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arXiv 2020年

作者： Queiroz, Saulo Vilela, Joao P. Monteiro, Edmundo PR Ponta Grossa Brazil CISUC CRACS INESCTEC Dep. of Computer Science Faculty of Sciences University of Porto rua do Campo Alegre s/n Porto4169-007 Portugal CISUC Department of Informatics Engineering University of Coimbra Portugal

In this paper, we study the impact of computational complexity on the throughput limits of the fast Fourier transform (FFT) algorithm for orthogonal frequency division multiplexing (OFDM) waveforms. Based on the spectro-computational complexity (SC) analysis, we verify that the complexity of an N-point FFT grows faster than the number of bits in the OFDM symbol. Thus, we show that FFT nullifies the OFDM throughput on N unless the N-point discrete Fourier transform (DFT) problem verifies as Ω(N), which remains a "fascinating" open question in theoretical computer science. Also, because FFT demands N to be a power of two 2i (i > 0), the spectrum widening leads to an exponential complexity on i, i.e. O(2ii). To overcome these limitations, we consider the alternative frequency-time transform formulation of vector OFDM (V-OFDM), in which an N-point FFT is replaced by N/L (L>0) smaller L-point FFTs to mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT by the straightforward DFT algorithm to release the V-OFDM parameters from growing as powers of two and to benefit from flexible numerology (e.g., L = 3, N = 156). Besides, by setting L to Θ(1), the resulting solution can run linearly on N (rather than exponentially on i) while sustaining a non null throughput as N grows. © 2020, CC BY.

关键词： Orthogonal frequency division multiplexing

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Performance Analysis of a 60-GHz Radar for Indoor Positioning and Tracking

Performance Analysis of a 60-GHz Radar for Indoor Positionin...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： A. Antonucci M. Corrà A. Ferrari D. Fontanelli E. Fusari D. Macii L. Palopoli Dep. of Information Engineering and Computer Science University of Trento Italy Tretec S.r.L. Trento Italy VNG Ingegneria Trento Italy Dep. of Industrial Engineering University of Trento Italy

Among the manifold wireless technologies recently adopted for indoor localization and tracking, radars based on phased-array transceivers at 60 GHz are gaining momentum. The main advantages of this technology are: high accuracy, good ability to track multiple target with a low computation burden and preservation of privacy. Despite the growing commercial success of low-cost radar platforms, accurate studies to evaluate their tracking performance are not frequent in the literature, the main reasons being the commercial policies that prevent a direct access to the processed data and the difficult calibration of indoor positioning systems under dynamic conditions. This paper aims to fill this gap providing an extensive and scientifically sound performance analysis of one of these sensors (i.e., the System-onChip (SOC) TI IWR6843) and exposing benefits and limitations of 60-GHz mm-wave sensors for people localization and tracking. Multiple experimental results show that the average standard positioning uncertainty is about 30 cm under dynamic conditions. Our study also reveals the critical impact of three parameters, which are not properly documented by the manufacturer. Localization accuracy and robustness are also significantly affected by the risk of delayed and spurious detections. In the paper, a possible mitigation strategy of these anomalies is presented.

关键词： Radar tracking Target tracking Antenna arrays Signal processing algorithms Sensors Radar antennas

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An Analysis of Haskell Parallel Programming Model in the HaLVM

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Junseok Cheon Yeoneo Kim Taekwang Hur Sugwoo Byun Gyun Woo Dep. of Electrical and Computer Engineering Pusan National University Dep. of Computer Science Kyungsung University Smart Control Center of LG Electronics

Recently, cloud computers are widely used due to the development of computing environment and network speed. Unikernel is considered to be an attractive operating system in the manycore environment that effectively provides cloud computers. HalVM is a unikernel coded in Haskell. In this paper, we analyze the performance of Haskell running in a unikernel, which has shown good parallel programming performance in a manycore environment. In this paper, we analyze the performance of Haskell running in a unikernel, while Haskell has shown good performance in a manycore environment. To do this, we select HaLVM as the target operating system and analyze the performance by testing various Haskell parallel programming models. Test result shows that HaLVM is scalable only on IVC. We also implement a library to make programming with IVC more convenient, and use this library to compare extensibility experiments. Our test shows HaLVM's scalability is 112.42% higher than Linux environment.

