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Towards efficient and real-time human activity recognition using wearable sensors: A shapelet-based pattern matching approach 13th

Towards efficient and real-time human activity recognition u...

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13th EAI international conference on Body Area Networks, BODYNETS 2018

作者： Yazdansepas, Delaram Saroha, Nitin Ramaswamy, Lakshmish Rasheed, Khaled Department of Computer Science University of Georgia AthensGA United States

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Application of Elastic Principal Component Analysis to Person recognition Based on Screen Gestures 18th

Application of Elastic Principal Component Analysis to Perso...

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18th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Baran, Mateusz Siwik, Leszek Rzecki, Krzysztof Cracow Univ Technol Fac Phys Math & Comp Sci Ul Warszawska 24 PL-31155 Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9783030209124;9783030209117

Person identification based on touch screen gestures is a well-known method of authentication in mobile devices. Usually it is only checked if the user entered the correct pattern. Taking into account other biometric data based on the speed and shape of finger movements can provide higher security while the convenience of this authorisation method is not impacted. In this work the application of Sequential Joint Functional Principal Analysis (FPCA) as a dimensionality reduction method for gesture data is explored. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of gestures collected from 12 people. The effects of sampling rate on classification performance is also measured. It is shown that the Support Vector Machine classifier reaches the accuracy of 79% using features obtained using the Sequential Joint FPCA, compared to 70% in the case of Euclidean PCA.

关键词： Touch screen gestures Biometrics Classification Elastic shape analysis pattern recognition

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2nd international conference on Innovative computing and Communication, ICICC 2019

2nd International Conference on Innovative Computing and Com...

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2nd international conference on Innovative computing and Communication, ICICC 2019

ISBN: (纸本)9789811503238

The proceedings contain 50 papers. The special focus in this conference is on Innovative computing and Communication. The topics include: Comparative Study of TDMA-Based MAC Protocols in VANET: A Mirror Review;node Authentication in IoT-Enabled Sensor Network Using Middleware;an Efficient Data Aggregation Approach for Prolonging Lifetime of Wireless Sensor Network;a Comprehensive Review of Keystroke Dynamics-Based Authentication Mechanism;performance Analysis of Routing Protocols in Wireless Sensor Networks;Controlled Access Energy Coding (CAEC) for Wireless Adhoc Network;ioT-Based HelpAgeSensor Device for Senior Citizens;Optimization of LEACH for Developing Effective Energy-Efficient Protocol in WSN;automated Vehicle Management System Using Wireless Technology;a Supply Chain Replenishment Inflationary Inventory Model with Trade Credit;experimental Analysis of OpenStack Effect on Host Resources Utilization;customer Churn Prediction in Telecommunications Using Gradient Boosted Trees;efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center;SCiJP: Solving computing Issues by Java Design pattern;permissioned Blockchain-Based Agriculture Network in Rootnet Protocol;load Balancing and Fault Tolerance-Based Routing in Wireless Sensor Networks;a Novel Context Migration Model for Fog-Enabled Cross-Vertical IoT Applications;equity Data Distribution Algorithms on Identical Routers;improved Leakage Current Performance in Domino Logic Using Negative Differential Resistance Keeper;an Efficient Parking Solution for Shopping Malls Using Hybrid Fog Architecture;Sensor’s Energy and Performance Enhancement Using LIBP in Contiki with Cooja;effect of Dropout and Batch Normalization in Siamese Network for Face recognition;Analysis and Mitigation of DDoS Flooding Attacks in software Defined Networks;Analysis of Impact of Network Topologies on Network Performance in SDN.

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The Extreme Learning Machine Algorithm for Classifying Fingerprints 39

The Extreme Learning Machine Algorithm for Classifying Finge...

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39th international conference of the Chilean-Computer-Science-Society (SCCC)

作者： Zabala-Blanco, David Mora, Marco Hernandez-Garcia, Ruber Barrientos, Ricardo J. Univ Catolica Maule Dept Comp Sci & Ind Talca 3480112 Chile Univ Catolica Maule Res Ctr Adv Studies Maule CIEAM Talca 3480112 Chile Univ Catolica Maule Lab Technol Res Pattern Recognit LITRP Talca 3480112 Chile

