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 Ultrasound Image Despeckling Approach Based on Principle Component AnalysisCSCJournals
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
Performance analysis of the convolutional recurrent neural network on acousti...journalBEEI
In this study, we attempted to find the optimal hyper-parameters of the convolutional recurrent neural network (CRNN) by investigating its performance on acoustic event detection. Important hyper-parameters such as the input segment length, learning rate, and criterion for the convergence test, were determined experimentally. Additionally, the effects of batch normalization and dropout on the performance were measured experimentally to obtain their optimal combination. Further, we studied the effects of varying the batch data on every iteration during the training. From the experimental results using the TUT sound events synthetic 2016 database, we obtained optimal performance with a learning rate of 1/10000. We found that a longer input segment length aided performance improvement, and batch normalization was far more effective than dropout. Finally, performance improvement was clearly observed by varying the starting points of the batch data for each iteration during the training.
SIGNAL DETECTION IN MIMO COMMUNICATIONS SYSTEM WITH NON-GAUSSIAN NOISES BASED...ijwmn
In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.
Signal Detection in MIMO Communications System with Non-Gaussian Noises based...ijwmn
In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
An Ultrasound Image Despeckling Approach Based on Principle Component AnalysisCSCJournals
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
Performance analysis of the convolutional recurrent neural network on acousti...journalBEEI
In this study, we attempted to find the optimal hyper-parameters of the convolutional recurrent neural network (CRNN) by investigating its performance on acoustic event detection. Important hyper-parameters such as the input segment length, learning rate, and criterion for the convergence test, were determined experimentally. Additionally, the effects of batch normalization and dropout on the performance were measured experimentally to obtain their optimal combination. Further, we studied the effects of varying the batch data on every iteration during the training. From the experimental results using the TUT sound events synthetic 2016 database, we obtained optimal performance with a learning rate of 1/10000. We found that a longer input segment length aided performance improvement, and batch normalization was far more effective than dropout. Finally, performance improvement was clearly observed by varying the starting points of the batch data for each iteration during the training.
SIGNAL DETECTION IN MIMO COMMUNICATIONS SYSTEM WITH NON-GAUSSIAN NOISES BASED...ijwmn
In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.
Signal Detection in MIMO Communications System with Non-Gaussian Noises based...ijwmn
In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
Energy Efficient Animal Sound Recognition Scheme in Wireless Acoustic Sensors...ijwmn
Wireless sensor network (WSN) has proliferated rapidly as a cost-effective solution for data aggregation and measurements under challenging environments. Sensors in WSNs are cheap, powerful, and consume limited energy. The energy consumption is considered to be the dominant concern because it has a direct and significant influence on the application’s lifetime. Recently, the availability of small and inexpensive components such as microphones has promoted the development of wireless acoustic sensor networks (WASNs). Examples of WASN applications are hearing aids, acoustic monitoring, and ambient intelligence. Monitoring animals, especially those that are becoming endangered, can assist with biology researchers’ preservation efforts. In this work, we first focus on exploring the existing methods used to monitor the animal by recognizing their sounds. Then we propose a new energy-efficient approach for identifying animal sounds based on the frequency features extracted from acoustic sensed data. This approach represents a suitable solution that can be implemented and used in various applications. However, the proposed system considers the balance between application efficiency and the sensor’s energy capabilities. The energy savings will be achieved through processing the recognition tasks in each sensor, and the recognition results will be sent to the base station.
ENERGY EFFICIENT ANIMAL SOUND RECOGNITION SCHEME IN WIRELESS ACOUSTIC SENSORS...ijwmn
Wireless sensor network (WSN) has proliferated rapidly as a cost-effective solution for data aggregation and measurements under challenging environments. Sensors in WSNs are cheap, powerful, and consume limited energy. The energy consumption is considered to be the dominant concern because it has a direct and significant influence on the application’s lifetime. Recently, the availability of small and inexpensive components such as microphones has promoted the development of wireless acoustic sensor networks (WASNs). Examples of WASN applications are hearing aids, acoustic monitoring, and ambient intelligence. Monitoring animals, especially those that are becoming endangered, can assist with biology researchers’ preservation efforts. In this work, we first focus on exploring the existing methods used to monitor the animal by recognizing their sounds. Then we propose a new energy-efficient approach for identifying animal sounds based on the frequency features extracted from acoustic sensed data. This approach represents a suitable solution that can be implemented and used in various applications. However, the proposed system considers the balance between application efficiency and the sensor’s energy capabilities. The energy savings will be achieved through processing the recognition tasks in each sensor, and the recognition results will be sent to the base station.
Investigation of the performance of multi-input multi-output detectors based...IJECEIAES
The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance.
