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Twin-Delayed DDPG-Based Multiuser Downlink Transmissions for RIS-Aided Wireless Communications

IEEE sensors JOURNAL 2024年第19期24卷 31215-31227页

作者： Faisal, K. M. Kim, Yonggang Choi, Wooyeol Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Kongju Natl Univ Dept Comp Sci & Engn Cheonan 31080 South Korea

Reconfigurable intelligent surface (RIS) can offer a customizable wireless transmission and is regarded as an incredibly crucial enabling technology when used as reflectors for current wireless base stations (BSs) to overcome the blockage challenges of millimeter-wave (mmWave) wireless communications systems. RISs are instrumental in driving the progress of sixth-generation (6G) wireless services and beyond, with the goal of achieving gigabit-per-second (Gbps) data rates for networks operating in the mmWave frequency bands. RIS has the significant potential to diminish the effects of signal blockages and unnecessary handovers because precise phase adjustment of RIS elements enhances the scattering environments and creates multiple reflective signal paths. However, the large number of RIS elements can complicate the optimization of BS and RIS reflector configurations and result in performance degradation. This article introduces a deep reinforcement learning (DRL) strategy to dynamically configure the numerous RIS elements for multiuser downlink transmissions. The proposed method employs the twin-delayed deep deterministic policy gradient (TD3) method to solve nonconvex optimization issues where the BS receives state information from the RIS, including feedback on user channel states. For real-world systems requiring collaborative control over phase shift and beamforming matrix, the BS determines the optimal actions, which comprise the allocation of transmission power and phase shift adjustments within a Nakagami-m fading environment. The experimental results indicate that the proposed TD3-based solution outperforms other existing benchmarks.

关键词： Optimization Reconfigurable intelligent surfaces Array signal processing Downlink Channel estimation Wireless sensor networks Communication systems Deep reinforcement learning (DRL) machine learning (ML) reconfigurable intelligent surface (RIS)

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An intelligent CMOS Image sensor System Using Edge information for Image Classification 19

An Intelligent CMOS Image Sensor System Using Edge Informati...

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Asia Pacific conference on Circuits and Systems (APCCAS)

作者： Kisku, Wilfred Khandelwal, Prateek Kaur, Amandeep Mishra, Deepak Indian Inst Technol Dept Elect Engn Jodhpur 342030 RJ India Indian Inst Technol Dept Comp Sci Engn Jodhpur 342030 RJ India

ISBN: (纸本)9798350381191

CMOS image sensors have drawn a lot of attention due to their superior performance in the past decade. The most demanding applications such as visual surveillance and intrusion detection in surveillance systems, and aerial monitoring in conflict zones, are made possible by recent technological advancements in near sensor-based systems. In the existing methodology, image sensors are being used along with a DSP processor to perform image classification and recognition. Continuously reading and transferring data of all pixels to CNN required high computation and power consumption of ADC. In this work, we propose a novel design for an intelligent CMOS image sensor wherein an analog Sobel edge detector is introduced before the ADC. Using the edge detector, instead of digitizing a natural image obtained off the sensor, only edges in the image are digitized and transferred for object classification and recognition tasks. This significantly reduces the power consumed by ADC as only edge-detected pixels are being converted into the digital domain. Analysis shows that object classification tasks on edge-detected images with reduced pixel information from thresholding operation result in pixel reduction by 67%, 79%, and 87% for threshold values of 0.2, 0.3, and 0.4. This work shows that CNN models can still be trained with acceptable accuracy on state-of-the-art models and can reduce operating power for intelligent edge devices.

