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Emerging Trends in Information Technology and Engineering (ic-ETITE), conference on

作者： Meherdeep Thakur Saira Banu J. Vellore Institute of Technology Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Object detection is not only shaping how Computers see and analyze things but it is also helping in the behavior of how an object reacts to the change in its environment. The main application of these object detection sensors or software is to find the location of an object in space or to track its movement. Object detection has infinitely many use cases and in this paper, we are introducing an application that will allow safety of users struck in a disaster and who need to be evacuated. In such cases the main thing to focus and to eradicate is camera noise, saturation and image compression. Our solution is to establish a connection between the person struck in a disaster with fire safety people. This works over a convolutional network that allows us to detect vulnerable things present inside a room that needs to be rescued and can also give an insight of any explosive inside the room. Our model uses Faster-RCNN and COCO which is a pretrained dataset. This allows real time object detection and classification on our network. Using this we were able to detect an object or a person and get him to rescue by providing them a shortest way out of that place. With this we were able to get an accuracy of more than 75% in our object detection model.

关键词： Object Detection RCNN Convolutional Neural Networks distributed Computing parallel processing Object detection distributed computing parallel processing (COMPUTERS) Detective cameras Disasters Persons

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Accelerated Training for Convolutional Neural Networks

Accelerated Training for Convolutional Neural Networks

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International conference on Information Science and Communications Technologies (ICISCT)

作者： Muhammadjon Musaev Mekhriddin Rakhimov Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9781728199696

ISBN: (纸本)9781728199702

Handwritten character recognition (HCR) is one of the ongoing research field in Artificial Intelligence. The recognition of handwritten characters is based on pattern recognition and image processing. One of the most common machine learning methods for solving this problem is Convolutional Neural Networks (CNN). To implement such tasks, a multilayer neural network is often used. CNN require as much data as possible to ensure high accuracy, while parallel processing can help us save time when neural network training. Such training time can be reduced by improving training operations using parallel computing technology and multi-core platforms. In this paper, exemplary parallelization of CNN training by dint of OpenMP technology and its libraries has been implemented. English alphabets were chosen as an experiment for the handwritten character recognition system, and CNN was used for its better accuracy. Each character data set contains 26 alphabets. That is, the image size of each character ranges from 16×16 to 256×256 pixels, and these pixels are taken as features for training the CNN. We analyzed how the speedup of CNN training depends on image size. Three multi-core processors with different specifications were chosen for the hardware implementation. The results show that the proposed parallel approach using OpenMP technology gives good acceleration, which reduces training time.

关键词： Training Handwriting recognition Neural networks Graphics processing units parallel processing Acceleration Character recognition

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Real Time Key Frame Extraction Through parallel Computation of Entropy Difference 18th

Real Time Key Frame Extraction Through Parallel Computation ...

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18th International conference on Computer Information Systems and Industrial Management

作者： Gautam, Nandita Das, Debdoot Khatua, Sunirmal Saha, Banani Univ Calcutta Comp Sci Dept Kolkata 700098 India

ISBN: (纸本)9783030289560;9783030289577

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition etc. Capturing real time video stream for multiple cameras within a region of interest has become a common phenomenon for an Intelligent Situation Awareness System. Video processing is an important application which is rapidly developing nowadays as an area of extensive research. Content retrieval as well as information collection from a video requires both syntactic and semantic analysis. For a large video data, some set of frames are used to represent the video content. These are identified as key frames. Several algorithms have been defined to extract key frames from a stored video file. The existing algorithms that have been defined for key frame extraction are based on sequential mode. This paper looks into the extraction of key frames for any real time video stream. The experimental results show that there is an effective reduction in the execution time to a huge extent in the case of distributed processing as compared to the sequential processing of frames. In this paper, we propose a distributed framework to regulate the speed of key frame generation for heterogeneous speed of incoming video.

