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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Wang Weihua Wang Peizao Niu Zhaodong Natl Univ Def Technol Elect Sci Coll ATR Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781538679463

Aiming at the difficulty of infrared target detection of "low and slow small" unmanned aerial vehicles (UAV) in complex low-altitude background, this paper proposes a new target detection algorithm based on multiscale fusion filtering. Combined with spatial multiscale decomposition filtering and temporal multiscale difference processing, the algorithm can effectively overcome many difficulties such as complex low altitude background interference, unknown target scale, unknown angular velocity and low target signal-to-noise ratio (SNR). The test result shows that the algorithm can effectively detect the UAV targets with different distances in complex low altitude background, and the false alarm rate is low. The algorithm is realized in TI 6657 DSP and realizes 100Hz real-time processing of mid-wave infrared images with 640*512 resolution, which has been effectively applied to the large-field circumferential scanning infrared search system developed by ATR Lab.

关键词： UAV target detection low-altitude background real-time algorithm low and slow small targets multiscale fusion filtering

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real-time algorithm Enabling High Dynamic Range Imaging and High Frame Rate Exploitation for Custom CMOS Image Sensor System Implemented by FPGA with Co-Processor

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Conference on real-time Image and Video Processing

作者： Jacquot, Blake C. Johnson-Williams, Nathan Aerosp Corp El Segundo CA 90245 USA

ISBN: (纸本)9781628414905

We present results from a prototype CMOS camera system implementing a multiple sampled pixel level algorithm ("Last Sample Before Saturation") in real-time to create High-Dynamic Range (HDR) images that approach the dynamic range of CCDs. The system is built around a commercial 1280 x 1024 CMOS image sensor with 10-bits per pixel and up to 500 Hz full frame rate with higher frame rates available through windowing. We provide details of system architecture and present images collected with the system.

关键词： real-time algorithm CMOS image sensor wide dynamic range day/night vision FPGA

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Implementation of a real-time Brain-to-Brain Synchrony Estimation algorithm for Neuroeducation Applications

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SENSORS 2024年第6期24卷 1776页

作者： Mendoza-Armenta, Axel A. Blanco-Tellez, Paula Garcia-Alcantar, Adaliz G. Ceballos-Gonzalez, Ivet Hernandez-Mustieles, Maria A. Ramirez-Mendoza, Ricardo A. Lozoya-Santos, Jorge de J. Ramirez-Moreno, Mauricio A. Tecnol Monterrey Sch Engn & Sci Mechatron Dept Monterrey 64700 Mexico

This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher-student or student-student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm's utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.

关键词： EEG bispectrum brain-to-brain synchronization real-time algorithm physiological signals Python

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Cross-step detection using center-of-pressure based algorithm for real-time applications

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2024年第1期21卷 161页

作者： Zadravec, Matjaz Matjacic, Zlatko Univ Rehabil Inst Republ Slovenia Linhartova 51 SI-1000 Ljubljana Slovenia

BackgroundGait event detection is crucial for assessment, evaluation and provision of biofeedback during rehabilitation of walking. Existing online gait event detection algorithms mostly rely on add-on sensors, limiting their practicality. Instrumented treadmills offer a promising alternative by utilizing the Center of Pressure (CoP) signal for real-time gait event detection. However, current methods have limitations, particularly in detecting cross-step events during perturbed walking *** present and validate a CoP-based algorithm to detect gait events and cross-steps in real-time, which combines thresholding and logic techniques. The algorithm was evaluated on CoP datasets from healthy participants (age range 21-61 years), stroke survivors (age range 20-67 years), and people with unilateral transtibial amputation (age range 28-63 years) that underwent perturbation-based balance assessments, encompassing different walking speeds. Detected gait events from a simulated real-time processing operation were compared to offline identified counterparts in order to present related temporal absolute mean errors (AME) and success *** proposed algorithm demonstrated high accuracy in detecting gait events during native gait, as well as cross-step events during perturbed walking conditions. It successfully recognized the majority of cross-steps, with a detection success rate of 94%. However, some misclassifications or missed events occurred, mainly due to the complexity of cross-step events. AME for heel strikes (HS) during native gait and cross-step events averaged at 78 ms and 64 ms respectively, while toe off (TO) AME were 126 ms and 111 ms respectively. A statistically significant difference in the algorithm's success rate score in detecting gait events during cross-step intervals was observed across various walking speeds in a sample of 12 healthy participants, while there was no significant difference among *** proposed algori