关键词： CMOS Image sensors Deep Learning Convolution Neural networks Sobel Filter Hardware Accelerators

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Adaptive Traffic Light Control Through Reinforcement Learning Based on sensor Integration 21st

Adaptive Traffic Light Control Through Reinforcement Learnin...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Uribe-Chavert, Pedro Lopez-Cuerva, Luis Posadas-Yague, Juan-Luis Poza-Lujan, Jose-Luis Univ Politecn Valencia Doctoral Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Informat Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Res Inst Ind Comp & Automat Camino VeraSn Valencia 46022 Spain

ISBN: (纸本)9783031820724;9783031820731

This paper explores intelligent traffic light management advancements, focusing on controlling intersection traffic opening times. The decision-making process is influenced by factors such as traffic density. The information for these decisions is gathered from sensors placed on the streets, whose accuracy can vary. Data collected are processed to aid control agents in decision-making. The paper proposes an intersection control algorithm that operates under the assumption of lacking sensorisation. To balance raw sensor data, control nodes implement a reinforced learning algorithm to select the most suitable combination of sensors to improve traffic parameters. The paper also introduces a method for calculating traffic density by combining sensors with imprecise data. This research contributes to intelligent traffic management by providing a novel approach to intersection control and traffic density calculation.

关键词： intelligent Traffic Light Management Reinforced Learning Algorithm Traffic Density Calculation

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Edge Perception: intelligent Wireless Sensing at Network Edge

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IEEE COMMUNICATIONS MAGAZINE 2025年第3期63卷 166-173页

作者： Cui, Yuanhao Cao, Xiaowen Zhu, Guangxu Nie, Jiali Xu, Jie Beijing Univ Posts & Telecommun Beijing Peoples R China Shenzhen Univ Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Future Network Intelligence Inst Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen Peoples R China

Future sixth-generation (6G) networks are envisioned to support intelligent applications across various vertical scenarios which have stringent requirements on high-precision sensing as well as ultra-low-latency data processing and decision making. Toward this end, a new paradigm of edge perception network emerges. This network integrates wireless sensing, communication, computation, and artificial intelligence (AI) capabilities at network edge for intelligent sensing and data processing. This article provides a timely overview on this emerging topic. We commence by discussing wireless edge perception, including physical layer transceiver design, network-wise cooperation, and application-specific data analytics, for which the prospects and challenges are emphasized. Next, we discuss the interplay between edge AI and wireless sensing in edge perception, and present various key techniques for two paradigms, namely edge AI empowered sensing and task-oriented sensing for edge AI, respectively. Finally, we emphasize interesting research directions on edge perception to motivate future works.

关键词： Wireless communication Wireless sensor networks Translation Text summarization Edge AI Predictive models Transformers Question answering (information retrieval) sensors Artificial intelligence

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Spatio-Temporal Synchronized Retention networks for Methane Concentration Prediction 9

Spatio-Temporal Synchronized Retention Networks for Methane ...

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9th International conference on intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Zhaozhao Dong, Wangzhi College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350376548

Methane accidents are one of the most dangerous accidents that threaten the safety and production of coal mines, therefore, how to accurately predict the methane concentration in the future period by utilizing the spatio-temporal data of methane concentration collected by sensors is crucial to reduce the probability of accidents. Usually, there are multiple methane concentration sensors arranged in the working face of underground coal mines, and the data collected by these sensors have strong spatio-temporal correlation. Existing methods either rely on the data from a single sensor for prediction or model the temporal and spatial characteristics of the data from multiple sensors separately by two different modules, which makes it difficult to explore the complex spatio-temporal correlation of the data from multiple sensors. Therefore, this article proposes a new model of encoder-decoder architecture based on retention mechanism. By connecting the same nodes at adjacent time steps, allowing the temporal and spatial information to freely propagate, and then synchronously modeling the temporal and spatial relationships with the parallel computing capability of the retention mechanism, the efficiency and accuracy of the prediction are enhanced. © 2024 IEEE.

关键词： Coal mines

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Compression Design for Efficient information Sharing in a Network of Radio Frequency sensors 32

Compression Design for Efficient Information Sharing in a Ne...

