This document discusses performance analysis of fingerprint image compression using two techniques: K-SVD-SR and SPIHT. K-SVD-SR is a novel compression algorithm based on K-singular value decomposition and sparse representation that has the ability to update the dictionary. The document compares the compressed image quality of K-SVD-SR to SPIHT in terms of mean square error and peak signal-to-noise ratio. It also describes the methodology used in K-SVD-SR, including constructing the dictionary from a training set of fingerprint patches and compressing new fingerprints by representing patches as sparse combinations of dictionary atoms.
Quality Prediction in Fingerprint CompressionIJTET Journal
A new algorithm for fingerprint compression based on sparse representation is introduced. At first, dictionary is constructed by sparse combination of set of fingerprint patches. Designing dictionaries can be done by either selecting one from a prespecified set or adapting a dictionary to a set of training signals. In this paper, we use K-SVD algorithm to construct dictionary. After computation of dictionary, the image gets quantized, filtered and encoded. The resultant image obtained may be of three qualities: Good, Bad and Ugly (GBU problem). In this paper, we overcome the GBU problem by prediction the quality of image.
Fuzzy Type Image Fusion Using SPIHT Image Compression TechniqueIJERA Editor
This paper presents a fuzzy type image fusion technique using Set Partitioning in Hierarchical Trees (SPIHT).
It is concluded that fusion with higher single levels provides better fusion quality. This technique can be used
for fusion of fuzzy images as well as multi model image fusion. The proposed algorithm is very simple, easy to
implement and could be used for real time applications. This is paper also provided comparatively studied
between proposed and previous existing technique and validation of the proposed algorithm as Peak Signal to
Noise Ratio (PSNR), Root Mean Square Error (RMSE).
An Image Steganography Algorithm Using Huffman and Interpixel Difference Enco...CSCJournals
Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Quality Prediction in Fingerprint CompressionIJTET Journal
A new algorithm for fingerprint compression based on sparse representation is introduced. At first, dictionary is constructed by sparse combination of set of fingerprint patches. Designing dictionaries can be done by either selecting one from a prespecified set or adapting a dictionary to a set of training signals. In this paper, we use K-SVD algorithm to construct dictionary. After computation of dictionary, the image gets quantized, filtered and encoded. The resultant image obtained may be of three qualities: Good, Bad and Ugly (GBU problem). In this paper, we overcome the GBU problem by prediction the quality of image.
Fuzzy Type Image Fusion Using SPIHT Image Compression TechniqueIJERA Editor
This paper presents a fuzzy type image fusion technique using Set Partitioning in Hierarchical Trees (SPIHT).
It is concluded that fusion with higher single levels provides better fusion quality. This technique can be used
for fusion of fuzzy images as well as multi model image fusion. The proposed algorithm is very simple, easy to
implement and could be used for real time applications. This is paper also provided comparatively studied
between proposed and previous existing technique and validation of the proposed algorithm as Peak Signal to
Noise Ratio (PSNR), Root Mean Square Error (RMSE).
An Image Steganography Algorithm Using Huffman and Interpixel Difference Enco...CSCJournals
Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Efficient Reversible Data Hiding Algorithms Based on Dual Predictionsipij
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of
prediction error histograms is proposed. The algorithm extends the efficient modification of prediction
errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data
embedding. The motivation behind using two predictors is driven by the fact that predictors have different
prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The
key feature of the proposed algorithm lies in using two predictors without the need to communicate
additional overhead with the stego image. Basically, the identification of the predictor that is used during
embedding is done through a set of rules. The proposed algorithm is further extended to use two and three
bins in the prediction errors histogram in order to increase the embedding capacity. Performance
evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in
boosting the embedding capacity while providing competitive quality for the stego image.
Graph Signal Processing: an interpretable framework to link neurocognitive ar...Nicolas Farrugia
This talk attemps to motivate the use of Graph Signal Processing to analyse neuroimaging data. After introducing recent paradigm shifts in neuroimaging research (network neuroscience and principal gradients of connectivity), we present our recent work in combining GSP and machine learning, which show substantial improvements in inference based approach using simple machine learning techniques. We finally open new perspectives regarding the potential of using GSP for interpretable neuroscientific research.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Compressive Sensing in Speech from LPC using Gradient Projection for Sparse R...IJERA Editor
This paper presents compressive sensing technique used for speech reconstruction using linear predictive coding because the
speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily.
