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IEEE International Workshop on Computer-Aided modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Chen Chen Emil Björnson Carlo Fischione Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Over-the-air computation (AirComp) is considered as a communication-efficient solution for data aggregation and distributed learning by exploiting the superposition properties of wireless multi-access channels. However, AirComp is significantly affected by the uneven signal attenuation experienced by different wireless devices. Recently, Cell-free Massive MIMO (mMIMO) has emerged as a promising technology to provide uniform coverage and high rates by joint coherent transmission. In this paper, we investigate AirComp in Cell-free mMIMO systems, taking into account spatially correlated fading and channel estimation errors. In particular, we propose optimal designs of transmit coefficients and receive combing at different levels of cooperation among access points. Numerical results demonstrate that Cell-free mMIMO using fully centralized processing significantly outperforms conventional Cellular mMIMO with regard to the mean squared error (MSE). Moreover, we show that Cell-free mMIMO using local processing and large-scale fading decoding can achieve a lower MSE than Cellular mMIMO when the wireless devices have limited power budgets.

关键词： performance evaluation Fading channels Distributed processing Distance learning Wireless networks simulation Channel estimation Massive MIMO Energy efficiency Decoding

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Fault Detection and Diagnosis Model for Photovoltaic Array Based on Multi-Scale Transformer

Fault Detection and Diagnosis Model for Photovoltaic Array B...

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International Congress on image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Haibing Liao Wang Wang Yinquan Li Shaoping Liu Chaoyang Tang Ye Yuan College of Engineering Shantou University Guangdong Botong New Energy Technology Co. Ltd

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

This paper presents an advanced automatic fault detection and diagnosis method for photovoltaic (PV) arrays aimed at enhancing the efficiency, reliability, and safety of PV systems. Traditional artificial intelligence methods, such as machine learning, although useful, are typically applied to structured, feature-rich two-dimensional data, thus lacking the capability to effectively classify samples represented as arrays of sampling points with only curve characteristics. This limitation may hinder the model's effectiveness and lead to local optimization issues. Existing deep learning techniques have limited feature extraction capabilities and often iterate repetitively over modules without extracting richer features from multiple levels and perspectives. To address these challenges, we propose a novel intelligent fault detection and diagnosis method utilizing a specifically designed multi-scale Transformer network. This network analyzes I-V curves through bilinear interpolation and incorporates irradiance and temperature data, ensuring a comprehensive input basis. We conducted thorough fault simulation experiments on the Simulink platform to evaluate the performance of our proposed method. The results highlight significant achievements in accuracy, generalization capability, reliability, and training efficiency.

关键词： Training Photovoltaic systems Interpolation Accuracy Computational modeling Fault detection Transformers Feature extraction Data models Arrays

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Proceedings - 2021 International Conference on signal processing and Machine Learning, CONF-SPML 2021

Proceedings - 2021 International Conference on Signal Proces...

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2021 International Conference on signal processing and Machine Learning, CONF-SPML 2021

ISBN: (纸本)9781665417341

The proceedings contain 65 papers. The topics discussed include: coarse-to-fine loss based on Viterbi algorithm for weakly supervised action segmentation;key parameter mapping method of signal-to-noise ratio from link simulation to system simulation;research on modeling and control of closed-loop fiber optic gyroscope;design of photoelectric signal parameter test system for liquid crystal filters;an overview of recommender systems and its next generation: context-aware recommender systems;examination and categorization of keywords associated with online review sorting;performance review of generative adversarial network for a bi-directional task;an overview on remote sensing image classification methods with a focus on support vector machine;and research on the consensus problem of multi-agent systems via event-triggered mechanism.

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A study on real-time image processing applications with edge computing support for mobile devices 25

A study on real-time image processing applications with edge...

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25th IEEE/ACM International Symposium on Distributed simulation and Real Time Applications (DS-RT)

作者： Mattia, Gabriele Proietti Beraldi, Roberto Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Ruber Rome Italy

ISBN: (纸本)9781665433266

Different libraries allow performing computer vision tasks, e.g., object recognition, in almost every mobile device that has a computing capability. In modern smartphones, such tasks are compute-intensive, energy hungry computation running on the GPU or the particular Machine Learning (ML) processor embedded in the device. Task offloading is a strategy adopted to move compute-intensive tasks and hence their energy consumption to external computers, in the edge network or in the cloud. In this paper, we report an experimental study that measure under different mobile computer vision set-ups the energy reduction when the inference of an image processing is moved to an edge node, and the capability to still meet real-time requirements. In particular, our experiments show that offloading the task - in our case real-time object recognition - to a possible next-to-the-user node allows saving about the 70% of battery consumption while maintaining the same frame rate (fps) that local processing can achieve.

