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Separate Least Mean Square Based Equalizer with Joint Optimization for Multi-CAP Visible Light communication

China communications 2022年第1期19卷 264-273页

作者： Jianli Jin Jianping Wang Huimin Lu Danyang Chen School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

Visible light communication(VLC) is expected to be a potential candidate of the key technologies in the sixth generation(6G) wireless communication system to support Internet of Things(IoT) applications. In this work, a separate least mean square(S-LMS) equalizer is proposed to compensate lowpass frequency response in VLC system. Joint optimization is employed to realize the proposed S-LMS equalizer with pre-part and post-part by introducing Lagrangian. For verification, the performance of VLC system based on multi-band carrier-less amplitude and phase(m-CAP) modulation with S-LMS equalizer is investigated and compared with that without equalizer,with LMS equalizer and with recursive least squares(RLS)-Volterra equalizer. Results indicate the proposed equalizer shows significant improved bit error ratio(BER) performance under the same conditions. Compared to the RLS-Volterra equalizer, SLMS equalizer achieves better performance under low data rate or high signal noise ratio(SNR) conditions with obviously lower computational complexity.

关键词： visible light communication Internet of Things equalization least mean square

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Piloting Performance Investigation in Virtual Reality Piloting Simulations

Piloting Performance Investigation in Virtual Reality Piloti...

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2024 International Symposium on Intelligent Signal Processing and communication Systems, ISPACS 2024

作者： Hsu, Mao-Hsiang Chang, Kang-Ming Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This study investigates the relationship between task difficulty and performance outcomes in virtual reality (VR) piloting tasks. Utilizing data from simulated flight sessions, we analyzed whether significant statistical differences exist in key performance metrics across varying levels of task difficulty. The results reveal that as task difficulty increases, performance variability, particularly in parameters like localizer error, glideslope error, and airspeed error, becomes more pronounced, indicating a direct correlation between task complexity and performance outcomes. © 2024 IEEE.

关键词： Virtual environments

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Infrared Single Pixel Imaging Based on Generative Adversarial Network

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Journal of Shanghai Jiaotong University (science) 2023年 1-11页

作者： Jiang, Yilin Zhang, Yilong Zhang, Fangyuan College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned. It is effective to use optical lens instead of traditional optical reflection system, which can reduce the loss in optical path transmission. At the same time, deep learning neural network is used for control. An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained. The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling, the system finally can realize the single pixel recovery imaging of the infrared image, which improves the resolution of the image while ensuring high sensitivity. © 2023, Shanghai Jiao Tong University.

关键词： Generative adversarial networks

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FERMixNet: An Occlusion Robust Facial Expression Recognition Model with Facial Mixing Augmentation and Mid-Level Representation Learning

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IEEE Transactions on Affective Computing 2024年第2期16卷 639-654页

作者： Huang, Yansong Peng, Junjie Zhang, Wenqiang Zhao, Tong Chen, Gan Tan, Shuhua Yi, Fen Wang, Lu Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Computer Engineering and Science The Shanghai Institute for Advanced Communication and Data Science Shanghai China Fudan University Academy for Engineering and Technology School of Computer Science and Technology Shanghai China YTO Express Co. Ltd. The National Logistics Engineering Laboratory Shanghai China

Facial expressions can provide a better understanding of people's mental status and attitudes towards specific things. However, facial occlusion in real world is an unfavorable phenomenon that greatly affects the performance of facial expression recognition models. Recent works addressing the occlusion problem have primarily relied on attention mechanisms or occlusion discarding methods that focus on non-occluded regions of the face. However, these methods have not achieved a good balance between occlusion robustness and model efficiency. In this paper, we propose a simple and efficient model, called FERMixNet, for occluded facial expression recognition. The model incorporates a novel facial mixing augmentation strategy (FERMix) that generates new training samples by simulating real-world facial occlusion and preserving high expression-related semantic information. By co-training the original and newly generated samples, the model's occlusion robustness is improved without increasing its complexity during inference. Additionally, to further enhance the model's occlusion robustness, we include mid-level representation learning in the network to learn the discriminative non-occluded local features of the samples with low computational cost. Extensive experiments on four public facial occlusion datasets: Occlusion-RAF-DB, Occlusion-FERPlus and FED-RO show that the proposed model achieves state-of-the-art results which demonstrates the good robustness of our method for occluded facial expression recognition. Meanwhile, the proposed model also achieves state-of-the-art results on the in-the-wild facial expression datasets RAF-DB, AffectNet-8, and AffectNet-7. It proves that the proposed model has good application prospects in real world. © 2010-2012 IEEE.

关键词： Face recognition

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Design and Implementation of a 4-Antenna Rx Beamforming System with Real-Time Calibration

Design and Implementation of a 4-Antenna Rx Beamforming Syst...

