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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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Multi-task Guided Blind Omnidirectional Image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： Image quality

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Failure Mode Analysis and Infrared Image Recognition of Crystalline Silicon PV Module 5

Failure Mode Analysis and Infrared Image Recognition of Crys...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Li, Ziyi Wang, Lijie Sun, Jianbo Lu, Xinyu Zhu, Xianwen School Of Measurement And Control Technology And Communication Engineering Harbin University Of Science And Technology China

ISBN: (纸本)9798400709272

In this paper, the fault analysis of crystalline silicon photovoltaic modules is studied. The Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods are used to analyze the failure mode and its causes. Based on the results of fault analysis, the infrared image analysis and recognition methods are studied for the two fault modes of infrared hot spot and component shedding of Photovoltaic(PV) modules. The YOLOv5s image recognition method based on clustering improvement and feature enhancement is discussed. The experimental results show that the YOLOv5s image recognition algorithm with clustering improvement and feature enhancement improves the training effect of the model by using the EIOU loss function to adaptively adjust the confidence loss balance coefficient, and the detection speed (Frame Per Second, FPS) can reach 42.37 FPS;by adding InRe feature enhancement modules before each detection layer, the extraction ability of target features is improved. The mean Average Precision (mAP) of hot spot and component shedding are 94.85 % and 90.67 %, respectively, which can fully meet the needs of UAV automatic inspection. © 2023 ACM.

关键词： Silicon

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LoBee: Bidirectional communication between LoRa and ZigBee based on Physical-Layer CTC

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IEEE Transactions on Wireless communications 2025年

作者： Gao, Demin Wang, Haoyu Chen, YongRui Ye, Qiaolin Wang, Weizheng Guo, Xiuzhen Wang, Shuai Liu, Yunhuai He, Tian Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China University of Minnesota The Department of Computer Science and Engineering 55414 United States University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Southeast University The School of Computer Science and Engineering Nanjing211189 China City University of Hong Kong Department of Computer Science 999077 Hong Kong College of Control Science and Engineering Zhejiang 310027 China Peking University The Department of Computer Science and Engineering Beijing100871 China University of Minnesota The Department of Computer Science and Engineering Minneapolis55414 United States

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-technology communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band. © 2002-2012 IEEE.

关键词： Zigbee

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Thai Beef Cattle Recognition for Fattening Farms: An Empirical Study of Cattle Cooperative Practices in Chiang Rai

Thai Beef Cattle Recognition for Fattening Farms: An Empiric...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Ant Maw Khon Myint Myat Pradorn Sureephong Qing Fan Santichai Wicha School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand Key Laboratory of China(Guangxi)-ASEAN CrossBorder Animal Disease Prevention and Control Ministry of Agriculture and Rural Affairs of China Guangxi Veterinary Research Institute Guangxi China Computer and Communication Engineering for Capacity Building (CCC) School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research focuses on developing an automated system to identify the breed and sex of Thai beef cattle using advanced computer vision technology. By using the YOLOv11 (You Only Look Once) model, a state-of-the-art object detection tool, this system can quickly and accurately identify cattle breeds and determine their sex. High-resolution image datasets were collected and augmented, yielding 81.2% accuracy for breed detection and 95.0% for sex classification, with mAP@50 scores of 85.1% and 99.3%, respectively. However, limited data for certain breeds, like Thai Native and Japanese cattle, reduced the model’s performance for those categories. To overcome this, we aim to expand the dataset from CCTV footage in future studies. The system will include user-friendly features, such as real-time alerts and dashboards, making it practical for farmers to use. This project demonstrates how AI can improve cattle farming by saving time, reducing errors, and supporting better decisions. Future improvements will explore applications in disease detection and behavior monitoring.

关键词： YOLO Deep learning Computer vision Cows Real-time systems Data models Object recognition Monitoring Farming Diseases

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Recurrence Formulas for the Mie Series

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Chinese Physics Letters 2011年第10期28卷 123-125页

作者： SUN Ji-Yu XIE Hong College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001 College of Measurement-Control Tech and Communications Engineering Harbin University of Science and TechnologyHarbin 150040

The Mie series has very important applications in electromagnetics(including optics).We employ a formulation for the Mie series which relies more on the derivatives of Legendre polynomials than Bessel *** formulas for derivatives related to Legendre polynomials are derived to realize the Mie series conveniently and to avoid treating special angles.

关键词： functions Legendre conveniently

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Electrical Characterization of Deep Trap Properties in High-k Thin-Film HfO2 Dielectric

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Chinese Physics Letters 2010年第7期27卷 218-220页

作者：杨录 National Key Laboratory for Electronic Measurement Technology School of Information and Communication Engineering North University of China Taiyuan 030051

Deep-trap properties of high-dielectric-constant （k） HfO2 thin films are investigated by deep-level transient spectroscopy and capacitance-voltage methods. The hole traps of the HfO2 dielectric deposited on a p-type Si substrate by sputtering are investigated in a metal-oxide-semiconductor structure over a temperature range of 300-500K. The potential depth, cross section and concentration of hole traps are estimated to be about 2.5eV, 1.8 ×10^-16 cm^2 and 1.0 × 10^16 cm^-3, respectively.

关键词： Traps Trapping Depth Potassium Curium

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FPGA-Oriented Target Detection Accelerator Design 5

FPGA-Oriented Target Detection Accelerator Design

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5th International Conference on Frontiers technology of Information and Computer, ICFTIC 2023

作者： Huang, Weizhe Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China

ISBN: (纸本)9798350309034

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method. © 2023 IEEE.

关键词： CNN FPGA target detection Winograd algorithm

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The system design of wireless network single loop regulator

The system design of wireless network single loop regulator

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2013 2nd International Conference on measurement, Information and control, ICMIC 2013

作者： Wu, Li-Hua Lian, Liu Yang, Ming-Ling Wang, Yan-Dong School of Measurement-control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781479913923

Single loop regulator is a process control level instrument in single loop distributed control system. The traditional single loop regulators based on the data bus and wire network exist limitations such as wiring and environment and so on. Aiming at these problems, this paper improved the traditional single loop regulator by combining wireless communication technology with information processing technology, and then designed a single loop regulator system based on wireless network. The system adopted PID adjustment. Practical tests proved that the regulator system could be used in practical applications. © 2013 IEEE.

关键词： Distributed parameter control systems

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