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检索条件"主题词=Signal Processing Algorithms"

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Task-Offload-Oriented Resource Optimization in MEC-Enabled System with Multiple BSs

Task-Offload-Oriented Resource Optimization in MEC-Enabled S...

引用

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

作者： Chang Wang Yanzan Sun Yong Fang Xiaojing Chen Shunqing Zhang School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In the extensive research on next-generation communication network architectures, task offloading and resource allocation problems become increasingly complex for mobile edge computing (MEC) systems with multiple base stations (BSs). This paper first formulates this multi-dimensional dynamic issue as an optimization problem. To minimize system overhead, we propose an attention-based multi-agent proximal policy optimization (A-MAPPO) algorithm. This algorithm employs a centralized training and decentralized execution (CTDE) framework, and leverages attention mechanisms to facilitate the convergence of the critic network, thereby enhancing the algorithm's performance. Experimental results show that the A-MAPPO algorithm can reduce system costs by up to 28.3% compared to other benchmark algorithms.

关键词： Training Costs Heuristic algorithms Scalability signal processing algorithms signal processing Resource management Time factors Optimization Next generation networking

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Optimization of a MIMO Antenna using the API metaheuristic algorithm

Optimization of a MIMO Antenna using the API metaheuristic a...

引用

International Conference on Image and signal processing and their Applications (ISPA)

作者： Bitat Hiba Ines Maamri Fouzia Khelfaoui Fatima Djellab Hanane Messai Yacine Aouissi Feriel SATIT Laboratory University Abbes Laghrour Khenchela Khenchela Algeria University Alarbi TBESSI Tebessa Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Antenna technology has undergone significant advancements with the integration of innovative techniques such as MIMO (Multiple Input Multiple Output) and phased array systems. By utilizing multiple antennas for transmission and reception, MIMO enhances data rates, spectral efficiency, and overall system performance. Phased array antennas, on the other hand, introduce beamforming capabilities, allowing for dynamic control and steering of radio wave beams without physical movement. The goal of this work is to optimize the control of the antenna network (MIMO) from the perspective of directivity and secondary lobe reduction using the API (pachycondyla apicalis algorithm) metaheuristic algorithm. Specifically, the API algorithm will be employed to determine the optimal element spacing for an N=16 element linear array operating at the LTE frequency of 2.5 GHz, and phase shift Ψ=2.3 radians with the objective of achieving the best value of the inter element spacing. while minimizing the secondary lobes.

关键词： Phased arrays Array signal processing Heuristic algorithms System performance Metaheuristics signal processing algorithms Space exploration

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Investigating Delay Compensation within the Context of Frequency-Aided PLL

Investigating Delay Compensation within the Context of Frequ...

引用

International Conference on Localization and GNSS (ICL-GNSS)

作者： Xinhua Tang Xinmin Zhu Shuguo Pan Tao Zhang Zhe Yan Laura Ruotsalainen Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology of Ministry of Education Southeast University Nanjing China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (数字)9798350380781

ISBN: (纸本)9798350380798

The transition from analog GNSS receivers to digital ones has spurred the emergence of various GNSS baseband signal processing algorithms. Among these, Frequency-aided PLL has been widely investigated in different forms, including FLL-assisted PLL, VFLL, etc. Great contributions have been made in the practical implementation and evaluation of different forms of Frequency-aided PLLs, but the impact of frequency aid information delay has not been thoroughly analyzed quantitatively. To date, only preliminary impact analysis in S domain is available in few literature sources. This paper mainly addresses the problem in Z domain that is more aligned with practical implementations. Two distinct types of Frequency-aided PLL are firstly proposed and a quasi-FLL-assisted PLL considering the frequency aid delay is chosen to be analyzed. Additionally, delay compensation is also derived using the Controlled-Root method to counteract the impact of delay, providing a valuable insight into the problem of frequency aid delay in other models.

关键词： Location awareness Global navigation satellite system Baseband signal processing algorithms Receivers Stability analysis Delays

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High-Speed Strassen Matrix Multiplication Accelerators for 2D Kalman Filter

High-Speed Strassen Matrix Multiplication Accelerators for 2...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Mohan Pudi Dayananda B N Achala G Pathipati Srihari Bethi Pardhasaradhi Linga Reddy Cenkeramaddi ECE Dept. National Institute of Technology Karnataka Surathkal India Department of ICT University of Adger Grimsted Norway

