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arXiv 2023年

作者： Bhattacharya, Swati Hari, K.V.S. Eldar, Yonina C. Statistical Signal Processing Lab Department of Electrical Communication Engineering Indian Institute of Science Bangalore India Faculty of Math and Computer Science Weizmann Institute of Science Israel

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for Multiple-Input Multiple-Output (MIMO) wireless communication systems. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) and its model-based neural network version JED using Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna configurations. Both proposed algorithms exploit the non-smooth constraint, that occurs as a result of the Quadrature Amplitude Modulation (QAM) data symbols, to effectively improve the performance using the ADMM iterations. The proposed unfolded network JED-U-ADMM consists of a few trainable parameters and requires a small training set. We show the efficacy of the proposed methods for both uncorrelated and correlated MIMO channels. For certain configurations, the gain in SNR for a desired BER of 10−2 for the proposed JED-ADMM and JED-U-ADMM is upto 4 dB and is also accompanied by a significant reduction in computational complexity of upto 75%, depending on the MIMO configuration, as compared to the complexity of JED-AM. © 2023, CC BY.

关键词： Channel estimation

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STARESNET: A NETWORK IN SPACETIME ALGEBRA TO SOLVE MAXWELL’S PDES

arXiv

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arXiv 2024年

作者： Pepe, Alberto Lasenby, Joan Buchholz, Sven Signal Processing and Communications Lab University of Cambridge Cambridge United Kingdom Department of Computer Science and Media Technische Hochschule Brandenburg University of Applied Sciences Brandenburg Germany

We introduce STAResNet, a ResNet architecture in Spacetime Algebra (STA) to solve Maxwell’s partial differential equations (PDEs). Recently, networks in Geometric Algebra (GA) have been demonstrated to be an asset for truly geometric machine learning. In [1], GA networks have been employed for the first time to solve partial differential equations (PDEs), demonstrating an increased accuracy over real-valued networks. In this work we solve Maxwell’s PDEs both in GA and STA employing the same ResNet architecture and dataset, to discuss the impact that the choice of the right algebra has on the accuracy of GA networks. Our study on STAResNet shows how the correct geometric embedding in Clifford Networks gives a mean square error (MSE), between ground truth and estimated fields, up to 2.6 times lower than than obtained with a standard Clifford ResNet with 6 times fewer trainable parameters. STAREsNet demonstrates consistently lower MSE and higher correlation regardless of scenario. The scenarios tested are: sampling period of the dataset;presence of obstacles with either seen or unseen configurations;the number of channels in the ResNet architecture;the number of rollout steps;whether the field is in 2D or 3D space. This demonstrates how choosing the right algebra in Clifford networks is a crucial factor for more compact, accurate, descriptive and better generalising pipelines. © 2024, CC BY.

关键词： Mean square error

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image processing

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Journal of computer and communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Hybrid Single Image Super-Resolution Algorithm for Medical Images

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computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid El-Shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Aswan UniversityAswan81542Egypt Electronics and Micro-Electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to ***,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)***,it is important to maintain the shape and size of the medical images while enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce *** proposed framework comprises five GAN models:Enhanced SR Generative Adversarial Networks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×***,several evaluation metrics like Peak signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage *** obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

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Enhanced Multidimensional Harmonic Retrieval in MIMO Wireless Channel Sounding

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IEEE Internet of Things Journal 2025年第11期12卷 16243-16255页

作者： Zhang, Yanming Xu, Wenchao Jin, A-Long Tang, Tianquan Li, Min Ma, Peifeng Jiang, Lijun Gao, Steven The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China HKU Zhejiang Institute of Research and Innovation Lab for Aerodynamics and Acoustics Hangzhou311305 China Tianjin University School of Microelectronics Tianjin300000 China Institute of Space and Earth Information Science The Chinese University of Hong Kong Department of Geography and Resource Management Hong Kong Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

This article introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a 2-D matrix format along the rth dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the rth dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding. © 2014 IEEE.

关键词： Eigenvalues and eigenfunctions

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Santoresh Kumari Dhimann Tinny Sawhney Rakesh Kumar Yadav Department of Computer Science & Engineering Maharishi University of Information Technology Lucknow India Department of Electronics Digital Signal Processing Lab University of Jammu Jammu India

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not available in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier.

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MoD-ART: Modal Decomposition of Acoustic Radiance Transfer

arXiv

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arXiv 2024年

作者： Scerbo, Matteo Schlecht, Sebastian J. Ali, Randall Savioja, Lauri De Sena, Enzo Institute of Sound Recording University of Surrey Guildford United Kingdom Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany Department of Computer Science Acoustics Lab Aalto University Espoo Finland

Modeling late reverberation at interactive speeds is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the late reverberation is dependent on the sound sources’ and listeners’ positions, and therefore must be adapted to their movements in real time. We present a novel approach to the task, named modal decomposition of Acoustic Radiance Transfer (MoD-ART), which can handle highly complex scenarios with efficiency. The approach is based on the geometrical acoustics method of Acoustic Radiance Transfer, from which we extract a set of energy decay modes and their positional relationships with sources and listeners. In this paper, we describe the physical and mathematical meaningfulness of MoD-ART, highlighting its advantages and applicability to different scenarios. Through an analysis of the method’s computational complexity, we show that it compares very favourably with ray-tracing. We also present simulation results showing that MoD-ART can capture multiple decay slopes and flutter echoes. Copyright © 2024, The Authors. All rights reserved.

关键词： Reverberation

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A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services

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Frontiers of Information Technology & Electronic engineering 2023年第7期24卷 1045-1061页

作者： Fatma KHALLAF Walid EL-SHAFAI El-Sayed M.EL-RABAIE Naglaa F.SOLIMAN Fathi EAbd E.L-SAMIE Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6^(th)October CityGiza 12451Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh I1586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia

Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive ***,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be *** paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLM)***,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive *** efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT ***,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVM)*** limitations of watermark embedding are also explained to conserve watermarking ***,3D-CLM encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy *** proposed security system is effective against many types of channel noise attacks,according to simulation results.

关键词： Color medical image Quaternion Adaptive watermarking Encryption Fractional transform Three-dimensional chaotic logistic map(3D-CLM)

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A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

arXiv

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arXiv 2023年

作者： Fister, Iztok Fister, Iztok Fister, Dušan Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Multi-Task Sparse signal Recovery with Dirichlet Process Priors Based on Expectation Propagation Technique 7

Multi-Task Sparse Signal Recovery with Dirichlet Process Pri...

引用

7th International Conference on signal and Image processing, ICSIP 2022

作者： Fu, Yin Wu, Qisong Zhang, Yimin D. Amin, Moeness G. Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing210096 China Temple University Department of Electrical and Computer Engineering PhiladelphiaPA19122 United States Villanova University Center for Advanced Communications VillanovaPA19085 United States

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Exploiting the shared information among tasks to significantly improve the sparse reconstruction performance lays the essence of multi-task compressive sensing. In this paper, a novel generative model of multi-task compressive sensing with Dirichlet process (DP) priors is proposed and the sharing mechanisms among tasks are revealed, yielding a principled means of inferring the clusters as well as performing compressive sensing inversion simultaneously. The spike-and-slab priors are first used to model the group sparsity among tasks within the identical cluster, and the DP priors are then introduced to automatically perform clustering for the tasks. The expectation propagation method is finally carried out to take the inference for posterior distribution approximation. The superiority of the proposed method over state-of-the-art algorithms is demonstrated by using experimental results on both numerical data and real data sets. © 2022 IEEE.

关键词： Compressed sensing

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