关键词： performance evaluation Computer vision image processing image edge detection Machine learning Particle measurements Real-time systems

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signal simulation of Dual-Polarization Weather Radar and Its Application in Range Ambiguity Mitigation

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ATMOSPHERE 2022年第3期13卷 432-432页

作者： Dai, Shaojun Li, Xuehua Bu, Zhichao Chen, Yajun He, Jianxin Li, Minghua Xiong, Maojie Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610225 Peoples R China China Meteorol Adm Key Lab Atmospher Sounding Chengdu 610225 Peoples R China China Meteorol Adm Meteorol Observat Ctr Beijing 100081 Peoples R China Meteorol Observat Ctr Inner Mongolia Meteorol Serv Hohhot 010051 Peoples R China

In this paper, a dual-polarization weather radar echo signal simulation method is proposed for the evaluation of the performance enhancement of dual-polarization weather radar systems, the optimization of signal processing algorithms and the improvement of scanning strategies. The actual weather radar base data are used in the simulation as the reference weather scene, which avoids using a complex algorithm for weather modeling. Moreover, based on radar weather equations, the radar system parameters are added into the radar echo signal modeling to establish the relationship between the simulated echo signal and radar system. As a result, the final simulated echo signal not only shows both the time and frequency domain characteristics of the weather target, but also includes the effects of the important performance of the dual-polarization weather radar system. In addition, to evaluate the performance of range ambiguity mitigation using phase coding and batch working modes, two different simulation methods for the radar signal are established on the method above;one is for batch working mode with long-PRT (pulse repetition time) and short-PRT transmission and receiving, and the other is for phase-coded mode with phase-coded transmission and phase-uncoded receiving. Under the same weather scene, the observation of these two different methods of range ambiguity mitigation are simulated and compared. Results show that the performance of the phase coding mode for mitigating range ambiguity is better than that of the batch mode. Obviously, the simulation method can be used to directly show the observation of different algorithms for mitigation range ambiguity under the same weather process, and quickly compare and evaluate the algorithm's performance, which is not possible for real radars.

关键词： dual-polarization echo simulation range ambiguity batch model phase coding

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simulation framework for activity recognition and benchmarking in different radar geometries

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IET RADAR SONAR AND NAVIGATION 2021年第4期15卷 390-401页

作者： Zhou, Boyu Lin, Yier Le Kernec, Julien Yang, Shufan Fioranelli, Francesco Romain, Olivier Zhao, Zhiqin Univ Elect Sci & Technol China Sch Informat & Commun Chengdu Peoples R China Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland Univ Cergy Pontoise Signal & Informat Proc Lab Cergy Pontoise France MS3 Microwave Sensing Signals & Syst Dept Microelect Delft Netherlands

Radar micro-Doppler signatures have been proposed for human monitoring and activity classification for surveillance and outdoor security, as well as for ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line of sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric (IF) radar. A simulator is presented to generate synthetic data representative of eight radar systems (monostatic, circular multistatic and in-line multistatic [IM] and IF) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of different radar systems. Six human activities are considered with signatures originating from motion-captured data of 14 different subjects. The classification performances are analysed as a function of aspect angles ranging from 0 degrees to 90 degrees per activity and overall. It demonstrates that IF configurations are more robust than IM configurations. However, IM performs better at angles below 55 degrees before IF configurations take over.

关键词： Doppler radar geometry image motion analysis radar signal processing assisted living

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performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Deepak Upadhyay Prakhar Prakhar Rashmy Moray Rashmi Sharma Nookala Venu Shipra Shukla Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India Symbiosis Institute of Management Studies (SIMS) A Constituent of Symbiosis International Deemed University Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of CSE (Data Science) G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions.

关键词： Fading channels Analytical models Adaptation models Array signal processing Atmospheric modeling Computational modeling Modulation Throughput Encryption Security

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MambaLiteSR: image Super-Resolution with Low-Rank Mamba Using Knowledge Distillation

MambaLiteSR: Image Super-Resolution with Low-Rank Mamba Usin...