Noise removal techniques for microwave remote sensing radar data and its eval...csandit
Microwave Remote Sensing data acquired by a RADAR sensor such as SAR(Synthetic Aperture
Radar) is affected by a peculiar kind of noise called speckle. This noise not only renders the
data ineffective for classification, texture analysis, segmentation etc. which are used for image
analysis purposes, but also degrades the overall contrast and radiometric quality of the image.
Here we discuss the various noise removal techniques which have been widely used by scientists
all over the world. Different filtering methods have their pros and cons, and no single method
can give the most satisfactory result. In order to circumvent those issues, better and better
methods are being attempted. One of the recent methods is that based on Wavelet technique.
This paper discusses the denoising techniques based on Wavelets and the results from some of
those methods. The relative merits and demerits of the filters and their evaluation is also done.
NOISE REMOVAL TECHNIQUES FOR MICROWAVE REMOTE SENSING RADAR DATA AND ITS EVAL...cscpconf
Microwave Remote Sensing data acquired by a RADAR sensor such as SAR(Synthetic Aperture Radar) is affected by a peculiar kind of noise called speckle. This noise not only renders the
data ineffective for classification, texture analysis, segmentation etc. which are used for image analysis purposes, but also degrades the overall contrast and radiometric quality of the image. Here we discuss the various noise removal techniques which have been widely used by scientists all over the world. Different filtering methods have their pros and cons, and no single method can give the most satisfactory result. In order to circumvent those issues, better and better methods are being attempted. One of the recent methods is that based on Wavelet technique. This paper discusses the denoising techniques based on Wavelets and the results from some of those methods. The relative merits and demerits of the filters and their evaluation is also done.
We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined ranges. Among the implemented architectures, the CRNN performed best under all testing conditions, followed by CNN. Although RNN was effective in tracking the time-correlation information in audio signals,it exhibited inferior performance compared to the CNN and the CRNN. Accordingly, it is necessary to develop more optimization strategies for implementing RNN in sound event detection.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
ENSEMBLE LEARNING APPROACH FOR DIGITAL COMMUNICATION MODULATION’S CLASSIFICATIONijaia
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
Novel Technique for Comprehensive Noise Identification and Cancellation in GS...IJECEIAES
Presence of noise significantly degrades the contents being transmitted over GSM channel. With the evolution of next generation of communication system, the challenges in noise cancellation in voice along with data transmission are not being addressed effectively by existing filters. Therefore, the proposed system offers a mechanism where emphasis is laid on identification of superior and inferior forms of GSM transient signal followed by cancellation of its noise level. Designed using analytical methodology, the proposed system harness the potential of probability theory to perform a modeling that associates allocated power of the transmitting device with the level of noise. The outcome of the study is found to offer a comprehensive identification of different forms of noise and can precisely determine the level of superior and inferior quality of signal. The outcome significantly assists in designing an accurate filter for noise cancellation in GSM signal.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Energy Efficient Animal Sound Recognition Scheme in Wireless Acoustic Sensors...ijwmn
Wireless sensor network (WSN) has proliferated rapidly as a cost-effective solution for data aggregation and measurements under challenging environments. Sensors in WSNs are cheap, powerful, and consume limited energy. The energy consumption is considered to be the dominant concern because it has a direct and significant influence on the application’s lifetime. Recently, the availability of small and inexpensive components such as microphones has promoted the development of wireless acoustic sensor networks (WASNs). Examples of WASN applications are hearing aids, acoustic monitoring, and ambient intelligence. Monitoring animals, especially those that are becoming endangered, can assist with biology researchers’ preservation efforts. In this work, we first focus on exploring the existing methods used to monitor the animal by recognizing their sounds. Then we propose a new energy-efficient approach for identifying animal sounds based on the frequency features extracted from acoustic sensed data. This approach represents a suitable solution that can be implemented and used in various applications. However, the proposed system considers the balance between application efficiency and the sensor’s energy capabilities. The energy savings will be achieved through processing the recognition tasks in each sensor, and the recognition results will be sent to the base station.
ENERGY EFFICIENT ANIMAL SOUND RECOGNITION SCHEME IN WIRELESS ACOUSTIC SENSORS...ijwmn
Wireless sensor network (WSN) has proliferated rapidly as a cost-effective solution for data aggregation and measurements under challenging environments. Sensors in WSNs are cheap, powerful, and consume limited energy. The energy consumption is considered to be the dominant concern because it has a direct and significant influence on the application’s lifetime. Recently, the availability of small and inexpensive components such as microphones has promoted the development of wireless acoustic sensor networks (WASNs). Examples of WASN applications are hearing aids, acoustic monitoring, and ambient intelligence. Monitoring animals, especially those that are becoming endangered, can assist with biology researchers’ preservation efforts. In this work, we first focus on exploring the existing methods used to monitor the animal by recognizing their sounds. Then we propose a new energy-efficient approach for identifying animal sounds based on the frequency features extracted from acoustic sensed data. This approach represents a suitable solution that can be implemented and used in various applications. However, the proposed system considers the balance between application efficiency and the sensor’s energy capabilities. The energy savings will be achieved through processing the recognition tasks in each sensor, and the recognition results will be sent to the base station.
