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检索条件"主题词=filtering algorithms"

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Long short-term memory assisted FIR filter for tracking human upper limb motion

Long short-term memory assisted FIR filter for tracking huma...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Rui Gao QingAo Zhao Yuan Xu School of Electrical Engineering University of Jinan Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To better correct the spatial trajectory of human motion, this paper proposes a Long Short-Term Memory (LSTM)-assisted correction algorithm for the Finite Impulse Response (FIR) filter, aimed at improving the accuracy of human joint motion capture based on inertial sensors. In the proposed algorithm, the forward data is obtained from the backward-updated data smoothed by the Rauch-Tung-Striebel (RTS) smoother and compared with the forward-predicted data from the FIR. The resulting error is input into the pre-trained LSTM model to predict the error data, and the backward data at that moment is updated, making the positional information more accurate. This calculation shows significant effectiveness when estimating the elbow data. In the experiment, the data obtained using the LSTM-assisted FIR filter showed a 35.83% improvement compared to the data obtained with the LSTM-assisted Kalman Filter.

关键词： Limbs Accuracy Finite impulse response filters Inertial sensors Predictive models filtering algorithms Prediction algorithms Motion capture Data models Long short term memory

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Underwater Robot Terrain Modeling Search and Rescue Algorithm

Underwater Robot Terrain Modeling Search and Rescue Algorith...

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Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)

作者： Hua Tian Xueyu An Binghang Wu Haowei Bian School of Intelligence & Electronic Engineering Dalian Neusoft University of Information Dalian China School of Healthcare Technology Dalian Neusoft University of Information Dalian China

ISBN: (数字)9798350352573

ISBN: (纸本)9798350352580

Solving the safety issues of underwater robot disconnection caused by loss of communication with the main ship or potential mechanical issues has become particularly important in today’s increasingly common era of underwater tourism. Firstly, we used ArcGIS software to conduct geographic modeling based on the real data of the Ionian Sea on Earth, which served as the data support for building the model. Establishing a realistic geographic model. Secondly, after analyzing the data of the Ionian Sea, we selected four important factors to improve the comprehensiveness of geographic modeling. We used some more advanced formula algorithms to calculate the required parameters of the model more accurately, and adopted the latest TEOS-10 [2] seawater thermodynamic equation as theoretical support to improve the accuracy of the model. Finally, we used the Kalman filtering algorithm to predict and calculate the actual position of the underwater robot in the Ionian Sea, and achieved a certain degree of accuracy to ensure the success rate of the search. And on the basis of mature models, Monte Carlo algorithm was applied to improve rescue efficiency, save rescue costs, and achieve the expected goals.

关键词： Autonomous underwater vehicles Thermodynamics Costs Accuracy Predictive models filtering algorithms Prediction algorithms

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Target Estimation with Angle-Only Observations of Distributed Passive Radar Networks

Target Estimation with Angle-Only Observations of Distribute...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Fabing Dou Linhai Wang Shenghua Zhou Mengchen Zhang Yidi Chen National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Academy of Space Information Systems Xi’an China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Passive radars that rely on angle-only measurements cannot obtain the distance between radars and targets. To obtain position estimates of targets, passive radars can be connected to a central processor through communication links. Therefore, fusion algorithms are required to combine observations from distributed passive radars. For the scenario where all radars and targets are moving, most existed fusion algorithms use filtering methods to estimate the velocity and position of moving targets. In fact, most filtering algorithms have some limitations which will limit the algorithm’s effectiveness to a large extent. In this paper, two fusion algorithms are proposed for the central processor. The first algorithm, known as the gross least squares (GLS), employs all observations from passive radars to implement moving target estimation, and thus needs significant computation at the central processor. The second algorithm, termed as the recursive least squares (RLS), iteratively optimizes the velocity and position of moving target. Exploiting the continuity of target motion, current target position and velocity estimates are used as initial values in the next moment. With good initial values, the number of iterations decreases and thus the computational cost is reduced at the central processor. Numerical results show that both algorithms benefit from more observations. The GLS algorithm provides more accurate position and velocity estimates of the target but requires substantial computation cost. The RLS algorithm may suffer a slight performance loss but offers a lower computation cost. The results demonstrate the trade-off between accuracy and computational cost, providing useful insights for practical applications.

关键词： Passive radar Costs Accuracy Radar measurements filtering Estimation filtering algorithms Data processing Computational efficiency

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Schizophrenia Detection Using EEG: A Study on Frequency Relevance

Schizophrenia Detection Using EEG: A Study on Frequency Rele...

