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Autonomous OA Removal in real-time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet algorithm

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2016年第1期16卷 20-20页

作者： Majmudar, Charvi A. Morshed, Bashir I. Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

Electroencephalography (EEG) is a non-invasive technique to record brain activities in natural settings. Ocular Artifacts (OA) usually contaminates EEG signals, removal of which is critical for accurate feature extraction and classification. With the increasing adoption of wearable technologies, single-channel real-time EEG systems that often require real-time signal processing for immediate real-time feedback are becoming more prevalent. However, traditional OA removal algorithms usually requiremultiple channels of EEG data, are computationally expensive, and do not perform well in real-time. In this article, a new hybrid algorithm is proposed that autonomously detects OA and subsequently removes OA from a single-channel steaming EEG data in real-time. The proposed single EEG channel algorithm also does not require additional reference electrooculography (EOG) channel. The algorithm has also been implemented on an embedded hardware platform of single channel wearable EEG system (NeuroMonitor). The algorithm first detects the OA zones using an Algebraic approach and then removes these artifacts from the detected OA zones using the Discrete Wavelet Transform (DWT) decomposition method. The de-noising technique is applied only to the OA zone, which minimizes loss of neural information outside the OA zone. A qualitative and quantitative performance evaluation was carried out with a 0.5s epoch in overlapping sliding window technique using time-frequency analysis, mean square coherence, and correlation coefficient statistics. The hybrid OA removal algorithm demonstrated real-time operation with 3s latency on the PSoC-3-microcontroller-based EEG system. Successful implementation of OA removal from single-channel real-time EEG data using the proposed algorithm shows promise for real-time feedback applications of wearable EEG devices.

关键词： Artifact removal EEG signal processing real-time algorithm wearable

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Implementation of the real-time algorithms Based on the Symbolic Dynamics of a Coarse-grained Heart Rate Variability in Ubiquitous Health Care Systems 10th

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World Congress on Medical Physics and Biomedical Engineering

作者： Yi, Sang-Hoon Park, Kyung-Tae Yoo, Cheol-Seung Jeon, Gye-Rok Inje Univ Dept Comp Aided Sci OeBang Dong 607 Gimhae 621749 South Korea Inje Univ Inst Math Sci Busan 602739 South Korea Busan Natl Univ Coll Med Dept Biomed Engn Busan 602739 South Korea

ISBN: (纸本)9783540368397

In order to perform a real-time analysis of a physiological signal, such as heart rate variability (HRV), obtained from a ubiquitous health care system, we propose some effective measures of complexity, which are basing on the symbolic dynamics. In symbolic dynamics, HRV extracted from an electrocardiogram is transformed into a symbolic sequence in the coarse-graining process where the difference of periods between consecutive heartbeats plays a threshold for determining the type of symbols in the symbolic return map. Quantification of a symbolic sequence was performed by using the generalized symbolic measures;pNNx, pWx, CNx, and CSEx in which x denotes a threshold of coarse-graining symbolization. By varying the threshold x, we comprehensively examined the symbolic dynamic parameters for normal controls with normal sinus rhythms and the presumed patients with atrial fibrillation, or congestive heart failure, and identified the optimal threshold distinguishing the normal group from the abnormal group. The range of optimal thresholds was found at 10 similar to 25 ms, which is far below from the conventional standard of 50 ms. We demonstrate that pNNx, pWx, CNx, and CSEx are useful algorithms for the real-time analysis of HRV acquired from the ubiquitous health care system as well as for the assessment of the emergent alterations in a heart dynamics.