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32nd European Signal processing conference (EUSIPCO)

作者： Coutts, Fraser K. Thompson, John Mulgrew, Bernard Univ Edinburgh Inst Imaging Data & Commun Edinburgh EH9 3BF Midlothian Scotland

ISBN: (纸本)9789464593617;9798331519773

There is a desire to maximise efficiency when sharing positioning, navigation, and timing (PNT) information captured by low size, weight, power, and cost (SWAP-C) radio frequency (RF) sensors in distributed sensor networks. Such information can be used to position the sensors or RF sources in both civilian and military applications. We propose a novel framework for improving efficiency when sharing PNT information in a network of sensors receiving radar waveforms in an urban, contested electromagnetic environment. We show that informed, at-edge compression strategies that prioritise the transmission of PNT information in the presence of interferers can be obtained using information theory, and that these techniques outperform the uninformed strategies typically utilised during compressed sensing. Furthermore, we exhibit the flexibility of these strategies when facilitating trade-offs between different types of PNT information. We demonstrate the impact of compression level and signal-to-noise ratio on PNT activities and we confirm that, for a given network bandwidth, sensing gains can be achieved by combining compressed measurements from multiple sensors.

关键词： Signal to noise ratio

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On cross-attention-based graph neural networks for fault diagnosis using multi-sensor measurement

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Ren, Zhenxing Zhou, Yu Taiyuan Univ Technol Coll Comp Sci & Technol Daxue St 209 Jinzhong 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Data Sci Daxue St 209 Jinzhong 030024 Shanxi Peoples R China

Rotating machinery fault diagnostics has received a lot of attention in recent years. As a result, there has been an increase in research interest in rotating machine intelligent fault detection, especially when using measurement from multi-sensors. However, an accurate fault diagnosis is still challenged based on nonlinear and non-stationary vibration signals. On the other hand, not enough research has been done on structural information fusion from multi-sensor measurement due to the complexity of spatial-temporal correlation. This paper explores the use of vibration signals in multi-sensor measurement fusion for rotating machinery fault diagnostics, and a method using a cross-attention-based dual-branch graph neural network (CA-GNN) is proposed. First, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose vibration signals. Variational mode decomposition is then used to further deconstruct the highest-frequency subsequence after its parameters have been optimized using the whale optimization algorithm. Next, we build the CA-GNN, which contains two space branches for high- and low-frequency signals after initially creating the graph using multiple sensor data sets. information across both branches for high- and low-frequency components can then be efficiently fused. Lastly, two experimental scenarios are used to illustrate the suggested technique and assess its viability and accuracy for rotating machinery fault diagnosis. Results indicate that the proposed method can diagnose rotating machinery health issues with an average accuracy of up to 99%, indicating that the method's performance can fulfill real-world requirements.

关键词： Fault diagnosis rotating machinery signal processing graph neural network cross-attention module

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Sensing-Assisted Communication in Vehicular networks With intelligent Surface

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第1期73卷 876-893页

作者： Meng, Kaitao Wu, Qingqing Chen, Wen Li, Deshi Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 201210 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China

The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet high-throughput and low-latency communication as well as high-resolution localization requirements in vehicular networks. However, considering the limited transmit power of the road site units (RSUs) and the relatively small radar cross section (RCS) of vehicles with random reflection coefficients, the power of echo signals may be too weak to be utilized for effective target detection and tracking. Moreover, high-frequency signals usually suffer from large fading loss when penetrating vehicles, which seriously degrades the communication service quality of users inside vehicles. To handle this issue, we propose a novel sensing-assisted communication scheme by employing an intelligent omni-surface (IOS) on the surface of vehicles to enhance both sensing and communication (S&C) performance. To this end, we first propose a two-stage ISAC protocol, including the joint S&C stage and the communication-only stage, to fulfil more efficient communication performance improvements benefited from sensing. The achievable communication rate maximization problem is formulated by jointly optimizing the transmit beamforming, the IOS phase shifts, and the duration of the joint S&C stage. However, solving this ISAC optimization problem is highly non-trivial since inaccurate estimation and measurement information renders the achievable rate lack of closed-form expression. To handle this issue, we first derive a closed-form expression of the approximate achievable rate under uncertain location information, and then unveil a sufficient and necessary condition for the existence of the joint S&C stage to offer useful insights for practical system design. Moreover, two typical scenarios including interference-limited and noise-limited cases are analyzed to provide a performance bound and a low-complexity algorithm for the considered systems. Finally, simulation results demonstra

