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conference Proceedings of the IEEE Magnetics Society Skyrmionic Synapse Implementation for pattern recognition Using Convolutional Neural Network

Conference Proceedings of the IEEE Magnetics Society Skyrmio...

引用

Magnetic conference - Short Papers (INTERMAG Short Papers), IEEE international

作者： Saumya Gupta Venkatesh Vadde Bhaskaran Muralidharan Abhishek Sharma Department of Electrical Engineering Indian Institute of Technology Bombay Powai Mumbai India Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar Punjab India

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Conventional neuromorphic computing faces the foreseeable limit of computational and power resources that can be mitigated by using spintronic-based devices. In this digest, we propose a 4-bit (16-state) skyrmion-based synaptic device for application in Convolutional Neural Network(CNN) consuming 0.971 fJ energy per weight update. Our proposed device offers compactness and scalability to higher bits. The skyrmionic synapse device is simulated using a micromagnetic simulation package(OOMMF) integrated with a software-based ReLU Max Pooled function to implement CNN for neuromorphic computing applications. We attained an accuracy of 98.15 % for pattern recognition on the MNIST handwritten data set.

关键词： Power demand Neuromorphic engineering Scalability pattern recognition Convolutional neural networks Magnetics Micromagnetics

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Efficient Mining of Regional Semantic patterns from Semantic Trajectories on Cloud computing 9

Efficient Mining of Regional Semantic Patterns from Semantic...

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9th international conference on Advanced Cloud and Big Data, CBD 2021

作者： Liu, Yushi Han, Jingjing Zhao, Bin Nanjing Normal University School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Jiangsu Open University Quality Assurance Office Nanjing China

ISBN: (纸本)9781665407458

Semantic trajectory pattern mining is becoming more and more important with the efficient semantic enrichment process methods, which is the process of semantic annotation for spatiotemporal trajectories. Existing works on semantic trajectory pattern mining focus on sequential patterns that occur frequently globally in the semantic trajectory data, ignoring the ones that are locally significant within a region, which is called regional semantic pattern. As a consequence, the existing regional semantic pattern mining algorithm RegMiner is time consuming. Based on this motivation, we propose an improved efficient regional semantic pattern mining algorithm called MRSP, which consists of three parts: candidate semantic category sequence discovery, regional semantic pattern recognition, and parallelization. The innovations of MRSP lie in that we use an improved neighborhood pruning algorithm (NPSP) and an improved neighborhood query algorithm (NSSI) in candidate semantic category sequence discovery mentioned above, and adopt a parallelization algorithm to do calculation based on Hadoop platform. Finally, we evaluate our approach on real dataset in Shanghai. Compared with RegMiner, MRSP has the same effect in mining regional semantic patterns, and the efficiency is greatly improved. © 2022 IEEE.

关键词： pattern recognition

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Deep Feature Learning for Image-Based Kinship Verification 40th

Deep Feature Learning for Image-Based Kinship Verification

引用

40th Computer Graphics international conference (CGI)

作者： Zhao, Shuhuan Wang, Chunrong Liu, Shuaiqi Cheng, Hongfang Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China Machine Vis Technol Innovat Ctr Hebei Baoding 071002 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR Beijing 100190 Peoples R China

ISBN: (纸本)9783031500688;9783031500695

Facial image-based kinship verification is one of the challenging tasks in computer vision. It has many potential applications, such as human trafficking, studying human genetics, generating family maps, family photo albums, etc. Therefore, we propose a deep feature learning method (DFLKV) which can extract more discriminative features for kinship verification. For a pair of facial images, we firstly design a network with multi-scale channel attention for the features extraction;then, select four methods for feature fusion;finally, infer kinship based on the fused features. We jointly adopt the contrastive loss and the binary cross-entropy loss to compute matching degree for paired samples. The experimental results on four widely used datasets KinFaceW-I, KinFaceW-II, Cornell KinFace and TS KinFace to validate the effectiveness of our approach.

