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arXiv 2024年

作者： Gilles, Jérome Castro, Richard Department of Mathematics & Statistics San Diego State University 5500 Campanile Dr San DiegoCA92182 United States

Due to their adaptive nature, empirical wavelets had several successes in many fields from engineering, science, medical signal/image processing. Recently, a general theoretical framework has been developed in the one-dimensional case, showing the possibility to build empirical wavelets from any classic mother wavelets. Given extensive literature both in theory and applications of classic wavelet frames, it is legitimate to ask about the feasibility of building empirical wavelet frames. We address this question in this paper. We prove several results which provide conditions on the existence of empirical wavelet frames taking into account the above mentioned *** Codes 42C40 © 2024, CC BY-NC-ND.

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Optimizing Hybrid Sparse and Block-Based Compressed Sensing: A Performance Study for Biomedical Imaging and Video Surveillance applications

Optimizing Hybrid Sparse and Block-Based Compressed Sensing:...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Nitish Vashisht M. Sudhakara Reddy Gajanan H Chavhan P. K. Miniappan Praney Madan Venkatasubramanian D Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka E&TC Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

One of the most promising fields, compressed sensing has found a home in video streaming applications, where it seeks to compress or sparsely represent signals by reducing their dimensions. The sparse representation is a good approximation to the signal since the amplitude of the coefficient’s decays according to the power law. signals are projected into low-dimensional space to generate measurement vectors. A sparse signal may be accurately rebuilt using the measurement vector. signals or noise that are not sparse but can be compressed and might result in an approximation. images in the frequency or wavelet domain are ideal for Compressed Sensing because they are compressible by nature. A more precise reconstruction of the original picture is possible due to an improvement in reconstructed image quality and a reduction in the compression ratio, which is the ratio of the dimensions of the number of pixels to the measurement *** Sparse and Block-based Compressed Sensing Algorithm (HSBCA) is the name of the approach that merged sparse-based and block-based compressed sensing algorithms. Block Compressed Sensing, the first module of HSSBCA, processes each message by dividing it into numerous picture regions, which results in decreased run time. In contrast, performance drops while picture reconstruction is underway; to remedy this, the research employs the second module, the sparse compressed sensing method, to boost reconstruction efficiency.

关键词： image coding wavelet domain Sparse approximation Magnetic resonance imaging signal processing algorithms Biomedical measurement Streaming media Vectors image reconstruction Compressed sensing

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Biosignal classification for human identification based on convolutional neural networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第7期34卷 e4685-e4685页

作者： Siam, Ali, I Sedik, Ahmed El-Shafai, Walid Abou Elazm, Atef El-Bahnasawy, Nirmeen A. El Banby, Ghada M. Khalaf, Ashraf A. M. Abd El-Samie, Fathi E. Kafrelsheikh Univ Fac Artificial Intelligence Dept Embedded Network Syst Technol Kafr Al Sheikh Egypt Kafrelsheikh Univ Fac Artificial Intelligence Dept Robot & Intelligent Machines Kafr Al Sheikh Egypt Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia Egypt Menoufia Univ Fac Elect Engn Dept Comp Sci & Engn Menoufia Egypt Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia Egypt Minia Univ Fac Engn Dept Elect & Elect Commun Al Minya Egypt

Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre-processing, and feature extraction/classification. The pre-processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre-processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky-Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

关键词： Biometrics deep learning human identification photoplethysmography (PPG) signal denoising

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Analytic and directional wavelet packets in the space of periodic signals

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APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2023年第1期67卷

作者： Averbuch, Amir Neittaanmaki, Pekka Zheludev, Valery Tel Aviv Univ Sch Comp Sci IL-69978 Tel Aviv Israel Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland

The paper presents a versatile library of analytic and quasi-analytic complex-valued wavelet packets (WPs) which originate from discrete splines of arbitrary orders. The real parts of the quasi-analytic WPs are the regular spline-based orthonormal WPs designed in [4]. The imaginary parts are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Tensor products of 1D quasi-analytic WPs provide a diversity of 2D WPs oriented in multiple directions. The designed computational scheme in the paper enables us to get fast and easy implementation of the WP transforms. The presented WPs proved to be efficient in signal/image processing applications such as restoration of images degraded by either additive noise or missing of up to 90% of their pixels.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： Directional wavelet packet Discrete periodic splines Analytic wavelet packet image denoising and impainting

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An Integrated Thresholding and Morphological Process with Histogram-based Method for Brain Tumor Analysis and MRI Tumor Detection

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Deepa, A.R. Chaurasia, Mousmi Ajay Vardhan, Peram Sai Harsha Ritwika, Ganishetti Kumar, Mamillapalli Samanth Nettm, Yaswanth Chowdary Koneru Lakshmaih Education Foundation A.P Guntur India Muffakham Jah College of Engineering & Technology Hyderabad India

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning. OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory, power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture. METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high *** proposed multiplier is delay efficient. RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture. CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay. © 2024 A. R. Deepa et al., licensed to EAI.

关键词： Discrete wavelet transforms

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Detection and Classification of Cardiovascular Diseases Using Neural Networks

Detection and Classification of Cardiovascular Diseases Usin...

