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检索条件"主题词=Signal Processing Algorithms"

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Robust Joint Optimization for Multi-Cell Backscatter Communication System: A Deep Reinforcement Learning Approach

Robust Joint Optimization for Multi-Cell Backscatter Communi...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Yuanyuan Lei Chenyang Du Meiying Yang Jing Guo Ming Zeng School of Information and Electronics Beijing Institute of Technology Beijing China Yangtze River Delta Graduate School of Beijing Institute of Technology (Jiaxing) Jiaxing China Datang Mobile Communications Equipment Co. Ltd

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Backscatter communication(BackCom) has emerged as an ultra-low power technology that enables battery-free devices for various Internet of Things scenarios. To improve the network performance, the resource allocation is important; however, most of prior works developed the strategy under the ideal case, i.e., the channel state information (CSI) is perfect, which can worsen the performance. Hence, in this paper, we investigate the robust resource allocation for a multi-cell BackCom system, where the imperfect CSI is considered. To maximize the sum rate of the considered system, a robust deep reinforcement learning-based algorithm is developed by jointly optimizing spectrum allocation, the reflection coefficients of tags, and the transmit power of readers. Specifically, the original non-convex optimization problem is transformed into a Markov decision process. Then, to effectively handle the discrete-continuous hybrid action space, we propose a robust multi-agent parametrized deep Q-network (MAPDQN) algorithm, where the artificial distorted CSI is introduced during the training phase to compensate for the error of imperfect CSI. The numerical results demonstrate the effectiveness and robustness of our proposed algorithm.

关键词： Training Performance evaluation Wireless communication signal processing algorithms Robustness Reflection coefficient Resource management Optimization Phase distortion Backscatter

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Artificial Intelligence Technique in Next Generation Healthcare Systems

Artificial Intelligence Technique in Next Generation Healthc...

引用

Communication, Computing and signal processing (IICCCS), IEEE International Conference on

作者： Chetan Pandey Sachin Sharma Shuchi Bhadula Department of Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Graphic Era Deemed to be University Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Advancement of science and technology makes human life very comfortable, healthy, and little bit secure. Current technology like Body Sensor Network, and Internet of Things, are making a healthcare system which is capable of detecting disease earlier on the basis of prior symptoms shown by the body. By using sensors we are now able to measure these symptoms like high or low blood pressure, body temperature, glucose level, heart beats, and more. Researchers are now incorporating the Artificial Intelligence with the next generation healthcare systems, making it a Self-Optimization system. In this paper, some advance technologies for healthcare system followed by some valuable artificial intelligence techniques are discussed. Based on some sorted research papers, author present a comparative analysis discussing about techniques used in next generation healthcare systems, objectives to achieve, and about the future aspects in terms of authors. Followed by these details, author also mention some applications, challenges and research trends in the field of healthcare system.

关键词： Temperature sensors Temperature measurement signal processing algorithms Medical services Prediction algorithms Market research Classification algorithms Reliability Artificial intelligence Next generation networking

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Synergistic PET/MR Reconstruction with VAE Constraint

Synergistic PET/MR Reconstruction with VAE Constraint

引用

European signal processing Conference (EUSIPCO)

作者： Valentin Gautier Claude Comtat Florent Sureau Alexandre Bousse Voichita Maxim Bruno Sixou Univ. Lyon INSA-Lyon UCBL UJM-Saint-Étienne CNRS Inserm CREATIS UMR 5220 Lyon France BioMaps Université Paris-Saclay CEA CNRS Inserm SHFJ Orsay France LaTIM INSERM U1101 Université de Bretagne Occidentale Brest France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this work, we propose a synergistic PET/MR reconstruction method based on the ADMM algorithm and a pre-trained bimodal Variational Auto Encoder (VAE) as a constraint. Because of the multiple modalities, balancing the VAE's loss becomes a challenge. To solve this, we adapt an adaptive loss balancing method and apply it to the training of the VAE. We evaluate our approach on 2D slices from 44 different patients and show that the presented approach performs particularly well on low dose/highly undersampled data.