关键词： Kinship verification Deep feature learning Channel attention

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Video-Based Semi-automatic Drivable Area Segmentation 27th

Video-Based Semi-automatic Drivable Area Segmentation

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27th international conference on pattern recognition, ICPR 2024

作者： Cheng, Zhengyun Zhang, Guanwen Wang, Changhao Zhou, Wei Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031784460

Drivable area segmentation is a crucial task in autonomous driving. While current works mainly focus on analyzing single image and overlook the intra-video associations, due to the limited availability of video-based datasets. We present a novel prototype-based approach, named DASeg, to tackle the challenge of annotating intra-video query images using annotated support images as guidance. The primary obstacle lies in aggregating representative prototypes while ensuring resilience to variations in appearance and position across the video. Our method consists of three key components: position embedding for utilizing positional priors, soft-pooling for alleviating the limited coverage of intra-class variations from the support provided, and prototype regularization for generalizability enhancement. We augmented the lane detection dataset VIL-100 by incorporating drivable area annotations, resulting in a new dataset named VDA-100, which was employed to evaluate the performance of the proposed method. Experiments show that our method achieves mIoU score of 88.3% with the pre-trained backbone from lane detection model, and 89.1% when trained from scratch. Our code and dataset is available at https://***/CZY-Code/DASeg. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drivable area segmentation Positional prior Prototype regularization soft-pooling

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Hybrid Electroencephalography-Induced Robot Navigation for Rehabilitative Platform 20

Hybrid Electroencephalography-Induced Robot Navigation for R...

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20th IEEE India Council international conference, INDICON 2023

作者： Ghosh, Ahona Ghosh, Lidia Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India Rcc Institute of Information Technology Department of Computer Application Kolkata India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application India

ISBN: (纸本)9798350305593

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms. © 2023 IEEE.

关键词： Electroencephalography Linear Discriminant Analysis OpenManipulatorX Phase-sensitive Common Spatial pattern Rehabilitation

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Automatic Attendance Monitoring System Using LBPH and HAAR Algorithm 9

Automatic Attendance Monitoring System Using LBPH and HAAR A...

引用

9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Rathinamala, S. Karthikeyan, A. Abishek, R. Kannan, C.K. KIT-Kalaignar Karunanidhi Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350397376

Every office and organization must have an attendance monitoring system. Every institution has a standard attendance policy that requires the professor or assistant to report the student's name and attendance for each and every session. An automatic attendance monitoring approach will be suggested to replace this human process. Face spotting, an image processing tool, and face identification techniques are used in the research project that is being suggested. This method assists in turning video frames into numbers so that the human profile may be quickly recognized for attendance monitoring. The image captured from the video should be compared with the data saved with the date and time, which will be automatically stored in the memory storage, before a recognized attendance database is formed. The Viola-Jones Face Detection Technique, also known as the HAAR cascade algorithm, and the Local Binary pattern Histogram (LBPH) algorithm were used in the development of the proposed automatic face recognition attendance monitoring system. Their combined features are used for attendance monitoring. The human face is first captured using the HAAR cascade technique, and then the LBPH algorithm is utilized to save the distinctive traits of each human face. During the face identification procedure, a predetermined user ID is retrieved from the algorithm data. This will produce the student's attendance data for their presence. The image processing system's data will need to be kept up to date and stored daily. The implementation of an automatic attendance tracking system will benefit from the suggested strategy. Anaconda software is used to implement and analyse the suggested attendance tracking system. © 2023 IEEE.

关键词： Local binary pattern

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Automated Machine Learning Evaluation of Squat Movement patterns Using Smartphone and CoreML 12

Automated Machine Learning Evaluation of Squat Movement Patt...