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IEEE International Symposium on Quality Electronic Design

作者： Romina Aalishah Mozhgan Navardi Tinoosh Mohsenin Department of Electrical and Computer Engineering Johns Hopkins University

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Generative Artificial Intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in high demand, particularly for deployment on edge devices where real-time processing is crucial. However, deploying such models on edge devices is challenging due to limited computing power and memory. In this paper, we present MambaLiteSR, a novel lightweight image Super-Resolution (SR) model that utilizes the architecture of Vision Mamba. It integrates State Space Blocks and a reconstruction module for efficient feature extraction. To optimize efficiency without affecting performance, MambaLiteSR employs knowledge distillation, transferring essential information from a larger Mamba-based teacher model to a smaller student model through hyperparameter tuning. Through a mathematical analysis of model parameters and their impact on the Peak signal-to-Noise Ratio (PSNR), we identify key factors and adjust them accordingly. Our comprehensive evaluation shows that MambaLiteSR outperforms state of the art edge SR methods by reducing power consumption while maintaining competitive PSNR and SSIM scores across benchmark datasets such as Set5, Set14, and BSD100. It also reduces the power usage during training by adopting low-rank approximation. Moreover, MambaLiteSR reduces the total number of parameters without degrading performance, enabling the efficient deployment of generative AI models on resource-constrained devices. Deployment on the embedded NVIDIA Jetson Orin Nano confirms the superior balance of MambaLiteSR size, latency, and resource efficiency. The experimental results show that MambaLiteSR achieves performance comparable to both the baseline and other edge models while using 15% fewer parameters than the baseline. It also improves the power consumption by up to 58% compared to state-of-the-art SR edge models, all while maintaining low energy consumpt

关键词： performance evaluation Training Power demand PSNR Generative AI image edge detection Computational modeling Superresolution Real-time systems Tuning

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performance appraisal of business administration based on artificial intelligence and convolutional neural network

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JOURNAL OF INTELLIGENT & FUZZY systems 2020年第2期39卷 1817-1829页

作者： Gui, Xin Huanghe Sci & Technol Coll Zhengzhou 450000 Henan Peoples R China

performance appraisal in business administration has a great impact on social and economic development, so a sound performance appraisal system should be established. Moreover, in the information age, scientific methods are needed to improve business management performance. Based on this, this study links artificial intelligence with convolutional neural networks, and builds a corresponding performance research model based on actual conditions. When building the model, this paper selects the data width of 8Bit and 32 data per line, and shifts storage 2 rows, and sets the read/write enable signal to be half of the clock signal. In addition, the image matrix of the input image subjected to nonlinear processing by the excitation function ReLU will exhibit sparsity. Finally, combined with the model and data constructed in this study, the model is validated and the relevant strategies for performance evaluation are obtained.

关键词： Artificial intelligence convolutional neural network business management performance evaluation simulation analysis

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Parameter-Efficient Transformer Network for Radar Anti-Jamming Strategy Design

Parameter-Efficient Transformer Network for Radar Anti-Jammi...

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Ubiquitous Communication (Ucom), International Conference on

作者： Liangqi Liu Tianpeng Pan Wenqiang Pu Bo Jiu Junkun Yan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

In modern electronic warfare (EW), the competition between radar and jammer systems is presenting a critical challenge. Recently, active countermeasures employ frequency agile technique and conceptualize anti-jamming as an online decision-making process. However, most traditional online methods suffer from poor sample efficiency and struggle with the real-world, large-scale scenarios. This paper addresses the limitations by introducing a transformer-based neural network tailored for radar anti-jamming strategy design. Our approach utilizes the transformer architecture's capabilities in representing large-scale scenarios, and incorporates prior knowledge of the jamming systems to significantly enhance sample efficiency. A parameter-efficient fine-tuning approach is further employed to optimize computational demands. Moreover, a signal-level simulation platform is developed to facilitate an in-depth evaluation of our approach against a range of established online algorithms and various jamming strategies. Rigorous experiments on this platform confirms our method's superiority in both sample efficiency and computational performance.

关键词： Computational modeling Neural networks Decision making Radar Computer architecture Transformers Radar countermeasures Computational efficiency Jamming Electronic countermeasures

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