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International conference on Internet of Things, Embedded systems and Communications (IINTEC)

作者： Sondes Gharsellaoui Majdi Mansouri Shady S. Refaat Haitham Abu-Rub Hassani Messaoud National Higher Engineering School of Tunis Monfleury Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems.

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A Gaussian Recursive Filter Parallel Implementation with Overlapping

A Gaussian Recursive Filter Parallel Implementation with Ove...

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International IEEE conference on Signal-image Technologies and Internet-Based System

作者： Pasquale De Luca Ardelio Galletti Livia Marcellino Department of Computer Science University of Salerno Italy Department of Science and Technology University of Naples Parthenope Naples Italy

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Gaussian convolutions computation is required in several scientific fields and, to this aim, efficient approximation methods, based on Recursive Filters (RFs), have been developed recently. Among them, Gaussian Recursive Filters (RFs) are designed to approximate the Gaussian convolution in a very efficient way. The accuracy of these methods, as is well known, can be improved by means of the use of the so-called K-iterated Gaussian recursive filters, that is in the repeated application of the basic RF. To improve the provided accuracy, K-iterated versions of these methods are also considered. Since it is often necessary to handle large size one-dimensional input signals, a parallel approach becomes mandatory. Recently, we proposed a parallel algorithm for the implementation of the K-iterated first-order Gaussian RF on multicore architectures. Here, using a similar parallelization strategy, based on a domain decomposition with overlapping, we propose a new implementation that would exploit, in terms of both accuracy and performance, the GPU (Graphics processing Unit) capabilities on CUDA environment. Tests and experiments confirm the reliability and the efficiency of the proposed implementation.

关键词： Radio frequency Convolution Graphics processing units Parallel algorithms Tools Boundary conditions Instruction sets

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1st International conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021)

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Journal of Physics: conference Series 2021年第1期1950卷

PREFACE It was a matter of pleasure to organize 1st International conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021) at Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram on 26th and 27th February 2021. The conference was organized in Association of Engineering & Technocrats, Faculty of Engineering and Technology, SGT University. The objective of this conference was to provide a common platform to scientists and researchers from all over India for exchanging their knowledge and views to deal with global challenges. The theme of this conference was not only the fundamental technology in the field of engineering science but also can change our society by exploring new horizons and continuous progress. A diverse range of topics from the neural network, digital image processing, machine learning, programmable logic controller, health care, IoT based systems, deep learning, lean manufacturing, abrasive flow machining, welding technology and simulation-based studies are featured in the conference. In addition to that, you will also hear about the incremental forming, automation integration techniques, wireless sensor techniques, non-invasive methods of diagnosis, robot assembly, data mining and various evolutionary algorithms in different applications. I hope, therefore, that you will get a chance to explore recent emerging technologies. We are delighted to deliver special thanks to IOP Publishing as our publication partner to publish the conference proceedings in JPCS. We sincerely thanks to our sponsors Council of Scientific and Industrial Research (CSIR) Human Resource Development Group (HRDG) and 'Sanranchana', SGT University Center of Research Innovation. We believe that the conference had provided the opportunity to the students and the young researchers for enriching their knowledge through interaction with eminent researchers from various NITs, IITs, state universities and reputed organizations. We hope that everybody had a great ex

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On the use of Artificial Intelligence techniques in Intelligent Transportation systems

On the use of Artificial Intelligence techniques in Intellig...

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IEEE Wireless Communications and Networking conference (WCNC)

作者： Machin, Mirialys Sanguesa, Julio A. Garrido, Piedad Martinez, Francisco J. Univ Zaragoza iNiT Res Grp Zaragoza Spain

ISBN: (纸本)9781538611548

Due to the progressive increase in the population and the complexity of their mobility needs, the evolution of transportation systems to solve advanced mobility problems has been necessary. Additionally, there are many situations where the application of traditional solutions is not entirely effective, e.g., when the processing of large amounts of data collected from in-vehicle sensors and network devices is required. To overcome these issues, several Artificial Intelligence-based techniques have been applied to different areas related to the transportation environment. In this paper, we present a study of the diverse Artificial Intelligence (AI) techniques which have been implemented to improve Intelligent Transportation systems (ITS). In particular, we grouped them into three main areas depending on the main field where they were applied: (i) Vehicle control, (ii) Traffic control and prediction, as well as (iii) Road safety and accident prediction. The results of this study reveal that the combination of different AI techniques seems to be very promising, especially to manage and analyze the massive amount of data generated in transportation.