关键词： symbolic dynamics heart rate variability real-time algorithm u-health care system

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A real-time Target Detection algorithm for Panorama Infrared Search and Track System

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Procedia Engineering 2012年 29卷 1201-1207页

作者： Wang Weihua Li Zhijun Liu Jing He Yan Chen Zengping ATR Lab Electronic Science and Engine Institute NUDT ChangSha410073 China NUDT ChangSha410073 China

With regard to target detection in high resolution panorama images attained by circumferential scan Infrared Search and Tracking system, a rough-to-meticulous real-time target detection algorithm is proposed based on analysis of characteristics of targets and background. In the rough detection phase, it attains initial high rate target detection by quick real-time algorithm, based on the gray high frequency and movement characteristics of the target in the whole panorama image. In the meticulous detection phase, focusing on the detected suspected target sliced images, it has further delicate detection and recognition on the basis of targets’ characteristics to exclude those false jamming. The detection result of the test images shows, the algorithm enables stable detection with low-rate false alarm for distant dim small targets, and has been applied to the development of engineering sample of the Panorama Infrared Search and Tracking system.

关键词： PIRST dim small target target detection real-time algorithm

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A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Devindi, Isuri Liyanage, Sashini Jayarathna, Titus Alawatugoda, Janaka Ragel, Roshan Univ Peradeniya Dept Comp Engn Peradeniya Sri Lanka Western Sydney Univ MARCS Inst Brain Behav & Dev Westmead NSW Australia Rabdan Acad Res & Innovat Ctr Div POB 114646 Abu Dhabi U Arab Emirates Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

关键词： ECG compression Hybrid PWM Low-complexity Nearly-perfect reconstruction real-time algorithm

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real-time Front Vehicle Detection algorithm Based on Local Feature Tracking Method

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Journal of Measurement Science and Instrumentation 2011年第3期2卷 244-246页

作者： Jae-hyoung YU Young-joon HAN Hern-soo HAHN Dept.of Electronic Engineering Soongsil University

This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous *** features in back side of the vehicle are vertical and horizontal edges,shadow and *** comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting *** features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the *** method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and *** it can be performed on real-time through applying it to the embedded system.

关键词： vehicle detection object tracking real-time algorithm Haarlike edge detection

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Descriptor-Based Tracking algorithm Using a Depth Camera

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2017年第6期62卷 638-647页

作者： Miramontes-Jaramillo, D. Kober, V. I. Diaz-Ramirez, V. H. Karnaukhov, V. N. Res & Higher Educ CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico Russian Acad Sci Kharkevich Inst Informat Transmiss Problems Moscow 127051 Russia Inst Politecn Nacl CITEDI Ave Parque Tijuana 22510 BC Mexico Chelyabinsk State Univ Ul Bratev Kashirinykh 129 Chelyabinsk 454001 Russia

The appearance of inexpensive high-quality video cameras and depth cameras resulted in the development of a large number of object-tracking algorithms. In this paper, a new descriptor-based algorithm for real-time object tracking using the information from a Microsoft Kinect depth camera is proposed. As a descriptor for the object tracked, histograms of oriented gradients calculated from the circular sliding regions of the scene image are used. The information on the depth of the scene is used when the image of the object of interest is partially occluded by other objects in the scene. To speed up the tracking process, a model for predicting the object motion is used. To ensure real-time tracking with the proposed algorithm, a multicore graphics processor is used.

关键词： object tracking histogram of oriented gradients real-time algorithm

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An optimal time-space algorithm for dense stereo matching

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JOURNAL OF real-time IMAGE PROCESSING 2012年第2期7卷 69-86页

作者： Guerra-Filho, Gutemberg Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

The increasing demand for higher resolution images and higher frame rate videos will always pose a challenge to computational power when real-time performance is required to solve the stereo-matching problem in 3D reconstruction applications. Therefore, the use of asymptotic analysis is necessary to measure the time and space performance of stereo-matching algorithms regardless of the size of the input and of the computational power available. In this paper, we survey several classic stereo-matching algorithms with regard to time-space complexity. We also report running time experiments for several algorithms that are consistent with our complexity analysis. We present a new dense stereo-matching algorithm based on a greedy heuristic path computation in disparity space. A procedure which improves disparity maps in depth discontinuity regions is introduced. This procedure works as a post-processing step for any technique that solves the dense stereo-matching problem. We prove that our algorithm and post-processing procedure have optimal O(n) time-space complexity, where is the size of a stereo image. Our algorithm performs only a constant number of computations per pixel since it avoids a brute force search over the disparity range. Hence, our algorithm is faster than "real-time" techniques while producing comparable results when evaluated with ground-truth benchmarks. The correctness of our algorithm is demonstrated with experiments in real and synthetic data.