关键词： Real time streaming Key frames parallel computing Entropy

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A review of Machine Learning based Recommendation approaches for cricket

A review of Machine Learning based Recommendation approaches...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Vidisha Vandana Bhatia Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Cricket is one of the most watched sports in the world. Analyzing the factors, activities that affect the aftermath of the matches is of interest to many cricket lovers. Machine learning (ML) has demonstrated favorable outcomes in various fields like medical diagnosis, image processing, prediction, classification, regression etc. and is evidenced to be accurate. The ingenious frameworks built on ML have scope to learn from past experiences. The cricket pitch is the prime segment along with home game advantage, coin toss, innings, day/night match, physical fitness etc. A compendious examination of the cricket pitch, if done with imminent objectives, will be useful to precisely speculate outcomes match. This paper focuses on the existing research for analyzing the cricket pitch, predicting the performance of players and outcomes of the matches. A comparison among machine learning techniques has also been done and Naïve Bayes gave the best results due to its capability of producing accurate results with small samples while in other researches random forest turned out to be the precise classifier as it gave more accuracy than any other approach used.

关键词： Games Sports Soil Videos image segmentation Tools Random forests

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A Concept of Smart Medical Autonomous distributed System for Diagnostics Based on Machine Learning Technology 19th

A Concept of Smart Medical Autonomous Distributed System for...

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19th International conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems (NEW2AN) / 12th conference on the Internet of Things and Smart Spaces ruSMART (Are You Smart)

作者： Velichko, Elena Nepomnyashchaya, Elina Baranov, Maxim Galeeva, Marina A. Pavlov, Vitalii A. Zavjalov, Sergey V. Savchenko, Ekaterina Pervunina, Tatiana M. Govorov, Igor Komlichenko, Eduard Peter Great St Petersburg Polytech Univ St Petersburg Russia Almazov Natl Med Res Ctr St Petersburg Russia

ISBN: (纸本)9783030308599;9783030308582

Telemedicine is a promising direction in the development of medical technologies for the interaction of patients with doctors at a distance. In this paper, we consider the use of telemedicine technologies for the development of smart medical autonomous technology. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods and machine learning algorithms. Some examples of medical system processing results are presented.

关键词： Telemedicine Medical diagnostics image processing

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PowerSGD: Practical Low-Rank Gradient Compression for distributed Optimization 33

PowerSGD: Practical Low-Rank Gradient Compression for Distri...

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33rd conference on Neural Information processing Systems (NeurIPS)

作者： Vogels, Thijs Karinireddy, Sai Praneeth Jaggi, Martin Ecole Polytech Fed Lausanne Lausanne Switzerland

We study lossy gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well, or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD using highly optimized off-the-shelf tools for distributed communication. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets. Our code is available at https://***/epfml/powersgd.

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ASPEN: An Efficient Algorithm for Data Redistribution Between Producer and Consumer Grids 24th

ASPEN: An Efficient Algorithm for Data Redistribution Betwee...

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International European conference on parallel and distributed Computing (Euro-Par)

作者： Foyer, Clement Tate, Adrian McIntosh-Smith, Simon Cray EMEA Res Lab Bristol Avon England Univ Bristol High Performance Comp Res Grp Dept Comp Sci Bristol Avon England

ISBN: (纸本)9783030105495;9783030105488

HPC applications and libraries have frequently moved parallel data from one distribution scheme to another, for reasons of performance. In modern times, a resurgence of interest in this data redistribution problem has emerged due to the need to relocate data distributed across one Producer grid onto a different distribution scheme across a Consumer grid. In this paper, we study the efficient algorithms to perform redistribution, and show how the best methods from the literature are still dependent on the number of processors in both grids. We describe a new algorithm ASPEN that exploits more cyclic patterns and relations in the distribution, is not dependent on the total number of processors and is thus well suited for use in a workflow management systems. We describe a preliminary implementation of the algorithm within such a workflow system and show performance results that indicate a significant performance benefit in data redistribution generation.

