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检索条件"主题词=Signal Processing Algorithms"

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SA-Divider: A Novel Automatic EEG Artifact Removal Method Based on Self-Attention Convolutional Neural Network

SA-Divider: A Novel Automatic EEG Artifact Removal Method Ba...

引用

International Conference on Cyberworlds

作者： Dongguan Qian Yu Liu Zhenyan Zhang Motonobu Hattori Hong Zeng Xianglei Tang Hangzhou Dianzi University Hangzhou China University of Yamanashi Yamanashi Japan Zhejiang Electronic Information Products Inspection and Research Institute Hangzhou China

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Various artifacts, often accompanied during the acquisition of electroencephalograph (EEG), hinder further progress of EEG-based applications. However, hand-craft and semi-automatic-based methods are inaccurate and timeconsuming, and automatic artifact removal remains challenging. In this study, we propose a novel automatic EEG artifact removal method based on self-attention convolutional neural network (CNN), named SA-Divider. SA-Divider consists of a Self-attention module (SA) and a Divider module. The SA is designed to capture more accurate temporal and spatial features of EEG, and the Divider, which comprises an Encoder, a Decoder, and a Decomposer, aims to acquire better performance of artifact removal in EEG with less computation time. Experimental results on two EEG datasets show that SA-Divider achieved significant performance in four metrics and time consumption, indicating that SA-Divider can effectively remove artifacts and retain clean EEG-related information from raw EEG signals. These findings suggest that the proposed SA-Divider provides an efficient method to remove artifacts and extract brain cognitive-related EEG components while reducing computational complexity.

关键词： Measurement Convolution Computational modeling signal processing algorithms Computer architecture Feature extraction Brain modeling Electroencephalography Decoding Convolutional neural networks

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Deep Learning-Enabled One-Bit DoA Estimation

Deep Learning-Enabled One-Bit DoA Estimation

引用

Sensor Array and Multichannel signal processing Workshop

作者： Farhang Yeganegi Arian Eamaz Tara Esmaeilbeig Mojtaba Soltanalian University of Illinois Chicago Chicago IL USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoAs requires recovering a sparse signal from a limited number of severely quantized linear measurements. In particular, we exploit covariance recovery from one-bit dither samples. To recover the covariance of transmitted signal, the learned iterative shrinkage and thresholding algorithm (LISTA) is employed fed by one-bit data. We demonstrate that the upper bound of estimation performance is governed by the recovery error of the transmitted signal covariance matrix. Through numerical experiments, we demonstrate the proposed LISTA-based algorithm's capability in estimating target locations. The code employed in this study is available online 1 1 https://***/TaraEsmaeilbeig/one_bit_DoA_***.

关键词： Direction-of-arrival estimation Upper bound Quantization (signal) Atmospheric measurements Estimation signal processing algorithms Particle measurements

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A Novel Unsupervised Frequency Recognition Method for SSVEP-Based BCIs Using Adaptive Updating and Transfer Learning Strategy

A Novel Unsupervised Frequency Recognition Method for SSVEP-...

引用

International Conference on Computer and Communications (ICCC)

作者： Manqi Lin Xiaoyan Deng Zhuliang Yu Jiayi Li College of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The application of supervised recognition algorithms based on steady-state visual evoked potentials (SSVEP) has gained increasing attention in the field of signal processing. However, these methods typically demand a large amount of training data to ensure accurate recognition, resulting in high training costs and poor human-computer interaction experiences. To address these issues, a novel method called filter bank unsupervised adaptive transfer-extended CCA has been proposed for SSVEP recognition. Unlike previous methods, this approach eliminates the need for calibration data from the current subject for training. It employs a transfer learning strategy to construct a transfer average template, utilizes an adaptive updating strategy to update the transfer template and the spatial filter with the current subject's data, and incorporates the filter bank analysis strategy to achieve high-performance unsupervised SSVEP recognition. This method was compared with three state-of-the-art unsupervised approaches, and experimental results demonstrated its superior recognition accuracy and information transfer rate. Specifically, when the time window length is 1.0 s, FBUAT-eCCA achieves an accuracy of 84.69 ± 6.33% and an information transfer rate of 148.90 ± 15.60 bits/min. Additionally, FBUAT-eCCA shows strong performance in extracting inter-class separation features.