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Congress on Evolutionary Computation

作者： Luís A. Alexandre Włodzisław Duch NOVA LINCS Universidade da Beira Interior Covilhã Portugal Dept. of Informatics Institute of Engineering and Technology Faculty of Physics Astronomy & Informatics Nicolaus Copernicus University Torun Poland

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

One important application of machine learning is the analysis of electroencephalographic recordings (EEG). Such oscillatory signals are noisy, non-stationary, full of artifacts, contain transients, and chaotic transitions between meta-stable states. EEG data rarely consists of recordings obtained for more than 100 patients with some brain disorder. This makes objective, reliable clinical diagnosis of many mental disorders, such as schizophrenia, very difficult. The standard approach splits the frequency of EEG oscillations focusing on five classical bands: delta, theta, alpha, beta, and gamma. In this paper, we investigate three main questions: (i) Are there certain frequencies that will allow for a reliable diagnosis of schizophrenia, and (ii) if they are, is there a connection to what is known about their neural basis? (iii) How long segments of EEG recordings are sufficient for reliable classification? We filter EEG signals, obtaining a set of 64 very narrow 1Hz frequency bands. A genetic algorithm, with a simple k-NN classifier, finds an optimal combination of these bands. The method is sufficiently simple to be used in clinical settings. A public schizophrenia EEG data set, containing 60 seconds of EEG recordings in the resting state, with only 16 electrodes, for 45 adolescent patients and 39 healthy controls, was used for testing. Signal duration of about 30 seconds enables to reach an accuracy of over 96% in 5-fold cross-validation tests. We compare our results to much more sophisticated state-of-the- art methods and discuss insights gained by our analysis into the brain basis of schizophrenia.

关键词： Electrodes Accuracy Mental disorders filtering algorithms Electroencephalography Recording Frequency measurement

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Location Enhancement and Multi-template Fusion for Fast Long-Term Tracking

Location Enhancement and Multi-template Fusion for Fast Long...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Shixin Chen Department of Computer Science Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Owing to the challenge of target occlusion leading to tracking failure during the target tracking process, achieving efficient and robust tracking of targets under occlusion scenarios has become a focal point of research. In this paper, we addresses the issue of tracking failure caused by the reduction or disappearance of target appearance information in occlusion scenarios. It proposes a study on target tracking under occlusion scenes based on deep learning methodologies. A foundation tracker is constructed that leverages location enhancement and multi-template fusion. Built upon this, graph attention mechanisms and target loss detection mechanisms are introduced. In cases where the target is lost due to occlusion, a re-detection strategy is employed to resume tracking once the target reappears. Experiments conducted on datasets demonstrate that the proposed algorithm can effectively determine whether a target is undergoing occlusion and significantly enhances tracking performance under such conditions.

关键词： Deep learning Target tracking Correlation Automation Attention mechanisms Neural networks filtering algorithms Artificial intelligence

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New Affine Projection Tanh-Based algorithms for Active Impulsive Noise Control

New Affine Projection Tanh-Based Algorithms for Active Impul...

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Measurement and Simulation (ATOMS), Advanced Topics on

作者： Felix Albu Ion Caciula Daniela Hagiescu Dept. of Electronics and Telecommunications Valahia University of Targoviste Targoviste Romania R&D Department Advanced Slisys SRL Bucharest Romania

Two new affine projection and improved proportionate affine projection tanh-based algorithms for active impulsive noise control are presented. It is shown that they can obtain better performance for active impulsive n... 详细信息

ISBN: (数字)9798350358377

ISBN: (纸本)9798350358384

关键词： Noise filtering algorithms Attenuation measurement Atoms Control systems Attenuation Robustness Complexity theory Noise measurement Projection algorithms

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A Hybrid Adaptive Technique for Barker Code Matched Filter Sidelobe Cancellation

A Hybrid Adaptive Technique for Barker Code Matched Filter S...

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Telecommunications Conference (ITC-Egypt), International

作者： Hazem Kamel Mohamed G. Shehata Samer Emad Eldin Ali Radar Department Military Technical College Cairo Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Barker phase coding is a well-known technique utilized in pulse compression radars employing binary phase-coded waveforms. Despite its effectiveness, a common issue associated with this method is the generation of undesired sidelobes in the output of the sub-pulse matched filter. These sidelobes have the potential to obscure the reflected echoes from nearby weak targets, consequently hampering the radar's detection capabilities. To address this challenge, this study introduces an innovative approach that combines the use of an Adaptive Normalized Least Mean Square (NLMS) filter for noise reduction and a Recursive-Least-Square (RLS) filter to mitigate the sidelobes produced by the matched filter. The integration of the RLS filter with the NLMS filter presents a promising strategy for combating radar active jamming. Through extensive simulation testing, the performance of this hybrid filtering technique is assessed in comparison to an optimal filter. The results indicate that both filters exhibit robust performance under high signal-to-noise ratios (SNRs). However, the proposed adaptive filter demonstrates superior performance over the optimal filter, especially in environments characterized by low signal-to-noise ratios (SNRs). This capability is particularly advantageous for detecting targets with low SNR levels, such as small Radar Cross Section (RCS) targets.

