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A distributed cellular approach of large scale SOM models for hardware implementation 3

A distributed cellular approach of large scale SOM models fo...

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3rd IEEE International conference on Image processing, Applications and Systems (IPAS)

作者： Rodriguez, Laurent Khacef, Lyes Miramond, Benoit Univ Cote Azur LEAT CNRS UMR 7248 Cote Dazur France

ISBN: (纸本)9781728102474

Self-organization is a powerful bio-inspired feature that has been poorly investigated in the context of hardware computing architectures. The neural foundations of brain self-organization enable structural plasticity, continuous learning, and tolerance to lesions but at the cost of massive lateral synaptic connections. A cellular formulation of the related neural models would be able to tackle this limitation by iterating the propagation of the information in the network. This paper introduces the SOMA hardware architecture, which relies on this principle to develop a Self-Organizing Machine Architecture. We show the benefits of our cellular implementation compared to the existing neural models. This contribution leads to a hardware compliant algorithm thanks to decentralized operations and communications. A generalization of the technique is proposed to implement different Kohonen-like self-organizing models. This particular cellular implementation reaches the same behavior as the centralized models but drastically reduces their computing complexity. We present the performance results and discuss the benefits of cellular computing approaches in the perspective of designing hardware neural processors for embedded applications.

关键词： self-organizing maps cellular computing neuromorphic architectures hardware neural networks

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Kannada Handwritten Script Recognition using Machine Learning Techniques

Kannada Handwritten Script Recognition using Machine Learnin...

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IEEE distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Roshan Fernandes Anisha P Rodrigues NMAM Institute of Technology Nitte India

ISBN: (数字)9781728137353

ISBN: (纸本)9781728137360

Many researchers have contributed to automate the optical character recognition. But handwritten character recognition is still an uncompleted task. In this paper we are proposing two techniques to recognize handwritten Kannada script, which yields high accuracy compared to previous works. There are lot of challenges in recognizing handwritten Kannada scripts. Few of the challenges include: each person have their own handwriting, there is no uniform spacing between alphabets, words and lines. Another main problem when it comes to Kannada language is that there is no large dataset available to train the recognition system, and it is challenging to write all combinations of each alphabet in Kannada script. In the proposed work, we have gathered the handwritten training set from the Web and from the students of our campus and segmented each letter. We have proposed two methods to recognize the handwritten Kannada characters. The first techniques is by Tesseract tool, and second is by using Convolution neural network (CNN). With Tesseract tool we have achieved 86% accuracy and through Convolution neural network we achieved87% accuracy although it might improve with the data set chosen and further enhanced image processing. The main idea behind this work is to extract text from the scanned images, identify the Kannada letters in it accurately and display or store it for further usage.

关键词： Character recognition Training Handwriting recognition Tools Optical character recognition software neural networks Optical imaging

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Brain Functional Connectivity Analysis and Crucial Channel Selection Using Channel-Wise CNN 25th

Brain Functional Connectivity Analysis and Crucial Channel S...

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25th International conference on neural Information processing (ICONIP)

作者： Wang, Jiaxing Wang, Weiqun Hou, Zeng-Guang Liang, Xu Ren, Shixin Peng, Liang Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9783030042127;9783030042110

Brain functional connectivity analysis and crucial channel selection, play an important role in brain working principle exploration and EEG-based emotion recognition. Towards this purpose, a novel channel-wise convolution neural network (CWCNN) is proposed, where every group convolution operator is imposed only on a separate channel. The inputs and weights of the full connection layer are visualized by using the brain topographic maps to analyze brain functional connectivity and select the crucial channels. Experiments are carried out on the SJTU emotion EEG database (SEED). The results demonstrate that positive and neutral emotions evoke greater brain activities than negative emotions in the left frontal region, which is consistent with the result from the power spectrum analysis in the literature. Meanwhile, 16 crucial channels, which are mainly distributed in the frontal and temporal regions, are selected based on the proposed method to improve emotion recognition performance. The classification accuracy by using the selected crucial channels is similar to that without channel selection. But the model with the 16 selected channels is more memory-efficient and the computation time can be reduced substantially.

关键词： Channel-wise convolution neural network Brain topographic maps Full connection layer Weights Crucial channels

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Convolutional neural network based processing of code-multiplexed coulter signals 22

Convolutional neural network based processing of code-multip...

