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5th international conference on Computer, communication, and signal processing (ICCCSP – 2021)

5th International Conference on Computer, Communication, and...

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international conference on Computer, communication and signal processing (ICCCSP)

The international conference on Computer, communication, and signal processing (ICCCSP – 2021) is an event that sheds the spotlight on the recent trends in the research areas of Computer Science, communications, and signal processing. The 2021 edition of the annual conference holds its lens on leveraging AI techniques for healthcare. AI in Healthcare is an interdisciplinary, dynamic field with significant relevance to the current situation. This event brings together researchers, students, and practitioners who are inclined towards health care research and applications. Research techniques may include Big Data Analytics, Data Engineering, Data Mining, Statistical Learning and Modelling, Machine Learning, Visualization, Predictive Analytics, ensuring Security & Privacy, and more. The AI in Healthcare conference helps to hear how leading healthcare researchers and organizations have leveraged AI's power to improve patient care and ROI.

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international conference on VLSI, signal processing, Power Electronics, IoT, communication and Embedded Systems, VSPICE 2020

International Conference on VLSI, Signal Processing, Power E...

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international conference on VLSI, signal processing, Power Electronics, IoT, communication and Embedded Systems, VSPICE 2020

ISBN: (纸本)9789811604423

The proceedings contain 35 papers. The special focus in this conference is on signal processing, Power Electronics, IoT, communication and Embedded Systems. The topics include: Automatic Modulation Recognition Using Machine Learning Techniques: A Review;Performance Analysis of TCP in Presence of Nonresponsive Traffic in Wireless Networks;microstrip Patch Antenna Analysis for 5G Millimeter-Wave communication: A Survey;Kannada Text-to-Speech System using MATLAB;design of Dynamic Induction Charging Vehicle for Glimpse of Future: Cutting Down the Need for High-Capacity Batteries and Charging Stations;Generation of ECG for Heart Block Cases;iterative Thresholding-Based Spectral Subtraction Algorithm for Speech Enhancement;Implementation of Cryptographic Algorithm (One-Time-Pad) with a RISC-V Processor;Bayes’ Classifier for Mapping Intermediate and Heterogeneous RS Imagery;Implementation of a Hebbian Learning Algorithm as an Accelerator to a RISC-V-Based Processor;design and Implementation of Agricultural Drone for Areca Nut Farms;underwater Marine Life and Plastic Waste Detection Using Deep Learning and Raspberry Pi;Land Cover Mapping Capability of Chaincluster, K-Means, and ISODATA techniques—A Case Study;microcontroller-Based Control Circuit for the Automatic Orientation of Solar Panels in the Direction of Sun;development of an Automated Plant Classification System Using Deep Learning Approach;design and Implementation of Multi-class Logistic Regression for Effective Classification of Low, Medium and High Risk Lung Cancer Problem;voice Controlled IoT Based Grass Cutter Powered by Solar Energy;human Body Measurement Extraction from 2D Images;Generation of ECG Arrhythmias Using Fourier Analysis;a Study on the Effect of Dimensionality Reduction on Classification Accuracy of Myoelectric Control Systems.

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I-KAN: Reconstructing Over-Range Inertial signals

I-KAN: Reconstructing Over-Range Inertial Signals

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Yifeng Zhang, Shu Zhao, Yi Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Inertial sensors are widely used in modern society, covering automotive, aerospace, robotics, and consumer electronics. However, these widely used sensors suffer from range limitations, causing signal saturation and information loss under high-dynamic motion conditions. To address this problem, we introduce the Kolmogorov-Arnold Networks (KAN) into inertial sensor signal processing for the first time and propose the I-KAN model. Unlike traditional deep learning models that treat signals as discrete point sequences, KAN treats the signal as a continuous curve through embedded spline functions, which allows it to infer the lost signal segments by exploring the continuity and fluctuation patterns of signal curves. Moreover, considering deep learning models suffer from the hallucination problem that generates chaotic and spurious outputs, we propose the Generation Hallucination Entropy (GHE), which quantifies and reduces hallucinations by enhancing the consistency of outputs for similar inputs, thus improving the stability and reliability of the model. Given the absence of research and dataset for reconstructing inertial over-range signals, we release the first inertial over-range signal recovery dataset (IOSRD) in Github, which consists of inertial sensor data from 10 smartphones. The results demonstrate that I-KAN effectively handles varying degrees of signal saturation and sets a benchmark in inertial over-range signal reconstruction. © 2025 IEEE.