关键词： Intelligent Transportation systems Artificial Intelligence Artificial Neural Networks Genetic algorithms Fuzzy Logic Expert systems

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GAP-8: A RISC-V SoC for AI at the Edge of the IoT 29

GAP-8: A RISC-V SoC for AI at the Edge of the IoT

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29th Annual IEEE International conference on Application-Specific systems, Architectures and Processors (ASAP)

作者： Flamand, Eric Rossi, Davide Conti, Francesco Loi, Igor Pullini, Antonio Rotenberg, Florent benini, Luca GreenWaves Technol Villard Bonnot France Univ Bologna DEI Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781538674796

Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to process data from richer sensors such as image, video, audio, or multi-axial motion/vibration has huge application potential. However, edge processing of data-rich sensors poses the extreme challenge of squeezing the computational requirements of advanced, machine-learning-based near-sensor data analysis algorithms (such as Convolutional Neural Networks) within the mW-range power envelope of always-ON battery-powered IoT end-nodes. To address this challenge, we propose GAP-8: a multi-GOPS fully programmable RISC-V IoT-edge computing engine, featuring a 8-core cluster with CNN accelerator, coupled with an ultra-low power MCU with 30 mu W state-retentive sleep power. GAP-8 delivers up to 10 GMAC/s for CNN inference (90 MHz, 1.0V) at the energy efficiency of 600 GMAC/s/W within a worst-case power envelope of 75 mW.

关键词： Edge processing Internet of Things Convolutional Neural Networks Near Sensor processing Parallel Architectures

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VLSI Architecture for Fine Grained Pipelined Feature Extraction using Histogram of Oriented Gradient

VLSI Architecture for Fine Grained Pipelined Feature Extract...

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IEEE conference on systems, Process & Control (ICSPC)

作者： Arif Sasongko Bima Sahbani School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9781728118260

ISBN: (纸本)9781728118277

The most popular field to support the massive growth of the intelligent based system in the recent year is object detection. The fundamental of the object detection technology is feature extraction. It becomes the main challenge due to the variability of application and the computation requirement. One of the challenging issue is to implement this computing system into hardware level architecture to get lower power, high speed, and lower cost computing system. According to some study, Histogram of Oriented Gradient (HOG) is the current most robust feature extraction. This paper proposes a novel hardware architecture for HOG based feature extraction. The architecture brings performance improvement and it opens various benefit in the utilization of VLSI system instead of GPU. This architecture implements tasks for HOG feature extraction including gradient extractor using edge filter algorithms, polar data representation using vectoring mode CORDIC, and histogram normalization using Barni's approximation method. The simulation shows very low (fraction of frame rate) output latency for various image size, therefore the system could support real-time processing, for those imaging rate. The processor successfully implemented for Stratix IV FPGA EP4SGX230 with logic utilization about 10%. With 100 MHz clock rate, it potentially performs up to 1.5 times faster than the GPU fast HOG implementation.

关键词： Histograms Feature extraction Computer architecture Object detection Graphics processing units image edge detection Process control

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Semantic processing of Radar Spectral Information for Air Object Recognition

Semantic Processing of Radar Spectral Information for Air Ob...

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International Scientific-Practical conference Problems of Infocommunications Science and Technology

作者： Svitlana Solonska Volodymyr Zhyrnov Oleksiy Holovin Kharkiv National University of Radio Electronics Kharkiv Ukraine Central Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine Kiev Ukraine

ISBN: (数字)9781728141848

ISBN: (纸本)9781728141855

The results of the study of semantic processing of spectral information in the intelligent radar systems are presented. A method for formalizing the processes of perception and transformation of spectral images based on algebra of finite predicates is developed. A model for semantic processing of spectral image for surveillance radars has been developed and implemented. To analyze the features of clutter spectra and air object signals authors investigate the decision making algorithms of a human-operator. Spectral pattern is described as a predicate on the set of spectral channels that exceed the certain threshold value. The predicates-signs are introduced to indentify spectral types. Instantaneous spectrum is corresponded to one type of spectrum by a combination of these predicates. A functional diagram for automatic identification of spectral types, based on these equations, is built. The algorithm for analyzing of the clutter spectrum and recognizing of the air objects, based on algebra of finite predicates, which is proposed in the paper, allows to define all signs and automatically recognize air objects in real time.