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Monitoring system of agricultural performance based on multisensor data and cross-validation technique in merging temporal images

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Agronomy Journal 2022年第1期115卷

作者： N. P. G. Bhavani Prameeladevi Chillakuru Ramana Lakshmi Adusumilli Roobaea Alroobaea Saeed Rubaiee Abdulkader S. Hanbazazah Suman Mann Saveetha School of Engineering Chennai Tamil Nadu 602105 India Contribution: Data curation Formal analysis Rajalakshmi Engineering College Chennai Tamil Nadu 602105 India Contribution: Investigation Methodology Dep. of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh 520007 India Contribution: Resources Software Dep. of Computer Science College of Computers and Information Technology Taif Univ. P. O. Box 11099 Taif 21944 Saudi Arabia Contribution: Funding acquisition Supervision Dep. of Industrial and Systems Engineering College of Engineering Univ. of Jeddah Jeddah Saudi Arabia Contribution: Funding acquisition Visualization Contribution: Funding acquisition Writing - original draft Writing - review & editing Maharaja Surajmal Institute of Technology New Delhi 110058 India Contribution: Conceptualization Writing - original draft

Several approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high-quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

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Evaluation of material structure changing after ultrasonic milling of aluminum foam by Computed Tomography (CT) 16

Evaluation of material structure changing after ultrasonic m...

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16th IMEKO TC10 Conference 2019 on Testing, Diagnostics and Inspection as a Comprehensive Value Chain for Quality and Safety

作者： Líska, Janos Kovács, Zsolt Ferenc Morovic, Ladislav Buransky, Ivan Kuruc, Marcel Viharos, Zsolt János Kritikos, Michaela John von Neumann University GAMF Faculty of Engineering and Computer Science Dep. of Vehicle Technology Kecskemét H-6000 Hungary Slovak University of Technology in Bratislava Faculty of Materials Science and Technology in Trnava Slovakia H-1111 Kende str. 13-17. Budapest Hungary

ISBN: (纸本)9789299008416

In our everyday lives, we spend more and more time in our vehicles, where we are exposed to growing accident hazards. That is why we are increasingly focusing on security. This is particularly true for electric cars, where the size of the power source and the firewall are not the same as for conventional vehicles. The so-called energy-absorbing zone has a key role during an accident. In the era of modern materials the aluminum foam can works as an energy-absorbing structure. However, the machining of this material is hard at this time. This article attempts to investigate, through Computed Tomography-CT technology, whether the material has a permanent deformation or dimensional variation in the structure of the material can be achieved by the ultrasonic milling process. © 2019 International Measurement Confederation (IMEKO). All rights reserved.

关键词： Milling (machining)

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Investigation on the Effect of the Feature Extraction Backbone for Small Object Segmentation using Fully Convolutional Neural Network in Traffic Signs Application

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IOP Conference Series: Materials science and engineering 2021年第1期1051卷

作者： H M Elhawary M I Shapiaib A Elfakharany Dep. of Computer Engineering Arab Academy for Science and Technology Cairo Egypt. Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia.

Small objects are widely found in different applications such as traffic signs and to segment those objects make it difficult to extract features due to the small number of pixels. Previous research has been done to show how error prone the semantic segmentation networks to small objects in variance of application such as medical images and remote sensing and how it leads to class imbalance. However, small object segmentation seems to be tricky and making the network struggle. Recently there are small amount of research has been done in the effect of the feature extraction backbone to the small object datasets. In this paper we investigate the effect of different backbone feature extraction such as AlexNet, VGGNet, GoogleNet on an imbalanced small objects dataset after grouping them by shape and colour in the Fully Convolutional Networks (FCN). We measure the performance on PASCAL VOC and Malaysian Traffic Sign Dataset (MTSD) showing the pixel accuracy, mean accuracy per class, mean IoU and frequency weighted IoU for each backbone and FCN. The results show that VGGNet as a backbone with Cross Entropy (CE) combined with Dice Loss (DL) achieves the highest score in mean IoU for imbalanced dataset but not for balanced dataset. However, in the imbalanced dataset major classes have a higher probability to confuse with minor classes due to the class imbalance. In conclusion we investigate different backbone networks with grouped labels dataset in shape and colour and we recommend using VGGNet FCN with CE combined with DL for imbalanced datasets.

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An algorithm for automatic grasping an UHF RFID passive tag

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IOP Conference Series: Materials science and engineering 2020年第1期922卷

作者： Emidio DiGiampaolo Francesco Martinelli Dep. of Industrial and Information Engineering and Economics University of L'Aquila Italy Dep. of Civil Engineering and Computer Science University of Rome Tor Vergata Italy

This paper proposes an automatic system which allows a mobile robot to reach a tagged object in an unknown environment, exploiting the Radio Frequency IDentification (RFID) technology. It is assumed that the robot has an odometry system (e.g., wheel encoders) and an RFID reader which provides it with phase-shift measurements of the RFID signal backscattered by the tagged object. Since phases are ambiguous measurements of the tag-reader distance, a Multi-Hypothesis Extended Kalman Filter is proposed to on-line extract, by fusing phase measurements with odometry readings, the range and the bearing of the unknown tag. Once the relative position of the tag is known, a control algorithm is applied to drive the robot toward the desired destination. Numerical results are reported to illustrate the approach.