Investigation of the performance of multi-input multi-output detectors based...IJECEIAES
The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance.
Noise removal techniques for microwave remote sensing radar data and its eval...csandit
Microwave Remote Sensing data acquired by a RADAR sensor such as SAR(Synthetic Aperture
Radar) is affected by a peculiar kind of noise called speckle. This noise not only renders the
data ineffective for classification, texture analysis, segmentation etc. which are used for image
analysis purposes, but also degrades the overall contrast and radiometric quality of the image.
Here we discuss the various noise removal techniques which have been widely used by scientists
all over the world. Different filtering methods have their pros and cons, and no single method
can give the most satisfactory result. In order to circumvent those issues, better and better
methods are being attempted. One of the recent methods is that based on Wavelet technique.
This paper discusses the denoising techniques based on Wavelets and the results from some of
those methods. The relative merits and demerits of the filters and their evaluation is also done.
NOISE REMOVAL TECHNIQUES FOR MICROWAVE REMOTE SENSING RADAR DATA AND ITS EVAL...cscpconf
Microwave Remote Sensing data acquired by a RADAR sensor such as SAR(Synthetic Aperture Radar) is affected by a peculiar kind of noise called speckle. This noise not only renders the
data ineffective for classification, texture analysis, segmentation etc. which are used for image analysis purposes, but also degrades the overall contrast and radiometric quality of the image. Here we discuss the various noise removal techniques which have been widely used by scientists all over the world. Different filtering methods have their pros and cons, and no single method can give the most satisfactory result. In order to circumvent those issues, better and better methods are being attempted. One of the recent methods is that based on Wavelet technique. This paper discusses the denoising techniques based on Wavelets and the results from some of those methods. The relative merits and demerits of the filters and their evaluation is also done.
We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined ranges. Among the implemented architectures, the CRNN performed best under all testing conditions, followed by CNN. Although RNN was effective in tracking the time-correlation information in audio signals,it exhibited inferior performance compared to the CNN and the CRNN. Accordingly, it is necessary to develop more optimization strategies for implementing RNN in sound event detection.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
ENSEMBLE LEARNING APPROACH FOR DIGITAL COMMUNICATION MODULATION’S CLASSIFICATIONijaia
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
Novel Technique for Comprehensive Noise Identification and Cancellation in GS...IJECEIAES
Presence of noise significantly degrades the contents being transmitted over GSM channel. With the evolution of next generation of communication system, the challenges in noise cancellation in voice along with data transmission are not being addressed effectively by existing filters. Therefore, the proposed system offers a mechanism where emphasis is laid on identification of superior and inferior forms of GSM transient signal followed by cancellation of its noise level. Designed using analytical methodology, the proposed system harness the potential of probability theory to perform a modeling that associates allocated power of the transmitting device with the level of noise. The outcome of the study is found to offer a comprehensive identification of different forms of noise and can precisely determine the level of superior and inferior quality of signal. The outcome significantly assists in designing an accurate filter for noise cancellation in GSM signal.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
Similar to Attention gated encoder-decoder for ultrasonic signal denoising (20)
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Attention gated encoder-decoder for ultrasonic signal denoising
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 4, December 2023, pp. 1695~1703
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i4.pp1695-1703 1695
Journal homepage: http://ijai.iaescore.com
Attention gated encoder-decoder for ultrasonic signal denoising
Nabil Jai Mansouri1,2
, Ghizlane Khaissidi2
, Gilles Despaux1
, Mostafa Mrabti2
, Emmanuel Le Clézio1
1
Laboratory Institue of Electronics and Systems (IES), University of Montpellier,
French National Centre for Scientific Research (CNRS), Montpellier, France
2
Laboratory of Computer and Inter-disciplinary Physics (LIPI), Ecole Normale Supérieure (ENS),
Sidi Mohamed Ben Abdellah University, Fez, Morocco
Article Info ABSTRACT
Article history:
Received Jun 24, 2022
Revised Jan 18, 2023
Accepted Jan 30, 2023
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.
