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Experimental Demonstration of Video Compression Sensing Free Space Optical Transmission System Based on Spatial Coding 9

Experimental Demonstration of Video Compression Sensing Free...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Li, Jinwang Dong, Keyan Liu, Tianci Song, Yansong Chen, Ci School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Changchun University of Science and Technology Institute of Space Optoelectronics Technology Changchun130022 China Henghui Optoelectronics Technology Co. Ltd Changchun130000 China

ISBN: (纸本)9798350376548

Wireless video image transmission is now widely used in scientific research and daily life, and laser beams have received more and more attention as an excellent information carrier. We combined the compressed sensing image algorithm with space laser communication technology and developed a free-space optical image transmission system utilizing the compressed sensing algorithm. However, due to the influence of atmospheric channels, the communication bit error rate increases, and there are errors in compressed image transmission, which seriously affects the quality of the deconstructed image at the receiving end. In response to this phenomenon, firstly, we built a space optical video transmission board based on FPGA and conducted experiments to quantitatively analyze the relationship between environmental impact and receiver image quality. Then, we propose a spatial coding method that divides the compressed image into different subframes for encoding according to position information. After collecting the data at the receiving end, the position is first allocated according to the encoding and the compressed image is reorganized. This solution solves the problem of individual data loss leading to overall line misalignment of the image. Finally, we conducted comparative experiments using the algorithm proposed in this paper and the traditional compressed sensing algorithm. Experimental results show that the method proposed in this paper improves the PSNR index by more than 3dB and the SSIM index by more than 15% under different algorithm sampling rates, which has a significant optimization effect on the reconstruction quality of atmospheric channel optical transmission images. © 2024 IEEE.

关键词： Laser beams

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A Secure Channel Service Based on Multi-connection communication in Power System 11

A Secure Channel Service Based on Multi-connection Communica...

引用

11th international conference on information Systems and computing technology, ISCTech 2023

作者： Wang, Wanqiao Gao, Kaiqiang Zhang, Hui Lu, Jun Liao, Chensu China Electric Power Research Institute Beijing China North China Electric Power University School of Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9798350342406

The security of data transmission has become more important in modern network communications in new power system. In order to guarantee the secure and reliable communication of power load management terminals under the new power system, this paper proposes a Secure Channel Service based on multi-connection communication. With the multi-connection feature, we divide the data into multiple slices and encrypt each slice before transmitting it over a plurality of connections. Meanwhile, this paper proposes a simple and efficient encryption algorithm for transmitted data that utilizes the multi-connection feature to ensure the simplicity and efficiency of the transmission computation and processing without generating a large amount of key data. Using time-slot scheduling strategy and multi-connection strategy as the main strategy and slice encryption strategy as the auxiliary strategy, the proposed Secure Channel Service can satisfy the requirements of higher level of secure data transmission of power load management terminals. © 2023 IEEE.

关键词： Cryptography

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Real-time Asset False Matching Detection via Attentive Siamese Networks 6

Real-time Asset False Matching Detection via Attentive Siame...

引用

6th international conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Miao, Ziwan Zhang, Li School of Computing Beijing Information Science & Technology University Beijing China School of Management Science and Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798331541750

Accurate and efficient asset matching is crucial for remote management and diversified investment entities. To address the challenges of low efficiency, high labor costs, and false matching, this paper proposes a real-time asset false matching detection method based on an attentive Siamese network. By integrating the Convolutional Block Attention Module (CBAM) into a Siamese neural network framework, the model enhances feature extraction capabilities, focusing on discriminative regions within asset images. Pretrained weights are transferred using transfer learning, and data augmentation techniques are applied to enhance the model's generalization ability. Experiments on various types of asset data demonstrate that the proposed method achieves an accuracy of 92.42% and an F1 score of 92.48%, significantly improving the precision and efficiency of asset similarity calculations. This research not only advances asset identification and matching but also showcases exceptional performance in intelligent asset inventory, providing reliable support for asset management. Future work will focus on improving model robustness and optimizing the network structure for large-scale and complex environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Cooperative Completing Tasks of Charging WRSNs with Multiple MUVs and Laser-charged UAVs 21

Cooperative Completing Tasks of Charging WRSNs with Multiple...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Chuanwen Liu, Ning Chen, Ting Cao, Jia Hong, Yi Chen, Zhibo Li, Deying Beijing Forestry University Information Science and Technology Beijing China University of Electronic Science and Technology of China Chengdu China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350329223