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22nd International conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2018

作者： Wang, Ningquan Liu, Ruxiu Sarioglu, A. Fatih Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781510897571

Coulter counters are used to count and size small particles suspended in an electrolyte by measuring the number and the amplitude of intermittent changes in electrical current as particles flow through a small aperture between two electrodes. We have recently introduced a technique, Microfluidic CODES, which transforms the Coulter counter into a code-multiplexed sensor network for distributed sensing of particles dispersed over a lab-on-a-chip device. Microfluidic CODES relies on micromachined electrode patterns to produce distinguishable signal waveforms that provide information on both the location and the physical properties of target particles. In this paper, we introduce a machine learning-based decoding algorithm for interpreting signals from a Microfluidic CODES device. Our approach utilizes a convolutional neural network, which reduces the constraints on the design of coded electrodes and also increases the data processing speed. Copyright © (2018) by Chemical and Biological Microsystems Society. All rights reserved.

关键词： Microfluidics

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Accelerating Training for distributed Deep neural networks in MapReduce 25th

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25th International conference on Web Services (ICWS) held as Part of the Services conference Federation (SCF)

作者： Xu, Jie Wang, Jingyu Qi, Qi Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9783319942896;9783319942889

Parallel training is prevailing in Deep neural networks (DNN) to reduce training time. The training data sets and layered training processes of DNN are assigned to multiple Graphics processing Units (GPUs) in parallel training. But there are some obstacles to deploy parallel training in GPU cloud services. DNN has a tight-dependent layering structure where the next layer feeds on the output of its former layer. It is unavoidable to transmit big output data between separated layered training processes. Since cloud computing offers separated storage services and computing services, data transmission through network harms the performance in training time. Thus parallel training leads to an inefficient training process in GPU cloud environment. In this paper, we construct a distributed DNN training architecture to implement parallel training for DNN in MapReduce. The architecture assigns GPU cloud resources as a web service. We also address the concern of data transmission by proposing a distributed DNN scheduler to accelerate the training time. The scheduler makes use of minimum cost flows algorithm to assign GPU resources, which considers data locality and synchronization into minimizing training time. Compared with original schedulers, experimental results reveal that distributed DNN scheduler decreases the training time by 50% with least data transmission and synchronizing parallel training.

关键词： Deep neural networks Parallel training MapReduce Data transmission Synchronization

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Advanced Driving Assistance System using distributed computation on single-board computers

Advanced Driving Assistance System using distributed computa...

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RSI/ISM International conference on Robotics and Mechatronics (ICROM)

作者： Karen Abrinia Moosa Ayati Hadi Niknam Shirvan Amin Abazari Ali Haddad Tabrizi Mostafa Shahbazzadeh Zeinab Maroufi Mohammadreza Akrami Erfan Safaee Amirhossein Oliaei Fasakhodi Amirhossein Panahi School of Mechanical Engineering University of Tehran Tehran Iran Faculty of New Sciences and Technologies University of Tehran Tehran Iran University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

Due to increasing use of vehicles and road traffic, drive safety has become an important issue. Therefore, an Advanced Driving Assistant System (ADAS) can be a convenient option to increase driving safety. This paper presents a model of an ADAS that is capable of driving from one place to another in different paths such as curved, straight and straight line followed by curved lines. This area of research is divided into several sub-domains, such as deep learning, hardware platform, computer vision, and control. Every self-driving car must be aware of its surroundings and act accordingly. Advances in neural networks and deep learning made it possible to extract information from cameras easily and robustly. In this paper, a neural network is trained to identify various objects such as traffic lights and pedestrians. In addition, image processing is used to detect road lines. due to high computational costs of image processing operations, a network of embedded systems are utilized. Furthermore, MPC control method is used for automated and intelligent steering that makes the right decisions in a timely manner with a small error.

关键词： Cameras Roads Automobiles Mechatronics Accidents Object detection

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A Proposed License Plate Classification Method Based on Convolutional neural network 6

A Proposed License Plate Classification Method Based on Conv...

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6th IEEE International conference on network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Ni, Kaili Fu, Meixia Huang, Zhongjie Sun, Songlin Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Secur Key Lab Trustworthy Distributed Comp & Serv BUPT Sch Informat & Commun EngnMinist Educ Beijing Peoples R China

ISBN: (纸本)9781538660676

Nowadays convolutional neural network(CNN) has been successfully applied in image processing. At the same tune license plate recognition is more and more universal. Before recognition with deep learning, we need to collect enough images to train the network. The quality of data is very important. The current methods of getting license plate based on deep learning are increasingly various, however, there are still many images where illumination, size and blurriness make it is extremely difficult to recognize. As a result, images with low quality eventually affect the accuracy of recognition. Therefore, license plate classification is essential to eliminate low quality images so that improve the quality of the dataset. In this paper, a method based on CNN is proposed to deal with license plate classification. We use a seven layers CNN and ultimately the best result reached 98.79%.

