The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based on mean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.
Compressed sensing applications in image processing & communication (1)Mayur Sevak
This document discusses applications of compressed sensing in image processing and communication. It begins by explaining what compressed sensing is and how it works by acquiring signals using fewer samples than required by the Nyquist criterion through exploiting sparsity and incoherence. It then discusses how to implement compressed sensing using properties of sparsity and incoherence along with convex optimization for reconstruction. Applications discussed include image fusion, representation, denoising, remote sensing, MIMO and cognitive radio communications, and ultra-wideband channel estimation. Compressed sensing is shown to provide benefits like reducing data acquisition and transmission costs while enabling reliable signal reconstruction.
In this paper we are interested to calculate the resonant frequency of rectangular patch antenna using artificial neural networks based on the multilayered perceptrons. The artificial neural networks built, transforms the inputs which are, the width of the patch W, the length of the patch L, the thickness of the substrate h and the dielectric permittivity ε_r to the resonant frequency fr which is an important parameter to design a microstrip patch antenna.The proposed method based on artificial neural networks is compared to some analytical methods using some statistical criteria. The obtained results demonstrate that artificial neural networks are more adequate to achieve the purpose than the other methods and present a good argument with the experimental results available in the literature. Hence, the artificial neural networks can be used by researchers to predict the resonant frequency of a rectangular patch antenna knowing length (L), width (W), thickness (h) and dielectric permittivity 〖(ε〗_r) with a good accuracy.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Many algorithms have been developed to find sparse representation over redundant dictionaries or
transform. This paper presents a novel method on compressive sensing (CS)-based image compression
using sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the three levels of
wavelet transform coefficients of the input image for compressive sampling. We have used three different
measurement matrix as Gaussian matrix, Bernoulli measurement matrix and random orthogonal matrix.
The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct each level of
wavelet transform separately. Experimental results demonstrate that the proposed method given better
quality of compressed image than existing methods in terms of proposed image quality evaluation indexes
and other objective (PSNR/UIQI/SSIM) measurements.
IRJET- Reconstruction of Sparse Signals(Speech) Using Compressive SensingIRJET Journal
This document discusses reconstructing sparse speech signals using compressive sensing. Compressive sensing allows reconstructing signals from fewer samples than the Nyquist rate by exploiting signal sparsity. The paper proposes using the basis pursuit algorithm to reconstruct speech signals sampled below the Nyquist rate. Basis pursuit formulates reconstruction as an l1-norm optimization problem to find the sparsest solution matching the samples. The algorithm is implemented in MATLAB and results show basis pursuit can accurately reconstruct speech signals from undersampled measurements without noise.
A Scheme for Joint Signal Reconstruction in Wireless Multimedia Sensor Networksijma
In context aware wireless multimedia sensor networks, scenarios are usually such that
signals of multiple distributed sensors contain a common sparse component and each individual
signal owns an innovation sparse component. So distributed compressive sensing based on joint
sparsity of a signal ensemble concept exploits both these intra- and inter- signal correlation structures
and compress signals to the extent possible. This paper proposes an optimized reconstruction
scheme based on joint sparsity model which is derived from the distributed compressive sensing. In
this regard, based on distributed compressive sensing, a joint reconstruction scheme is proposed to
compress and reconstruct ensemble of signals even in large scale data transmission. Furthermore,
simulation results show the effectiveness of the proposed method in diverse compression ratios and
processing times in comparison with the joint sparsity model and individual compressive sensing
reconstruction methods.
The document summarizes an algorithm for image encryption and compression based on compressive sensing and chaos. It begins with background information on compressive sensing theory and multi-chaotic based image encryption. It then describes the proposed algorithm which uses compressive sensing and a multi-chaotic system together for both image encryption and compression in a single step. Simulation results showed that the encrypted images had a large key space, low storage and transmission requirements, high security, and good statistical properties. Recovered images also had good quality while preserving image characteristics.
Various Applications of Compressive Sensing in Digital Image Processing: A Su...IRJET Journal
1. The document discusses various applications of compressive sensing in digital image processing. It describes how compressive sensing has been used successfully in areas like video encoding, atomic force microscopy imaging, image encryption, and reconstructing missing areas in images.
2. Compressive sensing allows reconstruction of images from incomplete information using fewer measurements than traditional methods. It takes advantage of sparsity and redundancy in images.
3. The applications discussed show that compressive sensing provides better results than other methods for tasks like video encoding, microscopy imaging, encryption, and filling in missing areas due to clouds in images.
Compressed sensing applications in image processing & communication (1)Mayur Sevak
This document discusses applications of compressed sensing in image processing and communication. It begins by explaining what compressed sensing is and how it works by acquiring signals using fewer samples than required by the Nyquist criterion through exploiting sparsity and incoherence. It then discusses how to implement compressed sensing using properties of sparsity and incoherence along with convex optimization for reconstruction. Applications discussed include image fusion, representation, denoising, remote sensing, MIMO and cognitive radio communications, and ultra-wideband channel estimation. Compressed sensing is shown to provide benefits like reducing data acquisition and transmission costs while enabling reliable signal reconstruction.
In this paper we are interested to calculate the resonant frequency of rectangular patch antenna using artificial neural networks based on the multilayered perceptrons. The artificial neural networks built, transforms the inputs which are, the width of the patch W, the length of the patch L, the thickness of the substrate h and the dielectric permittivity ε_r to the resonant frequency fr which is an important parameter to design a microstrip patch antenna.The proposed method based on artificial neural networks is compared to some analytical methods using some statistical criteria. The obtained results demonstrate that artificial neural networks are more adequate to achieve the purpose than the other methods and present a good argument with the experimental results available in the literature. Hence, the artificial neural networks can be used by researchers to predict the resonant frequency of a rectangular patch antenna knowing length (L), width (W), thickness (h) and dielectric permittivity 〖(ε〗_r) with a good accuracy.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Many algorithms have been developed to find sparse representation over redundant dictionaries or
transform. This paper presents a novel method on compressive sensing (CS)-based image compression
using sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the three levels of
wavelet transform coefficients of the input image for compressive sampling. We have used three different
measurement matrix as Gaussian matrix, Bernoulli measurement matrix and random orthogonal matrix.