This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete
points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient
Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The
experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
Fast and robust tracking of multiple faces is receiving increased attention from computer vision researchers as it finds potential applications in many fields like video surveillance and computer mediated video conferencing. Real-time tracking of multiple faces in high resolution videos involve three basic tasks namely initialization, tracking and display. Among these, tracking is quite compute intensive as it involves particle filtering that won’t yield a real time performance if we use a conventional CPU based system alone.
This paper presents a study of the efficiency and performance speedup achieved by applying Graphics Processing Units for Face Recognition Solutions. We explore one of the possibilities of parallelizing and optimizing a well-known Face Recognition algorithm, Principal Component Analysis (PCA) with Eigenfaces. In recent years, the Graphics Processing Units (GPU) has been the subject of extensive research and the computation speed of GPUs has been rapidly increasing.
Efficient Reversible Data Hiding Algorithms Based on Dual Predictionsipij
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of
prediction error histograms is proposed. The algorithm extends the efficient modification of prediction
errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data
embedding. The motivation behind using two predictors is driven by the fact that predictors have different
prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The
key feature of the proposed algorithm lies in using two predictors without the need to communicate
additional overhead with the stego image. Basically, the identification of the predictor that is used during
embedding is done through a set of rules. The proposed algorithm is further extended to use two and three
bins in the prediction errors histogram in order to increase the embedding capacity. Performance
evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in
boosting the embedding capacity while providing competitive quality for the stego image.
Graph Signal Processing: an interpretable framework to link neurocognitive ar...Nicolas Farrugia
This talk attemps to motivate the use of Graph Signal Processing to analyse neuroimaging data. After introducing recent paradigm shifts in neuroimaging research (network neuroscience and principal gradients of connectivity), we present our recent work in combining GSP and machine learning, which show substantial improvements in inference based approach using simple machine learning techniques. We finally open new perspectives regarding the potential of using GSP for interpretable neuroscientific research.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Compressive Sensing in Speech from LPC using Gradient Projection for Sparse R...IJERA Editor
This paper presents compressive sensing technique used for speech reconstruction using linear predictive coding because the
speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily.
This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete
points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient
Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The
experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
Fast and robust tracking of multiple faces is receiving increased attention from computer vision researchers as it finds potential applications in many fields like video surveillance and computer mediated video conferencing. Real-time tracking of multiple faces in high resolution videos involve three basic tasks namely initialization, tracking and display. Among these, tracking is quite compute intensive as it involves particle filtering that won’t yield a real time performance if we use a conventional CPU based system alone.
This paper presents a study of the efficiency and performance speedup achieved by applying Graphics Processing Units for Face Recognition Solutions. We explore one of the possibilities of parallelizing and optimizing a well-known Face Recognition algorithm, Principal Component Analysis (PCA) with Eigenfaces. In recent years, the Graphics Processing Units (GPU) has been the subject of extensive research and the computation speed of GPUs has been rapidly increasing.
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
Design and Implementation of EZW & SPIHT Image Coder for Virtual ImagesCSCJournals
The main objective of this paper is to designed and implemented a EZW & SPIHT Encoding Coder for Lossy virtual Images. .Embedded Zero Tree Wavelet algorithm (EZW) used here is simple, specially designed for wavelet transform and effective image compression algorithm. This algorithm is devised by Shapiro and it has property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. SPIHT stands for Set Partitioning in Hierarchical Trees. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images. The SPIHT algorithm was powerful, efficient and simple image compression algorithm. By using these algorithms, the highest PSNR values for given compression ratios for a variety of images can be obtained. SPIHT was designed for optimal progressive transmission, as well as for compression. The important SPIHT feature is its use of embedded coding. The pixels of the original image can be transformed to wavelet coefficients by using wavelet filters. We have anaysized our results using MATLAB software and wavelet toolbox and calculated various parameters such as CR (Compression Ratio), PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and BPP (Bits per Pixel). We have used here different Wavelet Filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal Filters .In this paper we have used one virtual Human Spine image (256X256).
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
Enhanced Latent Fingerprint Segmentation through Dictionary Based ApproachEditor IJMTER
The accuracy of latent finger print matching compared to roll and plain finger print
matching is significantly lower due to background noise, poor ridge quality and overlapping
structured noise in latent images. In this paper the proposed algorithm is dictionary-based approach
for automatic segmentation and enhancement towards the goal of achieving “lights out” latent
identifications system. Total variation decomposition model with L1 fidelity regularization in latent
finger print image remove background noise. A coarse to fine strategy is used to improve robustness
and accuracy. It improves the computational efficiency of the algorithm.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Similar to Performance Analysis on Fingerprint Image Compression Using K-SVD-SR and SPIHT (20)
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers