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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3477 条记录，以下是431-440 订阅

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Maximizing Energy and Spectral Efficiency Tradeoff in MISO-RSMA Systems Under Coarse Quantization

Maximizing Energy and Spectral Efficiency Tradeoff in MISO-R...

引用

European signal processing conference (EUSIPCO)

作者： Seokjun Park Jiwon Sung Jinseok Choi Jeonghun Park Wonjae Shin Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Electrical and Electronic Engineering Yonsei University Seoul South Korea Electricalq Engineering Korea University Seoul South Korea

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Using low-resolution quantizers reduces power consumption increasing energy efficiency (EE) but lowers spectral efficiency (SE) in wireless communication systems. To compensate the SE degradation, we consider rate-splitting multiple access (RSMA) which reduces inter-user interference by introducing a common stream in the downlink multi-user multi-antenna system with low-resolution digital-to-analog converters. We propose a precoding method that optimizes a weighted SE and EE. Key challenges that come with optimizing the trade-off between the SE and EE include transmit power optimization, a minimum rate constraint for a RSMA common stream, and quantization error mitigation. To tackle these challenges, we employ the Dinkelbach method and separate the optimization problem into two parts: optimizing the precoding direction and the transmit power. In terms of the precoding direction, we approximate the SE as a smooth function and derive the first-order optimality condition. Viewing this as an eigenvalue problem, we efficiently identify the principal eigenvector which corresponds to the best stationary point. For transmit power, we utilize a gradient descent algorithm. Both the precoding direction and the transmit power are updated in an alternating manner. Simulations validate the advantages of the proposed algorithm over other state-of-the-art algorithms.

关键词： Wireless communication Quantization (signal) Spectral efficiency Precoding Simulation Prevention and mitigation signal processing algorithms Downlink Eigenvalues and eigenfunctions Optimization

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A Robust Design of Real-Time Resilient Smile Recognition System using Hybrid Deep Learning Principles

A Robust Design of Real-Time Resilient Smile Recognition Sys...

引用

International conference on Communications and signal processing

作者： Aswathy K Cherian S. Devipriya Bhushan P. Saoji B. Mallikeswari R. Thiagarajan R. Krishnamoorthy Department Of Computing Technologies SRM Institute Of Science And Technology Kattankulathur Chennai Department of CSE Jerusalem College of Engineering Chennai India Instrumentation Engineering Bharati Vidyapeeth college of Engineering Navimumbai Dept of Computer JusticeBasheer Ahmed Sayeed college for Women Chennai India Department of IT Prathyusha Engineering College Chennai Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

We often convey our happiness via smiling or making a happy face. Simple, latent feelings like contentment and joy are brought to the surface in this way. In terms of social communication, this is the most difficult and potent task there is. One function of digital cameras is smile recognition, which prevents taking pictures of people unless they are smiling. There are two distinct stages to smile detection. After identifying a face in a video or picture, it watches for a grin. To be more precise, we may state that a motion detector takes a video or image and breaks it down into frames. It then examines the frameworks, such as the balanced level and flash, for the face area. The camera can tell whether someone is laughing or smiling because it looks for certain facial features—a jutted jaw, narrowed eyes, and an elevated cheekbone among others. The components of computer architecture and software advancements are advancing at a rapid pace, which is leading to a progressive increase in customer needs for electric products. A growing number of people are interested in human computer interaction systems that differ from the conventional user interface, which includes the keyboard and mouse. This article depicts the realtime implementation of deep learning algorithms in computer settings with limited resources. In this study, we provide HLSR, a new kind of deep learning model that combines the XGBoost and Convolutional Neural Network (CNN) algorithms for smile recognition. To get a clear picture of how well the suggested technique works, we cross-validate the HLSR model with the current CNN model.

关键词： Deep learning Face recognition Computational modeling Neural networks signal processing algorithms signal processing Software

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An Adaptive Estimation Method for Group Target Number Based on Information Theoretic Criterion

An Adaptive Estimation Method for Group Target Number Based ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Libin Dou Qi Jiang Rui Wang Mengxin Shi Jichuan Zhang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The number of individuals within a group is crucial information for the correct association, robust tracking, and situation awareness. Traditional methods using the range profile’s peaks are not suitable when targets are densely distributed. The target number estimation algorithms based on monopulse systems have high computational complexity, while those based on array systems require additional sensor resources. To address these issues, this paper proposes a method based on multiple incoherent observations using one monopulse radar. By combining multiple incoherent observations from a single sensor with information theoretic criterion, this approach can estimate the number of targets even when the resolution of a single sensor is insufficient. The effectiveness of the algorithm is verified through simulations, and the algorithm's performance is analyzed.

关键词： Analytical models Target tracking Estimation Radar Radar tracking Data processing Adaptive estimation Computational complexity signal resolution Sensor arrays

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Real-Time Monitoring of Three-Phase Unbalanced Devices and Real-Time Adjustment Analysis of Action Characteristics

Real-Time Monitoring of Three-Phase Unbalanced Devices and R...