The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct each level of
wavelet transform separately. Experimental results demonstrate that the proposed method given better
quality of compressed image than existing methods in terms of proposed image quality evaluation indexes
and other objective (PSNR/UIQI/SSIM) measurements.
IRJET- Reconstruction of Sparse Signals(Speech) Using Compressive SensingIRJET Journal
This document discusses reconstructing sparse speech signals using compressive sensing. Compressive sensing allows reconstructing signals from fewer samples than the Nyquist rate by exploiting signal sparsity. The paper proposes using the basis pursuit algorithm to reconstruct speech signals sampled below the Nyquist rate. Basis pursuit formulates reconstruction as an l1-norm optimization problem to find the sparsest solution matching the samples. The algorithm is implemented in MATLAB and results show basis pursuit can accurately reconstruct speech signals from undersampled measurements without noise.
A Scheme for Joint Signal Reconstruction in Wireless Multimedia Sensor Networksijma
In context aware wireless multimedia sensor networks, scenarios are usually such that
signals of multiple distributed sensors contain a common sparse component and each individual
signal owns an innovation sparse component. So distributed compressive sensing based on joint
sparsity of a signal ensemble concept exploits both these intra- and inter- signal correlation structures
and compress signals to the extent possible. This paper proposes an optimized reconstruction
scheme based on joint sparsity model which is derived from the distributed compressive sensing. In
this regard, based on distributed compressive sensing, a joint reconstruction scheme is proposed to
compress and reconstruct ensemble of signals even in large scale data transmission. Furthermore,
simulation results show the effectiveness of the proposed method in diverse compression ratios and
processing times in comparison with the joint sparsity model and individual compressive sensing
reconstruction methods.
The document summarizes an algorithm for image encryption and compression based on compressive sensing and chaos. It begins with background information on compressive sensing theory and multi-chaotic based image encryption. It then describes the proposed algorithm which uses compressive sensing and a multi-chaotic system together for both image encryption and compression in a single step. Simulation results showed that the encrypted images had a large key space, low storage and transmission requirements, high security, and good statistical properties. Recovered images also had good quality while preserving image characteristics.
Various Applications of Compressive Sensing in Digital Image Processing: A Su...IRJET Journal
1. The document discusses various applications of compressive sensing in digital image processing. It describes how compressive sensing has been used successfully in areas like video encoding, atomic force microscopy imaging, image encryption, and reconstructing missing areas in images.
2. Compressive sensing allows reconstruction of images from incomplete information using fewer measurements than traditional methods. It takes advantage of sparsity and redundancy in images.
3. The applications discussed show that compressive sensing provides better results than other methods for tasks like video encoding, microscopy imaging, encryption, and filling in missing areas due to clouds in images.
Photoacoustic tomography based on the application of virtual detectorsIAEME Publication
This document discusses using virtual detectors to improve photoacoustic tomography (PAT) image reconstruction when full scanning data is unavailable. It proposes interpolation and compressed sensing methods to generate virtual detector data and increase the number of measurements. Simulation results show applying these methods to preprocessed photoacoustic data significantly improves the peak signal-to-noise ratio of reconstructed images compared to direct reconstruction with limited detectors. Dictionary-based compressed sensing provides the best performance by learning an over-complete dictionary to sparsify signals. The methods allow better quality PAT imaging when hardware and spatial constraints limit actual detector positions and sampling angles.
Compressive Data Gathering using NACS in Wireless Sensor NetworkIRJET Journal
The document proposes a Neighbor-Aided Compressive Sensing (NACS) scheme for efficient data gathering in wireless sensor networks. NACS exploits both spatial and temporal correlations in sensor data to reduce data transmissions compared to existing compressive sensing models like Kronecker Compressive Sensing (KCS) and Structured Random Matrix (SRM). In NACS, each sensor node sends its raw sensor readings to a uniquely selected nearest neighbor node, which then applies compressive sensing measurements and sends the compressed data to the sink node. Simulation results show NACS achieves better data recovery performance using fewer transmissions than KCS and SRM, improving energy efficiency for data gathering in wireless sensor networks.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This document presents a neural network approach to channel equalization using a multilayer perceptron with a variable learning rate parameter. Specifically, it proposes modifying the backpropagation algorithm to allow the learning rate to adapt at each iteration in order to achieve faster convergence. The equalizer structure is a decision feedback equalizer modeled as a neural network with an input, hidden and output layer. Simulation results show the proposed variable learning rate approach improves bit error rate and convergence speed compared to a standard backpropagation algorithm.
Improved performance of scs based spectrum sensing in cognitive radio using d...eSAT Journals
Abstract
Tremendous growth in current wireless networks raises the demand of more frequency spectrum, over the finite availability of spectrum resource. Although, the research has specifies that the available primary users (i.e. licensed user) has not occupying the channel all the time. The most effective technology known as Cognitive radio giving promises for a solution of under utilization of available frequency spectrum in wireless communication. In cognitive radio network two types of wireless user can be define as primary user and secondary user. Primary users have highest priority to utilize the available band of frequency and secondary user can utilize these services only when the channel is vacant by primary user and there will be no any interference. The optimization of this may be implemented by a smart technique such as cognitive radio, which is fully automated intelligent wireless sensor tool having capability to sense, learn & adjust relevant operating parameters dynamically in radio atmosphere. This can be happen if we prefer the appropriate window technique to evaluate system parameter for sensing the availability of vacant band. We show that by comparing the different windows techniques, cognitive radios not only provide better spectrum opportunity but also provide the chance to huge number of wireless users.