关键词： Generation hallucination Inertial sensor KAN Over-range signal reconstruction

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Optical performance monitoring of mode division multiplexing signal based on multi-task convolutional neural network 7

Optical performance monitoring of mode division multiplexing...

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7th Optics Young Scientist Summit, OYSS 2024

作者： He, Ting Chang, Huan Gao, Ran Wang, Fei Guo, Dong Beijing Institute of Technology No.5 Zhongguancun South Street Haidian District Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

Mode division multiplexing (MDM) optical fiber communication technology enhances bandwidth utilization and communication capacity to address the increasing demand for high-capacity data transmission in the era of rapid network traffic growth. For MDM optical fiber communication systems affected by various impairments, optical performance monitoring (OPM) can effectively characterize the impairments in received optical signals, directly reflecting the transmission signal state and channel conditions, and help ensure efficient and stable network operation. In this paper, we propose a multi-task convolutional neural network (MT-CNN) aided OPM method for MDM signals. The proposed MTCNN combines convolutional neural networks and a multi-task learning framework to extract the transmission link information and predict the optical signal-to-noise ratio (OSNR) of few-mode channels synchronously. Simulation results show that the proposed MT-CNN-based OPM scheme can achieve accurate OSNR performance evaluation for MDM fiber transmission signals. © 2025 SPIE.

关键词： Convolutional neural networks

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Received signal Strength Interpolation Method for One-Dimensional Movable Antenna Positioning

Received Signal Strength Interpolation Method for One-Dimens...

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2025 international conference on Electronics, Information, and communication, ICEIC 2025

作者： Kim, Jaehong Sung, Myeonggi Joung, Jingon School of Electrical and Electronics Engineering Chung-Ang University Seoul06974 Korea Republic of

ISBN: (纸本)9798331510756

This study proposes a received signal strength (RSS) interpolation method to find the optimal position of the movable antenna (MA). The receiver with one-dimensional MA measures the RSS at the uniformly sampled antenna positions and interpolates them to reconstruct the RSS for the entire antenna region. The optimal MA position with the maximum interpolated RSS value is chosen. Using the minimum required number of sampling positions from the Nyquist sampling theorem, the proposed MA positioning with the RSS interpolation method outperforms other MA positioning and the fixed antenna position schemes. © 2025 IEEE.

关键词： Interpolation

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Received signal-to-Noise Ratio Evaluation for Hybrid Intelligent Reflecting Array Systems

Received Signal-to-Noise Ratio Evaluation for Hybrid Intelli...

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2025 international conference on Electronics, Information, and communication, ICEIC 2025

作者： Choi, Taehee Kim, Jaehong Joung, Jingon School of Electrical and Electronics Engineering Chung-Ang University Seoul06974 Korea Republic of

ISBN: (纸本)9798331510756

This study considers a direction-of-arrival (DOA)-based hybrid intelligent reflecting array (HIRA) phase shift method. Concretely, the HIRA sensors estimate the DOA of the signal transmitted from the user equipment (UE), and the phase shift values of the HIRA reflectors are calculated using the estimated DOA. Here, the DOA estimation accuracy and the HIRA beamforming gain are proportional to the numbers of the sensors and the reflectors, respectively. Thus, there exists a trade-off between the DOA estimation accuracy and the HIRA beamforming gain when the total number of sensors and reflectors is bounded. From the numerical simulation, the received signal-to-noise ratio was evaluated to verify the trade-off of the DOA-based HIRA phase shift method. Also, the optimal number of sensors was numerically demonstrated. © 2025 IEEE.

关键词： Beamforming

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Capacity Enhancement for 6G communication with Densification of Terrestrial Network and Integration with Non-terrestrial Network

Capacity Enhancement for 6G Communication with Densification...

