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arXiv 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Yu, Junzhi Zhai, Wenlong Zheng, Changwen Xu, Fanjiang Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China College of Engineering Peking University China Department of Computer Science and Technology Tsinghua University China

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10, 000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning

Epileptic Seizure Detection Using the EEG Signal Empirical M...

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Smart Computing and Application (ICSCA), International Conference on

作者： Jana Nassir Majeda Alasabi Saeed Mian Qaisar Muzammil Khan Computer Science Department Effat University Jeddah Saudi Arabia Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne France Department of Computer and Software technology University of Swat Swat Pakistan

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%.

关键词： Support vector machines Empirical mode decomposition Epilepsy Medical treatment Pattern classification Nonlinear filters Sensitivity and specificity

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Numerical Analysis of Bi-fluid PV/T Hybrid Collector Using the Finite Difference Method 6th

Numerical Analysis of Bi-fluid PV/T Hybrid Collector Using t...

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6th International Conference on Wireless Technologies, Embedded and Intelligent systems, WITS 2020

作者： El Manssouri, Oussama Hajji, Bekkay Gagliano, Antonio Tina, Giuseppe Marco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco DIEEI: Department of Electric Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789813368927

The PV/T is called as a hybrid bi-fluid type solar collector when both fluids (water and air) are used as working fluids. This type of collector provide a wide range of thermal applications and several modes can be executed depending on energy needs and applications: air mode, water mode and simultaneous mode (air and water). This paper presents a numerical analysis of PV/T bi-fluid collector using the finite difference method. Energy balance equations were established for each layer and solved using the Gauss–Seidel iteration method. The performance of the PV/T hybrid is evaluated according to the operating mode of the fluid (independently and simultaneously). The simulation results are compared with literature (Modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case. https://***/10.1109/iceit48248.2020.9113233, [10]), and indicate that when both fluids are used simultaneously, the overall electrical and thermal performance of the hybrid collector is considered satisfactory compared to the situation where two fluids operate independently. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Finite difference method

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Low level Syntax Elements Study in Intra HEVC/H.265 Video Codec 7

Low level Syntax Elements Study in Intra HEVC/H.265 Video Co...

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7th International Conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Menasri, Wahiba Meddah, Karim University of Sciecnes and Technology Houari Boumediene Usthb Laboratory of Image Processing and Radiation Algiers Algeria University Yahya Fares of Medea Nouveau Pôle Urbain Faculty of Technology Medea Algeria Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontrealQC Canada Usthb Laboratory of Instrumentation Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9781665480420

Ultra High Definition Television (UHDTV) imposes extremely high throughput requirement on video encoders based on High Efficiency Video Coding (H.265/HEVC). HEVC adopt many advanced developed techniques in order to compress and decompress the video sequence for ensuring the real time requirements with keeping the quality. The decoded video is obtained according to the syntax elements language specified by the HEVC standard. Consequently, HEVC adopt 74 syntax elements at the low level which are divided into four partitions: including quad tree partitioning, intra and inter prediction, transform level, quantization and loop filtering. This work propose a profound study of HEVC low level syntax elements. The four partitions of syntax elements are defined, detailed and specified (role). After that, the encoding process and order of each syntax element is given by diagram blocks followed by an examples of encoding process syntax elements for 4x4 and 8x8 transform blocks (references blocks) by using Matlab ***, performance comparison of different benchmarks is given in order to highlights the decoding efficiency of HEVC compared it predecessor AVC (Advenced Video Coding). This work give the only detailed study that demonstrate the main functionality of the all intra syntax elements encoding process in CABAC HEVC. © 2022 IEEE.

关键词： MATLAB

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Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease

arXiv

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arXiv 2024年

作者： Kim, So Yeon Wang, Sehee Choe, Eun Kyung Department of Artificial Intelligence Ajou University Suwon Korea Republic of Department of Software and Computer Engineering Ajou University Suwon Korea Republic of Department of Surgery Seoul National University Hospital Healthcare System Gangnam Center Seoul Korea Republic of

Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation. © 2024, CC BY.