Keywords: Primary user, Secondary user, Spectrum Sensing and Window technique etc.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
This document summarizes various image compression techniques including lossy and lossless methods. For lossy compression, it discusses transformation coding techniques like discrete cosine transform (DCT) and discrete wavelet transform (DWT). It also covers vector quantization, fractal coding, block truncation coding, and subband coding. For lossless techniques, it describes run length encoding, Huffman coding, LZW coding, and area coding. Finally, it provides an overview of using neural networks for image compression, including backpropagation networks, hierarchical/adaptive networks, and multilayer perceptron networks.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
Provably secure and efficient audio compression based on compressive sensingIJECEIAES
The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.
This document discusses dimensionality reduction techniques for hyperspectral images. It proposes using k-means clustering based on statistical measures like variance, standard deviation, and mean absolute deviation to select bands from hyperspectral images. The number of bands is first estimated using virtual dimensionality. Bands are then clustered based on their statistical properties and one band is selected from each cluster with the maximum value of the statistical measure. Finally, endmembers are extracted from the selected bands using N-FINDR.
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
This document presents a dual transform method for medical image compression that uses both singular value decomposition (SVD) and Haar wavelet transform. It compares the proposed dual transform method to existing Haar wavelet-SPIHT and DCT-SPIHT compression methods on 3 medical images. The dual transform method achieved higher compression ratios and PSNR values at 0.4 bits per pixel compared to the other methods, indicating better preservation of image quality at higher compression. The dual transform is thus concluded to be suitable for compressing medical images where no deterioration of image quality is acceptable.
Medical image analysis and processing using a dual transformeSAT Journals
Abstract The demand for images in medical field has increased drastically over the years. The need for reducing the storage space has resulted in image compression. This paper presents a dual transform for medical image compression algorithm. The experimental results determines how the compression ratio (CR), peak signal to noise ratio (PSNR) and SNR (signal to noise ratio) of different compression algorithms responds to dual transform algorithm. Keywords: DCT, SPIHT, Haar Wavelet, Linear approximation transform, image compression, Singular Value Decomposition (SVD).
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
1) The document presents a method for image compression that combines linear vector quantization and discrete wavelet transform.
2) Linear vector quantization is used to generate codebooks and encode image blocks, achieving better PSNR and MSE than self-organizing maps.
3) The encoded blocks are then subjected to discrete wavelet transform. Low-low subbands are stored for reconstruction while other subbands are discarded.
4) Experimental results show the proposed method achieves higher PSNR and lower MSE than existing techniques, preserving both texture and edge information.
Attention gated encoder-decoder for ultrasonic signal denoisingIAESIJAI
Ultrasound imaging is one of the most widely used non-destructive testing methods. The transducer emits pulses that travel through the imaged samples and are reflected by echo-forming impedance. The resulting ultrasonic signals usually contain noise. Most of the traditional noise reduction algorithms require high skills and prior knowledge of noise distribution, which has a crucial impact on their performances. As a result, these methods generally yield a loss of information, significantly influencing the final data and deeply limiting both sensitivity and resolution of imaging devices in medical and industrial applications. In the present study, a denoising method based on an attention-gated convolutional autoencoder is proposed to fill this gap. To evaluate its performance, the suggested protocol is compared to widely used methods such as butterworth filtering (BF), discrete wavelet transforms (DWT), principal component analysis (PCA), and convolutional autoencoder (CAE) methods. Results proved that better denoising can be achieved especially when the original signal-to-noise ratio (SNR) is very low and the sound waves’ traces are distorted by noise. Moreover, the initial SNR was improved by up to 30 dB and the resulting Pearson correlation coefficient was maintained over 99% even for ultrasonic signals with poor initial SNR.
An ECG Compressed Sensing Method of Low Power Body Area NetworkNooria Sukmaningtyas
Aimed at low power problem in body area network, an ECG compressed sensing method of low
power body area network based on the compressed sensing theory was proposed. Random binary
matrices were used as the sensing matrix to measure ECG signals on the sensor nodes. After measured
value is transmitted to remote monitoring center, ECG signal sparse representation under the discrete
cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstruct the
ECG signals. The simulation results show that the 30% of overall signal can get reconstruction signal
which’s SNR is more than 60dB, each numbers in each rank of sensing matrix can be controlled below 5,
which reduces the power of sensor node sampling, calculation and transmission. The method has the
advantages of low power, high accuracy of signal reconstruction and easy to hardware implementation.
CHANNEL ESTIMATION AND MULTIUSER DETECTION IN ASYNCHRONOUS SATELLITE COMMUNIC...ijwmn
In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Similar to Performance analysis of compressive sensing recovery algorithms for image processing using block processing
Photoacoustic tomography based on the application of virtual detectorsIAEME Publication
This document discusses using virtual detectors to improve photoacoustic tomography (PAT) image reconstruction when full scanning data is unavailable. It proposes interpolation and compressed sensing methods to generate virtual detector data and increase the number of measurements. Simulation results show applying these methods to preprocessed photoacoustic data significantly improves the peak signal-to-noise ratio of reconstructed images compared to direct reconstruction with limited detectors. Dictionary-based compressed sensing provides the best performance by learning an over-complete dictionary to sparsify signals. The methods allow better quality PAT imaging when hardware and spatial constraints limit actual detector positions and sampling angles.