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2024 International Symposium on Intelligent Signal Processing and communication Systems, ISPACS 2024

作者： Lai, Je-An Lin, Shou-Sheu Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

Real-time calibration for a beamforming receiver is a popular topic. We designed and implemented a 4-antenna Rx beamforming system with real-time calibration. This system sends an out-of-band calibration signal from the transmitter (Tx) side to calibrate the phase error of in-band received signals in real time at the receiver (Rx) side. The system was successfully implemented and validated on the M3force Software Defined Radio (SDR) platforms. It was verified using not only the accuracy of the direction of arrival (DOA) on the beampattern but also the array gain from the error vector magnitude (EVM) of the demodulated signals with beamforming in the receiver. The system achieved 4.60 dB of array gain and 2.9 degrees of the accuracy of DOA with calibration. © 2024 IEEE.

关键词： Direction of arrival

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An Efficient Cooperative Active Caching Strategy in Vehicular Edge Network Based on Asynchronous Federated Learning 22

An Efficient Cooperative Active Caching Strategy in Vehicula...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Pang Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

Edge caching is a promising technique for effectively reducing backhaul pressure and content access latency in the Internet of Vehicles (IoV). However, the high mobility of vehicles and dynamic user requests often lead to outdated cached content. Expired cache wastes the limited storage space and transmission in vehicular edge computing. Improving the cache hit rate is an effective approach to address these issues. In this work, we propose a Cooperative Active Caching Strategy (CACS) which works as follows. First, user preferences are analyzed from the historical content data of vehicle users. After that, multiple vehicles cooperate to learn the global model under an asynchronous federated learning framework, and get the content popularity from user preferences and content features. To explore the problem of local models being outdated in asynchronous federated learning, CACS integrates model compression algorithms, enhancing system efficiency and prediction accuracy. Finally, a heuristic cooperative caching content placement algorithm is proposed based on a greedy policy to minimize average access latency. Simulation results show that the CACS can improve the cache hit rate by 15% at most compared to existing state-of-the-art caching strategies. © 2024 IEEE.

关键词： Federated learning

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A Task Dependency-based Deduplicated Task Offloading Mechanism in Vehicular Edge Computing 22

A Task Dependency-based Deduplicated Task Offloading Mechani...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shao, Zhenyi Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

The increasing demand for in-vehicle applications has raised the complexity and computational load, while the in-vehicle tasks exhibit a sensitivity to latency. Previous research has proposed utilizing the idle computational resources of roadside vehicles to alleviate this contradiction. However, the high mobility of vehicles leads to communication interruptions, and the time-varying nature of vehicle density makes resource allocation challenging. In this work, we leverage the dependencies between vehicular computing tasks and design a deduplication offloading mechanism for stable reduction of latency. This mechanism consists of two stages, named the Multi-hop Clustering Deduplication Offloading (MCDO) mechanism. Firstly, a Multi-hop Two Layer Clustering (MTLC) algorithm is designed to divide vehicles based on task dependencies, speed, and position information. Then, a Deduplication Layered Offloading (DLO) algorithm is proposed to identify and remove duplicated tasks within each cluster while maintaining their inter-dependencies. Simulation results demonstrate that MCDO effectively divides vehicle clusters and offloads tasks efficiently under various road conditions. Compared to existing approaches, MCDO significantly enhances system performance, achieving a minimum improvement of 15.1% in terms of latency. © 2024 IEEE.

关键词： Computation offloading

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JobViz:Skill-driven visual exploration of job advertisements

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Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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Face Tracking using Deep Reinforcement Learning on Zenbo Junior

Face Tracking using Deep Reinforcement Learning on Zenbo Jun...

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2024 International Symposium on Intelligent Signal Processing and communication Systems, ISPACS 2024

作者： Tseng, Shih-Huan Lin, De-Yu Huang, Shih-Shinh National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This paper aims to develop a deep reinforcement learning-based face tracking system for Zenbo Jr. As robots become increasingly integrated into daily life, human-robot interaction (HRI) has emerged as a critical research area, especially in adhering to social norms. This project combines deep learning with visual servo systems to enhance the accuracy and response speed of robotic face tracking. The primary objectives of the study include: adjusting robot movements based on face position, designing a virtual environment using OpenAI Gym that matches Zenbo Jr.'s perspective for model training, and testing and validating the model on Zenbo Jr. The research methods involve designing the virtual environment, establishing reward functions, and constructing and training the DQN model. Experimental results compare the training outcomes of Q-Learning and Deep Q-Learning, confirming that Q-Learning leads to an excessively large Q-table in scenarios with numerous state-action combinations, making effective training impossible, whereas Deep Q-Learning achieves stable convergence and good results. © 2024 IEEE.

关键词： Deep reinforcement learning

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