ISBN: (纸本)9781728183282

Fingerprint recognition is the most employed biometric method for identification and verification purposes. Fingerprint images are classified into five categories according to the morphology of their ridges, which decreases the database penetration rate on an identification scheme. The classification procedure mainly starts with the feature extraction from the fingerprint sample, based on minutiae obtained from terminations and bifurcations of ridges. Afterward, the classification process is usually carried out by some artificial neural networks under supervised learning. Recently, convolutional neural networks are utilized as a potential alternative, by showing accuracies close to 100% with a high cost of learning times even using high-performance computing. On the other hand, the extreme learning machine (ELM) has emerged as a novel algorithm for the single-hidden layer feed-forward neural network, because of its good generalization performance at extremely fast learning speed. In this work, we introduce the ELMs for the fingerprint classification problem. The superior ELMs are given by the mapping activation function and the number of hidden nodes that maximize the accuracy of the classification;a heuristic approach is carried out to find these parameters. The studied databases are the NIST-4 and SFINGE, which are composed by different quantity and quality of fingerprint samples. Results show that ELM classification by using the feature descriptor of Hong08 achieves very high accuracy and low penetration rate, reducing severally the training time in comparison with deep learning approaches.

关键词： Extreme learning machine Fingerprint classification Supervised learning

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Face Identification via Strategic Combination of Local Features 1st

Face Identification via Strategic Combination of Local Featu...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Rakshit, Rinku Datta Kisku, Dakshina Ranjan Asansol Engineering College Vivekananda Sarani Kanyapur AsansolWest Bengal713305 India National Institute of Technology Mahatma Gandhi Road A-Zone DurgapurWest Bengal713209 India

ISBN: (纸本)9789811390418

Face identification systems that use a single local descriptor often suffer from lack of well-structured, complementary and relevant facial descriptors. To achieve notable performance, a face identification system is presented, which makes use of multiple representations of a face by means of different local descriptors derived from Local Binary pattern (LBP) and Local Graph Structure (LGS). Then, max, average and L2 pooling operations are applied on each representation of face image to scale down the features. Further, different representations are combined together and formed a strategically concatenated feature vector. Three classifiers including two correlation based and k-nearest neighbor (kNN) are used to produce matching proximities which are used to characterize a probe user with rank identity. The produced ranks are then fused together using rank level fusion techniques. The experimental results determined on the Extended Yale face B database and the Plastic Surgery database are encouraging and convincing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Gas Discrimination & Quantification using Sensor Array with 3D Convolution Regression Dual Network

Gas Discrimination & Quantification using Sensor Array with ...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Smart sensor system design requires intelligent data processing, which analyzes raw time-series sensor data to efficiently and precisely discriminate and quantify target gases. The work presented here utilizes the response of twin gas sensor arrays for gases such as ethanol, ethylene, methane, and carbon monoxide to discriminate and quantify the target gases. We propose a 3D convolution neural-based regression dual network (3D-CNRDN) for both gas quantification and discrimination. The spatiotemporal correlation of sensor array responses inspired us to design the deep neural network for the gas concentration estimation model. The sensor array set is spatially correlated, and all the twin array responses are temporally related. 3D-CNRDN uses raw time-series gas sensor array data. The data is fed to the network as the 3D pattern, which contains 2D spatial information varying with third dimension time to recognize patterns that eventually predict the concentration. The model evaluation shows that the proposed methods are an effective technique for gas quantification and identification with $\text{RMSE}=0.3179$ and classification accuracy 94.37%. Furthermore, the proposed method outperforms and provides higher discrimination accuracy and lower RMSE than other machine learning and deep learning methods.

关键词： Deep learning Gases Three-dimensional displays Convolution Neural networks Spatiotemporal phenomena pattern recognition

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Remarks on recognition of Aromas from Tea Leaves Using Deep Neural Network based on PPF-coated QCR Sensor Signals 3

Remarks on Recognition of Aromas from Tea Leaves Using Deep ...

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3rd IEEE international conference on Robotic computing (IRC)

作者： Takahashi, Kazuhiko Sugimoto, Iwao Doshisha Univ Fac Sci & Engn Kyoto 6100321 Japan Tokyo Univ Technol Sch Comp Sci Tokyo 1920982 Japan

ISBN: (纸本)9781538692455

This study investigated the capacity of a deep neural network to distinguish tea types based on their aromas. The data set of aromas from tea leaves, which contained sensor responses measured with a gas-sensing system using a mass-sensitive chemical sensor comprised of an array of quartz crystal resonators coated with plasma organic polymer films, was used to evaluate the recognition accuracy. To define the input vectors of the deep neural network in aroma recognition experiments, frequency analysis using a continuous wavelet transform, with the Morlet function as the mother wavelet, was used to extract features from the sensor signals of the data set. The deep neural network achieved a recognition accuracy of 100% for the three tea types: oolong, jasmine and pu'erh, and the base gas of dehumidified indoor air. Comparing the recognition accuracy of the deep neural network to that obtained from other pattern recognition methods, such as naive Bayes and random forests, the experimental results demonstrated the effectiveness of applying a deep neural network to this task.

