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Spatiotemporal pattern recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses 22

Spatiotemporal Pattern Recognition in Single Mixed-Signal VL...

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international conference on Neuromorphic Systems (ICONS)

作者： Nilsson, Mattias Liwicki, Foteini Sandin, Fredrik Lulea Univ Technol EISLAB Lulea Sweden

ISBN: (纸本)9781450397896

Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing. However, realizing the potential of such neuromorphic hardware requires efficient use of its heterogeneous, analog neurosynaptic circuitry with neurocomputational methods for sparse, spike-timing-based encoding and processing. Here, we investigate the use of balanced excitatory-inhibitory disynaptic lateral connections as a resource-efficient mechanism for implementing a thalamocortically inspired Spatiotemporal Correlator (STC) neural network without using dedicated delay mechanisms. We present hardware-in-the-loop experiments with a DYNAP-SE neuromorphic processor, in which receptive fields of heterogeneous coincidence-detection neurons in an STC network with four lateral afferent connections per column were mapped by random input-sampling. Furthermore, we demonstrate how such a neuron was tuned to detect a particular spatiotemporal feature by discrete address-reprogramming of the analog synaptic circuits. The energy dissipation of the disynaptic connections is one order of magnitude lower per lateral connection (0.65 nJ vs 9.6 nJ per spike) than in the former delay-based hardware implementation of the STC.

关键词： Neuromorphic Spatiotemporal pattern recognition Ultra-low-power Device mismatch Neural heterogeneity Temporal code Excitatory-inhibitory balance

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Combined Local pattern (CLP): A Novel Descriptor for Face recognition 4

Combined Local Pattern (CLP): A Novel Descriptor for Face Re...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Karanwal, Shekhar Cse Department UK Dehradun India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

LBP is renowned as most powerful texture descriptor. But major issue which LBP possesses is the noisy thresholding function. This sacrifices the discriminativity of the descriptor. To complement that three LBP variants are introduced and these are Mean LBP (MLBP), Median LBP (MnLBP) and Combined Local pattern (CLP). In MLBP, the mean of the whole patch (3×3) is used for neighborhoods comparison. In MnLBP, the median of whole patch (3×3) is used for neighborhood comparison. The Mean and Median value proves out very effective in contrast to LBP, in which center pixel is used for the neighborhoods comparison. Both MLBP and MnLBP comprehensively conquer the performance of the LBP. To build more productive descriptor features of LBP, MLBP and MnLBP are integrated into one framework. This joined descriptor is called as CLP. The CLP conquer the results of all alone descriptors. Additionally it outrun the performance of the various literature methods. The compression in feature size is accomplished by PCA and classification is assisted from SVMs. Experiments are conducted on ORL and GT face datasets. © 2022 IEEE.

关键词： Local binary pattern

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and pattern recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Muthu, Shanmugam Kalyan, G Ramesh Samuthira Pandi, V. Shobana, D. Lakshmi Priya, J. Jaishika, S. Shipt Inc 420 20th St N # 100 BirminghamAL35203 United States Sns College of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798331508685

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study. © 2025 IEEE.

关键词： Data accuracy

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PROS: an Efficient pattern-Driven Compressive Sensing Framework for Low-Power Biopotential-basedWearables with On-chip Intelligence 22

PROS: an Efficient Pattern-Driven Compressive Sensing Framew...

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28th Annual international conference on Mobile computing and Networking (ACM MobiCom)

作者： Pham, Nhat Jia, Hong Tran, Minh Dinh, Tuan Bui, Nam Kwon, Young Ma, Dong Nguyen, Phuc Mascolo, Cecilia Vu, Tam Univ Oxford Oxford England Univ Cambridge Cambridge England Univ Wisconsin Madison Madison WI USA Univ Colorado Boulder Boulder CO USA Singapore Management Univ Singapore Singapore Univ Texas Arlington Arlington TX USA

ISBN: (纸本)9781450391818

While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. This study proposes PROS, an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-off between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) efficiently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing efficiency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath. We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high fidelity signal. It boosts the power efficiency of the wearable device by more than 1200% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals.