关键词： Training Visualization Accuracy Transfer learning signal processing algorithms Filter banks signal processing Feature extraction Classification algorithms Steady-state

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Performance Analysis of Quaternary Inverter Implemented Using FinFET Models

Performance Analysis of Quaternary Inverter Implemented Usin...

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Mohit Kumar Goel Kulbhushan Sharma VLSI Centre of Excellence Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Due to their improved energy economy and reduced circuit complexity, multivalue logics have shown to be beneficial in the field of digital system design. In the field of multivalue logic, quaternary logic is typically regarded as an inventive method. This article describes the design of a quaternary inverter using 18 nm FinFET technology. Further, the performance of quaternary inverter is examined by employing different FinFET models. Based on simulation outcomes, the LVT, SVT, and HVT FinFET models display power consumption values of 8.61, 4.80, and 4.98 µ W, transient delay values of 10.02, 10.08, and 10.29 ns, and power delay product values of 86.33,48.38, and 51.24 (X10 −15 J), respectively. The designed quaternary inverter architecture is beneficial in designing quaternary arithmetic circuits for applications that requires quaternary signals.

关键词： Analytical models Biological system modeling signal processing algorithms FinFETs Inverters Delays Logic

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The Application of Two Bayesian Personalized Ranking Approaches based on Item Recommendation from Implicit Feedback

The Application of Two Bayesian Personalized Ranking Approac...

引用

IEEE signal processing and Communications Applications (SIU)

作者： Burak Tağtekin Zeynep Şahin Tuna Çakar Yassine Drias Predictive Development İstanbul Türkiye Insider Inc. İstanbul Türkiye Information Technologies İstanbul Türkiye Eve Retails Inc. İstanbul Türkiye Computer Engineering İstanbul Türkiye MEF University İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.

关键词： signal processing algorithms Stochastic processes Manuals signal processing Bayes methods Data mining Optimization

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Research on Blockchain Anomalous Behavior Detection Technology

Research on Blockchain Anomalous Behavior Detection Technolo...

引用

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

作者： Xia Sheng Aihua Li Fengxu Sun School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

For blockchain transactions, how to distinguish normal transactions from abnormal transactions is a very critical issue. To this end, we introduce deep learning as a capable tool for abnormal behavior detection detection, and its excellent learning ability shines in this field. In this paper, we propose a deep learning time series Transformer model based on deep learning combined with ResNet (Residual Network) for anomaly detection and classification of Bitcoin dataset. The Elliptic dataset is used and the dataset is top-filled by manually crawling the latest Bitcoin dataset, and the Random Forest algorithm is used for feature selection, and finally 16 features are selected for classification. After testing under different sampling methods the Transformer model combined with residual network shows good detection results compared to structures such as single machine learning algorithm SVM (Support Vector Machine) and other deep learning models CNN (Convolutional Neural Network).

关键词： Deep learning Support vector machines Machine learning algorithms Time series analysis signal processing algorithms Bitcoin Transformers Blockchains Convolutional neural networks Residual neural networks

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A Multi-Sensor Approach for Multi-Joint Tracking

A Multi-Sensor Approach for Multi-Joint Tracking

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Bruno Andó Salvatore Graziani Mattia Manenti Danilo Greco DIEEI University of Catania Catania Italy

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

This paper deals with the development of a low-cost multi-sensor and multi-node system aimed to reconstruct the orientation of a multi-joint model compatible with several application fields, such as robotics, human rehabilitation or monitoring systems. As an example, human kinetic parameters can be harnessed to prevent and diagnose diseases, enhance physical performance, and expedite rehabilitation. The approach adopted exploits features of an embedded multi-sensor system and signal processing aimed at the extraction of quantities of interest for the reconstruction of the model pose. In particular, the main contribution of this work is a dedicated signal processing implemented to reconstruct time series of joint angles and to provide a real-time 3D representation of the model. The assessment phase, consisting of a series of angles configurations imposed to the model, proved that mean and standard deviation of the residuals (as the absolute value of the difference between nominal and reconstructed values) are, in the worst case, 1.23° and 0.98° respectively, proving the capability of the algorithm to appropriately reconstruct the pose of the structure in real time.