关键词： Gait event detection Center of pressure Cross-step real-time algorithm Treadmill walking Balance assessment Instrumented treadmill Perturbation-based balance training Biofeedback

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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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Style Optimization Networks for real-time semantic segmentation of rainy and foggy weather

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2025年 130卷

作者： Huang, Yifang He, Haitao He, Hongdou Zhao, Guyu Shi, Peng Fu, Pengpeng Yanshan Univ Qinhuangdao 066004 Hebei Peoples R China

Semantic segmentation is an essential task in the field of computer vision. Existing semantic segmentation models can achieve good results under good weather and lighting conditions. However, when the external environment changes, the effectiveness of these models are seriously affected. Therefore, we focus on the task of semantic segmentation in rainy and foggy weather. Fog is a common phenomenon in rainy weather conditions and has a negative impact on image visibility. Besides, to make the algorithm satisfy the application requirements of mobile devices, the computational cost and the real-time requirement of the model have become one of the major points of our research. In this paper, we propose a novel Style Optimization Network (SONet) architecture, containing a Style Optimization Module (SOM) that can dynamically learn style information, and a Key information Extraction Module (KEM) that extracts important spatial and contextual information. This can improve the learning ability and robustness of the model for rainy and foggy conditions. Meanwhile, we achieve real-time performance by using lightweight modules and a backbone network with low computational complexity. To validate the effectiveness of our SONet, we synthesized CityScapes dataset for rainy and foggy weather and evaluated the accuracy and complexity of our model. Our model achieves a segmentation accuracy of 75.29% MIoU and 83.62% MPA on a NVIDIA TITAN Xp GPU. Several comparative experiments have shown that our SONet can achieve good performance in semantic segmentation tasks under rainy and foggy weather, and due to the lightweight design of the model we have a good advantage in both accuracy and model complexity.

关键词： Rainy and foggy weather Semantic segmentation real-time algorithm

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The WQN algorithm for EEG Artifact Removal in the Absence of Scale Invariance

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2663-2676页

作者： Dora, Matteo Jaffard, Stephane Holcman, David Ecole Normale Super IBENS UMR8197 PSL F-75005 Paris France Univ Paris Est Creteil Univ Gustave Eiffel CNRS LAMA UMR8050 F-94010 Creteil France

Electroencephalogram (EEG) signals reflect brain activity across different brain states, characterized by distinct frequency distributions. Through multifractal analysis tools, we investigate the scaling behaviour of several classes of EEG signals and artifacts. We show that brain states associated to sleep and general anaesthesia are not in general characterized by scale invariance. The lack of scale invariance motivates the development of artifact removal algorithms capable of operating independently at each scale. We examine here the properties of the wavelet quantile normalization (WQN) algorithm, a recently introduced adaptive method for real-time correction of transient artifacts in EEG signals. We establish general results regarding the regularization properties of the WQN algorithm, showing how it can eliminate singularities introduced by artefacts, and we compare it to traditional thresholding algorithms. Furthermore, we provide evidence that the algorithm performance is independent of the wavelet basis. We finally examine its continuity and boundedness properties and illustrate its distinctive non-local action on the wavelet coefficients through pathological examples.