关键词： Data distribution Redistribution Data placement Data locality Memory layout Communication pattern parallel programming distributed memory

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Color image watermark decoder by modeling quaternion polar harmonic transform with BKF distribution

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SIGNAL processing-image COMMUNICATION 2020年 88卷

作者： Liu, Yunan Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn PCA LabMinist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Peoples R China

It still remains a challenging problem to improve both of the imperceptibility and robustness for the digital image watermarking task. In this study, a color image watermarking scheme is proposed based on quaternion polar harmonic transform (QPHT) with maximum likelihood decoder, which better balances imperceptibility and robustness in a novel fashion. Thanks to the strong robustness of QPHT, the digital watermark is embedded into the QPHT magnitudes using a multiplicative approach. M the receiver, the watermark decoder can be formulated as a problem of weak signal detection, as the QPHT magnitudes are highly non-Gaussian. In view of this, selection of proper models is of great importance to construct a suitable watermark decoder. Further, we validate that the probability density function of Bessel K form suitably models the heavy tail behavior of QPHT magnitudes, and thus the maximum likelihood decoder is developed by modeling QPHT magnitudes with Bessel K form distribution. Experimental results on standard benchmarks demonstrate that the proposed approach provides better imperceptibility and robustness against various kinds of attacks compared to previous state-of-the-art methods.

关键词： Color image watermarking Quaternion polar harmonic transforms Bessel K form distribution Maximum likelihood decoder

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CONVERGENCE BOUNDS FOR COMPRESSED GRADIENT methods WITH MEMORY BASED ERROR COMPENSATION 44

CONVERGENCE BOUNDS FOR COMPRESSED GRADIENT METHODS WITH MEMO...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Khirirat, Sarit Magnusson, Sindri Johansson, Mikael Royal Inst Technol KTH Sch Elect Engn & Comp Sci Div Decis & Control Syst Stockholm Sweden Harvard Univ Sch Engn & Appl Sci 33 Oxford St Cambridge MA 02138 USA

ISBN: (纸本)9781479981311

The veritable scale of modern data necessitates information compression in parallel/distributed big-data optimization. Compression schemes using memory-based error compensation have displayed superior performance in practice, however, to date there are no theoretical explanations for these observed advantages. This paper provides the first theoretical support for why such compression schemes yields higher accuracy solutions in optimization. Our results cover both gradient and incremental gradient algorithms for quadratic optimization. Unlike previous works, our theoretical results explicitly quantify the accuracy gains from error compensation, especially for ill-conditioned problems. Finally, the numerical results on linear least-squares problems validate the benefit of error compensation and demonstrate tightness of our convergence guarantees.

关键词： Quadratic optimization quantization gradient descent incremental gradient methods

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Transmission Tomography Extension to the Open-source OMEGA Software

Transmission Tomography Extension to the Open-source OMEGA S...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Ville-Veikko Wettenhovi Marko Vauhkonen Ville Kolehmainen Department of Applied Physics University of Eastern Finland

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

This paper describes the transmission tomography extension to the Open-source MATLAB Emission Tomography Software (OMEGA), now named as Open-source MATLAB Emission and Transmission Tomography Software. The OMEGA toolkit was originally designed for the easy image reconstruction of positron emission tomography (PET) data. Now we extend many of the features available for PET imaging to also for transmission tomography, with examples provided for cone beam computed tomography (CBCT). OMEGA supports both MAT-LAB and the open-source alternative GNU Octave. All built-in algorithms for PET are available for the transmission tomography case. These include 10 (Poisson) maximum likelihood algorithms, 8 maximum a posteriori algorithms and 11 priors. Furthermore, custom algorithms using Gaussian-based reconstruction can be implemented with a MATLAB/Octave class object that can be used to compute either the forward projection or backward projection. OMEGA reconstructions, whether using the built-in functions or the class object, can be performed by using parallel computing utilizing either OpenCL for GPUs or OpenMP for CPUs. Both methods allow for completely matrix-free computations, enabling the use of high resolution scanners with the optimized use of memory. OpenCL and OpenMP also enable the use of any computer hardware in the reconstruction. Additionally, as with PET data, built-in support exists for GATE simulated CBCT data allowing easy reconstructions of Monte Carlo simulated data. This extension to transmission tomography extends the usability of OMEGA software to the efficient reconstruction of transmission tomography data.

关键词： Monte Carlo methods Tomography parallel processing Logic gates Software Hardware Positron emission tomography

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