With the rapid development of unmanned aerial vehicles (UAVs), they can not only carry communication equipment for intercepting and interfering with signals to protect communication security, but also carry energy transmission devices to transfer energy to wireless sensor networks and enhance network lifespan. This paper investigates the network framework of multiple UAVs-based wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs). On the basis of this network, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the maximum time consumption of UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the MUTSP problem to balance the charging tasks assigned to each UAV. Then, according to the MUTSP problem, we propose the TOUM algorithm(TOUMA) to design the detailed travel plan of UAVs and MUVs. We have validated the effectiveness of our algorithm through extensive simulation experiments. The experimental results demonstrate that the TOUMA algorithm outperforms both the solar charging method and the base station charging method in terms of time efficiency. Furthermore, under various parameter settings, the ratio of the TOUMA algorithm consistently remains below 1.56. © 2023 IEEE.

关键词： Wireless sensor networks

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Advanced Auditing Solutions Through the Blockchain Cutting Edge technology 10

Advanced Auditing Solutions Through the Blockchain Cutting E...

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10th international conference on Advanced computing and communication Systems, ICACCS 2024

作者： Siva, K.P. Sudhakar, S. Sahaya Gurus Raja, M. Kanishka, G. Niranjan, V. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

In the modern business landscape, organizations seek technology-driven auditing solutions. This Abstract empowers auditors by reducing their workload and providing precise questionnaires to extract detailed financial information for a comprehensive examination process, ensuring accuracy and efficiency. The scourge of manual data entry is relegated to history, as this system seamlessly connects to bank accounts for effortless data import or accommodates the upload of data in universally accepted document formats. At its core, an advanced algorithm works tirelessly to analyze extensive transaction data, swiftly pinpointing potential fraud or suspicious activities and expediting the investigative process. After analysis, this system meticulously compiles transaction data, associates them with the relevant parties, and diligently flags anomalies, facilitating the identification of stolen assets and the methodologies employed in the transgressions. Security is paramount through the implementation of blockchain technology, ensuring data integrity, while the project fosters collaboration by facilitating discussions with senior auditors and aligning with established audit standards. The culmination of these features streamlines the creation of auditable, court-ready reports, simplifying the transformation of findings into comprehensive documents fit for legal *** represents a revolutionary paradigm shift, poised to redefine auditing by harmonizing technology, efficiency, security, and adherence to professional principles. Overall, your abstract is comprehensive and well-structured, conveying the significance of your auditing initiative effectively. © 2024 IEEE.

关键词： Efficiency

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Multi-Beam Detection Geometric Model Based on Genetic Algorithm 6

Multi-Beam Detection Geometric Model Based on Genetic Algori...

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6th international conference on communications, information System and Computer Engineering, CISCE 2024

作者： Zhang, Yichi Wang, Ning Wang, Shuo School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9798350353174

This paper develops a geometric model for multi-beam detection in seabed mapping, emphasizing the application of a genetic algorithm to optimize the layout of survey lines. Focused on addressing the challenges associated with acoustic signal projection over sloped seabeds, the study introduces novel methods for determining overlap rates, depth of seawater, and coverage width under diverse terrain conditions. It establishes a unique multi-beam space seabed coordinate system to precisely model and analyze the seafloor's geometry, facilitating the accurate mapping of marine terrains. Through the innovative use of genetic algorithms, the research optimizes the arrangement of survey lines, ensuring thorough coverage and appropriate overlap rates for effective seabed exploration. This approach not only advances the technical methodologies in marine geodesy but also broadens the scope for future applications in marine exploration and environmental monitoring, marking a significant step forward in the use of computational algorithms for enhancing the accuracy and efficiency of seabed mapping. © 2024 IEEE.

关键词： Genetic algorithms

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Early Detection and Accurate Prediction of Heart Disease: The Role of Stacked GRU-TCN Model 1

Early Detection and Accurate Prediction of Heart Disease: Th...