Keywords:
Attention
Deep learning
Noise reduction
Ultrasonic signal
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nabil Jai Mansouri
Institute of Electronics and Systems, University of Montepllier
Montepllier, France
Email: nabil.jai-mansouri@umontpellier.fr, nabil.jaimansouri@usmba.ac.ma
1. INTRODUCTION
One of the most used non-destructive control methods is ultrasound imaging. It is applied for medical
purposes, as it allows the acquisition of images of internal organs, that help diagnose pain causes [1],
cancers [2], and fetal assessments [3]. Its application extends as well to the industrial domain where it was
deployed in the fault diagnosis of rolling element bearings [4] or for non-destructive testing of nuclear
reactors [5]. The main idea of this method aims to emit ultrasounds, using a transducer, that will penetrate
materials and be reflected on the different layers of the imaged sample. The reflected power or the
corresponding time of flight (ToF) of the ultrasonic signals will be used to quantify the grayscale of each pixel
in the final image. The ToF can be estimated using different methods such as Threshold, Akaike information
criterion method, and Cross-correlation [6]. Unfortunately, these methods’ performance can be heavily
degraded due to poor signal-to-noise ratio (SNR), hence the necessity of an efficient noise reduction. Because
of the noise randomness, noise reduction becomes a challenging task requiring high skills, knowledge of the
signal properties, and advanced denoising algorithms. At present moment, the methods used for signal
denoising can be classified into two clusters: classical signal processing methods based on mathematical
models, and learning methods. The performances of these methods depend on the complexity of the noise to
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be suppressed. Traditional frequency filtering methods are recommended when the noise does not share the
same frequency properties as the noise-free signal. These methods permit the selection of the informative
frequency range of the signal. But in certain cases, the noise spectrum overlaps with the spectrum of the clean
signal, and the classical filtering methods’ results are no longer in perfect adequation. For this reason, wavelet
filtering methods are recommended. These methods reduce noise on signal following three steps. First, the
signal is transformed to the wavelet domain using Mallat’s algorithm [7], resulting in a set of approximation
and detail coefficients. These latters are then thresholded as they refer to high-frequency terms (noise), contrary
to the approximation coefficients that identify the relevant low-frequency information. Last, a reconstruction
of the signal is performed leveraging the thresholded detail and the approximation coefficients. From the widely
used wavelet-based filtering methods, we cite the discrete wavelet transform (DWT), the stationary wavelet
transform (SWT), and the wavelet packets (WP) [8]. Matz et al. [9] compared these methods and proved the
WP's greatness. The main limitation of wavelet-based denoising methods is their requirement of an adequate
choice of the basic function, threshold method as well as a precise estimation of the threshold value. Therefore,
learning methods were proposed to overtake the necessity of these signal processing skills and to perform a
greater noise reduction.
The learning methods are categorized into two sub-fields, the supervised ones that learn during the
training to map from an input sample to a corresponding output, and the unsupervised algorithms that aim to
blindly extract hidden features yielding sufficient details to perform the clustering, dimensionality reduction,
or denoising. The Machine Learning algorithms as principal components analysis (PCA), independent
component analysis (ICA), and singular value decomposition (SVD), were combined by researchers with
traditional signal processing methods for better noise reduction [10]. Lately, deep learning (DL) has gained
researchers’ attention in a wide variety of fields, in parallel with the improvement of computing powers [11].
In signal and image processing, several researchers used DL algorithms for denoising applications [12]–[15].
Precisely, Gao et al. [16] designed a reversible mapping algorithm between a two-dimensional visual image
and a one-dimensional ultrasonic signal. They built an autoencoder able to extract complex features, and
perform the denoising of the ultrasonic signal. Their method showed more adaptability and robustness than
PCA, SVD, and wavelet algorithms. Xu et al. [17] removed grain noise by clustering the correlative signals
using the K-means algorithm. They trained autoencoders on the different configurations, and leveraged the
trained models to perform the noise reduction. Contrariwise, Antczak [18] proposed deep recurrent neural
networks (RNN) to denoise Electrocardiographic signals. The model was trained on simulated data, and fine-
tuned on real data. The results showed better performances when compared to undecimated wavelet transform
and bandpass filtering. In addition to that, the results proved that pretraining on synthetic data before fine-
tuning on real ones improved the DL model performances.
RNNs such as gated recurrent unit (GRU) or long short-term memory (LSTM) were proposed to deal
with sequential data [19]. This type of neural networks was employed for serval tasks as time-series’
forecasting [20], text classification [21], speech translation [22], and many more natural language processing
(NLP) applications. The main challenge in training RNN is the vanishing gradient problem, which tends to
penalize the network performances in extracting relevant information from data. Alongside, convolutional
neural networks (CNN) have known promising improvements like the ResNet architecture [23] that tackles
vanishing gradient phenomena through skip connections and ended up outperforming RNNs. Thus, CNN
offered higher performances and attracted researchers for sequential problem modeling. Song et al. [24]
proposed an unsupervised multispectral denoising method applied to satellite imagery using a wavelet sub-
band cycle-consistent adversarial network. Results proved a particularity of preserving high-frequency
information, representing edges in the case of satellite images. In the meanwhile, it removed successfully the
noise patterns. Sharma and Pramanik [25] proposed a U-net-based DL model to reduce noise and enhance the
resolution in acoustic resolution photoacoustic microscopy. The model performance has been validated on in
vivo rat vasculature imaging. Furthermore, Dang et al. [26] designed a dual-path-transformer-based full-band
and sub-band fusion network for speech enhancement purposes. The method is based on an encoder of a
Transformer model. This submodel is formed by a positional encoding layer, multi-head attention, and fully
connected layers. Their method outmatched the state-of-the-art methods on the voice cloning toolkit (VCTK),
diverse environments multichannel acoustic noise database (DEMAND), and deep noise suppression (DNS)
benchmarking datasets. However, the drawback of the Transformer based architectures is the need for very
large datasets limiting their application to several domains.