引用

12th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Sokolowska, Marta Podpora, Michal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland

ISBN: (纸本)9798350358056

There is a vast diversity of Machine Learning use cases, and both the hardware and the methodology itself are constantly getting better. This article presents a very specific use case - the recognising, classifying, and evaluation of human physical activity by using just a video stream from a smartphone and potentially instructing people how to exercise to get better results. The paper presents two key aspects of the challenge: (1) defining the problem in the physical activity domain and choosing a proper ML approach and tool, and (2) conducting the experiment using consumer-grade hardware (a smartphone) to evaluate if the proposed methodology is applicable using an easy-to-use tool and widely accessible hardware for users with limited (or without) Machine Learning experience. Chapter II presents the process of gathering and preparing the dataset, while the next chapter presents the creation of a Machine Learning model, model training using the acquired dataset, and the model performance. It was possible to obtain the result of 92.4% validation accuracy for the squat movement pattern classification model. The trained model can be used in an ML smartphone application for evaluation of user-provided examples of squats. © 2023 IEEE.

关键词： action classiication Artiicial Intelligence Machine Learning movement patterns pattern recognition video analysis

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pattern recognition for Identifying a String Within a Text File Using Finite Automata

Pattern Recognition for Identifying a String Within a Text F...

引用

international conference on computing and Networking Technology (ICCNT)

作者： Mamidi Prajana Harsha Rajkumar Akepati Sai Sannidhi Kammari Vidyasri Niharika Panda Dept. of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

pattern recognition is crucial across diverse domains, including retrieval of information, data mining, and bioinformatics. Numerous algorithms exist for string matching, and finite state machines (FSM) provide a robust framework for implementing these pattern recognition algorithms. This study introduces an efficient pattern recognition application utilizing a finite state machine, using the SSFSM library in Python. The SSFSM library facilitates the creation and management of FSMs, offering methods to define states and transitions efficiently. An algorithm is proposed, that identifies strings within a text file with the help of the SSFSM library in Python. It employs deterministic finite automata (DFA) for pattern recognition, scanning text files word by word and then character by character, and recording execution times to evaluate efficiency. The algorithm’s effectiveness is demonstrated through a comparative analysis of its performance for the number of characters and size of the input file. Results demonstrate the algorithm’s effectiveness in accurately identifying and quantifying specific string occurrences within large text files. It also shows that the search time of a string increases with an increase in the frequency of the character, size of the text file, and length of the search key, underscoring its potential for broader applications. Further, our results also show that there is approximately a 20% increase in search time when the search key length increases by 1. Future enhancements may include extended functionalities, support for additional document types, and improved complexity management.

关键词： Time-frequency analysis Automata Approximation algorithms Libraries pattern recognition Complexity theory Recording Data mining Bioinformatics pattern matching

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Alzheimer's Diseases Detection by Using Xception Deep Neural Network with Data Augmentation

Alzheimer's Diseases Detection by Using Xception Deep Neural...

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2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sivakumar, Soubraylu Bharadwaj, R. Abhichandra, S. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

In our modern age, amidst the whirlwind of progress and change, there exists a silent epidemic that transcends time and technology, affecting individuals and families in profound ways: Alzheimer's disease. This illness, which slowly causes memory loss and difficulty thinking, reminds us how delicate our minds are and how complex brain health can be. This work focuses on leveraging deep learning algorithms with data augmentation to enhance the accuracy of AD detection. The study examines four cutting-edge deep learning models: Xception, InceptionV3, EfficientNetB3, and ResNet50. Robust feature extraction from MRI images is accomplished by using ResNet50, which is renowned for its deep design with skip connections. EfficientNetB3, with its efficient design and high accuracy, contributes to effective classification of AD stages. InceptionV3's multi-level feature extraction capabilities are utilized to capture intricate patterns indicative of AD progression. Xception's depth wise separable convolutions enhance pattern recognition, aiding in precise disease detection. Utilizing deep learning algorithms, notably Xception, and employing data augmentation techniques, our study attained an exceptional accuracy of 97.18% in detecting Alzheimer's Disease, utilizing the "Alzheimer's Diseases Detection"dataset. In order to improve early diagnosis and treatment planning for Alzheimer's patients, the research intends to give a thorough examination of these deep learning methods in AD detection. © 2024 IEEE.

关键词： Deep learning Accuracy Data augmentation Feature extraction Robustness Classification algorithms Planning pattern recognition Alzheimer's disease Residual neural networks

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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