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international conference on 6G communications Networking and signal processing, SGCNSP 2023

作者： Sarkar, Pia Saha, Arijit Banerjee, Amit Chakraborty, Vedatrayee Department of Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Dum Dum Motijheel Rabindra Mahavidyalaya Kolkata India Microsystem Design-Integration Lab Physics Department Bidhan Chandra College Asansol India

ISBN: (纸本)9789819778751

The development of 5G technology has opened up new investigation to embrace the terrestrial network with non-terrestrial network for forthcoming 6G communication. In this study the channel capacity has been analyzed for Terrestrial Network at 6 GHz. Densification of network benefits in terms of capacity. The work has been carried out for 10, 50 and 200 m inter-site distance. For non-terrestrial network capacity analysis has been done at 28, 70 and 150 GHz for the altitude of Low Earth Orbit (LEO), Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO). 100, 500 and 900 elevation angles are taken and rural and urban scenario have been selected to show the effect of blockage. High gain antenna of 50 dB has been selected to mitigate huge path loss due to millimetre Wave. The feasibility of Satellite communication at millimeter Wave frequency (mmWave) has been studied in this work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite communication systems

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Direct-sampling radar receiver based on photonic channelization and spectral stitching 2

Direct-sampling radar receiver based on photonic channelizat...

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2nd international Academic conference on Optics and Photonics, IACOP 2024

作者： Liu, Jianwei Wang, Ruixuan Ma, Weichao Li, Wangzhe National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Terahertz Quantum Electromagnetics GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

A novel photonic technique to achieve a direct-sampling radar receiver for high-resolution detection based on the conception of channelization and spectral stitching is proposed and experimentally demonstrated. The proposed system exploits multiple incoherent lasers as optical local oscillators to perform wideband spectral segmentation and thus reduce the requirement of sampling rate and bandwidth for the electronic analog-to-digital converters (ADCs). The precise signal reconstruction is achieved using digital signal processing to eliminate the intra-channel and inter-channel signal impairments. The proposed scheme is demonstrated through an inverse synthetic aperture radar (ISAR) imaging experiment using an NLFM signal with low-sidelobe characteristics. © 2025 SPIE.

关键词： Inverse synthetic aperture radar

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Training-Free Task Planning by Parsing Language signals With Common Sense

Training-Free Task Planning by Parsing Language Signals With...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Xianqi Wang, Wenrui Chai, Shitong Wang, Xingtao Fan, Xiaopeng Faculty of Computing Harbin Institute of Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9798350368741

Task planning refers to autonomously organizing actions in response to instruction signals, especially language signals. Previous reinforcement learning and imitation learning methods always require a large amount of task-related data (data interacting with the environment or expert demonstrations) for policy training. In this paper, we propose a method named Language signal Parse Tree (LSPT for short) for task planning. LSPT is a training-free method and consists of a language signal parser and a precondition action generator. The language signal parser translates goal predicates (neural language instructions) into a predicate parse tree. This tree contains essential information about position and state changes, which is subsequently employed to generate key actions. The precondition action generator leverages commonsense to produce additional pre-actions that complement each key action, thereby increasing the likelihood of successful execution of all actions. The final plan used to accomplish the task incorporates both key actions and pre-actions. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art method LID by 24.8% on In-Distribution dataset, 43.2% on Novel Scenes dataset, and 38.8% on Novel Tasks dataset. © 2025 IEEE.

关键词： common sense embodied agents Language signal processing robots task planning

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： He, Yubin Chen, C.L. Philip Chen, Bianna Zhang, Tong School of Computer Science&Engineering South China University of Technology Guangdong Guangzhou510006 China Pazhou Lab Guangdong Guangzhou510335 China

ISBN: (纸本)9798350368741

Learning a generalized EEG representation under limited data and subject variability is a long-standing challenge. Most studies utilized data augmentation to extend the distribution of training data, which may hinder the diversity of augmented samples to cover more subject variability. In this paper, we propose a decomposed statistics-diverse feature augmentation (DSFA) framework for generalized EEG learning. The wavelet-based decomposed representation module decomposes signals into approximation and detail features, thus deriving semantics from original signals into approximation features to avoid over-transformation. The statistics-diverse feature augmentation module augments features to extend beyond the original feature space by manipulating the statistics of features. Extensive experiments on three datasets demonstrate that our approach achieves state-of-the-art performance in different classification tasks. Our repository is public at https://***/1940653868/DSFA. © 2025 IEEE.

关键词： EEG Classification Feature Augmentation Model Generalization signal Decomposition Transformer

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