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International conference on Electronics Representation and Algorithm, ICERA 2019

International Conference on Electronics Representation and A...

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International conference on Electronics Representation and Algorithm 2019, ICERA 2019

The proceedings contain 63 papers. The topics discussed include: an analysis of FPGA hardware platform based artificial neural network;walking pattern for quadruped as observer robot;loading effect with distributed generation (DG) injection on distribution system on temperature and breakdown voltage of oil transformer in distributed system;carbon monoxide sensor based on non-dispersive infrared principle;identical instruments detection using least mean square based adaptive filter;fire detection use CCTV with image processing based raspberry Pi;improving internal BGP provide fast failover in multihoming environment mobile backhaul;optimization of a customized mixed model assembly using MATLAB/Simulink;and a prototype implementation of visible light communication based electrocardiography data transmission.

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Microwave tomography for estimating moisture content distribution in porous foam using neural networks

Microwave tomography for estimating moisture content distrib...

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European conference on Antennas and Propagation, EuCAP

作者： Rahul Yadav Marko Vauhkonen Guido Link Stefan Betz Timo Lähivaara Department of Applied Physics University of Eastern Finland Kuopio Finland Institute for Pulsed Power and Microwave Technology Karlsruhe Institute of Technology Karlsruhe Germany Votsch Industrietechnik GmbH Reiskirchen Germany

ISBN: (数字)9788831299008

ISBN: (纸本)9781728137124

Selective heating in industrial microwave drying could be more efficiently addressed by intelligent control of distributed microwave sources. As a result, increasing system efficiency and reducing thermal runaway while processing low loss dielectric samples. However, applying such a precise microwave control requires non-invasive in-situ measurement of the unknown distribution of moisture inside the material. In this work, the feasibility of integrating a microwave tomography (MWT) with the drying system is demonstrated. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam. To solve the estimation problem in a fast way, a neural network based approach is proposed in this work. Promising estimation results are shown using synthetic measurement data.

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Rating Estimation from Review Texts Using Long Short-Term Memory

Rating Estimation from Review Texts Using Long Short-Term Me...

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IIAI International conference on Advanced Applied Informatics (IIAIAAI)

作者： Ryo Takada Teruhisa Hochin Hiroki Nomiya Graduate School of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9781728173986

A lot of reviews of products have been posted on various web sites and services because of the spread of the Internet, and the estimation of ratings from review texts is actively performed. However, there are few such studies on Japanese review texts without limiting the product genre. In this paper, we propose a neural network model that takes as input a general Japanese product review text and estimates rating for it without limiting the product genre. By using Long Short-Term Memory (LSTM), which is one of the regression type neural network models that can handle sequential data, we analyze words in sentences considering their order. The rating estimation model is realized mainly by segmentation of texts, conversion to distributed representations, an LSTM layer, and a fully connected layer. In addition, we conduct evaluation experiments of the created model and consider the results.

关键词： Limiting neural networks Bit error rate Estimation Machine learning Data models Natural language processing

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ExNET: Deep neural network for Exercise Pose Detection 2nd

ExNET: Deep Neural Network for Exercise Pose Detection

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2nd International conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Haque, Sadeka Rabby, Akm Shahariar Azad Laboni, Monira Akter Neehal, Nafis Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Pose detection estimate human activity in images or video frames using computer vision technique. Pose detection has many applications, such as body to augmented reality, fitness, animation etc. ExNET represents a way to detect human pose from 2D human exercises image using Convolutional neural network. In recent time Deep Learning based systems are making it possible to detect human exercise poses from images. We refer to the model we have built for this task as ExNET: Deep neural network for Exercise Pose Detection. We have evaluated our proposed model on our own dataset that contains a total of 2000 images. And those images are distributed into 5 classes as well as images are divided into training and test dataset, and obtained improved performance. We have conducted various experiments with our model on the test dataset, and finally got the best accuracy of 82.68%. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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An Event-driven Neuromorphic System with Biologically Plausible Temporal Dynamics 38

An Event-driven Neuromorphic System with Biologically Plausi...

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38th IEEE/ACM International conference on Computer-Aided Design (ICCAD)

作者： Fang, Haowen Shrestha, Amar Zhao, Ziyi Li, Yilan Qiu, Qinru Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)9781728123509

Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there is an increasing demand to process streams of temporal data on embedded devices with limited energy and power resources. Among all potential solutions, neuromorphic computing with spiking neural networks (SNN) that mimic the behavior of brain, have recently been placed at the forefront. Encoding information into sparse and distributed spike events enables low-power implementations, and the complex spatial temporal dynamics of synapses and neurons enable SNNs to detect temporal pattern. However, most existing hardware SNN implementations use simplified neuron and synapse models ignoring synapse dynamic, which is critical for temporal pattern detection and other applications that require temporal dynamics. To adopt a more realistic synapse model in neuromorphic platform its significant computation overhead must be addressed. In this work, we propose an FPGA-based SNN with biologically realistic neuron and synapse for temporal information processing. An encoding scheme to convert continuous real-valued information into sparse spike events is presented. The event-driven implementation of synapse dynamic model and its hardware design that is optimized to exploit the sparsity are also presented. Finally, we train the SNN on various temporal pattern-learning tasks and evaluate its performance and efficiency as compared to rate-based models and artificial neural networks on different embedded platforms. Experiments show that our work can achieve 10X speed up and 196X gains in energy efficiency compared with GPU.