Compressive Data Gathering using NACS in Wireless Sensor NetworkIRJET Journal
The document proposes a Neighbor-Aided Compressive Sensing (NACS) scheme for efficient data gathering in wireless sensor networks. NACS exploits both spatial and temporal correlations in sensor data to reduce data transmissions compared to existing compressive sensing models like Kronecker Compressive Sensing (KCS) and Structured Random Matrix (SRM). In NACS, each sensor node sends its raw sensor readings to a uniquely selected nearest neighbor node, which then applies compressive sensing measurements and sends the compressed data to the sink node. Simulation results show NACS achieves better data recovery performance using fewer transmissions than KCS and SRM, improving energy efficiency for data gathering in wireless sensor networks.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This document presents a neural network approach to channel equalization using a multilayer perceptron with a variable learning rate parameter. Specifically, it proposes modifying the backpropagation algorithm to allow the learning rate to adapt at each iteration in order to achieve faster convergence. The equalizer structure is a decision feedback equalizer modeled as a neural network with an input, hidden and output layer. Simulation results show the proposed variable learning rate approach improves bit error rate and convergence speed compared to a standard backpropagation algorithm.
Improved performance of scs based spectrum sensing in cognitive radio using d...eSAT Journals
Abstract
Tremendous growth in current wireless networks raises the demand of more frequency spectrum, over the finite availability of spectrum resource. Although, the research has specifies that the available primary users (i.e. licensed user) has not occupying the channel all the time. The most effective technology known as Cognitive radio giving promises for a solution of under utilization of available frequency spectrum in wireless communication. In cognitive radio network two types of wireless user can be define as primary user and secondary user. Primary users have highest priority to utilize the available band of frequency and secondary user can utilize these services only when the channel is vacant by primary user and there will be no any interference. The optimization of this may be implemented by a smart technique such as cognitive radio, which is fully automated intelligent wireless sensor tool having capability to sense, learn & adjust relevant operating parameters dynamically in radio atmosphere. This can be happen if we prefer the appropriate window technique to evaluate system parameter for sensing the availability of vacant band. We show that by comparing the different windows techniques, cognitive radios not only provide better spectrum opportunity but also provide the chance to huge number of wireless users.
Keywords: Primary user, Secondary user, Spectrum Sensing and Window technique etc.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
This document summarizes various image compression techniques including lossy and lossless methods. For lossy compression, it discusses transformation coding techniques like discrete cosine transform (DCT) and discrete wavelet transform (DWT). It also covers vector quantization, fractal coding, block truncation coding, and subband coding. For lossless techniques, it describes run length encoding, Huffman coding, LZW coding, and area coding. Finally, it provides an overview of using neural networks for image compression, including backpropagation networks, hierarchical/adaptive networks, and multilayer perceptron networks.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
Provably secure and efficient audio compression based on compressive sensingIJECEIAES
The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.
This document discusses dimensionality reduction techniques for hyperspectral images. It proposes using k-means clustering based on statistical measures like variance, standard deviation, and mean absolute deviation to select bands from hyperspectral images. The number of bands is first estimated using virtual dimensionality. Bands are then clustered based on their statistical properties and one band is selected from each cluster with the maximum value of the statistical measure. Finally, endmembers are extracted from the selected bands using N-FINDR.
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
This document presents a dual transform method for medical image compression that uses both singular value decomposition (SVD) and Haar wavelet transform. It compares the proposed dual transform method to existing Haar wavelet-SPIHT and DCT-SPIHT compression methods on 3 medical images. The dual transform method achieved higher compression ratios and PSNR values at 0.4 bits per pixel compared to the other methods, indicating better preservation of image quality at higher compression. The dual transform is thus concluded to be suitable for compressing medical images where no deterioration of image quality is acceptable.
Medical image analysis and processing using a dual transformeSAT Journals
Abstract The demand for images in medical field has increased drastically over the years. The need for reducing the storage space has resulted in image compression. This paper presents a dual transform for medical image compression algorithm. The experimental results determines how the compression ratio (CR), peak signal to noise ratio (PSNR) and SNR (signal to noise ratio) of different compression algorithms responds to dual transform algorithm. Keywords: DCT, SPIHT, Haar Wavelet, Linear approximation transform, image compression, Singular Value Decomposition (SVD).
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
1) The document presents a method for image compression that combines linear vector quantization and discrete wavelet transform.
2) Linear vector quantization is used to generate codebooks and encode image blocks, achieving better PSNR and MSE than self-organizing maps.
3) The encoded blocks are then subjected to discrete wavelet transform. Low-low subbands are stored for reconstruction while other subbands are discarded.
4) Experimental results show the proposed method achieves higher PSNR and lower MSE than existing techniques, preserving both texture and edge information.
Attention gated encoder-decoder for ultrasonic signal denoisingIAESIJAI
Ultrasound imaging is one of the most widely used non-destructive testing methods. The transducer emits pulses that travel through the imaged samples and are reflected by echo-forming impedance. The resulting ultrasonic signals usually contain noise. Most of the traditional noise reduction algorithms require high skills and prior knowledge of noise distribution, which has a crucial impact on their performances. As a result, these methods generally yield a loss of information, significantly influencing the final data and deeply limiting both sensitivity and resolution of imaging devices in medical and industrial applications. In the present study, a denoising method based on an attention-gated convolutional autoencoder is proposed to fill this gap. To evaluate its performance, the suggested protocol is compared to widely used methods such as butterworth filtering (BF), discrete wavelet transforms (DWT), principal component analysis (PCA), and convolutional autoencoder (CAE) methods. Results proved that better denoising can be achieved especially when the original signal-to-noise ratio (SNR) is very low and the sound waves’ traces are distorted by noise. Moreover, the initial SNR was improved by up to 30 dB and the resulting Pearson correlation coefficient was maintained over 99% even for ultrasonic signals with poor initial SNR.
