Article
Acoustics
Sridhar Chintala, Jaisingh Thangaraj, Damodar Reddy Edla
Summary: A novel adaptive algorithm, based on a new step size, is proposed to eliminate ocular artifacts from recorded raw EEG signals. By using second and fourth-order power optimization algorithms, reference signals are processed and subtracted to obtain true EEG signals.
Article
Biology
Binish Fatimah, Amit Singhal, Pushpendra Singh
Summary: Healthy sleep is crucial for the body's rejuvenation and overall health. Automated assessment of sleep disorders using EEG and other signals can improve classification accuracy. The proposed method allows for real-time and cost-effective continuous patient monitoring and feedback.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Xuan Zhang, Dixin Wang, Hongtong Wu, Jinlong Chao, Jitao Zhong, Hong Peng, Bin Hu
Summary: This paper investigates the adaptability and performance improvement of the truncated l1 distance (TL1) kernel in physiological signal vigilance estimation based on sparse representation algorithm. Experimental results show that the TL1 kernel outperforms the traditional radial basis function (RBF) kernel in both performance and kernel parameter stability. The research contributes to the development of physiological signal recognition based on kernel methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Zhen Qin, Jun Tao, Yili Xia, Le Yang
Summary: This paper proposes an enhanced sparse recursive least squares (RLS) adaptive filter algorithm by combining a sparse regularization term and a proportionate matrix. Theoretical performance analysis is conducted, and guidance for selecting adaptive parameters is obtained. A fast implementation method is also derived. Simulation results support the theoretical analysis.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Zhen Qin, Jun Tao, Yili Xia
Summary: The proportionate recursive least squares (PRLS) algorithm proposed in this study is designed for sparse system estimation, assigning independent weight updates to each tap based on the estimated filter coefficient magnitude. Its mean square performance is analyzed using the energy conservation principle to improve steady-state performance. An explicit condition on the control parameter of the proportionate matrix of PRLS is derived to ensure better performance than traditional RLS, supported by simulation results in a system identification setting.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Neurosciences
Chao-Lin Teng, Yi-Yang Zhang, Wei Wang, Yuan-Yuan Luo, Gang Wang, Jin Xu
Summary: The proposed EEMD-based ICA method (EICA) effectively removes EOG artifacts from multichannel EEG signals by combining EEMD and ICA algorithms. It achieves the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal compared to other existing approaches, demonstrating its superior performance in eliminating blink artifacts. This study provides a novel promising method for high-performance elimination of EOG artifacts in EEG signals processing and analysis.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Maaz Mahadi, Tarig Ballal, Muhammad Moinuddin, Ubaid M. Al-Saggaf
Summary: Recursive least-squares (RLS) algorithms are widely used in various applications. This paper focuses on time-varying regularized RLS (RRLS) techniques and proposes a low-complexity update method using an approximate recursive formula. Simulation results demonstrate the superiority of the time-varying RRLS strategy over the fixed one.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Ruchi Juyal, Hariharan Muthusamy, Niraj Kumar
Summary: This study proposes a new method for removing ocular artifacts from multi-channel EEG signals using singular spectrum analysis (SSA) and non-negative matrix factorization (NMF). The results show that the proposed method achieves better performance in artifact removal compared to other existing methods.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Chemistry, Analytical
Ajay Kumar Maddirala, Kalyana C. Veluvolu
Summary: The use of portable electroencephalogram (EEG) devices has increased in recent years for recording brain signals in healthcare monitoring and other applications. However, the measured EEG signals often contain artifacts from eyelid blinking or eye movements, which mislead the understanding of the brain state. Traditional artifact removal techniques cannot be applied to single-channel EEG signals, calling for new techniques. In this paper, a method combining singular spectrum analysis (SSA), continuous wavelet transform (CWT), and k-means clustering algorithm is proposed to remove eye-blink artifacts from single-channel EEG signals without affecting the low frequencies.
Article
Geochemistry & Geophysics
Wei Pu
Summary: This article proposes a new deep neural network architecture, called the sparse autoencoder network (SAE-Net), to solve the sensitivity to motion errors in synthetic aperture radar (SAR) imaging. The SAE-Net implements SAR imaging and autofocus simultaneously and is trained using a joint reconstruction loss and entropy loss. Tests on synthetic and real SAR data demonstrate that the proposed architecture outperforms other state-of-the-art autofocus methods in sparsity-driven SAR imaging applications.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biology
Yonglin Wu, Xinyu Jiang, Yao Guo, Hangyu Zhu, Chenyun Dai, Wei Chen
Summary: This study aims to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. The study reaches a more general conclusion on modality comparison and fusion based on the regression of features or their combinations and the awake-to-drowsy transition. The most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Jammisetty Yedukondalu, Lakhan Dev Sharma
Summary: This study presents an automated method for removing electrooculogram (EOG) artifacts from electroencephalogram (EEG) signals. The approach decomposes the contaminated signals into intrinsic mode functions (IMFs) using Circulant Singular Spectrum Analysis (CiSSA) and removes the artifact components using 4-level discrete wavelet transform (DWT). The proposed technique effectively eliminates EOG artifacts while preserving low-frequency EEG information.
Article
Engineering, Electrical & Electronic
Po Li, Ying He, Jingrui Zhang
Summary: This paper presents a real-time detection method for harmonic extraction that is able to handle sudden fluctuations in amplitude, phase, and frequency of the fundamental wave. The method utilizes the Least Squares method with forgetting factor to extract the phase and frequency of the fundamental wave, and then uses this information to design a linear time-varying observer for the extraction of DC bias and harmonics. The proposed method is shown to be robust and accurate in various scenarios such as distorted power grid signals, white noise, inter-harmonics, and high-order harmonics.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Automation & Control Systems
Yelong Yu, Xiaoyan Huang, Zhaokai Li, Min Wu, Tingna Shi, Yanfei Cao, Geng Yang, Feng Niu
Summary: This article introduces a novel online full parameter estimation method for permanent magnet synchronous motors (PMSMs). By utilizing the recursive least squares algorithm in the alpha-beta frame, the proposed method can estimate all motor parameters simultaneously. Simulation and experimental results demonstrate the superiority of the proposed method in terms of convergence rate, computational cost, and accuracy compared to the traditional method in the d-q frame.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Mouncef El Marghichi, Azeddine Loulijat, Issam El Hantati
Summary: This article proposes a battery parameter update method based on the variable recursive least squares algorithm to improve the accuracy of battery state-of-charge estimation. Comparisons with other methods reveal that the VRLS algorithm outperforms others in terms of predictive performance indicators.
ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.