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Digital signal processing and its Applications (DSPA), International Conference on

作者： Anton Strelnikov Vladimir Lyashev Alexander Sherstobitov Alexey Volkov National Research University of Electronic Technology Moscow Russia Moscow Institute of Physics and Technology Moscow Russia Moscow Radio Transmission Technology Laboratory Moscow Russia

In this paper we investigate the properties of deterministic ray-tracing channel modeling considering extended options of diffraction and diffuse scattering of electromagnetic waves in modern wireless communications system. The feature of environment-specific simulation describes the geometrical properties, which can be utilized for specific scenarios of frequency-selective channels in Non-Line-of-Site conditions. The theory of channel characterization for diffraction and scattering allows to bring more significant multipath components in application to massive multiple-input multiple-output antenna technology with large signal bandwidth in wireless system. Here a base station utilizes large-scale antenna panels to provide high-performance throughput to the user-equipment by accurate beamforming technique. In this work we analyze spatial consistency of multiplexing problem, power delay profile evaluation and capacity assessments in the ray-tracing channel modeling under the different path propagation conditions.

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An Adaptive Learning Rate Based Novel Recurrent Neural Network modeling and Control of Complex Non-Linear Dynamical systems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND signal processing 2025年

作者： Sahu, Richa Srivastava, Smriti Kumar, Rajesh Netaji Subhas Univ Technol Dept Elect Engn Sect 3 Dwarka India Netaji Subhas Univ Technol Dept Instrumentat & Control Engn Sect 3 New Delhi India Natl Inst Technol Dept Elect Engn Kurukshetra India

A Double Internal Loop Recurrent Neural Network (DILRNN) with an adaptive learning rate is proposed for the modeling and control of non-linear dynamical plants. The structure of DILRNN is an alteration of the Fully Connected Recurrent Neural Network (FCRNN). DILRNN contains three feedback loops taken primarily from the context layer to the hidden layer, the time delay of the output layer to the hidden layer, and the time delay of output to output. The parameters of DILRNN are updated using the gradient descent-based dynamic Back-Propagation (BP) algorithm. The Adaptive Learning Rate (ALR) scheme is implemented to ensure that the learning rate value is determined properly in each iteration and improves the performance of the learning algorithm. The effectiveness of the suggested strategy is assessed by considering both the Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) systems and comparing them with state-of-the-art methods. The simulation outcomes show that the proposed model outperforms the Feed Forward Neural Network (FFNN) and other Recurrent Neural Network (RNN) models, which were taken into evaluation in terms of their output and error. Next, a DILRNN controller is implemented using the proposed model to control non-linear dynamical systems. The controller's response is evaluated both in the absence and presence of a disturbance signal to assess the recovery capability of the controller. The response and error of the proposed controller are compared with other neural network controllers and elementary PID controllers.

关键词： adaptive control back-propagation method non-linear dynamical systems non-linear modeling recurrent neural network

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Extreme Low Bitrate image Compression System for Mobile Deployment

Extreme Low Bitrate Image Compression System for Mobile Depl...

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IEEE Workshop on Multimedia signal processing

作者： Junqi Wu Wenhong Duan Xianping Ma Jianhui Chang Shanshe Wang Siwei Ma Chuanmin Jia Peking University Shanghai Jiao Tong University Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

End-to-end image compression has achieved satisfactory results in recent studies. However, existing methods suffer from high complexity of complicated neural network computation and cannot be directly deployed on mobile devices due to the limitations of computing ability and storage. Therefore, considering the resource and computing ability constrains of the mobile devices, we make a trade-off in this paper between rate-distortion (R-D) performance, inference time, and model complexity. Then we design a novel lightweight perceptual image compression framework to alleviate the storage and complexity burden of mobile devices. Moreover, we design a hardware-friendly deployment scheme to apply the proposed compression framework on high-end mobile devices, which can achieve efficient image compression. Based on the above structures, we propose the first mobile system that achieves image compression on mobile devices. The supplementary material of our system demo is on https://***/documents/extreme-low-bitrate-image-compression-system-mobile-deployment.