关键词： CNN License plate Classification Recognition

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A Study of Action Recognition using Pose Data toward distributed processing over Edge and Cloud

A Study of Action Recognition using Pose Data toward Distrib...

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IEEE International conference on Cloud Computing Technology and Science

作者： Chikako Takasaki Atsuko Takefusa Hidemoto Nakada Masato Oguchi Ochanomizu University 2-1-1 Otsuka Bunkyo-ku Tokyo Japan National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo Japan National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki Japan

With the development of cameras and sensors, and the spread of cloud computing, life logs can be acquired and stored in general households for various services using the logs. However, it is difficult to analyze moving images acquired by a home sensor in real time using machine learning because the data size and the computational complexity are large. New computing paradigm called edge computing or fog computing, which enables distributed computing over edge and cloud, has the possibility to address this issue. The feature vectors are extracted from moving images by preprocessing on the sensor side and the only small feature vectors are sent to the cloud and used for learning. But, it is not clear how accurately we can recognize actions using only the feature vectors in the learning and inferring. We investigate the accuracies of action recognition with various machine learning methods using feature vector information obtained from moving images. We use the pose estimation library OpenPose for detection of the feature vectors and recognize actions using logistic regression, random forest, support vector machine, and neural network (NN) models, general NN and LSTM, as machine learning methods. The experimental results show that it is possible to recognize an action with 80% accuracy or higher when using random forest and neural network models. We also discuss a method to further improve the accuracy based on the experimental results.

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Selection of Assembly Systems;Assembly Lines vs. Seru Systems

Selection of Assembly Systems;Assembly Lines vs. Seru System...

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Complex Adaptive Systems conference (CAS) - Cyber Physical Systems and Deep Learning

作者： Aboelfotoh, Aaya Suer, Gursel A. Abdullah, Md Ohio Univ Ind & Syst Engn Athens OH 45701 USA

In the world of manufacturing, a manufacturing strategy must be selected depending on which strategy would produce the higher output rate, in order to carry out a set of product operations. This study focuses on the steady demand domain, where two strategies are feasible;Classical Assembly Line and Seru, a recent Japanese strategy that mostly involves single-worker assembly lines. Mathematical modelling is used to determine the worker-to station and task-to-station assignments of product structures with different parameters to calculate the assembly line output rate. On the other hand, simpler calculations are sufficient to determine the Seru strategy output rate. The aim is to study the relationship between worker skill level variation and strategy selection by building a neural network model using NeuroSolutions. Skill levels were considered to be normally distributed from 1 to 7. Inputs such as number of tasks, product precedence network flexibility, and task processing time variation will also be incorporated to support the learning process of the neural network model. The neural network model will then be applied to predict the more favourable strategy that would produce the higher output rate, between the Classical Assembly and Seru approach. As a result, this will help serve as a guideline regarding when to use Classical Assembly vs. Seru strategy, using only the available information about the product and skill variation and without the need to solve any mathematical model. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： assembly line seru systems mathematical modeling neural networks worker skill level manufacturing

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Spiking neural network with synaptic plasticity for recognition 3

Spiking neural network with synaptic plasticity for recognit...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2018

作者： Li, Jing Liu, I. Bo Gao, Weixin Huang, Xiaoyan School of Electronic Engineering Xi'an Shiyou University Xi'an China Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education International Research Center for Intelligent Perception and Computation Xi'an China

ISBN: (纸本)9781538645086

The spiking neural network referred to the third generation of neural network simulates the mechanisms of neurons and networks in brain. It has the distributed computational mechanism and robust information processing way like the nervous system. This paper describes that a spiking neural network with the synaptic plasticity recognizes the input scenes. The Digital spiking silicon neuron (DSSN), a mathematical structure-based qualitative model, is used to reproduce the various behaviors of neurons. We also designed the synapse model in our spiking neural network to precisely describe the dynamics of the transmitter release and the postsynaptic current generation. There are three layers in our network. The spiking neurons in layer 1 and 2 with special receptive fields perform the edge detection and orientation selection, respectively. The synaptic plasticity is realized in synaptic connections between spiking neurons in layer 2 and the output layer. The changing of connection is based on the Hebbian learning rule which supposes that the time difference of two spikes modifies the value of connection. We evaluated our spiking neural network with the task of image recognition. The spiking neurons in the output layer fire with the high frequency in response to their relevant input scenes. The simulation results show that our spiking neural network can successfully recognize the input scenes learned before. The recognition is robust against various distortions. © 2018 IEEE.

关键词： neural networks

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