关键词： FPGA Spiking neural network Neuromorphic computing

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Single High-Value Region Detection Based on Image Equal-Segmentation

Single High-Value Region Detection Based on Image Equal-Segm...

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International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Jinlong Chen Lei Huang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Computer Information and Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728184487

ISBN: (纸本)9781728184494

With the development of computer hardware equipment and deep neural network (DNN) technology, computer vision has received more and more attention from AI developers and researchers. In the field of digital image processing, people try to use some calculation methods and techniques to identify objects in images. In the process of finding the rules, this method is called target detection, which is also a research hotspot in the field of computer vision. High-value area detection is based on target detection technology. On the basis of image detection of digital images, some methods of image are used to segment more concerned areas in the target. In this paper, the method of equally segmenting the image takes the image containing the detected object as input and performs detection to achieve the purpose of detecting high-value areas. The steps of the algorithm are divided into high-value area estimation, image equal segmentation, image data attenuation, algorithm acceleration, target fusion and classification.

关键词： Image segmentation Computer vision Digital images neural networks Object detection Classification algorithms Object recognition

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Real-time modeling of audio distortion circuits with deep learning 16

Real-time modeling of audio distortion circuits with deep le...

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16th Sound and Music Computing conference, SMC 2019

作者： Damskägg, Eero-Pekka Juvela, Lauri Välimäki, Vesa Acoustics Lab Department of Signal Processing and Acoustics Aalto University Espoo Finland

ISBN: (纸本)9788409085187

This paper studies deep neural networks for modeling of audio distortion circuits. The selected approach is black-box modeling, which estimates model parameters based on the measured input and output signals of the device. Three common audio distortion pedals having a different circuit configuration and their own distinctive sonic character have been chosen for this study: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. A feedforward deep neural network, which is a variant of the WaveNet architecture, is proposed for modeling these devices. The size of the receptive field of the neural network is selected based on the measured impulse-response length of the circuits. A real-time implementation of the deep neural network is presented, and it is shown that the trained models can be run in real time on a modern desktop computer. Furthermore, it is shown that three minutes of audio is a sufficient amount of data for training the models. The deep neural network studied in this work is useful for real-time virtual analog modeling of nonlinear audio circuits. Copyright: © 2019 Eero-Pekka Damskägg et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Deep neural networks

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Arrhythmia Detection Using Convolutional neural Models 15th

Arrhythmia Detection Using Convolutional Neural Models

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15th International conference on distributed Computing and Artificial Intelligence (DCAI)

作者： Torres Ruiz, Jorge Buldain Perez, Julio David Beltran Blazquez, Jose Ramon Accenture Maria Zambrano 31 Zaragoza 50018 Spain Univ Zaragoza Dept Elect & Commun Engn Zaragoza 50018 Spain

ISBN: (纸本)9783319946498;9783319946481

Mostly all works dealing with ECG signal and Convolutional network approach use 1D CNNs and must train them from scratch, usually applying a signal preprocessing, such as noise reduction, R-peak detection or heartbeat detection. Instead, our approach was focused on demonstrating that effective transfer learning from 2D CNNs can be done using a well-known CNN called AlexNet, that was trained using real images from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. From any temporal signal, it is possible to generate spectral images (spectrograms) than can be analysed by 2D CNN to do the task of extracting automatic features for the classification stage. In this work, the power spectrogram is generated from a randomly ECG segment, so no conditions of signal extraction are applied. After processing the spectrogram with the CNN, its outputs are used as relevant features to be discriminated by a Multi Layer Perceptron (MLP) which classifies them into arrhythmic or normal rhythm segments. The results obtained are in the 90% accuracy range, as good as the state of the art published with 1D CNNs, confirming that transfer learning is a good strategy to develop decision models in signal and image medical tasks.

关键词： neural network Transfer learning Electrocardiogram Arrhythmia detection Convolutional network Spectrogram

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A Disentangling Invertible Interpretation network for Explaining Latent Representations

A Disentangling Invertible Interpretation Network for Explai...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Patrick Esser Robin Rombach Björn Ommer Heidelberg Collaboratory for Image Processing IWR Heidelberg University Germany Heidelberg University Heidelberg Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.

关键词： Semantics Neurons Biological neural networks Task analysis Visualization Computer vision Training

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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