In this work, an attention u-shaped convolutional autoencoder (Att-CAE) for ultrasonic signal
denoising is proposed. The neural network was trained on a wide variety of synthetic ultrasonic signals offering
the possibility to learn a mapping from noisy signals to completely noise-free ones. Moreover, it enables
highlighting the proposed method’s performance in the most critical conditions. The proposed model was
compared to DWT, butterworth filtering (BF), PCA, and convolutional autoencoders (CAE) methods.
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2. METHODOLOGY
2.1. Denoising attention convolutional autoencoder
To perform the denoising of the ultrasonic signals, an attention U-shaped convolutional autoencoder
was proposed to learn the signal features, suppress the noise, and provide the corresponding denoised signal.
Firstly, the autoencoder is an unsupervised neural network that learns through an encoding-decoding process.
It compresses the input data into a reduced space, called a latent-space representation, which is then leveraged
to reconstruct the input along with the decoding operation. Vanilla autoencoders are artificial neural
networks (ANN). Due to the weak feature extraction performed by the ANN, and thanks to CNN's ability to
learn the spatial information, a transition to CAE was proposed, where the ANNs of the vanilla autoencoder
are replaced by CNNs. The output of each convolutional layer is expressed in (1).
ai = σ(Fi ∗ ai−1 + bi) (1)
Where 𝑎𝑖−1 is the activation of the previous layer, 𝐹𝑖 and 𝑏𝑖 are the filters weights and biases from the
previous layer to the current one and × denotes the convolution [27]. The main limitation of the convolutional
autoencoders is that the model could potentially learn to simply copy its inputs, and poorly perform on other
signal distributions. On that ground, denoising autoencoders were proposed to force the encoder to learn
complex features of its inputs in a noisy environment and end-up in providing the decoder with sufficient useful
information for optimal reconstruction along with noise suppression.
In the proposed model, the rectified linear unit activation function (ReLu) has been used, except for
the last layer where it was replaced by the hyperbolic tangent activation function (tanh). The tanh was required
to reconstruct the dynamic range of the denoised signal. The ReLu activation function is employed as it
prevents the vanishing gradient problem thanks to its derivative, allowing the autoencoder to learn faster,
perform better, and generalize well. Another benefit of using the ReLu activation function is pushing the latent-
space representation units to zero, enabling an indirect control of the average number of zeros in the latent
space, resulting in the representation sparsity [28]. Inspired by the U-Net architecture [29], the model was
designed very carefully in a manner to have a symmetrically shaped encoder and decoder, allowing the cross-
connection between their same-sized layers [30]. These connections are concatenations of the outputs of the
same shaped layers from the encoder and the decoder. They ensure the reusability of features lost along the
encoding process in the decoding operation, allowing the final computations to be aware of the small and basic
details of the input signal. These cross-connections permit the gradient injection in the top layers as well,
helping them decide in which direction the weights should be moved to minimize the cost function. Thus, the
top layers, performing the basic feature extractions, surpass the vanishing gradient phenomena and well adjust
their filters’ parameters. Furthermore, it has been confirmed that the introduction of skip connections affects
the loss landscape offering the possibility to minimize highly non-convex loss functions [31]. The main
limitation of these connections is that basic features extracted at the encoder's top layers are processed equally
to deeper features from the decoder. To tackle this issue, an attention mechanism was proposed for features
weighting before concatenation, discriminating the relevant information to the task learned by the DL
model [32]–[34]. These mechanisms filter the neurons’ activations, whether during the forward or the
backward pass. During the backpropagation, the gradients emerging from the noisy background are shrunk so
that the model parameters in the top layers get updated based on pertinent information to the denoising task.
The used attention mechanism architecture is shown in Figure 1. The use of the attention mechanism at the
level of the skip connections helps improve the DL model noise reduction using shallower models. Considering
overfitting phenomena, dropout was used as a regularization method [35].
