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IEEE International Symposium on Circuits and Systems

作者： Winnie Thomas Suryakant Toraskar Virendra Singh Computer Architecture and Dependable Systems Laboratory Dept. of Electrical Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781728192017;9781728192024

Massively parallel processors such as graphics processing units (GPUs) often face the challenge of resource underutilization due to varying resource proclivity of workloads. Running multiple applications on a GPU has been an efficient and known alternative to mitigate underutilization. This paper proposes a multi-application oriented framework that carries out dynamic optimizations based on the operational intensities of various applications. Our framework analyzes applications based on operational intensities to identify their bottleneck resources using Roofline model. We demonstrate that the proposed optimizations improve the utilization and system-wide throughput of the GPU co-running applications with irregular resource demands. The dynamic optimizations improve the performance by 14.8% on average and up to 72.4% over a state-of-the-art spatial multitasking technique.

关键词： Heuristic algorithms Graphics processing units Throughput Multitasking Resource management Optimization Faces

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kanchi Lohitha Lakshmi S.Shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar Sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

arXiv

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arXiv 2022年

作者： Yiǧit, Tuncay Şengöz, Nilgün Özmen, Özlem Hemanth, Jude Işik, Ali Hakan Dept. of Computer Engineering Faculty of Engineering Süleyman Demirel University Isparta32260 Turkey Dept. of Pathology Faculty of Veterinary Medicine Burdur Mehmet Akif Ersoy University Burdur15030 Turkey Karunya Institute of Technology & Sciences Coimbatore641114 India Dept. of Computer Engineering Faculty of Engineering and Architecture Burdur Mehmet Akif Ersoy University Burdur15030 Turkey

Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorithm, and visualizes the output with gradient-weighted class activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a detailed questionnaire survey was conducted with the pathologists on these images. Both the decision-making processes and the explanations were verified, and the accuracy of the output was tested. The research results greatly help pathologists in the diagnosis of *** Codes 68T07 © 2022, CC BY.

关键词： Medical imaging

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Intelligent Optimization of Blasting Parameters in Railroad Tunnels Based on Blasting Quality Control

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Journal of Construction Engineering and Management 2025年第8期151卷

作者： Ma, Zhaoxi Gu, Jingtian Zhao, Qin Yang, Mingsong Qian, Chengwei He, Min Hei, Xinhong Dept. of Civil Engineering and Architecture Xi'an Univ. of Technology Shaanxi Xi'an710048 China Dept. of Computer Science and Engineering Xi'an Univ. of Technology Shaanxi Xi'an710048 China

Blasting parameters are crucial factors that directly affect the quality of tunnel excavation. To achieve optimal blasting results, it is necessary to continuously optimize the blasting parameters throughout the construction process, taking into account geological conditions. However, current research mainly focuses on optimizing single-type borehole parameters and fails to simultaneously address the requirements for minimizing overexcavation, underexcavation, and fragment size. This study proposes an intelligent optimization method for blasting construction parameters that combines support vector regression (SVR) with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Through grid search and genetic optimization algorithms, the SVR regression model is refined, establishing an accurate nonlinear mapping relationship between the borehole parameters of peripheral holes, auxiliary holes, and slot holes, and the resulting blasting effects. The NSGA-II algorithm is then employed to search for the Pareto optimal set of blasting construction parameters, with the goal of minimizing average linear overexcavation and the maximum fragment diameter. The technique for order of preference by similarity to the ideal solution (TOPSIS) method is used for multiattribute decision-making to identify the optimal blasting plan. The results show that the SVR model, optimized by the genetic algorithm, provides high prediction accuracy for blasting construction parameters, with determination coefficients of 0.89 and 0.97. Multiobjective optimization of blasting parameters using NSGA-II explores the effects of different parameter combinations on tunnel blasting outcomes. In designing and optimizing blasting parameters, particular attention should be paid to the optimization of peripheral and slot hole parameters to effectively control overexcavation, underexcavation, and fragment size. The intelligent optimization method proposed in this study, which integrates advanced intelligent al

关键词： Blasting

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Emotion detection in infants using an ensemble classifier with a novel member selection technique

Emotion detection in infants using an ensemble classifier wi...

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2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Fahmy, Hesham Ahmed Fahmy, Sherif Fadel Del Barrio García, Alberto A. Juan, Guillermo Botella Dept. of Electronics and Communications Dept. of Computer Engineering Arab Academy for Science Technology and Maritime Transport 2401 Smart Village Campus Giza12577 Egypt Dept. of Computer Architecture and Automation Complutense University of Madrid Ciudad Universitaria C/ Jose Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781713819462

In this paper, we develop a method for detecting infant emotion from images using machine learning techniques. It is important to develop automatic infant emotion detection algorithms as they can become part of an integrated system that monitors the well-being of infants at home and in institutional settings. In particular, we develop an ensemble classifier that combines the results from diverse individual classifiers to produce better classification results. We present a novel method for selecting members of the ensemble that is based on the Pearson correlation coefficient of the location of the errors in the classifiers. The results show that the ensemble classifier outperforms individual classifiers by at least 1.4 times, and outperforms a state-of-the-art open-source algorithm for adult emotion detection by a larger margin. The results also indicate that Pearson Correlation among classifiers at a certain number of classifier count is a good criterion for selecting ensemble members. ©2020 Society for Modeling & Simulation (SCS) International

关键词： Machine learning

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Predicting directional sound-localization of human listeners in both horizontal and vertical dimensions 148

Predicting directional sound-localization of human listeners...