关键词： Biosignal Compressive Sensing On-chip Intelligence Edge-AI Wearable devices Cyber-Physical systems

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Leveraging Segment-Anything model for automated zero-shot road width extraction from aerial imagery

Leveraging Segment-Anything model for automated zero-shot ro...

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2023 international conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Xu, Nan Nice, Kerry Seneviratne, Sachith Stevenson, Mark Faculty of Engineering and It Department of Infrastructure Engineering Australia Melbourne School of Design Transport Health and Urban Systems Research Lab Australia The University of Melbourne Optimization and Pattern Recognition Group Faculty of Engineering and It ParkvilleVIC Australia

ISBN: (纸本)9798350382204

Segment-Anything model (SAM) is a foundation segmentation model published in April 2023. Trained on an unprecedented 11 million annotated images, the model can generate segmented masks bearing clear-cut contours by integrating user-provided prompts. It is zero-shot transferable, requiring no task-specific training. Nevertheless, its applicability for geographic vision tasks has not been fully evaluated. There is no automated prompt-feeding method incorporating with SAM that can work efficiently for purposeful batch processing as well. To fill these gaps, we developed a process that can be executed automatically from visual-prompts extraction to road width measurement, utilizing OpenStreetMap (OSM) and SAM. By examining the quality of segmentation in various image contexts, we evaluated the capacity and limitations of SAM working on aerial imagery. Through comparing measured widths to VicRoads records, we validated the specially designed width-measuring algorithm for high precision and accuracy. After this process, prompt-indicated zero-shot approach in solving basic geographic vision tasks is to be shaped synchronously on both theory and application ends. © 2023 IEEE.

关键词： Remote sensing

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Determining the maximum power point location of the PV array under partial shading using the shading pattern recognition

Determining the maximum power point location of the PV array...

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Electrical, Electronic and computing Engineering (IcETRAN), international conference on

作者： Nikola Krstić Dragan Tasić Teodora Denić dept. of Power Engineering Faculty of Electronic Engineering University of Niš Niš Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

This paper proposes an approach for finding the maximum power point (MPP) location of the photovoltaic (PV) array in the case of partial shading based on the shading pattern recognition. Shading pattern recognition is achieved by determining the necessary solar irradiation for each part of the PV array surface to recreate the existing power-voltage (P-V) curve of the PV array. This is done using the metaheuristic particle swarm optimization (PSO) method in which criterion function is based on comparing the measured powers of the PV array (fitting power points) and the powers calculated using the PV array model for the same voltages. Model of the PV array is created from the PV cell model considering the topology and the partial shading of the PV array surface. The effect of partial shading is included by reducing the photocurrent of the PV cells in the shaded area of the PV array. When the shading pattern is known the location (voltage) of the MPP is found using the P-V curve obtained from the PV array model. Results generated for three different shading patterns show great precision of the proposed approach in determining the MPP location if the PV cell is accurately modeled, and enough number of fitting power points are used.

关键词： Photovoltaic systems Voltage measurement Accuracy Computational modeling Fitting Metaheuristics Surface fitting pattern recognition Topology Particle swarm optimization

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A Back Propagation Based Optimized Regression Learning Algorithm for Load Estimation in Power Systems

A Back Propagation Based Optimized Regression Learning Algor...

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Sustainable Power & Energy (ICSPE), international conference on

作者： Kapil Deep Shakya Preeti Jain Department of Electrical Engineering Jabalpur Engineering College India

ISBN: (数字)9798350366051

ISBN: (纸本)9798350366068

The strengths of soft computing and machine learning based algorithms are being harnessed globally to improve and optimize power system performance. One such application happens to be load forecasting in power systems, with the objective of minimizing the difference between demand and generation of electrical power. However, it is a nontrivial challenge as electrical load exhibits high volatility owing to several variables such as date, time, time of the year, socio economic factors, atmospheric conditions etc. Hence it is extremely difficult to estimate future trends in electrical load with high accuracy. Previous, conventional statistical techniques have been used in conjugation with existing load curves to estimate future load trends. However with emerging machine learning and deep learning algorithms rendering high accuracy of pattern recognition, forecasting electrical load employing machine learning and deep learning algorithms has become an active area of research. This paper presents a combination of data preprocessing and back propagation based optimized regression learning for electrical load forecasting. It has been shown that the proposed approach outperforms existing baseline approaches in the domain and attains a mean absolute percentage error (MAPE) of only 1.3% and an $\mathbf{R}{}^{\wedge}\boldsymbolrecognition$ value of 0.97.