关键词： performance evaluation image coding Multimedia systems Conferences Computational modeling Neural networks Rate-distortion signal processing Mobile handsets Complexity theory

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Edge Federated Learning via Unit-Modulus Over-The-Air Computation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第5期70卷 3141-3156页

作者： Wang, Shuai Hong, Yuncong Wang, Rui Hao, Qi Wu, Yik-Chung Ng, Derrick Wing Kwan Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Sifakis Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.

关键词： image edge detection Servers Training Computational modeling Collaborative work Array signal processing Wireless communication Autonomous driving federated learning large-scale optimization over-the-air computation

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Advancing image Domain performance evaluation in Photon-Counting Computed Tomography: A Physics-Guided Deep Learning Approach

Advancing Image Domain Performance Evaluation in Photon-Coun...

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2025 Conference on Medical Imaging

作者： Qin, Wenhui Zhong, Tao Yu, Xiaopeng Lai, Xiaochun ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China

ISBN: (纸本)9781510685888;9781510685895

Photon-counting computed tomography (PCCT) represents a transformative advancement in clinical imaging. A fundamental aspect of its development involves evaluating the performance of various photon-counting detector (PCD) and application-specific integrated circuit (ASIC) configurations. Traditional evaluation methods rely on metrics derived from the Cramer-Rao Lower Bound (CRLB), which establishes the theoretical minimum for material decomposition noise;however, these approaches face significant limitations. The conventional CRLB methodology requires computing partial derivatives of the covariance matrix, a process susceptible to numerical instability. This instability becomes particularly pronounced in the presence of high noise levels or limited datasets, potentially compromising the accuracy of derivative calculations and subsequent CRLB estimates. Although larger datasets can enhance precision, the computational demands of modeling individual X-ray photon pulses-based on specific sensor and ASIC characteristics-render such approaches challenging for image-domain evaluation, especially in complex CRLB calculations. To address these limitations, we present a deep-learning framework for assessing PCCT design performance in the image domain. Our approach employs neural networks to characterize statistical properties, specifically Gaussian-correlated noise, across energy bins in each detector pixel. These networks, trained on standardized simulation datasets incorporating detector and ASIC responses, provide an efficient alternative to traditional photon interaction and signal processing models. The framework enables direct material decomposition while circumventing CRLB calculations, facilitating comprehensive performance evaluation in the image domain. This methodology accelerates the development and optimization of PCCT technologies, enhancing the evaluation of system designs for clinical applications.

关键词： Photon Counting Computed Tomography Deep Learning Monte Carlo simulation performance evaluation

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modeling of Hybrid Fiber Optic and Free-Space Optical Communication systems

Modeling of Hybrid Fiber Optic and Free-Space Optical Commun...

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IEEE signal processing and Communications Applications (SIU)

作者： Serap KILINÇ EVRAN N. Özlem ÜNVERDİ Elektronik ve Haberleşme Mühendisliği Yıldız Teknik Üniversitesi İstanbul Türkiye

Free-space optical (FSO) communications systems have many advantages, such as large bandwidth, high data transmission rate, low cost, and easy installation process. In this study, a hybrid system incorporating both wired optical communication and free-space optical communication was designed, and its performance was analyzed. OptiSystem 20.0 simulation program was utilized in designing and performance evaluation of the hybrid communication system.

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Blind quality-based pairwise ranking of contrast changed color images using deep networks

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signal processing-image COMMUNICATION 2024年 121卷

作者： Chetouani, Aladine Qureshi, Muhammad Ali Deriche, Mohamed Beghdadi, Azeddine Univ Orleans Lab PRISME Orleans France Islamia Univ Bahawalpur Bahawalpur 63100 Pakistan Ajman Univ Coll Engn Artificial Intelligence Res Ctr Ajman U Arab Emirates Univ Sorbonne Paris Nord Inst Galilee L2TI Villetaneuse France

Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional Neural Networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of 3-10% compared to state-of-the-art IQEA measures.