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

High-speed and low-area Kalman filter (KF) algorithms are critical in autonomous vehicles, robotics, military, target tracking, and other applications. KF requires a large number of matrix multiplications (complexity of order O(n 3 )), which increases the number of cycles needed to process data. There is a vital requirement to develop a matrix multiplication accelerator module to enhance the efficiency of the entire Kalman Filter (KF) process. This paper proposes two accelerator models for KF, namely pipelined Strassen matrix multiplication and hybrid Strassen matrix multiplication. We propose pipelining the existing Strassen matrix multiplication architecture to reduce critical time to a single multiplication operation. To accelerate the KF and target the low area, we present a hybrid Strassen matrix multiplication architecture that reduces higher dimensions using the Strassen algorithm and computes the 2 × 2 matrix using standard matrix multiplication. The Strassen algorithm, pipelined Strassen algorithm, and hybrid Strassen algorithm-based accelerators are designed for the Vertix-7 FPGA to determine the maximum possible frequency and space limitations. For demonstration purposes, a 2D-KF is studied with a four-dimensional state vector.

关键词： Target tracking signal processing algorithms Computer architecture Vectors Kalman filters Robots Field programmable gate arrays

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Reconstruction Method for Radio Fuze signals Based on Compressed Sensing

Reconstruction Method for Radio Fuze Signals Based on Compre...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Biwen Jiang Shenghua Fu Chubao Li Jiuxing Yuan Xi’an Institute of Electromechanical Information Technology Xi’an China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

In response to the challenges posed by the advancement of radio fuze towards millimeter-wave and higher frequency bands, which enhance target detection accuracy and anti-jamming capabilities, this paper addresses the resulting burdens on signal reception, storage, and transmission. Under constraints of limited hardware resources and costs, we propose a reconstruction method for sampled data based on compressed sensing, leveraging the frequency domain sparsity of radio echoes. This approach aims to reduce the equivalent sampling rate of the received signals, thereby alleviating storage and transmission loads. The method employs an observation matrix for compressive sampling and utilizes the interior-point method to solve the convex optimization problem inherent in the reconstruction of sparse coefficients. Simulation results demonstrate that the proposed method significantly reduces the equivalent sampling rate of the receiver and mitigates data storage and transmission pressures while maintaining signal accuracy. This provides a technological foundation for the efficient and cost-effective implementation of signal processing in radio fuze.

关键词： Accuracy Simulation System performance signal processing algorithms Receivers Reconstruction algorithms Hardware signal reconstruction Convex functions Compressed sensing

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Remaining Life Prediction of Lithium Battery Based on Data Preprocessing CNN-GRU Model

Remaining Life Prediction of Lithium Battery Based on Data P...

引用

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

作者： Yang Li Zhengang Shi School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to overcome the non-stationarity and non-linearity of the capacity change of traditional lithium batteries during cyclic charging, which will be affected by the regeneration capacity change when life prediction is carried out, a neural network lithium battery life prediction technique using Whale Optimisation Algorithm (WOA) optimised Variational Modal Decomposition (VMD) and Convolutional Neural Network (CNN) combined with Gated Recycling Unit (GRU) is proposed. Firstly, the number of decomposition layers and penalty factor in the VMD were optimised by WOA, secondly, the optimised VMD was used to divide the capacity of Li-ion batteries into several components, and in the third step, the CNN-GRU neural network model was used for prediction. The WOA-VMD-CNN-GRU model is validated by NASA dataset, and the experimental results show that compared with other methods such as CNN, GRU, feed-forward neural network (FNN), the results of this study have a maximum average absolute error of no more than 0.0092, a maximum root-mean-square error of no more than 0.0096, a minimum relative accuracy of 0.9937, and a maximum prediction error of no more than 2 cycles for RUL, indicating that the study has a maximum prediction error of no more than 2 cycles. exceed 2 cycles, indicating that the study has good robustness and high accuracy.

关键词： Analytical models Accuracy Computational modeling NASA signal processing algorithms Predictive models Lithium batteries Prediction algorithms Data models Convolutional neural networks

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Hyperspectral Object Tracking with Low-Rank Adaptation

Hyperspectral Object Tracking with Low-Rank Adaptation

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Workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Langkun Chen Yunhe Zhang Yuze Ke Long Gao Weiying Xie State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Hyperspectral data, with its rich material information, offers significant advantages over RGB data, leading to an increased focus on hyperspectral object tracking. However, the limited availability of hyperspectral tracking datasets presents a major challenge, hindering the development of robust deep learning-based tracking algorithms. To address this issue, we propose a novel method called Low-Rank Adaptation-based Hyperspectral Tracking (LA-HST). Initially, we propose a spectral enhancement module with a low intrinsic dimension to enhance spectral details in images. Additionally, we introduce an adapted transformer encoder that effectively extracts hyperspectral features with rich spectral information. During training, we freeze the parameters of the pre-trained network and only train the parameters of the proposed modules, which account for just 0.5% of the total parameters. This approach allows the model to fully leverage the tracking capabilities of the pre-trained network while significantly enhancing training efficiency and improving generalization. Extensive experiments on the VIS, NIR, and RedNIR videos from the HOTC2024 dataset validate the effectiveness of LAHST, demonstrating its ability to deliver robust and efficient tracking performance across diverse spectral bands.