关键词： Backpropagation Deep learning Machine learning algorithms Load forecasting Neural networks Market research Power systems pattern recognition Discrete wavelet transforms Load modeling

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Predicting Student Attention And Engagement Through Eye-Tracking Using Machine Learning

Predicting Student Attention And Engagement Through Eye-Trac...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： Kirubakaran R Suganthi N Lokeshwaran V Akilan V E Shahidha S Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a comprehensive approach to predicting student engagement and learning behavior by analyzing eye-tracking data combined with behavioral analysis using camera-based recognition algorithms. The proposed system leverages WebGazer for eye-tracking to monitor gaze patterns and uses computer vision algorithms to recognize behaviors and emotional states. Feelings like engagement, frustration, or perplexity are identified using facial expression analysis. The system aims to provide real-time, actionable insights to students and educators by relating to emotional and concentration states by combining different modalities, thereby improving personalized learning strategies and educational outcomes.

关键词： Emotion recognition Computer vision Gaze tracking Machine learning Educational technology Prediction algorithms Real-time systems Telecommunication computing pattern recognition Monitoring

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Fusion of pattern recognition and Motion for Multi-Channel Motion Imagery EEG Based on Deep Spatiotemporal Feature 16

Fusion of Pattern Recognition and Motion for Multi-Channel M...

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16th th international conference on Intelligent computing Technology and Automation, ICICTA 2023

作者： Zhang, Yingjie Geng, Xiaozhong Yan, Hui Jiang, Huijie Zhang, Guangqiao Department of Computer Technology and Engineering Changchun Institute of Technology Jilin Province Changchun130012 China College of Information Technology Jilin Agricultural University Jilin Province Changchun130022 China Northeast Electric Power Design Institute. LTD. China Power Engineering Consulting Group Jilin Province Changchun130022 China

ISBN: (纸本)9781643684864

Changes in the membrane potential of neurons in the cerebral cortex produce measurable electrical brain activity. Electroencephalograms (EEGs) measure and record these electrical activity signals through the montage of the brain electrical activity signal channels formed by non-invasive sensing electrode arrays attached to the cortex. As a non-invasive detection method, EEG is widely used in the research fields of neuroscience and cognitive psychology. With the rapid development of electronic and computer technology, the development of Brain Computer Interface (BCI) technology has been promoted. This paper proposed a self-adaptive coupled motion control strategy based on the current working pose and micro-torque feedback. Compared with the conventional control way, no matter the coherence or the stability, this self-adaptive coupled motion control strategy manifests the better performance than conventional non-feedback mechanism. © 2024 The Authors.

关键词： Electroencephalography

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FPGA Implementation of pattern Detection using Convolutional Neural Networks

FPGA Implementation of Pattern Detection using Convolutional...

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international conference on Edge computing and Applications (ICECAA)

作者： Lahari P. L Rahul Gowtham Poola Siva Sankar Yellampalli Department of Electronics and Communication Engineering SRM University Andhra Pradesh India

The main objective of this research work is the detection of number of patterns from the input image. Artificial Neural Network (ANN) is used for solving may complex problems. pattern recognition is generalized according to the type of learning procedure used to generate the output value. In image processing, pattern recognition has may real world applications such as identification and authentication. Eg: license plate recognition, finger print analysis, face detection/verification and voice-based authentication. For detecting number of patterns, at first input image and input pattern need to be converted into the text file through MATLAB and that text files will be given as input in the Verilog coding. So, based on the pattern, detection of number of times pattern repeated is calculated is shown and implementation of the same algorithm in Artix 7 FPGA board is shown.

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