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An Oscillator Based Energy Efficient computing Architecture for Smart Sensors 62

An Oscillator Based Energy Efficient Computing Architecture ...

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62nd IEEE international Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Zhang, Ting Lu, Ruikuan Haider, Mohammad Alexander, J. Iwan D. Massoud, Yehia Univ Alabama Birmingham UAB Sch Engn Birmingham AL 35294 USA Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781728127880

In the past decade, there is a fast-growing need for sensing technology applications. However, the implementation of sensing and proceeding creates a large burden on data processing. The idea for designing stable cluster state for coupled phase oscillator in pattern recognition is receiving significant attention. This paper gives an overview of an oscillator neural network (ONN) based hierarchical associative memory (AM) architecture using oscillator synchronization and stable cluster state for pattern recognition and how such architecture can be efficiently used in local processing units. The ONN based AM architecture can be easily achieved using CMOS technology on local hardware units. Unlike cloud computing system, our architecture provides an approach when it is under an offline mode which couldn't get responses from the web server. The proposed architecture can help perform sensing and data processing efficiently on the local device without connecting to the Internet.

关键词： ONN synchronization stable cluster state energy efficiency hierarchical AM pattern recognition

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2nd international conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018

2nd International Conference of Artificial Intelligence, Med...

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2nd international conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018

ISBN: (纸本)9783030120818

The proceedings contain 70 papers. The special focus in this conference is on Artificial Intelligence, Medical Engineering, Education. The topics include: On Genetic Unitary Matrices and Quantum-Algorithmic Genetics;genetic Code Modelling from the Perspective of Quantum Informatics;focal Model in the pattern recognition Problem;experimental Detection of the Parallel Organization of Mental Calculations of a Person on the Basis of Two Algebras Having Different Associativity;determinant Optimization Method;analysis of Oscillator Ensemble with Dynamic Couplings;the Genetic Language: Natural Algorithms, Developmental patterns, and Instinctive Behavior;planck–Shannon Classifier: A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells;ECG Signal Spectral Analysis Approaches for High-Resolution Electrocardiography;intelligent System for Health Saving;A Life Ecosystem Management with DNA Base Complementarity;novel Approach to a Creation of Probe to Stop Bleeding from Esophageal Varicose Veins Based on Spiral-Compression Method;parallel Mechanisms in Layout of Human Musculoskeletal System;methods of Mutual Analysis of Random Oscillations in the Problems of Research of the Vertical Human Posture;verification of Mathematical Model for Bioimpedance Diagnostics of the Blood Flow in Cerebral Vessels;evaluation of Adhesive Bond Strength of Dental Fiber Posts by “Torque-Out” Test;image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set;steganographic Protection Method Based on Huffman Tree;the Role of Hybrid Classifiers in Problems of Chest Roentgenogram Classification;patient-Specific Biomechanical Analysis in Computer Planning of Dentition Restoration with the Use of Dental Implants;a Viscoelastic Model of the Long-Term Orthodontic Tooth Movement.

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Recognizing Hand-Woven Fabric pattern Designs Based on Deep Learning

Recognizing Hand-Woven Fabric Pattern Designs Based on Deep ...

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international conference on Computer, Communication and Computational Sciences (IC4S)

作者： Puarungroj, Wichai Boonsirisumpun, Narong Loei Rajabhat Univ Fac Sci & Technol Comp Sci Dept Loei 42000 Thailand

ISBN: (纸本)9789811368615;9789811368608

Hand-woven fabric pattern designs commonly represent the tradition and culture of local communities. pattern recognition methods can help classify these pattern designs without having to find an expert. The research aims at recognizing woven fabric pattern designs of traditional fabrics called Phasin in Loei province, Thailand based on deep learning methods. In recognizing pattern design, three deep learning models were experimented: Inception-v3, Inception-v4, and MobileNets. The research collected images of real silk fabrics containing 10 pattern designs. For each pattern, there were 180 images segmented and there were 1,800 images in total in the dataset. The data were trained and tested based on 10-fold cross-validation approach. The results of the test show that MobileNets outperforms Inception-v3 and Inception-v4. The test accuracy rates of pattern design recognition for MobileNets, Inception-v3, and Inception-v4 are 94.19, 92.08, and 91.81%, respectively. The future work will be done by carrying out more data training to increase performance, obtaining more pattern designs, and developing an application based on a high-performance model.

关键词： pattern recognition Deep learning Convolutional neural network Weaving pattern Loei Phasin Fabric pattern design

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Diving deep in Deep Convolutional Neural Network

Diving deep in Deep Convolutional Neural Network

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international conference on Advances in computing, Communication Control and Networking (ICACCCN)

作者： Divya Arora Mehak Garg Megha Gupta Guru Gobind Singh Indraprastha University Delhi India

ISBN: (数字)9781728183374

ISBN: (纸本)9781728183381

Artificial Neural networks have been proved most efficient in Deep Learning mainly because of large number of datasets it can handle. The most widely used is the Convolutional Neural Network (CNN). It has been proved useful for computer vision, pattern recognition and Natural Language Processing (NLP). CNN is so vastly used, as, unlike traditional Neural Nets, it reduces number of parameters and focus more on domain specific features. There are various CNN architectures proposed, such as LeNet, AlexNet, GoogleNet. In this paper, we talk about structure of CNN and all the models of CNN which are proposed till date.

关键词： Deep learning Error analysis Learning (artificial intelligence) Tools Natural language processing pattern recognition Convolutional neural networks

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Why is the Winner the Best?

Why is the Winner the Best?

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conference on Computer Vision and pattern recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Comparison of EMG signal classification algorithms for the control of an upper limb prosthesis prototype

Comparison of EMG signal classification algorithms for the c...

