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Radar Waveform Recognition Based on Time-frequency Images Under Deteriorating Channel Conditions

Radar Waveform Recognition Based on Time-frequency Images Un...

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international conference on Computer and Communications (ICCC)

作者： Lai Shen Daying Quan Xiaofeng Wang Xiaoping Jin Ning Jin dept. of college of information Engineering China Jiliang University Hangzhou China

In the process of electronic countermeasures, most studies of radar signal recognition focus on Additive White Gaussian Noise (AGWN) channel scenarios, while pay less attention to the influence of channel deterioration on radar signal recognition. therefore, we propose a low interception rate radar signal recognition method based on time-frequency images under channel impairment. First, we obtain shallow features of LPI radar signals in fading channels by Choi-Williams distribution (CWD) time-frequency transform that converts the signals to time-frequency images. Second, we design a parallel residual network model for feature learning and signal classification to realize the automatic modulation recognition of radar waveforms under channel fading conditions. Simulation results show that the proposed method effectively achieves the recognition of 12 kinds of radar waveforms both in AGWN channels and in multipath channels, which demonstrates the superiority of this model.

关键词： Time-frequency analysis Image recognition Computational modeling Modulation Radar Radar imaging Radar signal processing

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Stochastic Computation Based Hybrid Artificial Neural Networks and Spike Neural Network

Stochastic Computation Based Hybrid Artificial Neural Networ...

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international conference on Computer and Communications (ICCC)

作者： Yakun Zhou Jienan Chen Yizhuo Zhou Ziyang Shao National Key Laboratory of Science and Technology on Communication University of Electronic Science and Technology of China Chengdu Sichuan China

Neuromorphic computing is a promising technology for future machine learning research, which is envisioned to provide lower power consumption, fault tolerance, and massive parallelism. However, the neuromorphic network, i.e., spike neural network (SNN), suffers from a broader range of its application compared with artificial neural networks (ANN). In this work, we propose a stochastic computation SC-based hybrid ANN and SNN scheme, which supports both ANN and SNN computation in SC domain. the ANN computation is converted to SC, which is implemented with simple logic gates. Moreover, we propose a new two-stage stochastic computation that is compatible with the sequence computation style of SNN. Consequently, the logic resource for the hybrid SNN and ANN can be shared. Compared to the state-of-the-art hybrid scheme, the proposed SC-based ANN and SNN exhibit much lower hardware costs and higher hardware efficiency.

关键词： Fault tolerance Power demand Neuromorphic engineering Fault tolerant systems Artificial neural networks Machine learning parallel processing

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Interpretable Sentencing Element-based Prison Term Prediction

Interpretable Sentencing Element-based Prison Term Predictio...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Yifang Zhang Duanyu Feng Wei Tian Hao Wang School of Mathematics Sichuan University Chengdu China School of Law Southwest Petroleum University Chengdu China

We construct a Bayesian Network (BN) model for the task of prison term prediction in the field of Legal Judgment Prediction (LJP). Our BN model takes sentencing elements as the input and constructs further feature representations of these elements by employing expert knowledge and an auto-encoder. the prediction of the prison term is made by using the conditional probability distribution (CPD) table of the pooling of the two representations. Compared with the Natural Language processing (NLP) techniques which are often based on deep neural networks and are prevailing in prison term prediction, our model greatly improves the interpretability since the sentencing elements incorporate the prior knowledge of the specific crime and the probability inference framework of the Bayesian Network conforms with the human decision behavior. In addition, our model has a simple architecture so that it can be easily extended. We conduct a series of experiments on a dataset of the crime of fraud in mainland China and the results demonstrate that our model achieves comparable performance on all evaluation metrics.

关键词： Measurement Uncertainty Neural networks Semantics Predictive models Natural language processing Probability distribution

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Optimized Smart Agricultural Production Systems using Enhanced parallel Green Anaconda Convolutional Neural Network

Optimized Smart Agricultural Production Systems using Enhanc...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S. Gomathi Meena Himanshu Rai Goyal Lavanya E. Muthukumar Mamtaben Kamleshbhai Megha Swapnil Parikh PG Department of Computer Science and Computer Applications PERI College of Arts and Science Mannivakkam Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Viruthunagar Tamil Nadu India Department of MBA Nehru College of Management Thirumalayampalayam Coimbatore Tamil Nadu India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Vallabh Vidyanagar Gujarat India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). this system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. the EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. Computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). these results highlight the efficiency of the approach and its potential for further advancement in smart agriculture.