关键词： Training signal processing algorithms signal processing Feature extraction Transformers Object tracking Data mining Hyperspectral imaging Videos Overfitting

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Deep Learning-Based Bayoud Disease Recognition Using Palm Fronds Database From Biskra Region, Algeria

Deep Learning-Based Bayoud Disease Recognition Using Palm Fr...

引用

International Conference on Image and signal processing and their Applications (ISPA)

作者： Betka Abir Hendaoui Mounira Noureddine Samia Toumi Abida Medaoukh Saadia Departement of Electrical Engineering University Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Early Recognition of plant diseases is required to provide appropriate treatments and protect agricultural production. Automated image detection techniques can provide early, fast, accurate, and inexpensive detection results. However, scientific research on date palm diseases is even rare due to the limited number of databases, including the Bayoud disease. In this paper, a new database, Biskra Algeria Palm Fronds Database (BAPFD), containing 400 images of date palm fronds is proposed, it includes images of both healthy and Bayoud disease-affected fronds. The Deep Learning (DL) technique is then used to classify our database and recognize Bayoud disease in date palm fronds. The performance of our approach is measured in terms of accuracy, loss, precision, and recall. The mentioned experimental results show that the proposed approach based on DL achieved high performance in recognizing Bayoud disease in date palm fronds, up to 100% for precision and recall and in order of 4×10 –4 for loss. The results show also that the model is capable to accurately classify infected and not infected fronds images even with a small percentage of bayoud. This demonstrates that our database contains significant information concerning bayoud diseases, which enables DL to design an accurate model that aids in correctly classifying palm fronds and identifying Bayoud disease. We further acknowledge the effectiveness of the DL approach in the recognition of healthy and not healthy fronds.

关键词： Deep learning Plant diseases Databases signal processing algorithms Production signal processing Loss measurement

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High-Dimensional Constrained Huber Regression

High-Dimensional Constrained Huber Regression

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Sensor Array and Multichannel signal processing Workshop

作者： Quan Wei Ziping Zhao School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In big data analytics, the collected data may be contaminated by heavy-tailed noises or outliers, and the sample size may be insufficient. In this paper, we study robust sparse regression under the presence of asymmetric heavy-tailed errors within a high-dimensional setting, where the ambient dimension can exceed the sample size. The estimation problem is formulated as an $\ell_{1}$ constrained regression with Huber loss function. We propose a simple projected gradient descent algorithm to solve the problem and establish its convergence properties, accounting for both computational and statistical errors. Under mild conditions, we demonstrate that the successive iterates converge at a linear rate to an estimate within the statistical precision of the model. Numerical experiments validate the robust estimation performance of the proposed method across various heavy-tail distribution settings.

关键词： Heavily-tailed distribution Conferences Computational modeling Noise signal processing algorithms Estimation Distributed databases

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Breaking the Gaussian Barrier: Leveraging ReGVAMP to Extend EKF and IEKF

Breaking the Gaussian Barrier: Leveraging ReGVAMP to Extend ...

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Asilomar Conference on signals, Systems & Computers

作者： Fangqing Xiao Dirk Slock Communication Systems Department EURECOM France

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

In the non-linear hidden Markov chain (HMC) model, commonly employed in robotics, navigation, signal processing, and control systems, various variants of Kalman filter (KF) like the Extended Kalman Filter (EKF) and Iterated Extended Kalman Filter (IEKF) have been developed for different scenarios, with their limitations and advantages. However, their effectiveness can diminish in the presence of non-Gaussian noise, which is prevalent in many real-world situations. This paper presents a novel approach to tackle non-Gaussian noise leveraging the revisited Generalized Vector Approximate Message Passing (ReGVAMP) within the context of EKF and IEKF. ReGVAMP extends these KF variants to break the Gaussian barrier, thereby enhancing the accuracy of state estimation. A tracking simulation validated the feasibility of our proposed algorithms.

关键词： Stochastic systems Noise Hidden Markov models signal processing algorithms Filtering algorithms Vectors Kalman filters State estimation Nonlinear systems Robots

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