关键词： Training signal processing algorithms Europe signal processing Reconstruction algorithms Image reconstruction

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Scalarization of Multi-Function Sensor Scheduling Problem

Scalarization of Multi-Function Sensor Scheduling Problem

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New Trends in signal processing (NTSP)

作者： Jerguš Suja Pavel Kulmon Institute of Mathematics of Faculty of Mechanical Engineering University of Technology Brno Czech republic Research Department ERA a.s Pardubice Czech republic

ISBN: (数字)9788080406370

ISBN: (纸本)9798350362688

The VERA-NG system is a multifunctional passive surveillance system that simultaneously fulfills several functions. Concerning the hardware resources, it must solve the problem of scheduling the tasks of individual functions. This problem is defined as a multiobjective optimization. This work aims to solve this problem with the goal programming scalarization technique. Using a simple simulation, we demonstrate the properties of this approach and summarize its advantages over other scalarization techniques.

关键词： Surveys Target tracking Surveillance signal processing algorithms Programming signal processing Linear programming Market research Optimization Tuning

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

引用

signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Pallab Banerjee Sweety Kumari Biresh Kumar Kanika Thakur Kumar Gaurav Purushottam Kumar Dept. of Computer Science Engineering Amity University Jharkhand Ranchi Jharkhand India Dept. of Computer Science Engineering Galgotia University Greater Noida campus Amity School of Information Technology Amity University Jharkhand Ranchi Jharkhand India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

An innovative data structure called a “dynamic tree” has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the “Maximum Flow method,” a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios.

关键词： Runtime Heuristic algorithms signal processing algorithms Forestry signal processing Data structures Prediction algorithms Time complexity Optimization Periodic structures

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When Audio Denoising Meets Spiking Neural Network

When Audio Denoising Meets Spiking Neural Network

引用

Artificial Intelligence (CAI), IEEE Conference on

作者： Xiang Hao Chenxiang Ma Qu Yang Kay Chen Tan Jibin Wu Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China College of Design and Engineering National University of Singapore Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Audio denoising techniques are essential tools for enhancing audio quality. Spiking neural networks (SNNs) offer promising opportunities for audio denoising, as they leverage brain-inspired architectures and computational principles to efficiently process and analyze audio signals, enabling real-time denoising with improved accuracy and reduced computational overhead. This paper introduces Spiking-FullSubNet, a real-time audio denoising model based on SNN. Our proposed model incorporates a novel gated spiking neuron model (GSN) to effectively capture multi-scale temporal information, which is crucial for achieving high-fidelity audio denoising. Furthermore, we propose the integration of GSNs within an optimized FullSubNet neural architecture, enabling efficient processing of full-band and sub-band frequencies while significantly reducing computational overhead. Alongside the architectural advancements, we incorporate a metric discriminator-based loss function that selectively enhances the desired performance metrics without compromising others. Empirical evaluations show the superior performance of Spiking-FullSubNet, ranking it as the winner of Track 1 (Algorithmic) of the Intel Neuromorphic Deep Noise Suppression Challenge.

关键词： Computational modeling Noise reduction Neurons signal processing algorithms Computer architecture Spiking neural networks Brain modeling

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Modeling the Energy Consumption of the HEVC Software Encoding Process Using Processor Events

Modeling the Energy Consumption of the HEVC Software Encodin...

引用

IEEE Workshop on Multimedia signal processing

作者： Geetha Ramasubbu André Kaup Christian Herglotz Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Developing energy-efficient video encoding algorithms is highly important due to the high processing complexities and, consequently, the high energy demand of the encoding process. To accomplish this, the energy consumption of the video encoders must be studied, which is only possible with a complex and dedicated energy measurement setup. This emphasizes the need for simple energy estimation models, which estimate the energy required for the encoding. Our paper investigates the possibility of estimating the energy demand of a HEVC software CPU-encoding process using processor events. First, we perform energy measurements and obtain processor events using dedicated profiling software. Then, by using the measured energy demand of the encoding process and profiling data, we build an encoding energy estimation model that uses the processor events of the ultrafast encoding preset to obtain the energy estimate for complex encoding presets with a mean absolute percentage error of 5.36% when averaged over all the presets. Additionally, we present an energy model that offers the possibility of obtaining energy distribution among various encoding sub-processes.