关键词： Smart agriculture Deep learning Production systems Energy consumption Crops Water conservation Convolutional neural networks Internet of things Data mining Water resources

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Line-Structured Light Based 3D Reconstruction Algorithm for Ice Surface

Line-Structured Light Based 3D Reconstruction Algorithm for ...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Chengzhi Jiang Zhen Ren Yuxiang Li Zhisheng Gao School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Line structured light has a wide range of applications in 3D measurement, such as metal parts, weld seams and other precision shaft parts, and the surface of ice cubes has similar characteristics and can also be measured in 3D using line structured light. However, due to the distinctive optical characteristics of the ice surface, the brightness of the captured laser stripes at certain positions may be either insufficient or excessively intense, posing difficulties in accurately identifying the centerline of the laser stripes; and the high transparency of the ice surface, part of the light will be projected into the interior of the ice resulting in a weakened reflection, and the light projected into the interior of the light will also diffuse and affect the imaging. therefore, how to accurately measure the three-dimensional structure of the surface has become a topic to be studied. To accurately identify the centerline of the laser stripes within the interference image, this paper introduces a laser stripe center extraction algorithm that combines deep learning with the Steger algorithm. By leveraging the robust feature learning capabilities of deep neural networks, the algorithm can effectively capture the overall characteristics of the laser stripes on the ice surface, enabling precise detection and segmentation of the stripes. Subsequently, the Steger algorithm is applied to determine the centerline of the laser stripes with high accuracy. Subsequently, Steger's algorithm is employed to precisely determine the centerline of the laser stripes. this approach effectively mitigates the problem of not being able to accurately extract the center of the laser stripes caused by the high transparency of ice and complex surface reflection characteristics. Subsequent experiments have proved that this method can greatly reduce the extraction time of laser stripe center and significantly improve the accuracy of laser stripe center extraction and 3D reconstruction.

关键词： Surface reconstruction three-dimensional displays Accuracy Laser noise Ice surface Lasers Signal processing algorithms Measurement by laser beam Surface emitting lasers Convolutional neural networks

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Recognition of micro-objects with adaptive models of image processing in a parallel computing environment 14

Recognition of micro-objects with adaptive models of image p...

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14th international Scientific and Technical conference on applied Mechanics and Systems Dynamics, AMSD 2020

作者： Jumanov, I.I. Djumanov, O.I. Safarov, R.A. Samarkand State University 15 University Boulevard Samarkand Uzbekistan

Scientific and methodological foundations for optimizing the processing of micro-objects, in particular, pollen grains, have been developed on the basis of models and methods of preliminary information processing with mechanisms for filtering, identifying, and using textural, specific characteristics, and geometric features of images. the efficiency of image filtering mechanisms is investigated based on the use of statistical control rules, adaptive two-threshold control, trend functions, and control of the contour description error, traditional detectors of random noise, and interference. the mechanisms of image identification using biquadratic, orthogonal algebraic polynomials, 4th order splines are proposed. A technique for optimizing image identification by finding a function by its integrals in a family of straight lines based on the Mellin and Fourier transforms has been developed. Combined models with mechanisms for smoothing reference points, segmentation of contours, filtering with detection of changes in dynamics, optimization by the conjugate gradient method, time relaxation have been built. A software complex for the recognition and classification of images of pollen grains in C ++ has been developed, which includes testing identification algorithms based on the Daubechies spline function, orthogonal algebraic polynomials of 4, 8 orders in the parallel computing environment "CUDA". © Published under licence by IOP Publishing Ltd.

关键词： Fourier transforms

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Mono and stereo performance of the two SST-1M telescope prototypes 38

Mono and stereo performance of the two SST-1M telescope prot...

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38th international Cosmic Ray conference, ICRC 2023