Figure 1. Architecture of the employed additive attention mechanism. Each decoder layer’s output is scaled
using coefficients learned from the same decoder layer’s activations, representing the input features (𝑥𝑙
), and
the gating signals (g) originating from the same-sized encoder ‘s layers [32]
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Another important factor in the Att-CAE training is the optimization algorithm. In this study, the
results of the most popular gradient-based algorithms were compared: Firstly, the stochastic-gradient descent
algorithm (SGD) [36], as an accelerated schemes method, and then two common adaptative methods called
adaptative momentum estimation (Adam) [37], and AdaBelief [38]. The Adam optimization algorithm was
selected to optimize the model parameters. The adopted algorithm showed better denoising results and
converged slightly faster than Adabelief. This optimization has been performed by maximizing the peak signal-
to-noise ratio (PSNR) as a loss function. The latter has been chosen as it prevents the DL model from over-
smoothing the dynamic range of the signal since the denoised signals should conserve the echoes considered
as the most relevant segments of the ultrasonic signal. The architecture of the DL model is shown in Figure 2.
Figure 2. The proposed Att-CAE architecture. The U-shaped model compresses the noisy signals along with
the encoding branch in silver, and then decompressed in the decoding violet branch. The skip connections
concatenate weighted features learned by the encoder’s blocks to the same-size decoder blocks’ outputs. The
weighting is performed by the Attention mechanism presented in Figure 1
2.2. Data generation
Deep neural networks (DNN) require a very large amount of data to learn. As it was proven that
pretraining on synthetic data before fine-tuning on experimental one’s results in better performances [39], two
datasets were developed to train and validate the proposed denoising DL model. As most of the present
ultrasonic imaging devices digitalize the signals through an analog to digital converter (ADC) [40], these two
sets are composed of 100,000 ultrasonic noise-free signals of 1,000 data points Figure 3(a). They contain a
3 cycles pulse with a 10 MHz center frequency transducer and its first echo. The original pulse acting as a
reference signal is positioned at 0.25 ms and shifted by an 𝜀 ranging between −∆𝑡 2
⁄ and +∆𝑡 2
⁄ , where ∆𝑡 is
the signal temporal sampling step. The 𝜀 is made to represent a non-synchronization between the excitation
trigger and the signal sampling at the level of the signal generation. Echoes are then arbitrarily shifted between
200∆𝑡 + 𝜀 and 500∆𝑡 + 𝜀. In this simulation, ∆t is equal to 5 ns, corresponding to a 0.2 GHz sampling
frequency, and the propagation times range between 1 μs and 2.5 μs.
For the first training database, gaussian white noise (GWN) was added at 10 dB SNR. Considering
the second training database, GWN noise was added to the clean signals, but at different intensities ranging
from 10 dB to 50 dB Figure 3(b), in a manner to have a uniform SNR distribution. The distributions of the
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databases are explained by the fact that during our experimental study, it has been observed that training the
model at first on the 10 dB SNR database and then on the second database results in better noise reduction on
poor SNR signals. Providing the model at first with low SNR signals to denoise forces the learning of features.
(a) (b)
Figure 3. Comparaison between, (a) denoised signals in green and (b) highly noisy signals in red at 10 dB, 15
dB and 20 dB SNR for (1), (2), and (3) respectively, and the initial clean signals in black
In more complex conditions. Afterward, training on the second database permits the generalization to
other noise intensities. The noise was added as expressed in (2).
yi = xi + ni (2)
Where 𝑥𝑖 represents the clean signal, n is a GWN and 𝑦𝑖 the resulting noisy signal from the addition of the
noise to the clean signal. The noise amplitude added to the clean signals is calculated in (3).
𝐴𝑛𝑜𝑖𝑠𝑒 = 𝐴𝑠𝑖𝑔𝑛𝑎𝑙. 10
−𝑆𝑁𝑅𝑑𝐵
20 (3)
3. RESULTS AND DISCUSSION
3.1. Noise reduction
In order to validate our Att-CAE, we created 9 databases similar to the initial ones as test sets
containing 100 noise-free signals 𝑥𝑖 along with their corresponding noisy versions 𝑦𝑖. These noisy signals’
SNR range between 10 dB and 50 dB with a step of 5, denoted from database 1 to 9 respectively. These
databases’ noisy signals were denoised by means of the Att-CAE, CAE, Machine Learning methods such as
PCA, and classical signal processing methods such as BF and DWT. We then analyzed the denoising
performance of each algorithm comparing the SNR enhancement and the Pearson correlation coefficient (P’r)
by (4) and (5).
𝑆𝑁𝑅 = 10 𝑙𝑜𝑔10 (
𝑃𝑠
𝜎𝑛𝑜𝑖𝑠𝑒
2) (4)
Where Ps refers to the clean signal power, and 𝜎𝑛𝑜𝑖𝑠𝑒 donates the noise standard deviation.