关键词： Energy consumption Analytical models Software algorithms Estimation Energy measurement signal processing algorithms Streaming media Encoding Software Energy efficiency

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EFIM: An Exploring Feature-Invariant Module for Domain Generalization

EFIM: An Exploring Feature-Invariant Module for Domain Gener...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Yangsen Li Weiyu Chen Zhenjie Ke Yuzheng Liu Yiqi Hu Chenyu Ma Xiaotong Tu State Grid Fujian Electric Power Co. Ltd. Fujian Electric Power Transmission and Transformation Engineering Co. Ltd. State Grid Fujian Electric Power Co. Ltd. Construction Branch. School of Informatics Xiamen University China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Deep learning suffers from performance degradation when evaluated with different distributions of training and testing data. Domain generalization (DG) enables the model to generalize to data distributions in unseen domains, i.e., to learn domain-invariant representations. In this paper, we learn domain invariant features in terms of internal and mutual invariance. Internal invariance means that we can capture the intrinsic semantics of the data, i.e., properties within a domain that are independent of other domains, and mutual invariance means that features can be learned in multiple domains. We design an Exploring Feature-Invariant Module (EFIM). EFIM employs a knowledge distillation framework to capture Fourier phase information as internally invariant features and a triplet loss metric for multi-source domain features as mutually invariant features. Extensive experiments conducted on the Case Western Reserve University rolling bearing (CWRU) dataset and SJTU dataset validate the effectiveness of the EFIM.

关键词： Training Measurement Degradation Deep learning Semantics signal processing algorithms Rolling bearings signal processing Feature extraction Testing

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Research and Implementation of Solution and Optimization Methods for Nested Algebraic Loops

Research and Implementation of Solution and Optimization Met...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Jing Zang Shiyi Yi Haokun Rong School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To improve simulation efficiency and accuracy of simulation results, point at communication errors and data transmission issues caused by temporal variability between simulation models, Using Kosaraju - Breadth Bidirectional search method to detect the nested algebraic loop structure in the simulation system. On the basis of studying the traditional solution method of nonlinear equations, the structure of Jacobi matrix in the imprecise Newton's method is adjusted by using the iteration between H and S matrix in the NPHSS method, the Newton NPHSS iterative method is obtained to improve the low efficiency of imprecise Newton's method for solving large dimension algebraic loops, and improve the efficiency of the algorithm. The error analysis of the Newton NPHSS iterative method and the iterative results of Newton method and inexact Newton method show that the Newton NPHSS iterative method converges faster and has less iteration. And the new algorithm was applied to the FMI simulation system constructed by Simulink, achieving fast detection and effective solution of nested algebraic loops, verifying its effectiveness and feasibility in practical applications.

关键词： Jacobian matrices Software packages Search methods Simulation Software algorithms signal processing algorithms Mathematical models Software reliability Iterative methods Newton method

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Adaptive Feature Optimization in fMRI Brain Networks for Alzheimer's Disease Detection via GA-SA

Adaptive Feature Optimization in fMRI Brain Networks for Alz...

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

作者： Shusheng Zhou SS Zhou Yan Yang Y Yang School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

This study explores the use of Genetic Algorithm (GA), Simulated Annealing (SA), and a combined GA-SA approach for feature selection in the classification of Alzheimer's Disease (AD) based on fMRI data. The aim is to improve classification model accuracy by selecting the most relevant features through these optimization methods. Experiments were carried out on fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), with each test repeated 20 times to ensure stability, and average outcomes reported. The GA-SA method achieved the highest best cost value of 64.6944%, demonstrating its effectiveness in selecting key features, specifically features 1, 2, 5, 9, 10, and 11, which significantly improved the classification accuracy compared to using all features without optimization. The comparison with individual GA and SA approaches, as well as the non-optimized feature set, underscores the critical role of feature selection in boosting model performance. Our results suggest that hybrid optimization techniques like GA-SA are highly effective in enhancing feature selection processes for fMRI-based AD classification.

关键词： Costs Accuracy signal processing algorithms Simulated annealing Functional magnetic resonance imaging Feature extraction Stability analysis Alzheimer's disease Genetic algorithms Testing

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