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检索条件"主题词=Signal Processing Algorithms"

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Tangled Program Graphs as an alternative to DRL-based control algorithms for UAVs

Tangled Program Graphs as an alternative to DRL-based contro...

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Hubert Szolc Karol Desnos Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Kraków Poland Univ Rennes INSA Rennes CNRS IETR – UMR 6164 Rennes France

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Deep reinforcement learning (DRL) is currently the most popular AI-based approach to autonomous vehicle control. An agent, trained for this purpose in simulation, can interact with the real environment with a human-level performance. Despite very good results in terms of selected metrics, this approach has some significant drawbacks: high computational requirements and low explainability. Because of that, a DRL-based agent cannot be used in some control tasks, especially when safety is the key issue. Therefore we propose to use Tangled Program Graphs (TPGs) as an alternative for deep reinforcement learning in control-related tasks. In this approach, input signals are processed by simple programs that are combined in a graph structure. As a result, TPGs are less computationally demanding and their actions can be explained based on the graph structure. In this paper, we present our studies on the use of TPGs as an alternative for DRL in control-related tasks. In particular, we consider the problem of navigating an unmanned aerial vehicle (UAV) through the unknown environment based solely on the onboard LiDAR sensor. The results of our work show promising prospects for the use of TPGs in control related-tasks.

关键词： Measurement Laser radar Navigation signal processing algorithms Process control Computer architecture Deep reinforcement learning Autonomous aerial vehicles Safety

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Machine Learning-Based Gait Analysis for Assessing Balance Ability in Cancer Patients

Machine Learning-Based Gait Analysis for Assessing Balance A...

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

作者： Tao Liu Lisheng Gao Xu Zhou Qingqing Cao Chao Yu Dandan Chen Wanqiu Zhang Wei Lu Wanqing Yao Cuihong Guan Institutes of Physical Science and Information Technology Anhui University Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Hefei Cancer Hospital Chinese Academy of Sciences Hefei China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

During the treatment process, cancer patients often experience a decline in balance ability due to various reasons such as surgery, which increases the risk of falls and poses a serious threat to their health. Therefore, it is crucial to quickly and accurately assess the balance ability of cancer patients to take preventive measures against falls at an early stage. This study aims to establish an objective classification model for the quantitative assessment of balance ability in cancer patients using machine learning-based gait analysis techniques. Using wearable devices such as pressure insoles and IMUs, we collected and organized a gait dataset from 78 patients with varying balance abilities. Through feature engineering and machine learning techniques, we employed various feature selection algorithms to obtain the optimal features and then used these features to evaluate the performance of three different models in assessing patients' balance abilities. Experimental results show that SVM achieved the best performance, with average accuracy, precision, recall, F1 score, and area under the curve (AUC) of the test set being 0.8831, 0.8810, 0.8645, 0.8667, and 0.97, respectively. These results indicate that the proposed gait analysis method has significant practical value in assessing the balance ability of cancer patients.

关键词： Support vector machines Analytical models Accuracy Surgery signal processing algorithms Machine learning Feature extraction Wearable devices Gait recognition Cancer

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Passive radar multi-target detection experimental study at sea

Passive radar multi-target detection experimental study at s...

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Computer and Information processing Technology (ISCIPT), International Symposium on

作者： Zaiyuan Wu Jie Song Zhongxun Wang Qian Wang Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai University School of Physics and Electronic Information Yantai University Yantai China Naval Aviation University Yantai China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

The non-cooperative dual-base radar provides superior performance in terms of safety and robustness of target detection. This paper presents a series of experimental maritime target detections using a passive radar system and the collection of a large amount of data. The first is an introduction to the principle of passive radar and the key technologies involved, the next step is to describe the experimental scenario, finally, the experimental data were used for the plotting of a radar system diagram and the analysis of the resultant graphs. Experimental results showed that the system could succeed in detecting targets at sea.

关键词： Passive radar Radar cross-sections Radar detection signal processing algorithms Object detection Radar signal processing Stability analysis

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Enhanced Binary Classification of Gait Disorders Using a Machine Learning Majority Voting Approach