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148th Audio Engineering Society International Convention 2020

作者： Barumerli, Roberto Majdak, Piotr Reijniers, Jonas Baumgartner, Robert Geronazzo, Michele Avanzini, Federico Dept. of Information Engineering University of Padova Italy Acoustics Research Institute Austrian Academy of Sciences Austria Dept. of Engineering Management University of Antwerp Belgium Dept. of Architecture Design and Media Technology Aalborg University Denmark Dept. of Computer Science University of Milano Italy

Measuring and understanding spatial hearing is a fundamental step to create effective virtual auditory displays (VADs). The evaluation of such auralization systems often requires psychoacoustic experiments. This process can be time consuming and error prone, resulting in a bottleneck for the evaluation complexity. In this work we evaluated a probabilistic auditory model for sound localization intended as a tool to assess VAD's abilities to provide static sound-localization cues to listeners. The outcome of the model, compared with actual results of psychoacoustic experiments, shows the advantages and limitations of this systematic evaluation. © 2020 148th Audio Engineering Society International Convention. All rights reserved.

关键词： Audition

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OCT based scanning system to enable 3D viewing, sharing and printing of artworks

OCT based scanning system to enable 3D viewing, sharing and ...

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3D Image Acquisition and Display: technology, Perception and Applications, 3D 2021 - Part of Imaging and Applied Optics Congress 2021

作者： Zhou, Xingyu In, Darlene Xiong, Xinchang Yang, Kunze Chen, Xing McCune Bruhn, Heather Liu, Xuan Yang, Yi Dept. of Electrical and Computer Engineering New Jersey Institute of Technology University Heights NewarkNJ07102 United States Division of Science and Engineering Penn State Abington AbingtonPA19001 United States The College of Arts and Architecture Penn State University ParkPA16802 United States

We acquired 3D volumetric data of artworks with an OCT-based hybrid scanning platform. The data is then exported into standard 3D data formats that can be 3D printed, shared, and viewed in VR and AR. © 2021 The A... 详细信息

ISBN: (纸本)9781557528209

关键词： Volumetric analysis

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

Recommendation System Towards Residential Energy Saving Base...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Shadi Atalla Yassine Himeur Wathiq Mansoor Abbes Amira Fodil Fadli Abigail Copiaco Shahab Saquib Sohail College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah UAE Dept. of Architecture & Urban Planning Qatar University Doha 2713 Qatar School of Engineering Sciences & Technology Jamia Hamdard New Delhi 110062 India

ISBN: (纸本)9781665492669

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users’ activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors’ algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size.

关键词： Training Power demand Machine learning algorithms Shape Signal processing algorithms Signal processing Feature extraction

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A Dual Band Split Ring Electromagnetic Band Gap using Interdigital Technique and its Applications

A Dual Band Split Ring Electromagnetic Band Gap using Interd...

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2020 International Conference on Power, Energy and Innovations, ICPEI 2020

作者： Archevapanich, Tuanjai Chomtong, Pongsathorn Akkaraekthalin, Prayoot Rajamagala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Dept. of Electronics and Communication Engineering Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Dept. of Teacher Training in Electrical Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Dept. of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9781728172408

This paper proposes the electromagnetic band gap (EBG) reflector using split ring resonator with interdigital technique. The capacitive value between split ring gaps has the results of slow-wave effect in transmission line, that can reduce the size of split ring resonator unit cell from λ/2 to about λ/4. Moreover, it can control the second harmonics to resonance at the required frequency for the second band operation. The unit cell will be designed at the fundamental frequency of 1.8 GHz and the second resonance frequency of 2.4 GHZ, which are the frequency bands of LTE and WLAN systems. The unit cells are arranged in an array to act as a reflector for a dipole antenna. The simulation results at both operating frequency bands show the antenna gains of 8.17 dB and 8.29 dB at 1.8 GHz (460 MHz bandwidth) and 2.4 GHz (470 MHz bandwidth), respectively. © 2020 IEEE.

关键词： Dipole antennas

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A Double Layer Frequency Selective Surface using Interdigital Split Ring Resonators and Applications

A Double Layer Frequency Selective Surface using Interdigita...

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2020 International Conference on Power, Energy and Innovations, ICPEI 2020

ISBN: (纸本)9781728172408

This paper proposes a split ring resonator unit cell using interdigital structure. The size can be reduced from λ/2 to about λ/4 caused by the slow wave effect. An array of 14×14 unit cells can be formed to act as a frequency selective surface (FSS). The proposed FSS is designed at frequency of 1.8 GHz, which the length of unit cell is 11.48 mm and the length of FSS is 172.50 mm. Using double layer FSS, the effect of coupling between layers will result in a larger bandwidth from 1.8 GHz to 2.45 GHz. The simulation results when applying a dipole antenna over the double layer FSS show that the high antenna gains of 8.34 dB and 9.75 dB are obtained at 1.8 GHz to 2.45 GHz, respectively. The proposed double layer FSS with a dipole can be applied for modern communication systems including LTE and WLAN. © 2020 IEEE.

关键词： Frequency selective surfaces

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