𝑃′𝑟 =
∑ ((𝑥𝑖−𝑥̅)(𝑦𝑖−𝑦
̅)
𝑛
𝑖=0
√∑ (𝑥𝑖−𝑥̅)2
𝑛
𝑖=0 √∑ (𝑦𝑖−𝑦
̅)2
𝑛
𝑖=0
(5)
Among them, 𝑥̅ and 𝑦
̅ are the mean values of (𝑥𝑖) and (𝑦𝑖) respectively, and n is the length of the
signal. SNR donates the ratio between the power of the ultrasonic signal and the corrupting noise power. The
higher the SNR value, the better the noise reduction. The P’r donates the linear relationship between the clean
signal and the denoised one. The coefficient ranges from -1 to 1, for a negative correlation and a positive
correlation, respectively. The closer the value to 1, the greater the correlation between the clean signal and the
de-noised one.
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To evaluate the method's effectiveness, we tuned the parameters of the wavelet algorithm, the results
of which will be compared to the Att-CAE results. The wavelet algorithm decomposes the signals on a basis
of functions yielding coefficients corresponding to the local signal similarity to these basis elements [41].
These coefficients will then be filtered using the Bayes Shrink algorithm where each wavelet subband is
thresholded and reconstructed to provide the denoised signal [42]. The choice of the basis function is made in
a manner to maximize the SNR results on each evaluation database. It is also conventional to choose a mother
wavelet that correlates to the signal [43]. For the BF method, we designed a low pass filter that cuts off all the
frequencies superior to 25 MHz, and the filter order was set to 5. PCA used a Bayesian model to automatically
choose the components that should be retained. To allow a precise comparison between our Att-CAE model
and existing DL denoising protocols, CAEs were trained on the same data distribution and include the same
methodology and hyperparameters as our model.
To compare the proposed method results to the other methods, we denoised the evaluation databases’
samples using our Att-CAE, and compared our results with those from CAE, PCA, DWT, and the BF. The
SNR and P’r means of each database’s denoised signals were also calculated. Figure 4 presents the comparison
of the SNR distributions before and after denoising. It shows that the method proposed in this paper
demonstrates a ≈30 dB improvement on signals with SNR ranging from 10 dB to 20 dB. The second-best
method is CAE performing a 2 dB weaker SNR enhancement. Then PCA performs a 12 dB increase slightly
better than DWT and BF methods with SNR improvements limited to ≈10 dB and ≈6 dB respectively. For the
signals with initial SNR ranging between 25 dB and 40 dB, the proposed method raised the SNR values by
more than 25 dB outmatching the CAE by more than 4 dB. The wavelet method and BF were limited to 7 dB
increases, moderately surpassed by the PCA method which achieved a 9 dB SNR improvement. For SNR
values ranging from 45 dB to 50 dB corresponding to low noise levels, our denoising method performed ≈15
dB enhancement, outperforming all the other methods limited to an 8 dB upgrade. Furthermore, the proposed
method achieved better SNR enhancements than the method proposed by Gao et al. [16] and Xu et al. [17]
limited to 6 dB and 18 dB SNR increases, respectively. Moreover, Sun and Lu [44] improved a wavelet
threshold processing function for noise reduction on ultrasonic signals. Their work enabled the detection of
defect echoes lost by the traditional thresholding functions. However, their SNR improvement was limited to
6 dB compared to the method proposed in this paper.
Thereafter, the P’r is analyzed in Figure 5 as an accurate comparison metric linked to the covariance
computed between the denoised sample and its corresponding noise-free signal. For SNR higher than 30 dB,
all the methods have similar P’r coefficients. However, for higher noise powers, P’r coefficients of the PCA,
DWT, and BF methods undergo dramatic decreases. Meanwhile, the Att-CAE proposed in this paper alongside
a slightly lower CAE method resulted in considerably higher values (≈1). This metric then confirms that the
Att-CAE compared to other methods considerably reduces the noise, even for very poor SNR. In brief,
whatever the SNR and in particular in very noisy situations, the Att-CAE outperforms traditional and Machine
Learning methods. This yields better noise reductions that will lead to an optimization of signal position
identification essential for imaging techniques.
Figure 4. SNR distributions’ means before and after
denoising by mean of the compared methods
Figure 5. Correlation coefficients’ means of each
evaluation database after noise reduction using the
proposed method, BF, DWT, PCA, and the CAE
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3.2. Method’s reusability and carbon footprint
The method proposed in this paper requires minor reparameterization and no pre-knowledge of the
signals’ features or high skills, which prevents exhaustive parameters’ optimization computations, contrary to
other traditional methods where often several characteristics of the signal are essential to the denoising process
as well as an optimization of the parameters for each configuration. Moreover, the distributed-computing
feature promotes the proposed method compared to the other ones. Lately, huge DL models demand more and
more computation power and energy, which raises concerns about the environmental effects of these
algorithms. For this purpose, we proposed an Att-CAE that could be recycled using transfer learning
approaches [45]. These techniques permit retrieval of similar performances on different data distributions for
correlative applications preventing full retraining from scratch and resulting in a reduction of the carbon
footprint of the method for a greener algorithm. The model proposed in this paper was trained on NVIDIA’s
Tesla K80 GPU, and its training carbon footprint was estimated to be less than 5.04 𝑘𝑔𝐶𝑂2𝑒𝑞 [46].