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1st IEEE international conference on Electronics, computing, communication and Control technology, ICECCC 2024

作者： Mahammad Rafi, D. Mhatre, Manda Anil Sharma, Neelam Madhanagopal, R. Ahamed, Shaik Khaleel Srija, N. Hyderabad India C.K.Thakur A.C.S.College Department of Zoology New Panvel Maharashtra Mumbai India School of Physical Education Lovely Professional University Punjab India The Madura College Department of Statistics Tamilnadu Madurai India Methodist College of Engineering and Technology Department of CSE Hyderabad India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India

ISBN: (纸本)9798350371802

cardiovascular diseases represent a significant global health challenge, with the incidence of cardiac conditions rising steadily due to lifestyle factors such as poor diet and lack of exercise. Detecting and monitoring cardiovascular disease within large datasets is critical for effective public health management. This study presents a systematic approach for the efficient detection of cardiovascular disease, encompassing data preprocessing, feature selection, and model training. Sequential processing is essential for each stage of the methodology. Data preprocessing involves outlier removal and data standardization to ensure data quality and consistency. Feature selection techniques are employed to identify the most influential variables for model training. Careful consideration is given to feature selection during the training of a Stacked GRU-TCN model to optimize model performance. Comparative analysis demonstrates the superior performance of the proposed approach over TCN and GRU algorithms, achieving an impressive accuracy rate of 96.70%. These findings underscore the potential of advanced modeling techniques in enhancing cardiovascular disease detection and management. © 2024 IEEE.

关键词： Diseases

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Brain Tumor Diagnosis Using CNN 3

Brain Tumor Diagnosis Using CNN

引用

3rd international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2023

作者： Amritraj, Shoibam Mahendra, Jhalak Diana Cyril, C. Pretty SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9781665494007

Brain tumors are deadly neurological conditions caused by uncontrolled cell growth in the brain or skull. This disease's mortality rate continues to rise. Early detection of malignant tumors is essential for treating patients and improving survival rates. The evaluation and treatment of brain tumors rely heavily on the accurate categorization of the tumors. To spot malignant growths in the brain, neuroradiologists employ a variety of imaging methods. However, Magnetic Resonance Imaging is widely employed because it has a high picture quality as well as its absence of reliance on ionizing radiation. When it comes to classification and segmentation difficulties, among the machine learning subfields is deep learning (DL), which has lately demonstrated impressive results. Utilizing techniques of DL in the context of a big dataset medical field has shown to be quite advantageous, opening up completely new pathways and opportunities for the development of novel methods for the detection of illnesses. During this investigation, we are going to discuss a deep learning model that was created with the help of a CNN architecture to identify and categorize various forms of brain tumors from data found in the open-source dataset. The identification of brain tumors is accomplished with the help of a simple convolutional model, and the categorization of the primary kinds of brain tumors is carried out with the assistance of a transfer learning model known as ResNet50. © 2023 IEEE.

关键词： Brain

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A Comprehensive Study of Web Phishing Detection Using Machine and Deep Learning Architecture 14

A Comprehensive Study of Web Phishing Detection Using Machin...

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14th international conference on computing communication and Networking Technologies, ICCCNT 2023

作者： Prabhu, N. Senthil Kumar, T. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Phishing is the easiest way to compromise a user or an entity and first choice of malicious users;as it is hard to circumvent and exploit technology & security controls to gain unauthorised access or to steal information. Industry and institutions spending time, effort, money and invest knowledge on combating against phishing attack in two broad aspects;one is cyber security awareness program and the other mean is building machine and deep learning architectures to detect phishing attempts. This systematic study presents the trend analyses of the common attribute present in the dataset and significance of different machine and deep learning architectures and its hyperparameters in detecting web phishing attempts. © 2023 IEEE.

关键词： Cybersecurity

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A Review on Quality Speech Recognition under Noisy Environment 9

A Review on Quality Speech Recognition under Noisy Environme...

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9th international conference on Advanced computing and communication Systems, ICACCS 2023

作者： Muthumari, M. Valarmathi, R.S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Electronics and Communication Engineering Department Chennai India

ISBN: (纸本)9798350397376

Speech has always been the most important role in our day to day communication. People used to represent our thoughts through the some known language, which may be done through human to human interaction or human to machine interaction. Human Machine Interaction (HMI) plays a vital role on interaction in speech recognition system. Speech or word by word recognition process is finishing the process of separating the speech characteristics from the pre-recorded datasets, and classifying the same characteristics. A word or speech must be forwarded to more advanced software for syntactic and semantic analysis in order to be recognized. So the speech recognition system has to find a very good feature extraction method, speech classifier and performance evaluator to recognize well in all noisy environments. Hence this paper provides the overview of challenges accepted by different researchers as well as it provides the collective information about different assessment, methodology like Hidden Markov Model and Gaussian Mixture Model with MFCC and LSTM and performance improvement given by the researchers for accepted challenges to do the speech recognition in noisy environment. © 2023 IEEE.

关键词： Feature extraction

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