Enhanced Binary Classification of Gait Disorders Using a Mac...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ahmed Khalil Muhammad Saad Kareem Chaar Reza Tafreshi Shameel Abdulla MD Ferdous Wahid Department of Mechanical Engineering Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study introduces a machine learning-based methodology for classifying healthy individuals and those with gait disorders, employing a merged data set from ‘GaitRec’ and ‘Gutenberg.’ Key gait features were extracted from the normalized ground reaction force data that had 2435 subjects, including maximum peaks, load rate, lowest troughs, and area under the curve. Numerous machine learning models, such as Support Vector Machine, Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbor, Bagging, Adaboost, and Neural Network, were developed, and their hyperparameters were optimized using a grid search approach. The model’s performance was evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. These assessment metrics are calculated through a repeated hold-out strategy, where 80% of the data is utilized for training the model, and the remaining 20% is reserved for testing purposes. Notably, the study highlights the superiority of an ensemble model that combines these algorithms through majority voting. This majority voting model, which was compared against individual models, achieved an accuracy of 96.63%, surpassing existing benchmarks in the field. This approach underscores the efficacy of ensemble techniques, particularly majority voting, in enhancing classification accuracy, thus contributing significantly to the field of biomedical signal processing by offering a scalable and cost-effective solution for accurate gait disorder identification.

关键词： Legged locomotion Support vector machines Training Accuracy Biological system modeling signal processing algorithms Benchmark testing Feature extraction Tuning Load modeling

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Analysis of the Factors Affecting the Decoding Success Rate of High rate Shear Valve Pulser

Analysis of the Factors Affecting the Decoding Success Rate ...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zhiming Wang Kun Chen Lei Xiao Dingyi Wen Haizhen Wu COSL Well-Tech Division China Key Laboratory of Deep Sea Deep Formation Energy Engineering of Hainan Province China School of Energy Resources China University of Geosciences(Beijing) Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The "Xuanji" system is a revolutionary drilling technology independently developed by China Oilfield Services Limited(COSL). It is composed of two major technologies, namely rotary steering drilling "Welleader" and logging while drilling "Drilog", and has currently achieved large-scale application. While the High rate Shear Valve Pulser(HSVP), as a crucial component of the "Xuanji" system, can reach a maximum transmission rate of 20 bps under actual drilling conditions. The high-rate mud pulser utilizes mud pulses for signal transmission and can upload downhole logging data, wellbore trajectory, and engineering parameters to the surface system in real time. The surface data acquisition and demodulation system parses the received mud pulse signals to realize real-time communication between downhole instruments and the surface system. However, channel transmission characteristics such as acquisition noise and pump noise can interfere with the signal quality and affect the decoding success rate of surface signals. This paper analyzes the sources of noise, discusses the influences of channel characteristics, pump noise, acquisition noise, bottom hole assembly(BHA), drilling engineering parameters, mud parameters, and signal processing parameters on the decoding success rate, and adopts corresponding methods to handle the noise that affects the decoding success rate. This has greatly enhanced the decoding success rate of the signals and is of great significance for improving the real-time demodulation effect of high-speed mud pulse data transmission.

关键词： Drilling Noise Noise reduction signal processing algorithms Interference Valves Real-time systems Decoding Data communication Demodulation

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Online Canonical Correlation Analysis via Rayleigh-Ritz Projections

Online Canonical Correlation Analysis via Rayleigh-Ritz Proj...

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Asilomar Conference on signals, Systems & Computers

作者： Vassilis Kalantzis Panagiotis A. Traganitis Charilaos I. Kanatsoulis IBM Research MIT-IBM Watson AI Lab Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Canonical correlation analysis (CCA) is a classical statistical tool that enables processing of multiview data in a plethora signal processing, machine learning and data mining applications, by identifying a common linear subspace from the available data views. Most algorithms tackling the CCA task require all the data per view to be available. Nevertheless, in many cases, data are not available in batch form and may arrive in streaming fashion. This work puts forth a novel, computationally efficient, projection based method for identifying and updating the common subspace on-the-fly, as new data arrive, while retaining its' fidelity. Preliminary numerical tests, on synthetic and real data benchmarks, showcase the potential of the proposed method.

关键词： Computers Correlation Machine learning algorithms signal processing algorithms Machine learning signal processing Approximation algorithms Performance analysis Computational efficiency Data mining

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A Novel Machine Learning Approach for Medical Recommendation System

A Novel Machine Learning Approach for Medical Recommendation...

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International Conference on Communications and signal processing

作者： P Kumar P Yashini Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE REC Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Hospitals are using online recommender systems more and more frequently. Nowadays, the vast majority of consumers research potential prescriptions online before consulting their doctors for a variety of medical issues. When pandemics, floods, occur, the medical recommendation system may come in handy. Using fewer resources, recommender systems provide more precise, dependable, and accurate clinical predictions. The patient receives trustworthy information regarding the medication, dose, and potential side effects from the medication recommendation system. The patient's symptoms are taken into account while choosing the right medication, which is then delivered based on the user profile. This system uses K-means clustering algorithms to analyze patient data and provide personalized medical recommendations. These algorithms use characteristics like patient demographics, medical histories, and symptoms—all of which are gathered from a sizable dataset of medical records— to produce precise suggestions. This system's objective is to enhance patient outcomes by giving fast, precise recommendations that are tailored to each person's particular need.