关键词： sensors Array signal processing Closed-form solutions Channel estimation Vehicle-to-everything Fading channels Wireless sensor networks intelligent surface integrated sensing and communication sensing-assisted communication beamforming phase shift design vehicular networks

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Multi-Shadow Scenarios Tennis Ball Detection by an Improved RTMdet-Light Model

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IET IMAGE processing 2025年第1期19卷

作者： Zhu, Yukun Peng, Yanxia Yu, Cong Jianghan Univ Sch Phys Educ Wuhan Peoples R China Jianghan Univ Sch Intelligent Mfg Wuhan Peoples R China

The real-time and rapid recording of sport sensor data related to tennis ball trajectories facilitates the analysis of this information and the development of intelligent training regimes. However, there are three essential challenges in the task of tennis ball recognition using sport vision sensors: the small size of the ball, its high speed, and the complex match scenarios. As a result, this paper considers a lightweight object detection model named improved RTMDet-light to deal with these challenges. Specifically, it has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. Furthermore, GhosNet and ShuffleNet are used to replace the CSPLayers which reduce the parameters of our model. The lightweight model proposed addresses the inherent challenges of detecting small objects and muti scenarios in the match. After training, the proposed model performed better on four scenarios with different shades of tennis ball match, with results visualized through heatmaps and performance metrics tabulated for detailed analysis. The recall, FLOPs and number of parameters of the improved RTMDet-light are 71.4%, 12.543G, and 4.874M, respectively. The results demonstrate robustness and effectiveness of our model in accurate tennis ball detecting across various scales. In conclusion, our model for real-time detection in tennis ball detection offers a lightweight and faster solution for sport sensors.

关键词： complex networks image matching image recognition learning (artificial intelligence) sport

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Progress in Artificial Intelligence Applications Based on the Combination of Self-Driven sensors and Deep Learning 4

Progress in Artificial Intelligence Applications Based on th...

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4th International conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wan, Weixiang Sun, Wenjian Zeng, Qiang Pan, Linying Xu, Jingyu University of Electronic Science and Technology of China Electronics & Communication Engineering Chengdu China Yantai University Electronic and Information Engineering Shandong Yantai China Zhejiang University Computer Technology Zhejiang Hangzhou China Trine University Information Studies PhoenixAZ United States Northem Arizona University Computer Information Technology FlagstaffAZ United States

ISBN: (纸本)9798350384062

In the era of the Internet of Things, developing a smart sensor system with sustainable power supply, easy deployment, and flexible use has become a challenging problem. Traditional power supplies, prone to frequent replacements or requiring charging during usage, hinder the advancement of wearable devices. The contact-to-separate friction nanogenerator (TEN G), composed of polychotomy ethylene (PTFE) and aluminum (AI) foils, addresses this issue by harvesting human motion energy through body movement arrangement. This energy is then utilized to monitor human motion posture by detecting changes in output electrical signals. In 2012, Academician Wang Zhonglin and his team invented the triboelectric nanogenerator (TENG), which operates as a self-driven sensor primarily powered by Maxwell displacement current. This enables direct conversion of mechanical stimulation into electrical signals during action, making it suitable for self-powered sensor applications. TENG-based sensors boast a simple structure and high instantaneous power density, providing a crucial tool for constructing intelligent sensor systems. Additionally, when combined with machine learning characteristics such as low cost, short development cycles, robust data processing, and predictive capabilities, the processing of the numerous electrical signals generated by TENG has a significant impact. The integration of TENG sensors is poised to drive the rapid evolution of intelligent sensor networks in various fields such as transportation, security, water conservancy, and construction, where urban sound management is essential. The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: 1) Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range. These settings can be modified onl

关键词： Ethylene

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