An ECG Compressed Sensing Method of Low Power Body Area NetworkNooria Sukmaningtyas
Aimed at low power problem in body area network, an ECG compressed sensing method of low
power body area network based on the compressed sensing theory was proposed. Random binary
matrices were used as the sensing matrix to measure ECG signals on the sensor nodes. After measured
value is transmitted to remote monitoring center, ECG signal sparse representation under the discrete
cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstruct the
ECG signals. The simulation results show that the 30% of overall signal can get reconstruction signal
which’s SNR is more than 60dB, each numbers in each rank of sensing matrix can be controlled below 5,
which reduces the power of sensor node sampling, calculation and transmission. The method has the
advantages of low power, high accuracy of signal reconstruction and easy to hardware implementation.
CHANNEL ESTIMATION AND MULTIUSER DETECTION IN ASYNCHRONOUS SATELLITE COMMUNIC...ijwmn
In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Similar to Performance analysis of compressive sensing recovery algorithms for image processing using block processing (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
This study Examines the Effectiveness of Talent Procurement through the Imple...
Performance analysis of compressive sensing recovery algorithms for image processing using block processing
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 5, October 2022, pp. 5063~5072
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5063-5072 5063
Journal homepage: http://ijece.iaescore.com
Performance analysis of compressive sensing recovery
algorithms for image processing using block processing
Mathiyalakendran Aarthi Elaveini1
, Deepa Thangavel2
1
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
2
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
Article Info ABSTRACT
Article history:
Received Jun 10, 2021
Revised May 23, 2022
Accepted Jun 14, 2022
The modern digital world comprises of transmitting media files like image,
audio, and video which leads to usage of large memory storage, high data
transmission rate, and a lot of sensory devices. Compressive sensing (CS) is
a sampling theory that compresses the signal at the time of acquiring it.
Compressive sensing samples the signal efficiently below the Nyquist rate to
minimize storage and recoveries back the signal significantly minimizing the
data rate and few sensors. The proposed paper proceeds with three phases.
The first phase describes various measurement matrices like Gaussian
matrix, circulant matrix, and special random matrices which are the basic
foundation of compressive sensing technique that finds its application in
various fields like wireless sensors networks (WSN), internet of things (IoT),
video processing, biomedical applications, and many. Finally, the paper
analyses the performance of the various reconstruction algorithms of
compressive sensing like basis pursuit (BP), compressive sampling matching
pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard
thresholding (IHT), block processing-based basis pursuit (BP-BP) based on
mean square error (MSE), and peak signal to noise ratio (PSNR) and then
concludes with future works.
Keywords:
Basis pursuit
Compressive sampling
matching pursuit
Iterative hard threshold
Iterative reweighed least square
Mean square error
Peak signal to noise ratio
Recovery algorithm
This is an open access article under the CC BY-SA license.
Corresponding Author:
Deepa Thangavel
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology
Kattankulathur, Chennai-603203, India
Email: deepat@srmist.edu.in
1. INTRODUCTION
The digital revolution of the internet and social media leads to the transmission of many multimedia
signals over the internet. Compression plays a vital role in limiting the bandwidth and storage space of
multimedia signals like image audio and video. In 2004, David Donoho and his team proposed compressive
sensing a signal sampling theory with captures the signal efficiently and the signal is recovered back with
fewer samples thus reducing the dimensionality of signals known as sparse or compressible [1]–[3].
In traditional sampling, the signals are sampled and then compressed whereas in compressive
sensing the signals are based on the signal sparsity [4]. Sparsity is a property of an image with most of the
elements is zero and the image can be represented by a few non-zero elements image. The sparsity of the
matrix is the ratio of zero value elements to the total number of elements. A sparse basis matrix [5] can be
obtained from the traditional matrixes like discrete wavelet transform (DWT), discrete cosine transform
(DCT), and discrete Fourier transform (DFT). For example, natural images are highly sparse in wavelet while
speech signal and medical images have sparsity representation Fourier and random transform [6]. Let X be
the original image with NxN matrix elements [7] which is transformed into transform data Y then Y can be
expressed as (1).
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 5, October 2022: 5063-5072
5064
𝑌 = 𝜓(𝑋) (1)
If the entries in matrix Y are close to zero then X is said to be sparse on some basis ѱ.
The acquisition model [8] can be mathematically represented as Y= ѱ X where X is input signal of
size N, ѱ is random measurement matrix of size MxN and Y is the measurement vector of size M. At the
processing end of the reconstruction model [9], the measurement vector Y and reconstruction matrix ʘ is
space basis X then X is (2).
𝑥 = ∑ 𝑆𝑖𝜓𝑖
𝑛
𝑖=1 (2)
Let K be sparsity vector S [10], [11], then the matrix must satisfy restricted isometry property (RIP) and for
reconstruction, the measurement matrix ѱ and sparse basis should be incoherent [12].
2. RELATED WORKS
The image compression system presented in [13] uses the different techniques for the choice of
image compression techniques and for setting up a compression ratio. The image compression was carried
out in phases where the first step was to compress the image using DCT, the second to reconstruct, and
finally to train a set of images. The paper discusses the design of image compressed sensing (CS) to reduce
redundancy based on objective criteria and subjective criteria.
The hardware implementation of CS in [14] was done in field-programmable gate array (FPGA)
which is the best platform for reconfigurability. The image was stored in synchronous dynamic random-
access memory (SDRAM) and a sparse matrix is generated using the transformation process. The recovery of
the image was found to be more complex and time-consuming.
An efficient compression scheme in wireless sensor network (WSN) proposed in [15], [16] presents
a new architecture and protocol to achieve energy efficiency and transmit the image over WSN the
architecture optimizes high speed in image compression implemented with limited hardware and the power
consumption is low. The literature [17], [18] presents the image packet queue to minimize error rate and thus
improving the throughput of image transmission. The block compressive sensing proposed in [19] divides the
image into blocks thereby maximizing the sparsity and employs an adaptive threshold thereby allocating
measurements based on the sparsity of individual blocks with a predetermined measurement ratio.
