Design and Implementation of Low-Complexity Multiple Symbol Detection Algorithm Using Hybrid Stochastic Computing in Aircraft Wireless Communications by Yukai Liu, Rongke Liu, Kairui Tian, Zheng Lu, Ling Zhao

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Wireless key generation system for internet of vehicles based on deep learning by Han WANG, Liquan CHEN, Zhongmin WANG, Tianyu LU

“…In recent years, the widespread application of internet of vehicles technology has garnered attention due to its complex nature and point-to-point communication characteristics.Critical and sensitive vehicle information is transmitted between different devices in internet of vehicles, necessitating the establishment of secure and reliable lightweight keys for encryption and decryption purposes in order to ensure communication security.Traditional key generation schemes have limitations in terms of flexibility and expandability within the vehicle network.A popular alternative is the physical layer key generation technology based on wireless channels, which offers lightweight characteristics and a theoretical basis of security in information theory.However, in the context of internet of vehicles, the movement speed of devices impacts the autocorrelation of generated keys, requiring improvements to traditional channel modeling methods.Additionally, the randomness and consistency of generated wireless keys are of higher importance in applications in internet of vehicles.This research focused on a key generation system based on the wireless physical layer, conducting channel modeling based on line-of-sight and multipath fading effects to reflect the impact of vehicle speed on autocorrelation.To enhance the randomness of key generation, a differential quantization method based on cumulative distribution function was proposed.Furthermore, an information reconciliation scheme based on neural network auto-encoder was introduced to achieve a dynamic balance between reliability and confidentiality.Compared to the implementation of Slepian-Wolf low-density parity-check codes, the proposed method reduces the bit disagreement rate by approximately 30%.…”
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Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis by Kun Zhang, Zhaojian Yang, Qingbao Bao, Jianwen Zhang

“…Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. …”
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AI-Based Anomaly Detection and Optimization Framework for Blockchain Smart Contracts by Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi

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Accounting Management and Optimizing Production Based on Distributed Semantic Recognition by Ruina Guo, Shu Wang, Guangsen Wei

“…These results highlight the algorithm’s efficacy in enhancing the understanding and analysis of customer needs, market trends, competitors, and other pertinent information within the realm of commercial applications for companies.…”
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An overview of the CSI feedback based on deep learning for massive MIMO systems by Muhan CHEN, Jiajia GUO, Xiao LI, Shi JIN

“…The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.…”
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PSO–SOM Neural Network Algorithm for Series Arc Fault Detection by Na Qu, Jiatong Chen, Jiankai Zuo, Jinhai Liu

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Discrete Dynamics-Based Parameter Analysis and Optimization of Fuzzy Controller for Inverted Pendulum Systems Based on Chaos Algorithm by Xingguo Xia, Jianwei Xia, Mingyi Gang, Qingfeng Zhang, Jing Wang

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Tensor Calculus in Digital Colorimetry by Y. N. Saukova, M. A. Hundzina

“…According to this approach the color space is a set of directed vector fields undergoing sampling, quantization and coding operations. Also it is a dynamic open system exchanging information with the environment at a given level and to identify color with specified levels of accuracy, reliability, uncertainty and entropy.…”
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High-Bit-Depth Geometry Representation and Compression in MPEG Immersive Video System by Yoonseob Lee, Kwan-Jung Oh, Gwangsoon Lee, Byung Tae Oh

“…However, the original high-bit-depth geometry information is quantized to fit the conventional video codec during this process, leading to the loss of detailed sub-bit information. …”
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TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing by Amna Khatoon, Weixing Wang, Mengfei Wang, Limin Li, Asad Ullah

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QP-Adaptive Dual-Path Residual Integrated Frequency Transformer for Data-Driven In-Loop Filter in VVC by Cheng-Hsuan Yeh, Chi-Ting Ni, Kuan-Yu Huang, Zheng-Wei Wu, Cheng-Pin Peng, Pei-Yin Chen

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A Multi-Objective Gray Consistency Correction Method for Mosaicking Regional SAR Intensity Images with Brightness Anomalies by Jiaying Wang, Xin Shen, Deren Li, Litao Li, Yonghua Jiang, Jun Pan, Zezhong Lu, Wei Yao

“…In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, causing a loss of brightness information. …”
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SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation by Aravind R. Voggu, Kanish R, Nishith Akula, Lohitaksh Maruvada, Takanori Shimizu, Madhav Rao

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A novel reconstruction-based video anomaly detection with idempotent generative network by Wenmin Dong, Lifeng Zhang, Wenjuan Shi, Xiangwei Zheng, Yuang Zhang

“…Reconstruction-based VAD has received increasing research attention, but faces challenges such as missing anomalies for the reconstruction error as a criterion, and information loss when suppressing anomalous data, existing methods also struggle to detect unseen anomalies. …”
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Active Disturbance Rejection Vector Control for High-Power Asynchronous Motor to Suppress Measurement Noise by Xuesong ZHOU, Chenglong WANG, Youjie MA, Boning WU

“…Influenced by such factors as interference, quantization errors, and motor operating conditions, the detection elements gives the speed information mixed with noise that needs to be filtered, but the traditional speed filtering method cannot consider both real-time and precision requirements. …”
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FPG-AI RNN: A Technology-Agnostic Framework for the Automatic Acceleration of LSTM/GRU-Based Models on FPGAs by Tommaso Pacini, Pietro Nannipieri, Silvia Moranti, Luca Fanucci

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Low-Complexity Receiver Using Undersampling for Guard-Band SSB-DDO-OFDM by Ming Chen, Qinghui Chen, Hui Zhou, Zhiwei Zheng, Jing He, Lin Chen

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Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar by Seungchan Lim, Chaewoon Park, Seongjoo Lee, Yunho Jung

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Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV by Priyanshu Srivastava, Namita Shah, Kavita Jaiswal

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