关键词： Diseases

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Research on Image Captioning Based on Vision-language Pre-trained Models

Research on Image Captioning Based on Vision-language Pre-tr...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Shiwei Zou Yuxiang Xie Jie Yan Yingmei Wei Xidao Luan Department of System Engineering National University of Defense Technology Hunan China Key Laboratory of Big Data Department of System Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science Changsha University Hunan China

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively; 2) a multi-modal encoder to align the two modalities; and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance.

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Adapting the Simulated Annealing Algorithm to Enhance the Performance of the LEACH Protocol

Adapting the Simulated Annealing Algorithm to Enhance the Pe...

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International Conference on computer and Applications (ICCA)

作者： Mohammed Benhadji Kaddi Mohammed Omari Mohammed Material Sciences Department Laboratory of Energy Environment and Information System (LEESI) University of Adrar Algeria Mathematics and Computer Science Department LDDI Laboratory University of Adrar Algeria Computer Science and Engineering Department American University of Ras Al Khaimah Ras Al Khaimah United Arab Emirates

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

In parallel with the proliferation and extension of wireless sensor networks (WSNs), as well as the diversity of their applications, such networks continue to fail to operate for lengthy periods of time due to node failure and the inability to replace sensor batteries in most circumstances. This hinders the networks' ability to remain operational over extended periods. That necessitates making the best and most efficient use of the nodes' energy. Hierarchical clustering methods augmented with metaphysical algorithms have produced amazing benefits in terms of network longevity. One of the most significant of these algorithms that draws inspiration from nature and yields excellent results is the simulated annealing technique. In this research, we report on our analysis of this method and how it performs best when used to optimize routing in WSN.

关键词： Wireless sensor networks Protocols Heuristic algorithms Metaheuristics Energy conservation Clustering algorithms Simulated annealing Spread spectrum communication Routing Testing

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An intelligent guided-airborne virus detection 21

An intelligent guided-airborne virus detection

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. National Engineering School of Sfax Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Electronic Industrial ENISo Sousse University Tunisia ENIS Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Majmaah University Faculty of Sciences and Humanities Sciences Department of Computer Science Saudi Arabia Prince Sultan University Dean of Digital Transformation and E-transactions Majmaah11952 Saudi Arabia

ISBN: (纸本)9781665482615

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance. © 2022 IEEE.

关键词： Biosensors

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RSA-1024 Cryptography on Artix-7 FPGA: Medical Imaging Application 16

RSA-1024 Cryptography on Artix-7 FPGA: Medical Imaging Appli...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Ghayoula, Ridha Amara, Wided Smida, Amor Fattahi, Jaouhar Waly, Mohamed I. Bahroun, Sahbi Gmati, Issam El University of Moncton Faculty of Engineering New Brunswick Canada Enit University of Tunis El Manar SysCom Laboratory Tunis Tunisia Majmaah University College of Applied Medical Sciences Majmaah Saudi Arabia Laval University Department of Computer Science && Software Engineering Quebec Canada Institut Superieur d'Informatique University of Tunis El Manar Tunis Tunisia Umm Al Qura University College of Engineering Umm Al Qura Saudi Arabia

ISBN: (纸本)9798350371154

In the domain of medical imaging, ensuring the security of confidential patient data stands as a paramount concern. The imperative for robust encryption to safeguard medical images against unauthorized access has driven the widespread adoption of the RSA algorithm, celebrated for its efficacy in data security assurance. This study specifically centers on the deployment of RSA-1024 cryptography on an Artix-7 FPGA, targeting applications in medical imaging. Acknowledging the demand for heightened security and accelerated processing, we have engineered RSA crypto-accelerators as hardware implementations of the RSA algorithm. We present a comprehensive methodology for implementing the 1024-bit RSA encryption/decryption algorithm, leveraging the square and multiply technique for modular exponentiation. Our algorithms are meticulously instantiated through VHDL code, tailored for execution on the Artix-7 xc7a35tcsg324-1 FPGA manufactured by Xilinx. Rigorous testing using the Vivado 2022.2 tool validates the functionality and dependability of our design. This research underscores the pivotal role of advanced cryptographic methodologies in safeguarding medical images and underscores the strides made in FPGA-based security solutions. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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