关键词： Neural networks Feature extraction Robot sensing systems Continuous wavelet transforms pattern recognition

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Real-Time Texture Extraction Based on the Improved Median Robust Extended Local Binary pattern 2020

Real-Time Texture Extraction Based on the Improved Median Ro...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Mengnan Wang Yanjun Zhang School of Information and Electronics Beijing Institute of Technology Beijing China CN

ISBN: (纸本)9781450387835

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. The fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. The proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. The extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2].

关键词： real-time MRELBP feature extraction Field-Programmable Gate Array (FPGA)

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7th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2018

7th International Conference on Frontiers of Intelligent Com...

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7th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2018

ISBN: (纸本)9789813291850

The proceedings contain 72 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: An Efficient Privacy-Preserving Search Technique for Encrypted Cloud Data;optimal Path Selection in Wireless Sensor Networks Using Enhanced Spatial Reusability;Application of Classical Codes over GF(4) on Quantum Error Correction Codes;Improved GLIM in Multiple-Input Multiple-Output OFDM VLC;design and Implementation of an IoT-Based Water Purifier System Enabling Predictive Maintenance;grey Wolf Optimized Task Scheduling Algorithm in Cloud computing;adaptive Feature Selection and Classification Using Optimization Technique;how to Fairly Allocate Indivisible Resources Among Agents Having Lexicographic Subadditive Utilities;a Novel Approach of Ontology-Based Activity Segmentation and recognition Using pattern Discovery in Multi-resident Homes;colorectal Cancer Diagnosis with Complex Fuzzy Inference System;a Machine Learning Approach for Hot Topic Detection in News;a Proposal of Expert System Using Deep Learning Neural Networks and Fuzzy Rules for Diagnosing Heart Disease;Development of the Rules for Model Transformation with OCL Integration in UWE;nonintrusive Load Monitoring Algorithms: A Comparative Study;craftQuest—Mobile App for Collecting Craft Village Data;tracking Big5 Traits Based on Mobile User Log Data;prediction of Factors Associated with the Dropout Rates of Primary to High School Students in India Using Data Mining Tools;domain-Specific Versus General-Purpose Word Representations in Sentiment Analysis for Deep Learning Models;improvement of Machine Learning Method by Combining Flow Text and Layout Text in Extracting Information from Scanned Healthcare Documents;assessing the Learning Difficulty of Text-Based Learning Materials;watermark by Learning Non-saliency.

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pattern recognition Through Digital Image Processing for Unmanned Aerial Vehicles 26

Pattern Recognition Through Digital Image Processing for Unm...

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IEEE 26th international conference on Electronics, Electrical Engineering and computing (INTERCON)

作者： Ponce, Gabino Rey Vidangos Bhimani, Kishankumar Prakosa, Jalu A. Ana Beatriz Alvarez, M. St Petersburg Electrotech Univ LETI Fac Elect Engn & Automat St Petersburg Russia St Petersburg Electrotech Univ LETI Dept Robot & Automat Ind Syst St Petersburg Russia Indonesian Inst Sci LIPI Res Ctr Phys Tangerang Selatan Indonesia Univ Fed Acre UFAC Ctr Ciencias Exatas & Tecnol CCET Aldeia Do Rio Branc Brazil

ISBN: (纸本)9781728136462

This paper describes the implementation of two digital image processing methods for pattern recognition, by color boundary method and the Haar Cascade Classifier to detect objects in a video stream, both methods implemented on Python 3 and OpenCV. patterns detection of images obtained from drones has advantages over traditional video recording drones. The drone has a streaming video system, based on the Raspberry Pi 3 minicomputer, which is sent by wireless communication to the base station where a pattern recognition algorithm performs operations on the video source coming from the drone. This proposed system has excellent performance based on an integrated streaming video system with 5.8GHz Wi-Fi connection acceptable to the base station. Both methods proved to be valid for certain types of patterns and objects under different light conditions.

关键词： Digital image processing pattern recognition Boundary color detection Haar Cascade method for customized object detection Video Streaming Wireless communication Aerial robots Drones

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