4. CONCLUSION
In this paper, an Att-CAE is proposed for ultrasonic signal denoising. The DL model learns a
compressed representation of the noisy ultrasonic signals providing the most relevant features of the latter.
This representation is then leveraged to reconstruct the denoised signals. Attention gates were employed at the
level of the skip connections to filter the features shared between the encoder and the decoder layers. The
method proposed in this paper was compared to BF, DWT, PCA, and CAE methods, where the experiments
collated the signal-to-noise ratios and Pearson correlation coefficient results. The DL model showed
considerable improvements in the SNR values up to 30 dB outmatching the compared methods’ SNR
enhancement. Considering the correlation coefficients, the proposed method alongside the CAE resulted in
very high values (≈1) on signals with different noise intensities, which proves the efficient noise suppression,
while the PCA, DWT, and BF methods achieved similar values only when the SNRs of the noisy signals were
higher than 35 dB. The proposed deep learning model effectively denoises signals at different noise levels and
recovers the signal waveform even when the signal is heavily corrupted by the noise. Such an efficient
algorithm will lead to an improvement in the ultrasonic imaging process, enhancing the resolution of medical,
industrial, and other applications based on this technology. Future work will then focus on the application of
the proposed DL method to the estimation of the times of flight of ultrasonic signals. The quality of the Att-
CAE pulse detection should then allow an enhancement the axial resolution of imaging devices. With a similar
objective, this method will also be extended to higher frequencies where noise becomes a dominant problem
due to ultrasound attenuation.
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Attention gated encoder-decode for ultrasonic signal denoising (Nabil Jai Mansouri)
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BIOGRAPHIES OF AUTHORS
Nabil Jai Mansouri received his engineering degree from the National School
of Applied Sciences of Fez, Morocco. He is pursuing his Ph.D. degree at the Sidi Mohamed
Ben Abdellah University and the University of Montpellier. His Ph.D. thesis focuses on Deep
Learning methods for ultrasonic signal processing. He can be contacted at emails: nabil.jai-
mansouri@umontpellier.fr, nabil.jaimansouri@usmba.ac.ma.
Ghizlane Khaissidi , National Ph.D. holder in 2009 of the Sidi Mohamed Ben
Abdellah University in Fez in Image Processing and Computer science. Currently a Professor
at the National School of Applied Sciences (ENSA), University USMBA Fez (Morocco), and
member of the Lab of computing and interdisciplinary physics (L.I.P.I). Her research
activities concern image processing and its applications in medicine, heritage preservation
(indexing of old manuscripts), societal dimension of applications (applications for the blind
and visually impaired), and handwriting analysis for the detection of neurodegenerative
pathologies. Machine learning, Deep learning, and data analysis. She can be contacted at
email: ghizlane.khaissidi@usmba.ac.ma.
Gilles Despaux graduated from a School of Engineers in Robotics in 1990. He
received his Ph. D. in "Electrical Engineering" in 1993 from Montpellier University, France,
where he was Assistant Professor until 2006 and then Professor. He is currently head of the
acoustic team of the Institute of Electronics and Systems and is the Director of the master’s
degree in Electrical Engineering. His research area concerns material characterization and
imaging by Acoustic Microscopy which he first studied at Stanford Univ. during his Master's
Degree Training period in 1990. He is a member of the French Society of Acoustics and an
IEEE Member. He can be contacted at email: gilles.despaux@umontpellier.fr.
Mostafa Mrabti obtained a Ph.D. degree from the USMBA University in 1996,
Fes Morocco. He is a Professor at the National School of Applied Sciences (ENSA),
University USMBA Fez (Morocco), and a member of the LIPI laboratory. He is the author
of many publications. His research interests are automatic control, signal processing, and
information. He can be contacted at email: mostafa.mrabti@usmba.ac.ma.
Emmanuel Le Clézio graduated in mathematics in 1995 and electronics in 1997
from the Univ. of Rennes I, France, and received the Master of Acoustics from the Univ. of
Le Mans, France, in 1998. He received his Ph.D. degree in mechanics (acoustics) from the
Univ. of Bordeaux 1, France, in 2001. For 10 years he was an Assistant Professor of Physics
and Telecommunications at the University of Tours (IUT-Blois) and since 2011 Professor of
Electronics at the University of Montpellier. His research area concerns complex material
characterization by acoustic methods. He is a member of the French Society of Acoustics and
an IEEE Member. He can be contacted at email: emmanuel.le-clezio@umontpellier.fr.