关键词： Machine learning algorithms Pandemics Hospitals signal processing algorithms Clustering algorithms Machine learning signal processing

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Towards Neuromorphic processing for Next-Generation MU-MIMO Detection

Towards Neuromorphic Processing for Next-Generation MU-MIMO ...

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IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： G. N. Katsaros J. C. De Luna Ducoing Konstantinos Nikitopoulos 5GIC & 6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Upcoming physical layer (PHY) processing solutions, leveraging multiple-input multiple-output (MIMO) advances, are expected to support broad transmission bandwidths and the concurrent transmission of multiple information streams. However, the inherent computational complexities of conventional MIMO PHY algorithms pose significant practical challenges, not only in meeting the strict real-time processing latency requirements but also in maintaining practical computational power consumption budgets. Novel computing paradigms, such as neuromorphic computing, promise substantial gains in computational power efficiency. However, it is unknown whether it is feasible or efficient to realize practical PHY algorithms on such platforms. In this work, we evaluate for the first time the potential of neuromorphic computing principles for multi-user (MU)-MIMO detection. In particular, we developed the first spiking-based MU-MIMO simulator that meets practical error-rate targets, suggesting power gains of at least one order of magnitude when realized on actual neuromorphic hardware, compared to conventional processing platforms. Finally, we discuss the challenges and future research directions that could unlock practical neuromorphic-based MU-MIMO systems and revolutionize PHY power efficiency.

关键词： Wireless communication Power demand Neuromorphic engineering Neural networks signal processing algorithms signal processing Physical layer Real-time systems Streams Next generation networking

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Orthogonal Matching Pursuit-Based algorithms for the Birkhoff-Von Neumann Decomposition

Orthogonal Matching Pursuit-Based Algorithms for the Birkhof...

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European signal processing Conference (EUSIPCO)

作者： Damien Lesens Jérémy E. Cohen Bora Uçar LIP ENS de Lyon 46 allée d'Italie Lyon France Univ Lyon INSA-Lyon UCBL UJM CNRS Inserm CREATIS UMR 5220 U1294 Lyon France CNRS and LIP UMR5668 (Université de Lyon - ENS de Lyon - UCBL - CNRS - Inria) 46 allée d'Italie Lyon France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Birkhoff-von Neumann (BvN) decomposition writes a doubly stochastic matrix as a convex combination of permutation matrices. For a given doubly stochastic matrix, the decom-position in general is not unique. In many applications a sparsest decomposition, that is with the smallest number of permutation matrices is of interest. This problem is known to be NP-complete, and heuristics are used to obtain sparse solutions. We propose heuristics based on the well-known orthogonal matching pursuit for sparse BvN decomposition. We experimentally compare our heuristics with the state of the art from the literature and show how our methods advance the known heuristics.

关键词： Heuristic algorithms Matching pursuit algorithms signal processing algorithms Europe signal processing Matrix decomposition Sparse matrices Optimization

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Antenna Selection and Beamforming in ELAA-based ISAC System: A Distributed Approach

Antenna Selection and Beamforming in ELAA-based ISAC System:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qian Gao Ruikang Zhong Yuanwei Liu Queen Mary University of London London UK The University of Hong Kong Hong Kong

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

An extremely large-scale antenna array (ELAA) based integrated sensing and communication (ISAC) system is investigated. In contrast to existing near-field ISAC systems, we consider the complexity of beamforming design and antenna selection for ELAA under a user-roaming environment. The formulated problem is maximizing long-term sum data rate by jointly optimizing antenna selection and beamforming, subject to the constraints of transmitting power and sensing targets’ beam pattern gain. We propose a dimension reduction assisted multi-agent proximal policy optimization (DR-MAPPO) algorithm against the dynamic scenario: 1) an OrderConv layer is designed, which reduces the input dimension by removing the channel state information of unselected antenna while keeping the order information of selected ones, 2) multiple agents are employed to execute antenna selection and beamforming in a distributed manner. Numerical results demonstrate that the proposed DR-MAPPO algorithm improve the computational efficiency of joint antenna selection and beamforming optimization by reducing the size of the input dimension while obtaining comparable data rates and beampattern gain for sensing targets.

关键词： Wireless communication Array signal processing Heuristic algorithms signal processing algorithms Directive antennas Integrated sensing and communication Arrays Antennas Optimization Antenna arrays

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