3. STATE-OF-THE-ART COMPRESSIVE SENSING
Signal and image processing systems rely on the recovery of signals from the information [20]. When
the signal acquired is linear, then it can be solved using the linear system equations mathematically as (3):
𝑌 = 𝐴𝑥 (3)
where Y is observed data, which is 𝜖𝐶m
and connected to 𝑋𝜖𝐶N
.
The matrix 𝐴𝜖𝐶mxN
is the measurement matrix. Traditionally the number of measurements m should
be as large as N. If m<N then the linear system is underdetermined with infinite solution, which makes
recovery of X from Y impossible. The above technology is related to the Shannon sampling theorem. Figure 1
shows the block diagram of the compressive sensing and also describes the acquisition model as well as the
reconstruction model. The reconstruction algorithms that make this underlying assumption possible is
sparsity as shown in Figure 1.
3.1. Acquisition model
The measurement of the signal is made randomly [21] by different techniques like random
modulator, modulated wideband converter (MWC), random modulation pre-integrator (RMPI), random
filtering, random convolution compressive multiplexer, random equivalent sampling, random triggering
based modulated wideband compressive sampling, and quadrature analog to information converter. The
proposed a compressive sampler, random [22] which samples the signals below the Nyquist rate. The low
pass filter (LPF) results in a unique frequency signature in the low region. The high-frequency signal lies in
the low-frequency region. This unique frequency signature gives the original signal in random measurements
and finds its application for wideband signal acquisition. MWC uses a parallel architecture. The input signal
is multiplexed by different chipping sequences and then passed via LPF thus sampled below the Nyquist rate
and can be used for the wideband signal. RMPI is a combination of MWC with a parallel version [23] of
random and only the difference is RMPI uses integrator instead of LPF by which the resultant is a different
3. Int J Elec & Comp Eng ISSN:2088-8708
Performance analysis of compressive sensing recovery algorithms … (Mathiyalakendran Aarthi Elavein)
5065
reconstruction method that helps in acquiring the ultrawideband signal. The random filtering method
performs convolution with finite impulse response (FIR) and it finds its application in compressing
continuous and streaming signals. The random convolution consists of random pulses and then the pulses are
obtained by a circular shift of the first row and can be used as a universal acquisition. In compressive
multiplexer, the parallel architecture samples multichannel data to obtain a sub-Nyquist rate.
Figure 1. Block diagram for compressive sensing
3.2. Measurement matrix
The measurement matrix plays a vital in compressing the signal, therefore choosing a measurement
matrix is important for accuracy and less processing time to recover the signal [24]. Figure 2 gives the
different types of measurement matrices deployed for compressive sensing. It is broadly classified into the
deterministic and random matrix.
Figure 2. Different types of measurement matrix
a. Random matrix
Random matrices are identical and independent matrices that are generated by the distribution like
normal, random, and Bernoulli functions. The random matrix is broadly classified into structured and
unstructured matrix unstructured matrices are generated randomly. This matrix includes Gaussian, Bernoulli,
and uniform matrix.
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 5, October 2022: 5063-5072
5066
b. Gaussian matrix
The elements are distributed normally and the elements are independent except 0 and σ2
(variance).
The probability distribution function is (4).
𝑓 (
1
𝜇
, 𝜎2
) =
1
√2𝜎2𝜋
𝑒𝑥𝑝(1−𝜇2)
2𝜎2 (4)
Where μ is mean, σ is standard deviation, and 𝜎2
is variance.
𝐾 ≤ 𝐶1𝑁 𝑙𝑜𝑔 (
𝐿
𝑆
) + 𝐶2 𝑙𝑜𝑔 𝜀 − 1 (5)
Where S is sparsity, N is No. of measurements, L is length of sparse, and C1, C2 is circulant matrix.
c. Bernoulli matrix
Bernoulli matrix denoted as 𝐵𝜖𝑅, 𝑚𝑥𝑛 have equal probabilities at −
1
√𝑚
𝑜𝑟
1
√𝑚
, with outcomes n=0
and n=1. The PDF of the Bernoulli matrix is
𝐹(𝑛) = {
1
2
𝑓𝑜𝑟𝑛 = 0
1
2
𝑓𝑜𝑟𝑛 = 1
} Which satisfies RIP (6)
d. Structured random matrices
Unstructured matrices are slow and not advisable for large-scale problems since structured matrices
follow a structure [25] this reduces storage time and randomness.
e. Random partial Fourier matrix
Consider the matrix F 𝑁𝑥𝑁 whose entry is
(𝐹)𝑘𝑗 = 𝑒𝑥𝑝
2𝜋𝑖𝑘𝑗
𝑁
(7)
and 𝑘, 𝑗 takes the value of {1,2 … 𝑁}. The restricted isometric property (RIP) is also satisfied for the material
by randomly choosing M rows 𝑀 ≥ 𝐶𝐾 𝑙𝑜𝑔 𝑁 ∕ 𝜖. Where 𝑀 is No. of measurements, 𝐾 is sparsity, and 𝑁 is
length of sparse signal.
f. Random partial Hadamard matrix
The entries are 1 and -1 for the Hadamard matrix and the column is orthogonal. Consider a matrix H
with order n, Then H. 𝐻𝑇
= 𝑁𝐼𝑁. Where IN is Identity matrix and 𝐻𝑇
is transpose of H.
g. Deterministic matrices
The matrix follows a deterministic condition for satisfying the RIP. They are divided into semi-
deterministic and full-deterministic.
h. Semi-deterministic matrix
Semi-deterministic matrices are generated in two steps. The first column is generated randomly and
the full matrix is generated by applying a transformation to the first column thus generating the rows of the
matrix.
i. Circulant matrix
The entry for the matrix is 𝐶𝑗 = 𝐶𝑗 − 𝐼, where 𝐶 = (𝑐1 𝑐2 … 𝑐𝑛) and 𝑖, 𝑗 = {1 … 𝑁} thus the resultant
matrix is circulant.
