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Digital image Semantic Communications with Joint Source-Channel Coding and Constellation Optimization 16

Digital Image Semantic Communications with Joint Source-Chan...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Shen, Linzi Ma, Nan Chen, Jianqiao Song, Mengshu Dong, Chen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Zgc Institute of Ubiquitous-X Innovation and Applications The 6G R&d Department Beijing100876 China

ISBN: (纸本)9798350390643

Recently developed semantic communication facilitates an intelligent and minimalist approach to meet the growing demands for future sixth-generation (6G) communication sys-tems. However, since the constraints of backpropagation during neural network training, the predominant approach of most extant studies are based on differentiable channel inputs, which is difficult to match with existing digital communication systems effectively. Meanwhile, the research on modulation techniques considering constellation optimization for digital semantic communication is still in the early stage. To deal with these issues, we propose a novel deep joint source-channel coding (DJSCC)-based digital semantic communication with considering constellation optimization, which is referred to as DJSCC-C. Firstly, the digital semantic communication system is introduced. Secondly, the DJSCC-C system is proposed for image transmission, which is trained with a newly-constructed loss function, aiming to improve the transmission rate in the digital semantic communication system as well as optimize the joint source-channel coding capability. Finally, simulation results are provided to verify the superiority of the proposed scheme for future design of digital semantic communication systems. © 2024 IEEE.

关键词： Channel coding

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Bearing Vibration signal Restoration Method based on Fuzzy Adaptive Sliding Mode Algorithm

Bearing Vibration Signal Restoration Method Based on Fuzzy A...

引用

2023 IEEE international conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Mao, Yuze Shi, Lei Wang, Xiaokang Wang, Chao Shanghai Aerospace Control Technology Institute Shanghai China

ISBN: (纸本)9798350300826

With the development of intelligent algorithms on the internet and the widespread application of signal recovery technology in the current mechanical industry, signal recovery systems based on fuzzy adaptive sliding mode algorithms are becoming increasingly important for improving mechanical bearing fault diagnosis. In the construction of the entire mechanical bearing signal recovery model, how to improve the accuracy of signal recovery and reduce the time for model signal feature extraction is currently a key issue that needs to be urgently solved. This article conducts simulation experiments and analysis on the parameter dataset of mechanical bearing components that are prone to faults. based on the fuzzy adaptive sliding mode algorithm process, combined with adaptive law parameter calculation and fuzzy system model design, the following conclusion is drawn: the application of improved signal recovery models on the sampled four bearing component parameters has an average improvement of 6.65% in recovery accuracy and 9.25% in recovery efficiency. This method uses fuzzy adaptive sliding mode algorithm to adaptively extract fault features, accurately identify fault types and degrees, innovatively suppress noise and interference, and improve the accuracy of vibration signal recovery. © 2023 IEEE.

关键词： Failure analysis

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Implementation of a universal replicable DSP core in FPGA devices for cascade audio signal processing applications 28th

Implementation of a universal replicable DSP core in FPGA de...

引用

28th international conference on Knowledge based and Intelligent information and Engineering systems, KES 2024

作者： Golka, Lukasz Poczekajlo, Pawel Suszyński, Robert Koszalin University of Technology ul Sniadeckich 2 Koszalin75-453 Poland

The article presents the concept of a universal DSP core, enabling the creation of systems compliant with various processing architectures, such as Single Instruction, Multiple Data - SIMD, Multiple Instruction, Multiple Data - MIMD. The aim was to facilitate a simpler and faster configuration and programming process for complex and more efficient signal processing systems, while minimizing the need for hardware description languages. The architecture and instruction set for the DSP core are described, allowing for simultaneous addressing of multiple operands in a single instruction. Unidirectional signal flows between cores are proposed, resulting in a reduction of connections and increased performance. The testing process and comparison of results with computations performed in Matlab are described. The paper points out future research directions, such as hardware optimization, increasing the number of cores, developing floating-point computations, and modular system construction. The proposed solutions can significantly impact the efficiency and flexibility of future signal processing systems. © 2024 The Authors.

关键词： image segmentation

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Tucker Decomposition based Lossless image Compression for 3D Medical image 8

Tucker Decomposition Based Lossless Image Compression for 3D...