关键词： Art Electroencephalography time-frequency analysis Signal processing algorithms Transient analysis Transforms Task analysis Artifact replacement EEG real-time algorithm regularity property transport wavelet decomposition

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algorithm for Trajectory Simplification Based on Multi-Point Construction in Preselected Area and Noise Smoothing Processing

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DATA 2024年第12期9卷 140-140页

作者： Huang, Simin Yang, Zhiying Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points is proposed. The algorithm constructs the preselected area through the error measurement method based on the feature information of implicit trajectory points (IEDs) proposed in this paper, predicts the falling point of trajectory points, and realizes the one-way error-bounded simplified trajectory algorithm. Experiments show that the simplified algorithm has obvious progress in three aspects: running speed, compression accuracy, and simplification rate. When the trajectory data scale is large, the performance of the algorithm is much better than that of other line segment simplification algorithms. The GPS error cannot be avoided. The Kalman filter smoothing trajectory can effectively eliminate the influence of noise and significantly improve the performance of the simplified algorithm. According to the characteristics of the trajectory data, this paper accurately constructs a mathematical model to describe the motion state of objects, so that the performance of the Kalman filter is better than other filters when smoothing trajectory data. In this paper, the trajectory data smoothing experiment is carried out by adding random Gaussian noise to the trajectory data. The experiment shows that the Kalman filter's performance under the mathematical model is better than other filters.

关键词： trajectory simplification data compression spatio-temporal features real-time algorithm bounded error

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A Mathematical algorithm for Predicting the Crack in the Junction of a Steel Truss Bridge Using Acoustic Emission Testing

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2023年第3期23卷 747-757页

作者： Banerjee, Aishwarya Mukherjee, Arpita AcSIR Ghaziabad 201002 India CSIR Cent Mech Engn Res Inst Informat Technol Grp Durgapur West Bengal India

This paper presents a novel real-time algorithm for crack localization in the intersection of steel truss bridges using acoustic emission testing. It is well known that the welded junction or joint of the bridges is vulnerable to cracks due to long-duration dynamic loading. Here, the pencil lead-break-up (PLB) test is used to generate acoustic emission (AE) signals on the joint positions of the steel truss model and recorded using the R6D sensors. Crack location is determined in real-time using the proposed algorithm and a preset range of values for different AE signal parameters. A new modified equation calculates the distance between the source and the nearest sensor. Several experiments are performed with varying positions of sensors and PLB test locations on the model. It helps to set the range of different parameter values of the acquired AE signal by applying signal processing techniques. The proposed algorithm allows for locating the affected node number in the truss model. It also finds the corresponding shortest crack distance from the sensor. At the same time, a reduction in the number of sensors is also given importance.

关键词： Structural health monitoring Acoustic emission technique Signal processing Steel truss bridge model Crack localization real-time algorithm

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A Deep Reinforcement Learning Based real-time Solution Policy for the Traveling Salesman Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 5871-5882页

作者： Ling, Zhengxuan Zhang, Yu Chen, Xi Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

The rapid development of logistics and navigation has led to increasing demand for solving route optimization problems in real-time. The traveling salesman problem (TSP) tends to require fast and reliable online solutions, which may not be met by traditional iterative optimization algorithms. In this work, a real-time solution policy is proposed for TSP. The idea is to build a mapping between city information and optimal solutions using deep neural networks. Therefore, when given a new set of city coordinates, the optimal route can be directly and quickly calculated without iteration. Considering the recent advancement in computer vision with deep convolutional neural networks (DCNNs), an image representation is proposed to convert TSP to a computer vision problem. A problem decomposition method is introduced to reduce the mapping complexity. Taking advantage of the powerful fitting capabilities of DCNN, a deep reinforcement learning method is designed without any labeling requirement. The proposed method is superior for real-time applications compared with other algorithms.

关键词： Urban areas real-time systems Heuristic algorithms Reinforcement learning Neural networks Training Image representation Machine learning deep reinforcement learning real-time algorithm deep convolutional neural network traveling salesman

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