关键词： Radar imaging Semantics Reflection Algebra Atmospheric modeling Mathematical model

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Computational Vision and Medical image processing: Vipimage 2011

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2018年

作者： Joáo Manuel Rs S Tavares Renato M Natal Jorge

ISBN: (纸本)9781138112544

This book contains invited lecturers and full papers presented at VIPimage 2011 - iii ECCOMAS Thematic conference on Computational Vision and Medical image processing (Olho, Algarve, Portugal, 12-14 October 2011). International contributions from 16 countries provide a comprehensive coverage of the current state-of-the-art in:image processing and Analysis;Tracking and Analyze Objects in images;Segmentation of Objects in images;3D Vision;Signal processing;Data Interpolation, Registration, Acquisition and Compression;Objects Simulation;Medical Imaging;Virtual Reality;Software Development for image processing and Analysis;Computer Aided Diagnosis, Surgery, Therapy and Treatment;Computational Bioimaging and Visualization;Telemedicine systems and their Applications. Related techniques also covered in this book include the level set method, finite element method, modal analyses, stochastic methods, principal and independent components analyses and distribution models. Computational Vision and Medical image processing - VIPimage 2011 will be useful to academics, researchers and professionals in Computational Vision (image processing and Analysis), Computer Sciences, Computational Mechanics and Medicine.

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Myocardial Scar Segmentation from Magnetic Resonance images Using Convolutional Neural Network

Myocardial Scar Segmentation from Magnetic Resonance Images ...

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conference on Medical Imaging - Computer-Aided Diagnosis

作者： Zabihollahy, Fatemeh White, James A. Ukwatta, Eranga Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Univ Calgary Libin Cardiovasc Inst Alberta Calgary AB Canada

ISBN: (数字)9781510616400

ISBN: (纸本)9781510616400

Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper, we propose a semi-automated method for segmentation of myocardial scar from late gadolinium enhancement magnetic resonance image (LGE-MRI) using a convolutional neural network (CNN). In contrast to image intensity-based methods, CNN based algorithms have the potential to improve the accuracy of scar segmentation through the creation of high-level features from a combination of convolutional, detection and pooling layers. Our developed algorithm was trained using 2,336,703 image patches extracted from 420 slices of five 3D LGE-MR datasets, then validated on 2,204,178 patches from a testing dataset of seven 3D LGE-MR images including 624 slices, all obtained from patients with chronic myocardial infarction. For evaluation of the algorithm, we compared the algorithm-generated segmentations to manual delineations by experts. Our CNN-based method reported an average Dice similarity coefficient (DSC), precision, and recall of 94.50 +/- 3.62%, 96.08 +/- 3.10%, and 93.96 +/- 3.75% as the accuracy of segmentation, respectively. As compared to several intensity threshold-based methods for scar segmentation, the results of our developed method have a greater agreement with manual expert segmentation.

关键词： image segmentation 3D image processing Convolutional neural networks image processing algorithms and systems Algorithm development Magnetic resonance imaging

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algorithms of landmark robot navigation basing on monocular image processing 8

Algorithms of landmark robot navigation basing on monocular ...

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8th International conference Advanced Computer Information Technologies, ACIT 2018

作者： Koval, Vasyl Department of Information Computing Systems and Control Ternopil National Economic University 8 Chekhova str. Ternopil Ukraine

ISBN: (纸本)9789666544899

The application of mobile robots is very important in environments that are dangerous or inappropriate for human life. One of the problems arising for the mobile robot when targeting point within the indoor application during navigation is the provision of its localization. In this paper, the developments of the algorithms that provide and enable mobile robot to position itself within the indoor environment by using one video camera and a landmark template is presented. Copyright © 2018 for the individual papers by the papers'authors.

关键词： Mobile robots

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