作者： Juryšek, J. Tavernier, T. Novotný, V. Heller, M. Mandat, D. Pech, M. Alispach, C. Araudo, A. Beshley, V. Blazek, J. Borkowski, J. Boula, S. Bulik, T. Cadoux, F. Casanova, S. Christov, A. Chytka, L. della Volpe, D. Favre, Y. Gibaud, L. Gieras, T. Hamal, P. Hrabovsky, M. Jelínek, M. Karas, V. Lyard, E. Mach, E. Marek, W. Michal, S. Michalowski, J. Moderski, R. Montaruli, T. Muraczewski, A. Muthyala, S.R. Nagai, A. Nalewajski, K. Neise, D. Niemiec, J. Nikolajuk, M. Ostrowski, M. Palatka, M. Prouza, M. Rajda, P. Schovanek, P. Seweryn, K. Sliusar, V. Stawarz, L. Świerblewski, J. Świerk, P. Štrobl, J. Vícha, J. Walter, R. Zagdański, A. Ziȩtara, K. FZU - Institute of Physics the Czech Academy of Sciences Na Slovance 1999/2 Prague 8 Czech Republic Charles University Faculty of Mathematics and Physics Institute of Particle and Nuclear Physics Prague Czech Republic DPNC Université de Genève 24 Quai Ernest Ansermet GenèveCH-1211 Switzerland Astronomical Institute of the Czech Academy of Sciences Boční II 1401 PragueCZ-14100 Czech Republic ELI Beamlines Institute of Physics Czech Academy of Sciences Dolní BřežanyCZ-25241 Czech Republic Pidstryhach Institute for Applied Problems of Mechanics and Mathematics National Academy of Sciences of Ukraine 3-b Naukova St. Lviv79060 Ukraine Nicolaus Copernicus Astronomical Center Polish Academy of Sciences ul. Bartycka 18 Warsaw00-716 Poland Institute of Nuclear Physics Polish Academy of Sciences KrakowPL-31342 Poland Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warsaw00-478 Poland Palacky University Olomouc Faculty of Science 17. listopadu 50 Olomouc Czech Republic Faculty of Physics University of Bialystok ul. K. Ciolkowskiego 1L Bialystok15-245 Poland Astronomical Institute of the Czech Academy of Sciences Fričova 298 OndřejovCZ-25165 Czech Republic Département d’Astronomie Université de Genève Chemin d’Ecogia 16 VersoixCH-1290 Switzerland ETH Zurich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 Zurich8093 Switzerland Astronomical Observatory Jagiellonian University ul. Orla 171 Krakow30-244 Poland AGH University of Science and Technology al.Mickiewicza 30 Krakow30-059 Poland Centrum Badań Kosmicznych Polskiej Akademii Nauk 18a Bartycka str. Warsaw00-716 Poland

the Single-Mirror Small-Sized Telescope, or SST-1M, was originally developed as a prototype of a small-sized telescope for CTA, designed to form an array for observations of gamma-ray-induced atmospheric showers for energies above 3 TeV. A pair of SST-1M telescopes is currently being commissioned at the Ondřejov Observatory in the Czech Republic, and the telescope capabilities for mono and stereo observations are being tested in better astronomical conditions. the final location for the telescopes will be decided based on these tests. In this contribution, we present a data analysis pipeline called sst1mpipe, and the performance of the telescopes when working independently and in a stereo regime. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Stereo image processing

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parallel Construction of Information Technology Value Model: Design-Science Research Methodology 8

Parallel Construction of Information Technology Value Model:...

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8th international conference on Information and Communication Technology, ICoICT 2020

作者： Abdurrahman, Lukman Mulyana, Tatang Information Systems Study Program Telkom University School of Industrial Engineering Bandung Indonesia Industrial Engineering Study Program Telkom University School of Industrial Engineering Bandung Indonesia

ISBN: (纸本)9781728161426

To date, the study model of IT value usually takes the form of a serial construction relating its components. this paper tries to offer a parallel construction in modeling the IT value study. the applied methodology is the design-science research approach to create the construction relating its components. To perfect the construct, the paper applies the partial adjustment valuation that functions as an estimating technique to approximate and evaluate the mathematical magnitude of the performing returns of a component compared to the real figures. the developed pattern undertakes a case study with Mandiri's data, an Indonesian state-owned banking industry, shows that the estimated revenue of the parallel configuration has a deviation of only 0.28% when compared with the bank's real revenue during the period 2004-2014. thus, the parallel configuration may appear as the alternative configuration in the study of IT value as a result of its authenticity academically as well as pragmatically. © 2020 IEEE.

关键词： Functions

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MISA-MD: A New Design of Molecular Dynamics Software for GPU Architecture *

MISA-MD: A New Design of Molecular Dynamics Software for GPU...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Genshen Chu Tianyi Chen Dandan Chen He Bai Guannan Li Shuai Ren Yang Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Research Center Zhejiang Lab Beijing China

Molecular dynamics is a significant method in computational material and many other fields, and is computationally intensive. In this paper, we present a new molecular dynamics software, MISA-MD, which is mainly designed for high computational performance on GPU architecture. the main contributions include a redesign of the atom data structure, communication optimizations, parallel strategies and a series of GPU optimizations on memory accessing. Compared to the CPU version, the presented version of MISA-MD can achieve more than 130 speedup when running on GPU hardware. Moreover, our MISAMD code also demonstrates 51 % performance improvement, when compared to the well-known software LAMMPS on GPU.

关键词： Codes Memory management Layout Graphics processing units Organizations Software Hardware

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Massively parallel processing Database for Sequence and Graph Data Structures applied to Rapid-Response Drug Repurposing 8

Massively Parallel Processing Database for Sequence and Grap...

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8th IEEE international conference on Big Data (Big Data)

作者： Rickett, Christopher D. Maschhoff, Kristyn J. Sukumar, Sreenivas R. Hewlett Packard Enterprise Spring TX 77389 USA

ISBN: (纸本)9781728162515

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. the novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). Based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.

关键词： graph database graph analytics in-database analytics distributed processing parallel processing sequence analytics

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