关键词： Contrast enhancement evaluation (CEE) Contrast change Contrast enhancement (CE) image quality enhancement evaluation (IQEA) Convolutional neural network (CNN) Visual saliency image ranking

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Research on performance Optimization of Embedded Microcontrollers Based on Convolutional Neural Networks

Research on Performance Optimization of Embedded Microcontro...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Peng Ding School of Intelligent Manufacturing and Electronic Engineering Wenzhou University of Technology Wenzhou China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

This paper focuses on the performance optimization of embedded microcontrollers using convolutional neural networks (CNNs). With the continuous development of the Internet of Things and edge - computing technologies, embedded microcontrollers are required to handle complex tasks such as image and signal processing. CNNs, renowned for their outstanding performance in feature extraction and pattern recognition, offer a promising solution for enhancing the capabilities of embedded microcontrollers. This research first reviews the basic principles of CNNs and embedded microcontrollers. Then, it delves into the challenges of integrating CNNs into embedded microcontrollers, including limited computing resources and power consumption constraints. Through in - depth experimental studies, real - world data is collected and analyzed to evaluate the impact of different optimization strategies on the performance of embedded microcontrollers. The results show that through appropriate optimization methods, the performance of embedded microcontrollers in tasks related to CNN - based processing can be significantly improved, which has important practical significance for promoting the application of CNNs in the field of embedded systems.

关键词： performance evaluation Quantization (signal) Power demand Microcontrollers Computational modeling Memory management Optimization methods Convolutional neural networks Internet of Things Hardware acceleration

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Automated Detection of Roadway and Traffic Control Conditions Using On-Board image processing

Automated Detection of Roadway and Traffic Control Condition...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Ashmitha Jaysi Sivakumar L Pragati Sambhavi Roy Lelitha Devi Vanajakshi Department of Civil Engineering Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Accurate detection of roadway and traffic control conditions is crucial for enhancing Advanced Driver Assistance systems (ADAS) in dynamic environments. This study aims to leverage deep learning techniques, specifically YOLO series, for their identification in real-world scenarios. Detection of traffic signs, markings and signal along with road conditions such as potholes, and speed breakers are considered. The YOLO models perform well and demonstrates effective and real-time detection capacities.

关键词： YOLO performance evaluation Deep learning Pedestrians Roads Computational modeling Multitasking Real-time systems Vehicle dynamics Surface treatment

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Rate-Controllable and Target-Dependent JPEG-Based image Compression Using Feature Modulation

Rate-Controllable and Target-Dependent JPEG-Based Image Comp...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Jeong, Seongmoon Jeon, Kang Eun Ko, Jong Hwan Sungkyunkwan University Department of Artificial Intelligence Suwon-si Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon-si Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon-si Korea Republic of

ISBN: (纸本)9798350313154

While conventional image compression techniques are optimized for human visual perception, the rise of machine learning techniques has led to the emergence of image compression methods tailored for machine vision tasks. Although a few recent studies explored target-dependent reconfiguration of lightweight codecs such as JPEG, these approaches are limited to specific trained bitrates only. Moreover, existing deep learning-based compression frameworks entail a high computational cost, making them impractical for real-time compression on devices with limited resources. In this paper, we present a novel JPEG compression framework that can adaptively generate an optimal quantization table (QT) depending on both the target bitrate and the target metric (quality or accuracy). To provide fine controllability over a wide range of bitrates, we employ a feature modulation technique to a QT generator and bitrate predictor, which are trained by a novel training method called bitrate range partitioning. Our simulation results show that the proposed framework enhances the performance of standard JPEG by up to 2dB in PSNR and 10% in accuracy at the same bitrate, while incurring minimal computational overhead compared to JPEG. © 2023 IEEE.

关键词： image compression

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