𝐶 = ⌊
𝐶𝑛 𝐶𝑛−1 ⋯ ⋯ 𝐶1
𝐶1 𝐶𝑛 ⋯ ⋯ 𝐶2
⋮
𝐶 𝑛−1
𝐶𝑛−2 𝐶𝑛
⌋ (8)
j. Toeplitz matrix
The resultant Toeplitz matrix T is associated with 𝑡 = (𝑡1, 𝑡2 … 𝑡𝑛), 𝑇𝑖𝑗 = 𝑡𝑗−𝑖 with constant diagonal
𝑇𝑖𝑗 = 𝑡𝑗+1 𝑗+1.
𝑇 = [
𝑡𝑛⋱ 𝑡𝑛−1 ⋯ ⋯ 𝑡1
𝑡𝑛+1
⋮
⋮
𝑡𝑛 ⋯ ⋯ 𝑡2
𝑡2𝑛−1 𝑡2𝑛−2 ⋯ ⋯ 𝑡𝑛
] (9)
5. Int J Elec & Comp Eng ISSN:2088-8708
Performance analysis of compressive sensing recovery algorithms … (Mathiyalakendran Aarthi Elavein)
5067
k. Full deterministic matrices
The matrices are purely based on RIP properly.
l. Chirp sensing matrix
The matrix is based on non-cyclic codes where the columns are generated by chirp signal. The
matrix of the chirp is given by 𝐴𝑐ℎ𝑟𝑖𝑝 = [𝑢𝑟1 𝑢𝑟2 𝑢𝑟3 … 𝑢𝑟𝑤] and 𝑈𝑟𝑡 = (𝑡 = 1 … 𝑤) with 𝑀𝑥𝑀 matrix.
m. Binary BCH matrices
Binary Bose Chaudhuri Hocquenghem (BCH) matrix is a cyclic matrix and given by (10).
𝐻 = [
1 𝛼 𝛼2
1 𝛼3
𝛼3
⋮ ⋮ ⋮
⋯ 𝛼(𝑛−1)
⋯ 𝛼3(𝑛−1)
⋮ ⋮
] (10)
3.3. Recovery algorithm
The recovery of the original signal after compression is the essential step of compressive sensing.
The recovery algorithms are classified into six different categories as shown in Figure 3. Incoherence and
RIP are two important factors for the reconstruction of the compressed signal. The maximum correlation
between two elements in a pair of matrices is incoherent that involves quantity to measure the suitability of
φ [26]. Let the measurement metrics φ and sparse basis Ψ are said to be incoherent to each other. The
coherence range is 𝜓[𝜑, 𝜓] ∈ [1, √𝑛]. The recovery is better when coherence is small. Let the sparsity of
vector S be K, then, to recovery from measurements Y [27] the matrix should satisfy RIP with order K
1 − 𝛿 ≤
‖𝛩𝑈‖2
‖𝑈‖2
≤ 1 + 𝛿 (11)
ℓ1 is minimization or basis pursuit (BP). The convex optimization searches for a minimum ℓ1 norm
solution. The BP [28] recovers the signal only if its measurement is free from noise.
a. Algorithm for BP
Input: A, Y where A-measurement matrix, Y-measurement vector
Instruction: X=argmin‖𝑍‖, subject to AZ=Y
Output: vector x+
b. Greedy pursuit (GP)
Step by step method is implemented in greedy approach [28] and each iteration is selected by
updated columns that are similar to measurements.
c. Orthogonal matching pursuit (OMP)
The algorithm is described as
Input: A, Y where A-measurement matrix, Y-measurement vector
Initialization: s0=Φ; x0=0
Sn+1=SnU{jn +1} jn+1=argmax {‖𝐴(𝑌 − 𝐴𝑥)‖}
Xn+1
=argmin{‖𝑌 − 𝐴𝑧‖} supp ZCSn+1
Output:=x+=xn
d. Compressive sampling (CoSaMP)
Parallel greedy algorithm not only selects by one atom but operates by selecting K or multiplies
of K. CoSaMP selects 2K column of measurement matrix which will be added to previous k atoms and out of
this 3K atoms the best K atoms are taken and updated. Subspace pursuit (SP) [29] selects only K atoms for
each iteration which reduces the complexity.
e. Threshold approach
The solution set Si is updated using threshold operation. The iterative hard threshold (IHT) [30] uses
a non-linear threshold operator 𝜂𝑘 keeping the largest k entries in S all others are set to zero.
𝑆 = 𝜂𝑘(𝑠 + 𝜆ʘ𝑇(𝑌 − ʘ𝑠) (12)
Where λ-denotes step size, if the step size is fixed the algorithm will not recover while step size is adaptive
[31] it becomes complicated.
f. Combinal approach
The algorithms make use of two methods count min and count median. In the count min method, it
computes the minimum value from a measurement of the previous step. In the count median, the median is
calculated instead of the minimum value.
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g. Convex approach
The ℓ p norm approach is replaced in the place of ℓ1 norm where 0<p<1 and thus approach recovers
the signal from the fewer components. A weaker version of RIP is enough to reconstruct the signal perfectly.
h. Bayesian approach
The Bayesian approach is suitable for signals of a probability distribution. The signals coefficients
determining using maximum likelihood estimate or maximum posterior estimate.
i. Block processing-based basis pursuit (BP-BP)
The proposed basis pursuit along with block processing process the image using mean and standard
deviation and based on the processed image, the measurement matrix and the DCT transform are performed.