引用

8th IEEE international conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Makandar, Aziz Biradar, Rekha Ksa Wuv Computer Science department India

ISBN: (纸本)9798331505462

The successful operation of a telecommunication system depends on the maintaining and transferring of high compression three-dimension medical images that produce reconstructions of excellent quality after decompression. To address this issue, we presented a Tucker Decomposition based lossless image compression for three-dimension medical image. Tucker decomposition, the extended form of Singular Value Decomposition (SVD), can handle high dimensional information;SVD is limited to two dimensions. Use the Selective Bounding volume (SBV) to find and retrieve the Volume of Interest (VOI). Tucker decomposition is used to break down the extracted volume of interest (VOI) into its component major tensor, coefficient matrices, and distinct parameters. subsequently, an Adaptive Binary Range Coder (ABRC) is utilized for compress these values. By inverting the compression procedure and integrating the uncompressed VOI with the surrounding area employing the bound volume coordinates connected to the compressed three-dimensional image, the final uncompressed VOI is produced. utilizing a range of 3D medical images obtained through different forms of imaging, the proposed approach's performance was assessed using analytical evaluation metrics like the Compression Ratio (CR), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The proposed compression methodology produces consistently high-quality image compressions and produces larger-scale results, as shown by the method's outcome. © 2024 IEEE.

关键词： Voice/data communication systems

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Indoor Accidents Detection for Elderly based on Features Fusion and LSTM 21

Indoor Accidents Detection for Elderly Based on Features Fus...

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21st IEEE international conference on Computer Applications, ICCA 2024

作者： San, Win Pa Pa Zaw, Sai Maung Maung University of Computer Studies Mandalay Image and Signal Processing Lab Mandalay Myanmar University of Computer Studies Mandalay Faculty of Computer Systems and Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

Nowadays, with aging of the human society, the 'ratio of family care givers and elderly' is not equivalent and cannot give enough caring to the elderly in some countries. Therefore, automatic health monitoring system for elderly draws the interests of many researchers and become as special issue. In the implementation of elderly people monitoring system, the detection of fall events plays as an important role because the fall is the common cause which is very risky and threaten the health of elderly. The aim of this research is to implement the vision-based fall detection system which is important for health monitoring of elderly by applying the computer vision and deep learning technology. Most of the traditional vision-based fall detection systems which based on the height, width of human posture, fail to detect the fall events from different camera viewpoints. In this research, to accurately detect the fall from different camera viewpoint, the features fusion approach is applied over the deep learning model. In the implementation of the system, firstly, the human silhouette image is created by using the background subtraction. After that, the human silhouette image which have been extracted from two consecutive frames are fused as a single image called Silhouette History image (SHI). Then, the SHI results are used as shape feature. On the other hand, the motion features called Dense Optical Flow (DOF) is extracted over the two consecutive video frames and fuse SHI and DOF as a single input image. Then, the results of features fusion are feed as input for pretrained Convolution Neural Network (CNN) for extracting the deep CNN features. Finally, the Recurrent Neural Network (RNN) called Long-Short-Term-Memory (LSTM) is trained over those CNN features for recognizing the sequence of fall event. The experiments are performed using the publicly available dataset of UP-fall detection dataset for evaluating and confirming the effectiveness of the proposed method. © 2024

关键词： Fall detection

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th international conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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Enhanced Lane Keeping Assistance through IoT Sensor Fusion and Deep Learning 2

Enhanced Lane Keeping Assistance through IoT Sensor Fusion a...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Pachpol, Shreyash Vignesh, C. Chandra Jeslin, J. Gnana Kumar, B. V. Praveen Viswanathan, C. Srinivasan, C. Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India RAIK Coll Engn & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Usha Rama Coll Engn & Technol Dept Comp Sci & Engn Vijayawada Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

This research introduces a novel method for improving Lane Keeping Assistance (LKA) systems via the use of Convolutional Neural Networks (CNNs) and internet of Things (IoT) sensor fusion. The proposed system compiles information about the vehicle's environment from various sensors, such as cameras, LiDAR, radar, and GPS. Using sensor fusion methods, the system enhances its ability to recognize lane markers, obstructions, and other important road elements by combining the capabilities of each sensor modality. An important part of the system is using CNNs to analyze visual sensor input in real-time. To help drivers stay in their lanes, the system uses CNNs trained on massive datasets of tagged images to identify automobiles, lane markers, and other objects. The system learns from its surroundings and the driver's actions in real time. It can adjust to different roads and driving styles, providing individualized support while keeping everyone safe. The proposed method improves LKA systems' accuracy, dependability, and adaptation to different driving situations, as shown experimentally. Due to cutting-edge vehicle safety technology that combines IoT sensor fusion with CNN-based deep learning, drivers may now travel with more assurance and serenity.