关键词： Matched filters Radar cross-sections Codes Noise reduction Adaptive filters Object detection filtering algorithms

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A Machine Learning Model for Recognition and Classification of Emotion Using Facial Expressions

A Machine Learning Model for Recognition and Classification ...

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Delhi Section Conference (DELCON), IEEE

作者： R. Kamalraj Sandeep Kumar Jain Trapty Aggarwal Khushmeen Kaur Brar Mohammad Al-Farouni Anita Gehlot Department of CSE and IT Jain Deemed to be University Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India MSET Maharishi University of IT Uttar Pradesh India Department of Electronics and Communication Engineering Chandigarh University Mohali Punjab India The Islamic University Najaf Iraq Department of ECE Uttaranchal Institute of Technology Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Emotion recognition has become a major area of multidisciplinary research in recent years and has many potential applications in the development of artificial intelligence and human-computer interface related design of human health detection. Expansion experiments are carried out by researchers using various stimuli, attributes, and categorization methods. This paper makes use of hybrid feature optimization algorithms i. e, IWSS Embedded and PSO for recognizing emotions using facial expressions. A pretrained model ResNet50V2 is used as a feature extractor and an image filter called Discretization is applied in order to improve the accuracy of the proposed approach. The results revealed that the methodology attained an overall accuracy of 75%.

关键词： Emotion recognition Accuracy Face recognition filtering algorithms Multilayer perceptrons Feature extraction Brain modeling Electroencephalography Optimization Load modeling

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Noise Reduction of Bird Calls Based on a Combination of Spectral Subtraction, Wiener filtering, and Kalman filtering

Noise Reduction of Bird Calls Based on a Combination of Spec...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Zenghui Chen College of Photonic and Electronic Engineering Fujian Normal University Fujian China

ISBN: (数字)9781665471978

ISBN: (纸本)9781665471985

Speechis a channel of communication for birds, and by analyzing bird calls, biologists can obtain much important biological information. However, in the speech recognition process, there is inevitably the influence of environmental noise. However, the recognition rate in the presence of ambient noise is not yet satisfactory, so strengthening the noise immunity is a problem that needs further research and solution in the speech recognition process. This paper proposes a bird call noise reduction method based on a combination of spectral subtraction, Wiener filtering, and Kalman filtering to enhance the noisy speech information. In this paper, through white noise addition and using three methods of independent use and combined into one filter use, through the listening side, image analysis, signal-to-noise ratio, and coherence analysis of out, the noise reduction effect is very significant, and can be applied to the background noise filtering of the real environment in practice, for human understanding of bird habits a step further.

关键词： Wiener filters Speech recognition filtering algorithms White noise Birds Information filters Biology

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Graph Convolutional Networks for Designing Collaborative filtering-Based Health Recommender Systems

Graph Convolutional Networks for Designing Collaborative Fil...

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International Conference on New Technologies of Information and Communication (NTIC)

作者： Boudjemaa Boudaa Imen Bestani Noureddine Benadjrouda Department of Computer Science University of Tiaret Tiaret Algeria

Recommender systems provide useful item suggestions (products or services) to users as part of their decision-making processes. The effectiveness of recommender systems is now clearly confirmed in various fields of application (e.g., YouTube, Amazon, Facebook, ResearchGate). In the literature, many research works have addressed the application of recommendations in the field of health in what are called health recommender systems (HRS). HRS is an innovative alternative when it comes to providing information to help doctors in the diagnosis/treatment of diseases, as well as helping patients with recommendations on how to maintain their well-being. However, the proposed development approaches in this field are limited to traditional models that lack the accuracy and effectiveness, which are vital in healthcare. This paper presents a design model for collaborative filtering-based health recommender systems using graph neural networks (GNN) via its promising Graph Convolutional Network (GCN) architecture. In this model, the convolution layer works with a simplified and efficient GCN algorithm named LightGCN. GCN-based methods are among the new cutting-edge approaches in recommender systems, and LightGCN has proven its superiority in recommendation accuracy.

关键词： Video on demand Social networking (online) Decision making Collaboration Medical services filtering algorithms Graph neural networks

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