The processed image is then recovered using BP and the compression ratio is better when compared to other
algorithms.
Figure 3. Classification of recovery algorithms
4. RESULT AND DISCUSSION
The various recovery algorithms are analyzed by simulating the output for two different images,
cameraman and Lena images. The peak signal to noise ratio (PSNR) and MSE of both the images for various
algorithms are tabulated. The peak signal to noise ratio describes the quality metrics of the image. The PSNR
is expressed as the ratio of the maximum value of the signal to the noise and it is expressed in the logarithmic
decibel scale. PSNR is also defined in terms of MSE. MSE is defined as (13), (14), and (15).
𝑀𝑆𝐸 =
1
𝑚𝑛
∑ ∑ [𝐼(𝑖, 𝑗) − 𝐾(𝑖, 𝑗)]
𝑛−1
𝑗=0
𝑚−1
𝑖=0
(13)
𝑃𝑆𝑁𝑅 = 10 𝑙𝑜𝑔 10 (
𝑀𝑎𝑥2
𝑀𝑆𝐸
) (14)
𝑃𝑆𝑁𝑅 = 20 𝑙𝑜𝑔 10 (
𝑀𝑎𝑥2
√𝑀𝑆𝐸
) (15)
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Performance analysis of compressive sensing recovery algorithms … (Mathiyalakendran Aarthi Elavein)
5069
Figure 4 shows the images of the cameraman, which was originally taken for compression,
measurement matrix, the compressed image, and the recovered image with the calculated PSNR. Figure 5 gives the
comparison of various recovery algorithm for the image’s cameraman and Lena at compression ratio 3:1. The
PSNR and the MSE for the images are calculated and the tabulation of the images along with their recovery
algorithms is tabulated in Table 1.
Figure 4. Images of cameraman compressed and recovered
CS Algorithms
BASIS PURSUIT CoSAMP GP IHT
Recovered
cameraman
image
Recovered
Lena image
IRLS OMP SP BP-BP
Recovered
cameraman
image
Recovered
Lena image
Figure 5. Recovered images of cameraman and Lena with various recovery algorithms
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Table 1. Comparison table of various recovery algorithms, PSNR, and MSE
CS Algorithm Cameraman Lena
MSE PSNR in dB MSE PSNR in dB
BP 26.14 19.79 18.12 22.97
CoSamp 40.09 16.07 26.50 19.67
GP 29.64 18.69 20.66 21.83
IHT 44.40 15.18 37.86 16.57
IRLS 23.69 16.67 23.69 16.67
OMP 32.08 18.01 20.03 22.10
SP 34.66 17.34 22.30 21.16
BP-BP 26.04 19.80 18.10 23.01
Figures 6 and 7 provide the comparison chart of MSE and PSNR in dB for different images with
different recovery algorithm. The results of various algorithms with two different images conclude that based
on the sparsity of the images the recovery algorithms reconstruct the image with a compression ratio of 3:1
which means 30% of the data are compressed and thus limits the storage and bandwidth of the system.
Figure 6. Comparison chart for mean square error
Figure 7. Comparison chart for PSNR
5. CONCLUSION AND FUTURE WORK
The various compressive sensing recovery algorithms are compared and based on their analysis of
comparison the recovery of the image depends on the measurement matrix used and the nature of the image.
The proposed block processing along with Basis Pursuit is comparatively better in performance than the
other algorithms and the compression ratio of the algorithms is found to be 3:1 which is a moderate
compression ratio. Based on the present comparison the future work can be enhanced by compressing the
image and transmitting the image through a wireless network thereby reducing the storage capacity and
transmission rate.
0
20
40
60
BP CoSamp GP IHT IRLS OMP SP BP-BP
MSE
Recovery Algorithms
Comparison graph for MSE of cameraman and Lena
Cameraman MSE Lena MSE
0
5
10
15
20
25
BP CoSamp GP IHT IRLS OMP SP BP-BP
PSNR
in
dB
Recovery Algorithms
Comparision cahart of PSNR for Cameraman and Lena
Cameraman PSNR in dB Lena PSNR in dB
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Performance analysis of compressive sensing recovery algorithms … (Mathiyalakendran Aarthi Elavein)
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BIOGRAPHIES OF AUTHORS
Mathiyalakendran Aarthi Elaveini received her B.E degree in Electronics and
Communication Engineering from Madurai Kamaraj University, Tamil Nadu, India, and her
M.E degree in Embedded Systems Technologies from Anna University, Chennai, India in
2004 and 2012, respectively. She is currently pursuing her Ph. D at SRM University. She is
currently working as an Assistant Professor in the Department of ECE, SRM Institute of
Science and Technology, Ramapuram, Chennai, India. Her research interests are in the areas
of wireless communication systems, image processing, and visible light communication. She
can be contacted at email: aarthim@srmist.edu.in.
Deepa Thangavel received her B.Tech. degree in Electronics and
Communication Engineering from Madras University, Chennai, India, and her M. Tech degree
in Communication Engineering from VIT, Vellore, Chennai, India in 2004 and 2006,
respectively. She completed her Ph. D in 2015. She is currently working as an Associate
Professor in the department of ECE, SRM Institute of Science and Technology, Chennai,
India. She has more than 19 publications in SCI/SCOPUS indexed journals and 22
proceedings in international and national conferences. She is currently supervising 6 Ph.D
students. She is an active participant in both academic and research activities. Her research
interests are in the areas of wireless communication systems, signal processing, visible light
communication, multicarrier, and spread spectrum techniques for 5G communication. She can
be contacted at email: deepat@srmist.edu.in.