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An Implementation of IoT-Based Weather Monitoring System

An Implementation of IoT-Based Weather Monitoring System

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yung-Chung Tsao Yin Te Tsai Yaw-Wen Kuo Chaokung Hwang Dep.of Electrical Engineering National Chi Nan University Nantou Taiwan Dep. Of Computer Science & Communication Engineering Providence University TaiChung Taiwan Wu Tso Elementary School TaiChung Taiwan

ISBN: (纸本)9781728152103

The living environment monitoring is a critical issue today. Many related researches focused on developing the living environment monitoring systems by using the web-based technology, but such information systems would increase the difficulty of promotion and migration to others, because of needing well-trained engineers to achieve and maintain such information system. This research focuses on developing the weather monitoring system. The main goal of the research is to use the technology of message queuing telemetry transport (MQTT) as roles of the communication layer instead of direct-connecting database, which can isolate the system migration complexity from heterogeneous relational database management system (RDBMS) and construct a distributed information system easily. Finally the contributions are demonstrated as implementation of the IoT-based weather monitoring system. The prototyping of the weather monitoring system is implemented and dep.oyed at Wu-Tso elementary school for the students' learning of nature science and activity environment. In addition, the system is implemented using open sources and is easy to be dep.oyed and scalable.

关键词： geophysics computing Internet of Things relational databases weather forecasting geophysics computing weather forecasting Relational databases Internet of things weather monitoring computer based information systems Relational database systems

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NTEP-DB 1.0: A relational database for the national turfgrass evaluation program

International Turfgrass Society Research Journal

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International Turfgrass Society Research Journal 2021年第1期14卷

作者： Yiqun Xie Majid Farhadloo Ning Guo Shashi Shekhar Eric Watkins Len Kne Han Bao Aaron J. Patton Kevin Morris Dep. of Computer Science and Engineering Univ. of Minnesota Minneapolis MN 55455 USA Dep. of Geographical Sciences Univ. of Maryland College Park MD 20740 USA Center for Geospatial Information Science Univ. of Maryland College Park MD 20740 USA Dep. of Horticultural Science Univ. of Minnesota St. Paul MN 55108 USA U-Spatial Univ. of Minnesota Minneapolis MN 55455 USA Dep. of Horticulture and Landscape Architecture Purdue Univ. West Lafayette IN 47907 USA National Turfgrass Evaluation Program Beltsville MD 20705 USA

Given field experiment data collected by the National Turfgrass Evaluation Program (NTEP), we aim to design and create a relational database to store the data and support efficient queries. As one of the most widely-known turfgrass research programs in the world, NTEP has generated large volumes of data on turfgrass cultivars and experimental germplasm since the early 1980s, providing invaluable information for a variety of user groups (e.g., homeowners, seed companies, golf course managers, retailers, turfgrass researchers) to select cultivars that best fit their needs (e.g., winter survival, pest tolerance, turf quality). The datasets have historically been stored in large sets of text files and spreadsheets. Currently, NTEP data are delivered to users through a website ( *** ) as summary reports and it can be extremely tedious (e.g., hundreds of clicks, data merging, jargon) to perform a simple query (e.g., best cultivar selection with typical conditions). This significantly limits the use of NTEP data and hides its value from the public. To address these limitations, we carried out an interdisciplinary effort with horticulture and computer science researchers to design and create the first NTEP database – NTEP-DB 1.0 – to reduce the manual efforts and expert knowledge currently required to extract meaningful information from the data. Experiments confirm that the query results are correct, and that the database can greatly reduce manual efforts. Anticipating next-generation advances, we also recommend incorporating spatial data types and analytical techniques into future designs of the database.

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Explainable deep learning based medical diagnostic system

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Smart Health 2019年 13卷

作者： Xue, Qinghan Chuah, Mooi Choo Dep. of Computer Science and Engineering Lehigh University 27 Memorial Dr W Bethlehem 18015 PA United States

Recently, many researchers have conducted data mining over medical data to uncover hidden patterns and use them to learn prediction models for clinical decision making and personalized medicine. While such healthcare learning models can achieve encouraging results, they seldom incorporate existing expert knowledge into their frameworks and hence prediction accuracy for individual patients can still be improved. However, expert knowledge spans across various websites and multiple databases with heterogeneous representations and hence is difficult to harness for improving learning models. In addition, patients' queries at medical consult websites are often ambiguous in their specified terms and hence the returned responses may not contain the information they seek. To tackle these problems, we first design a knowledge extraction framework that can generate an aggregated dataset to characterize diseases by integrating heterogeneous medical data sources. Then, based on the integrated dataset, we propose an end-to-end deep learning based medical diagnosis system (DL-MDS) to provide disease diagnosis for authorized users. We also provide explanations for the diagnose results. Evaluations on real-world data demonstrate that our proposed system achieves good performance on diseases diagnosis with a diverse set of patients’ queries. © 2019 Elsevier Inc.

关键词： Deep learning Heterogeneous representation Knowledge extraction medical diagnosis Query processing

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