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signal processing Algorithms, Architectures, Arrangements and applications (SPA)

作者： Bastián Estay Zamorano Ali Dehghan Firoozabadi Alessio Brutti Pablo Adasme David Zabala-Blanco Pablo Palacios Játiva Cesar A. Azurdia-Meza Department of Electricity Speech Music & Image Processing Laboratory (SMIPL) Universidad Tecnológica Metropolitana Santiago Chile Center for Augmented Intelligence Fondazione Bruno Kessler (FBK) Trento Italy Department of Electrical Engineering Universidad de Santiago de Chile Santiago Chile Department of Computing and Industries Universidad Católica del Maule Talca Chile Escuela de Informática y Telecomunicaciones Universidad Diego Portales Santiago Chile Department of Electrical Engineering Universidad de Chile Santiago Chile

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

This research proposes the use of convolutional neural networks (CNNs) for classifying the electrocardiograms (ECGs) using continuous wavelet transform (CWT). Various CNN architectures were compared and optimized, which employ an algorithm that halts training when the network converges. Three PhysioNet databases were collected, temporally segmented, and transformed into scalograms using CWT. Training was conducted in MATLAB, initially with the stochastic gradient descent (SGD) optimizer with Momentum and secondly with adaptive moment estimation (ADAM) optimizer, to assess its impact on learning. The results indicate that both AlexNet and GoogleNet are the best networks in terms of accuracy and training time. ADAM improves performance and training time across all networks compared to SGD, and a decrease in performance was observed when the signals were affected by noise.

关键词： Training Heart Continuous wavelet transforms Accuracy Noise signal processing algorithms Real-time systems Classification algorithms Convolutional neural networks History

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Stockwell Transform and its Modifications in signal processing Courses: Comparison and Features

Stockwell Transform and its Modifications in Signal Processi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Snježana Maksimović Slavica Gajić Faculty for Architecture Civil Engineering and Geodesy University of Banja Luka Banja Luka Bosnia and Herzegovina Faculty of Electrical Engineering University of Banja Luka Banja Luka Bosnia and Herzegovina

It is well known that signal transformations are very powerful and popular methods for signal and image processing. Because of its ability to decompose signals, signal analysis on form level is enabled. Therefore, there are many more or less known signal transformations in the scientific community. The most popular ones are Fourier transform, short time Fourier transform, wavelet transform, Discrete cosine transform and those transforms are commonly mentioned in academic literature. On the other hand, Stockwell transform has self-adjusted window size, while fractional Stockwell transform improves the flexibility of time–frequency analysis and energy of signal spectra. Because of the above, in this paper Stockwell transform advantages and applications in the signal and image processing area are analyzed. Its features and parity to the other transformations is shown. Also, the most commonly used fractional transformations that improve standard transformations, because their ability of rotation, were listed.

关键词： Time-frequency analysis image resolution image coding Transforms Discrete wavelet transforms Discrete cosine transforms Standards

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wavelet-like Transform with Subbands Fusion in Decoupled Structure for Deep image Compression

Wavelet-like Transform with Subbands Fusion in Decoupled Str...

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Picture Coding Symposium, PCS

作者： Ke Ma Yaojun Wu Zhaobin Zhang Semih Esenlik Xiaoyan Sun Kai Zhang Li Zhang University of Science and Technology of China HeFei Anhui P.R. China Bytedance Inc. San Diego CA USA

ISBN: (数字)9798350358483

ISBN: (纸本)9798350358490

wavelet-like transform, based on convolutional neural network (CNN), is content-adaptive and has made remarkable achievements in end-to-end image compression. However, the subsequent sequential processing of each subband in the entropy module takes a relatively long decoding time, resulting in incon-venience for real-world applications. In this work, for lossy image compression, the wavelet-like transform is transplanted into the prevailing autoencoder structure to enhance the analysis and synthesis transform due to its excellent decomposition capability. The obtained subbands of different frequencies will undergo a hierarchical decorrelation architecture for subband fusion, also called cross fusing module. The specialized treatment will be applied to different subbands according to their spatial resolution to attain a more compact latent representation. In addition, the proposed solution features an architecture that decouples the arithmetic decoding process from the sample prediction process, which significantly reduces the decoding complexity. Experiments on the Kodak test set show that the proposed method achieves −3.04% BD-Rate compared to existing decoupled end-to-end structure in RGB Peak signal-to-Noise Ratio (PSNR).

关键词： wavelet transforms image coding PSNR Redundancy wavelet analysis Entropy Decorrelation

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AN ATTENTION BASED wavelet CONVOLUTIONAL MODEL FOR VISUAL SALIENCY DETECTION

AN ATTENTION BASED WAVELET CONVOLUTIONAL MODEL FOR VISUAL SA...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Bhooshan, Reshmi S. Suresh, K. Coll Engn Dept ECE Thiruvananthapuram Kerala India Govt Engn Coll Dept ECE Thiruvananthapuram Kerala India

ISBN: (纸本)9781728176055

The emergence of deep neural architectures greatly enhanced the accuracy of salient region detection algorithms that plays a vital role in computer vision applications. However, the accurate extraction of regions with fine boundaries still remains as a challenge. In this work, an attention based wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. Further enhancement of the fine boundaries of the predicted map is made possible by the inclusion of a combinational loss function of balanced cross entropy loss, SSIM loss and edge loss. The effectiveness of the method is evaluated using three benchmark datasets and the results shows better performance achieving a minimum Mean Absolute Error (MAE) of 0.032 and maximum F-measure of 0.938.

关键词： Visual Saliency detection wavelet Convolutional model Feature Attention Network

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Utilising Machine Learning for Modulation and signal processing

Utilising Machine Learning for Modulation and Signal Process...

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Communication & Computing (WCONF), World Conference on

作者： Daljeet Pal Singh Madhav Singh Solanki Renuka Jyothi S Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform; wavelet-based totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Performance evaluation wavelet transforms Codecs Shape Modulation signal processing algorithms Feature extraction Classification algorithms Standards signal detection

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