关键词： Solid modeling Time-frequency analysis Three-dimensional displays Heuristic algorithms Time series analysis signal processing algorithms signal processing

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A New Method for Source Number Estimation in the Presence of Unknown Nonuniform Noise

A New Method for Source Number Estimation in the Presence of...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Mengxia He S. C. Chan Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Classical source number estimators are usually derived under the assumption of uniform white noise; hence, they are ineffective with unknown nonuniform noise. Current advanced estimators designed for nonuniform noise are computationally expensive and often unable to yield satisfactory performance in unfavorable conditions, such as low signal-to-noise ratio, small number of snapshots, and sources with different transmit power. To address this, this paper proposes a new method to estimate the number of sources under nonuniform noise. The proposed method first constructs a likelihood ratio statistic as a function of the maximum likelihood estimation of the array covariance matrix, which is estimated by a subspace estimation algorithm. Then, based on the asymptotic theory of the likelihood ratio, the number of sources is estimated via a sequence of hypothesis tests. Theoretical analysis demonstrates that the proposed estimator is consistent in the general asymptotic regime. Simulation results show that the proposed estimator achieves a higher correct detection probability in unfavorable conditions and is more robust against nonuniformity of noise than state-of-the-art estimators.

关键词： Maximum likelihood estimation Simulation Noise signal processing algorithms White noise Probability Parallel processing

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Neurophysiology signal Codecs for the DICOM® Standard: Preliminary Results

Neurophysiology Signal Codecs for the DICOM® Standard: Prel...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Filippo Battaglia Mattia Galanti Giovanni Gugliandolo Stefan Rampp Jan Remi Alexandra Parashos Sonali Sharma Sonal Bhatia Brian C. Dean Ekrem Kutluay Zeke Campbell Sarah Schmitt Nicola Donato Jonathan J. Halford Giuseppe Campobello Department of Engineering University of Messina Messina Italy School of Computing Clemson University Clemson SC USA Department of Neurosurgery and Department of Neuroradiology University Hospital Erlangen Germany Department of Neurology Ludwig Maximilian University Munich Germany Medical University of South Carolina Charleston SC USA Ralph H. Johnson VA Medical Center Medical University of South Carolina Charleston SC USA Department of Neurology Ralph H. Johnson VA Medical Center Medical University of South Carolina Charleston SC USA

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

In this paper we report experimental results on compression of neurophysiology signals obtained as part of the standardization activities conducted by the Working Group 32 (WG-32) of the Digital Imaging and Communications in Medicine (DICOM). WG-32 focuses on extending the DICOM® standard for clinical neurophysiology data exchange. With this aim, several compression techniques specifically devised for neurophysiology signals, as well as audio codecs, have been investigated and compared using real-world datasets. Moreover, a web-based application, named EEGnet and developed specifically for viewing and annotating electroencephalography (EEG) data, has been exploited for determining the maximum distortion that can be tolerated on neurophysiology signals. Through the EEGnet framework, eight neurologists, affiliated to different universities and medical centers, identified signals where they observed a clinically-significant difference. As one of the main results of our study, we found that, in the case of EEG signals, a percentage root mean square difference (PRD) of 5% can be accepted by clinicians and experts. On the other hand, all experts agreed that distortion is unacceptable when the PRD is greater than 15%. Finally, surprisingly enough, experimental results showed that audio codecs provide performance levels that, in some cases, are comparable to those of state-of-the-art algorithms specifically devised for compression of EEG signals.

关键词： Codecs signal processing algorithms Distortion Electroencephalography Electromyography ITU DICOM

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An Innovative System for Driver Monitoring and Vehicle Sound Interaction

An Innovative System for Driver Monitoring and Vehicle Sound...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Andrea Generosi Valeria Bruschi Stefania Cecchi Nefeli Aikaterini Dourou Roberto Montanari Maura Mengoni DIISM Università Politecnica delle Marche Ancona Italy DII Università Politecnica delle Marche Ancona Italy Re-Iab srl Italy Reggio Emilia Italy

ISBN: (数字)9798350384987

ISBN: (纸本)9798350384994

An important aspect of Advanced Driver-Assistance Systems is the real-time monitoring of the driver and the interaction with him/her. In this scenario, the proposed work is focused on the development of an innovative system capable of analyzing the driver's state and interact with him/her in a innovative way. In particular, the driver monitoring is obtained through the implementation of a multimodal approach that exploits deep learning and data fusion techniques while the interaction is achieved through sound signals elaborated with digital signal processing algorithm for the creation of an immersive scenario.

关键词： Deep learning Visualization Statistical analysis Heuristic algorithms signal processing algorithms Data integration Digital signal processing

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