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IEEE International conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Yan Ming Gu Haifeng Gu Xiaojun Gao Jinhua Zhang Shuguang Liang Ji Yu Pengjie Rugao Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. Nantong China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

This paper designs and implements a three-phase unbalanced monitoring and adjustment system based on advanced algorithms and model simulation. The system uses a variety of sensors and signal processing algorithms to achieve high-precision real-time monitoring of three-phase unbalanced parameters. By establishing a dynamic adjustment algorithm for action characteristics based on machine learning, the system can intelligently adjust the action characteristics of the three-phase unbalanced device according to real-time monitoring data to maintain the stability and efficiency of the system. The simulation platform uses MATLAB Simulink to establish a system model, and verifies the effectiveness and real-time response capability of the algorithm through multiple sets of simulation experiments. The experimental results show that the proposed system shows good adaptability under different loads and power grid environments, can effectively reduce the three-phase imbalance, and improve the power supply quality of the system. The real-time adjustment algorithm performs well in stability and response speed, and realizes the dynamic optimization of the action characteristics of the three-phase unbalanced device. This study provides innovative ideas and technical support for solving the three-phase imbalance problem in the power system.

关键词： Machine learning algorithms Heuristic algorithms Power system dynamics signal processing algorithms Power system stability Sensor phenomena and characterization Real-time systems Stability analysis Mathematical models Monitoring

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A novel multi-ellipsoidal approach to group target tracking using variational approximation

A novel multi-ellipsoidal approach to group target tracking ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Jichuan Zhang Qi Jiang Cheng Hu Mengxin Shi Libin Dou Sheng Liu Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Real-Time Information Processing Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Group target tracking that estimates the position and shape of collective objects is a hotspot area in radar observation. Group targets, such as birds and UAVs, often exhibit complex spatial structures and highly dynamic behaviors. However, traditional multi-ellipsoidal tracking methods rely on a simple random matrix to model sub-object extension, where orientation and extension size are coupled. The initial sub-object model may fail to adapt to dynamic changes in the sub-object state, especially when the sub-object rotates. This paper introduces a modified multi-ellipsoidal tracking method, where the orientation of each sub-object is represented as an independent Gaussian random vector, separate from its extension size. The extension size is described by a diagonal symmetric positive definite matrix, with its elements following inverse Gamma distributions. A variational Bayesian approach is applied to approximate the complex posterior distribution. Real-data experiments validate the effectiveness of the proposed method.

关键词： Gamma distribution Adaptation models Target tracking Symmetric matrices Heuristic algorithms Radar tracking Approximation algorithms Birds Vectors Bayes methods

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Detection of TEMPEST audio compromising signal using artificial intelligence

Detection of TEMPEST audio compromising signal using artific...

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International conference on Communications (COMM)

作者： Alexandru-Mădălin Vizitiu Lidia Dobrescu Cristian Molder Bogdan Sebacher Bogdan Cătălin Trip Vlad Florian Butnariu Faculty of Electronics Telecommunications and Information Technology National University of Sciences and Technologies Politehnica Bucharest Bucharest Romania Center of Excellence in Robotics and Autonomous Systems—CERAS "Ferdinand I" Military Technical Academy Bucharest Romania Department of Applied Informatics Military Technical Academy "Ferdinand I" Bucharest Romania

ISBN: (数字)9798350355956

ISBN: (纸本)9798350355963

In the contemporary landscape of cybersecurity, electronic devices are increasingly susceptible to various forms of exploitation, with TEMPEST (Transient Electromagnetic Pulse Emanation Standard) attacks posing a significant threat. This article delves into the application of artificial intelligence (AI) for detecting vulnerabilities in electronic devices through audio signal analysis within the TEMPEST domain. By leveraging advanced AI techniques, particularly machine learning algorithms, this study identifies a novel approach regarding the TEMPEST evaluation of electronic devices which process audio signal using artificial intelligence. The process involves the collection and processing of audio signals emanating from electronic devices, followed by the application of AI models to detect the presence of audio information in the received signal. The findings underscore the efficacy of AI involved in TEMPEST evaluation of electronic devices.

关键词： Machine learning algorithms Object recognition Artificial intelligence Transient analysis signal analysis Computer security Standards

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DRL Path Planning for UAV-Aided V2X Networks: Comparing Discrete to Continuous Action Spaces

DRL Path Planning for UAV-Aided V2X Networks: Comparing Disc...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Leonardo Spampinato Alessia Tarozzi Chiara Buratti Riccardo Marini WiLab CNIT / DEI University of Bologna Bologna Italy

It is expected that future 6G vehicular networks will rely on Unmanned Aerial Vehicles (UAVs) acting as flying Base Stations, namely Unmanned Aerial Base Stations (UABSs), in order to provide a wide range of services that current terrestrial networks cannot manage. Vehicles may exploit strong links with the UABS, enabling applications such as advanced driving and extended sensing. In this context, if vehicular users are satisfied with an appropriate Quality of Experience (QoE), they are able to upload a given amount of data for a given time window, continuously. To allow this, an efficient path planning is fundamental. This paper presents a Deep Reinforcement Learning (DRL)-based solution, where a novel reward function is proposed with the aim of offering a continuous service to vehicles. Results are presented in terms of the percentage of satisfied users and both, a continuous action space as well as a discrete action space, are considered exploiting two different DRL algorithms (i.e., Double Dueling Deep Q Network (3DQN) and Deep Deterministic Policy Gradient (DDPG)) in order to compare the two and select the best one according to the described scenario of interest.