关键词： Dense stereo matching Complexity and performance analysis Disparity map Optimal linear algorithm real-time algorithm

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real-time Power Balancing in Electric Grids With Distributed Storage

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2014年第6期8卷 1167-1181页

作者： Sun, Sun Dong, Min Liang, Ben Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 2J7 Canada Univ Ontario Inst Technol Dept Elect Comp & Software Engn Toronto ON L1H 7K4 Canada

Power balancing is crucial for the reliability of an electric power grid. In this paper, we consider an aggregator coordinating a group of distributed storage (DS) units to provide power balancing service to a power grid through charging or discharging. We present a real-time, distributed algorithm that enables the DS units to determine their own charging or discharging amounts. The algorithm accommodates a wide spectrum of vital system characteristics, including time-varying power imbalance amount and electricity price, finite battery size constraints, cost of using external energy sources, and battery degradation. We develop a modified Lyapunov optimization framework for real-time power balancing and provide a fast iterative method for distributed implementation. The two components interact through a novel cost cushion parameter that tunes the trade-off between system performance and convergence speed. We show analytically that the algorithm converges quickly and provides asymptotically optimal performance as the capacity of DS units increases. We further study through simulation the algorithm performance over a wide range of parameter values and demonstrate that it is highly competitive over a greedy alternative.

关键词： Energy storage renewable generation power balancing real-time algorithm distributed control

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An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 5148-5160页

作者： Kowsar, Yousef Moshtaghi, Masud Velloso, Eduardo Leckie, Christopher Kulik, Lars Univ Melbourne Sch Comp & Informat Syst Interact Design Lab Parkville Vic 3010 Australia Univ Melbourne Sch Comp & Informat Syst Parkville Vic 3010 Australia Alexa AI Amazon Manhattan CA USA

Short bursts of repeating patterns [intervals of recurrence (IoR)] manifest themselves in many applications, such as in the time-series data captured from an athlete's movements using a wearable sensor while performing exercises. We present an efficient, online, one-pass, and real-time algorithm for finding and tracking IoR in a time-series data stream. We provide a detailed theoretical analysis of the behavior of any IoR and derive fundamental properties that can be used on real-world data streams. We show that why our method, unlike current state-of-the-art techniques, is robust to variations in repeats of the same pattern adjacent to each other. To evaluate our algorithm, we build a wearable device that runs our algorithm to conduct a user study. Our results show that our algorithm can detect intervals of repeating activities on edge devices with high accuracy (over 70% F1-Score) and in a real-time environment with only a 1.5-s lag. Our experimental results from real-world datasets demonstrate that our approach outperforms state-of-the-art algorithms in both accuracy and robustness to variations of the signal of recurrence.

关键词： Activity monitoring online algorithm pattern mining real-time algorithm time-series analysis unsupervised learning wearable technology

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Intelligent current controller for an HVDC transmission link

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IEEE TRANSACTIONS ON POWER SYSTEMS 1998年第3期13卷 1076-1083页

作者： Narendra, KG Khorasani, K Sood, VK Patel, RV Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Hydro Quebec IREQ Varennes PQ J3X 1S1 Canada

This paper describes an intelligent current controller for the fast and flexible control of an HVDC trans mission link using Artificial Neural Network (ANN) and Fuzzy Logic (FL) paradigms. A simple yet effective ANN architecture is present-ed with am-line adaptation of the activation function and learning parameters. Two methods of adapting the learning parameters are presented. In the first method, a heuristic approach to evaluate the learning rate as a polynomial of an energy function is considered. In the second method, a Fk based on-line adaption of the learning parameters is discussed. Performance of ANN, ANN-FL based and PI controllers are compared. A feasibility analysis is carried out to implement ease proposed neural controller algorithm in real-time.

关键词： current controller artificial neural networks (ANN) fuzzy logic (FL) HVDC link real-time algorithm

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