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international conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Daniel Lorias Espinoza Leobardo Elí Sánchez Velasco CINVESTAV IPN México City

ISBN: (数字)9781728189871

ISBN: (纸本)9781728189888

In this paper, Machine Learning algorithms (Decision Trees and Support Vector Machines) are proposed and compared to select a classification system for EMG signals to improve the performance of pattern recognition for the control of a prosthesis prototype. The training, validation and signal classification were made using the Classification Learner application of the MatLab software, using a database captured with the commercial myoelectric armband MYO which contain the information of eight different hand movements. The results show that Support Vector Machines algorithms have a better performance than the decision trees, reaching the 99.8% of accuracy with linear and quadratic kernel and the 99.9% using a cubic kernel.

关键词： Support vector machines Electromyography Classification algorithms Robots pattern recognition Machine learning algorithms Kernel

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Arduino microcontroller based Cashline Security System based on vein recognition Using Dactylography Technology 3

Arduino microcontroller based Cashline Security System based...

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3rd international conference on computing Methodologies and Communication (ICCMC)

作者： Rubini, M. Kavisarathi, M. Kaviya, R. Shankarr, B. P. Kiruba Pavithra, A. Praveen, R. KS Rangasamy Coll Technol Dept Elect & Commun Engn Tiruchengode India

ISBN: (纸本)9781538678084

In recent times, Cashline Systems are highly used for most of the bank transactions. Cashline system security is becoming more and more important in all aspects. On other hand the ATM frauds such as Duplicating ATM cards and card skimming were increased. To overcome such frauds dactylography technology is used in the proposed system. The advantage of the dactylography technology results on an entirely card free transaction. Even for the identical twins, the finger vein pattern is matchless. The width of the vein may be changed due to temperature changes or blood pressure but the centre line become stable. Then the extracted line will be used for the future identification purpose. The cashline system is to increase authenticity, confidentiality as well as user trust towards ATM transaction. After the analyzation of finger vein of the authenticated person, the next step is followed by means of the analyzed one's finger vein matching with the recorded vein pattern of the related banks database. Then high security level GSM module will generate an OTP and sends it to the authorized users mobile then followed by entering the OTP to the ATM machine, the verification is done and then further transaction process is followed.

关键词： Dactylography finger vein pattern GSM OTP

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Online Gaussian LDA for Unsupervised pattern Mining from Utility Usage Data 19

Online Gaussian LDA for Unsupervised Pattern Mining from Uti...

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19th IEEE international conference on Machine Learning and Applications, ICMLA 2020

作者： Mohamad, Saad Bouchachia, Abdelhamid Bournemouth University Department of Computing Poole United Kingdom

ISBN: (纸本)9781728184708

Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns. © 2020 IEEE.

关键词： Energy utilization

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Non-symmetry and Anti-packing pattern Representation Model in Visual Tracking 15th

Non-symmetry and Anti-packing Pattern Representation Model i...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Zheng, Yunping Li, Ruijun Sarem, Mudar School of Computer Science and Engineering South China University of Technology GuangzhouGuangdong510006 China School of Software Engineering Huazhong University of Science and Technology WuhanHubei430074 China General Organization of Remote Sensing Damascus Syria

ISBN: (纸本)9783030325909

Based on the non-symmetry and anti-packing pattern representation model (NAM), we propose a new division model in object tracking, whose most important characteristic is asymmetry. For the target region, we divide it into homogeneous blocks of different sizes and irregular positions. Pixels in the same homogeneous block have similar values, so we can use mean value and area to represent the feature of this block. When tracking the object, the proposed algorithm updates the mean values of each block to adapt to the change of the object. The characteristics of the whole target consist of the feature information and the position information of each block. Compared with the traditional division models, the NAM has fewer divided blocks but more highlight target structure characteristics. The experimental results in this paper show that our proposed tracking algorithm based on the NAM is better than those based on the quadtree or the compression tracking (CT) in both accuracy and speed. © 2020, Springer Nature Switzerland AG.

关键词： Computerized tomography

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international conference on soft computing and Signal Processing, ICSCSP 2018

International Conference on Soft Computing and Signal Proces...

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international conference on soft computing and Signal Processing, ICSCSP 2018

ISBN: (纸本)9789811335990

The proceedings contain 78 papers. The special focus in this conference is on soft computing and Signal Processing. The topics include: Comparative analysis of clustering algorithms with heart disease datasets using data mining weka tool;A machine learning approach for web intrusion detection: MAMLS perspective;white blood cell classification using convolutional neural network;analysis of mobile environment for ensuring cyber-security in IoT-based digital forensics;payment security mechanism of intelligent mobile terminal;Hybrid neuro-fuzzy method for data analysis of brain activity using EEG signals;gait recognition using J48-based identification with knee joint movements;cyber intelligence alternatives to offset online sedition by in-website image analysis through webcrawler cyberforensics;deep convolutional neural network-based diabetic retinopathy detection in digital fundus images;analysis of early detection of emerging patterns from social media networks: A data mining techniques perspective;a framework for semantic annotation and mapping of sensor data streams based on multiple linear regression;Cyclostationarity analysis of GPS signals for spoofing detection;implementation of fingerprint-based authentication system using blockchain;NSGLTLBOLE: A modified non-dominated sorting TLBO technique using group learning and learning experience of others for multi-objective test problems;homomorphic encryption scheme for data security in cloud using compression technique;efficient query clustering technique and context well-informed document clustering;motif shape primitives on fibonacci weighted neighborhood pattern for age classification;a novel virtual tunneling protocol for underwater wireless sensor networks;garbage monitoring system using internet of things;mobile learning recommender system based on learning styles;initial centroids for K-means using nearest neighbors and feature means.

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