关键词： Training Space vehicles Base stations signal processing algorithms Reinforcement learning signal processing Trajectory

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An Imperceptible and Robust Audio Watermarking Algorithm Based on SNGAN

An Imperceptible and Robust Audio Watermarking Algorithm Bas...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Weili Zhou Jiabei Zhou Shuangyuan Yang School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Digital audio watermarking has emerged as a significant research focus in the field of multimedia data hiding in recent years. However, existing deep learning-based watermarking algorithms have primarily concentrated on the image field. Moreover, current audio watermarking techniques mostly rely on traditional methods, exhibiting clear drawbacks in imperceptibility and robustness against common signal processing attacks, particularly Re-sampling and MP3 compression. To address this challenge, this paper introduces a novel digital audio watermarking approach. Leveraging the high-quality generation and outstanding generalization capabilities of Spectrally Normalization Generative Adversarial Network (SNGAN), we apply it for the first time to embed and extract watermarks in the audio domain. Additionally, we incorporate Dense Connections within the generator of the GAN to further enhance its learning capacity. Furthermore, we design a dedicated attack network tailored for common audio attacks to bolster the robustness of our model. Extensive experiments show that our proposed model achieves an advanced level of performance, with an average signal-to-noise ratio (SNR) of 26.59 and an average watermark extraction BER of 0.42%. These results conclusively establish the exceptional imperceptibility and robustness of our model.

关键词： Training Neural networks signal processing algorithms Watermarking QR codes Network architecture Generative adversarial networks

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Underwater Image Enhancement Using Gaussian Smoothing and Edge Detection

Underwater Image Enhancement Using Gaussian Smoothing and Ed...

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International conference on Inventive Computation Technologies (ICICT)

作者： V. Lambodara Aravind C. Bagyalakshmi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Underwater acoustic communication is used for oceanographic data collection, environmental monitoring, disaster alarm systems, and navy applications. Underwater has its peculiarities such strong signal attenuation and limited bandwidths, multipath nature of signals, and ambient noises. All these characteristics bring a great deal of interference probably in transmission with certainty and effectiveness. This work propounded an optimized hybrid model that works concurrently with advanced signal processing and noise-reducing techniques for communication enhancement. The purpose of the proposed model is to develop high signal-to-noise ratio (SNR) received signals with lesser transmission error using time-frequency domain analysis and filtering techniques, which are specifically designed for underwater applications. Thus, the main aim of this work, in fact, is towards the development of a robust underwater communication platform, which ensures a very reliable transfer of data under conditions of excessive noise even in the rapidly changing aquatic environments. Comparison of the proposed model with traditional techniques shows that this model stands much higher than the earlier techniques regarding transmission accuracy, noise resilience, and overall efficiency, thus extending the development of more reliable underwater communication systems toward future marine and defense applications.

关键词： Adaptation models Underwater communication Smoothing methods Image edge detection Computational modeling signal processing algorithms Reliability Underwater acoustics Image enhancement signal to noise ratio

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Edge-IoT Architecture-Based Visualization Service Platform for RTO Real-Time Monitoring

Edge-IoT Architecture-Based Visualization Service Platform f...

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International conference on advanced Communication Technology (ICACT)

作者： Hoon-Min Park Dal-Hwan Yoon Hyun-Min Jung Dae-Hee Lee Tae-Yeung Lim Hyun Kim Myung-Ki Jang Emsolution Co. Ltd Suwon Korea Department of Electronic Engineering Semyung University Korea Devicenet Co. Ltd Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

In this study, we present the Edge IoT Architecture (Architecture) for analyzing the real-time state information of the regenerative thermal oxidizer (RTO) and implement a visualization method. The Edge-IoT architecture system interfaces with the programmable logic control (PLC) that controls the monitoring of RTO operations and transmits real-time data to a remote server, analyzes the channel data received through the IoT to build a database, and visualizes the characteristics of database (DB) analysis. IoT architecture implementation for fault diagnosis and maintenance of RTO designs IoT circuits on PLC control panels that control monitoring. Compatibility and information security are considered to facilitate remote maintenance. The received data is segmented by each channel and analyzed by a visualization algorithm to detect anomalies in the segmented data. At this time, the visualized data is judged to be normal, insufficient, or warning, depending on the threshold.

关键词： Image edge detection Data visualization signal processing algorithms signal processing Real-time systems Maintenance Thermal analysis Visual databases Servers Monitoring

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