关键词： Automotive Safety Real-time Analysis image Recognition Sensor Integration Automated Driving Safety Enhancement

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IOT based Smart Wheelchair for Elderly Healthcare Monitoring

IOT Based Smart Wheelchair for Elderly Healthcare Monitoring

引用

2023 international conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Dar, Reyaz Ahmad Khatoon, Shahida Saleem, Basharat Khan, Huma Jamia Millia Islamia Department of Electrical Engineering New Delhi India Uttrakhand Technical University Department of Computer Science Dehradun India

ISBN: (纸本)9798350399769

With every field of technology witnessing advancement and innovation, application of such innovations with refined expertise has eased human welfare. In present times also, internet of Things (IOT) is proving to be panacea of all hiccups for handicapped patients. Patients enduring physical disabilities cannot take care of themselves sans help of other individuals, and therefore need artificial tools to move around like wheelchair. Under a promising technology, smart/capable wheelchairs are supposed to be developed which are laced with pro-active sensory system. The technology incorporated is internet of Things (IOT). The disabled patients using these rare wheelchairs, no longer need to depend on others rather sensors fitted with the wheelchairs, monitor the vital bio indices as that of blood, heart, lungs and neural system. In case of any irregularity, signal transaction using embedded systems, onto cloud takes place. This results in flagging a signal which can be accessed by doctors available in online mode. Hence the redressel of medical problems would become much more effective, easier and in less time. IOT is certainly a promising technology for medical care also. © 2023 IEEE.

关键词： internet of things

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Research on the Application of Computer image Vision technology in the Curriculum System of Preschool Education 6

Research on the Application of Computer Image Vision Technol...

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2023 IEEE 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Niu, Jun Meng, Yang Changchun Humanities and Sciences College Changchun China

ISBN: (纸本)9798350313444

An interactive learning system based on intelligent image recognition for young children is proposed. The platform consists of cloud server and learning platform. The learning machine is connected to the cloud server through the internet or WIFI, and the cloud server includes a cloud service module, a cloud data module, an information comparison module and an information feedback module. The learning machine includes an image scanning module, an image acquisition module, a central processing unit, an image display module, a 3D virtual model modeling module, a 3D scene design module and an interactive roaming experience module. A voice-based interactive system for young children is established. It can promote children's development of language goals. Children's interest in language can be increased through timbre, language input, language conversion, phonetic word conversion and other methods. It can build a "cognition"model in children's brains, enabling children to distinguish the differences between similar things, thereby improving children's cognition level. © 2023 IEEE.

关键词： image vision preschool education situational interaction system interaction

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Anomaly Detection in internet of Things Using Weighted Botox Optimization Algorithm with Gated Recurrent Unit 3

Anomaly Detection in Internet of Things Using Weighted Botox...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Sujatha, J. Patil, Nagaraj B. Supriya Abas, Hayder Muhamed Jagadesh, Gona Sir M Visvesvaraya College of Engineering Department of Computer Science and Engineering Raichur India Visvesvaraya Technological University Belagavi India Government S.K.S.J Technological Institute Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of technology Department of Information Science and Engineering Bengaluru India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq School of Engineering Anurag University Hyderabad India

ISBN: (纸本)9798331508456

Anomaly Detection (AD) play a significant part in risk management and data security among various domains like medical image recognition, economic insurance security, internet of Things (IoT) management. However, several types of AD have been established in traditional research that concentrate on identifying anomaly with the help of classification issues. To overcome this problem, Weighted Botox Optimization Algorithm with Gated Recurrent Unit (WBOA-GRU) is proposed in this research for AD. The WBOA chooses optimal features and helps to reduce dimensionality through updating their locations based on its behavior. The GRU effectively handle sequential data when requiring less computational sources thereby enhancing the classification accuracy. The preprocessing uses two methods where hash encoding is applied to convert categorical data into numerical format and then normalization is applied to scale the data for ensuing reliability. The WBOA-GRU achieves accuracy of 99.25% for UNSW-NB15 dataset which is better than Rat Swarm Optimization with Improved Self-Attention based Gated Recurrent Unit (RSO-ISAGRU). © 2025 IEEE.

关键词： Risk management

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