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SSRN 2024年

作者： Nodjigoto Blague, Alain Ousman, Boukar Libouga Li Gwet, David Kamgang, Jean Claude Ndogotar, Nelio University of Ngaoundéré ENSAI Department of Electrical Energy and Automatic Engineering Cameroon University of Ngaoundéré EGCIM Department of Fundamental Sciences and Engineering Techniques Cameroon University of Ngaoundéré ´ ENSAI Department of Mathematics and Computer Sciences Cameroon SARH University Department of Mathematics and Informatics Chad

Discrete Volterra equation is useful model for nonlinear problem. Demosaicing on frequencydomain analysis is an aliasing problem between luminance and chrominance components from a CFA image, which means a nonlinear problem. In order to solve demosaicing, the discrete Volterra equation may be used as a model. A suitable Volterra kernel is required. We show that there is an analogy between the weights and bias of the perceptron and the coefficients of the Volterra kernel. This analogy allows us to have a numerical resolution of the discrete Volterra equation and thus to compute the coefficients of the Volterra kernel. On the Kodak database images, this demosaicing algorithm is used according to the Volterra kernel's size and the Volterra equation's order. Several metrics are used to evaluate performance. The results show that the aliasing problem is attenuated depending on the order of the Volterra equation and the size of the Volterra kernel chosen. For the second-order Volterra equation and a kernel size of 3 × 3, we have an average signal-to-noise ratio performance of 37.7 dB. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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On the Evaluation of DHT22 Temperature Sensor for IoT Application 8

On the Evaluation of DHT22 Temperature Sensor for IoT Applic...

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8th International Conference on computer and Communication engineering, ICCCE 2021

作者： Ahmad, Yasser Asrul Surya Gunawan, Teddy Mansor, Hasmah Hamida, Belal Ahmed Fikri Hishamudin, Adam Arifin, Fatchul International Islamic University Malaysia Electrical and Computer Engineering Dept. Gombak Malaysia Universitas Negeri Yogyakarta Electronics and Informatics Eng. Dept. Yogyakarta Indonesia

ISBN: (纸本)9781728110646

Temperature is one of the most monitored items in many industrial and commercial applications. Internet of Things (IoT) temperature sensors are commonly small, low power, and able to measure humidity as well. Obtaining timely, accurate temperature information is critical in an IoT system. This paper aims to evaluate an IoT temperature system's performance by comparing it with an industrial system. In this project, an IoT temperature monitoring system has been developed using a DHT22 temperature sensor and a Raspberry Pi to represent an IoT temperature monitoring system. The system is evaluated by comparing its temperature measurement with a Keithley 6517-TP industrial grade temperature probe connected to Keithley 2110 benchtop digital multimeter as the acquisition system. The IoT temperature monitoring system utilizing the DHT22 sensor provides accurate measurement up to 0.10C but a slower response in detecting temperature change. This finding would be able to provide a reference in developing an IoT system that requires temperature monitoring. © 2021 IEEE.

关键词： Internet of things

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A Unified Approach for Binary-Class and Multi-Class Data Augmented Generation

A Unified Approach for Binary-Class and Multi-Class Data Aug...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Frederic Rizk Rodrigue Rizk Dominick Rizk Patrick Rizk Chee-Hung Henry Chu The Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA U.S.A. Department of Computer Science University of South Dakota Vermillion SD U.S.A. Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC U.S.A. Department of Mechanical Engineering University of Quebec at Trois-Rivières Drummondville QC Canada The Center for Advanced Computer Studies Informatics Research Institute University of Louisiana at Lafayette Lafayette LA U.S.A.

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Deep neural networks excel in a wide range of tasks but require diverse datasets to prevent overfitting. Overfitting occurs when a network fits training data too precisely, leading to poor generalization. Data Augmentation is often used to mitigate overfitting aiming at enlarging and improving the quality of training datasets, facilitating the construction of superior deep learning models. MAGAN algorithm emerges as an innovative approach that functions as a Meta-Analysis of Generative Adversarial Networks (GANs). MAGAN harnesses the latent space capabilities of GANs to confront the challenges presented by binary-class, multi-class, grayscale, and RGB images, effectively covering a wide spectrum of scenarios. In this paper, we propose the use of MAGAN algorithm for binary-class and multi-class data augmented generation. We also undertake an in-depth experimental analysis, evaluating the performance of the proposed MAGAN-based approach in comparison to two alternative baseline scenarios: one without any augmentation and another utilizing a conventional augmentation method. To gauge the effectiveness of the proposed technique, we employed diverse classification metrics, including accuracy, loss, precision, recall, F1-score, and the confusion matrix. Our results demonstrate that the proposed approach surpasses the other two scenarios achieving improvements in terms of accuracy by a factor of x1.15 and x1.03, respectively. This underscores the significant advantages of harnessing MAGAN, a meta-analysis of GANs, for data augmentation across a range of image types and classification tasks.

关键词： Deep learning Measurement Accuracy Training data Artificial neural networks Gray-scale Data augmentation

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Proposing a Novel Framework for Prediction of Stock using Machine Learning

Proposing a Novel Framework for Prediction of Stock using Ma...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Ankur Singh Bist Bhupesh Rawat Sandy Kosasi Qurotul Aini Fitra Putri Oganda Ahmad Bayu Yadila Computer Sc. & engg. Graphic Era Hill University Uttarakhand India Faculty of Informatics Engineering STMIK Pontianak West Kalimantan Indonesia Information System University of Raharja Tangerang Indonesia Computer System University of Raharja Tangerang Indonesia

Stock prices are highly volatile, dynamic, and non-linear, making it very difficult to predict the exact price at any given time. In addition, stock prices are influenced by several factors, such as political conditions, the global economy, unexpected events, company financial performance, and more. Up to this point, various machine learning techniques have been employed for stock prediction; however, none of these techniques can accurately predict stock prices due to the high volatility in stock prices. Machine learning approaches, such as random forest, SVM, KNN, and logistic regression, represent some of the algorithms used for stock prediction. This paper aims to propose a new framework based on machine learning and deep learning for stock prediction. The prediction relies on the company’s stock fundamentals, past performance, related stocks in the same sector, and other relevant factors.

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TasKar: A Research and Education Tool for Calculation and Representation of Binary Classification Performance Instruments 14

TasKar: A Research and Education Tool for Calculation and Re...

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14th International Conference on Information Security and Cryptology, ISCTURKEY 2021

作者： Canbek, Gurol Temizel, Tugba Taskaya Sagiroglu, Seref Pointr/Middle East Technical University Ankara Turkey Middle East Technical University Informatics Institute Ankara Turkey Gazi University Computer Engineering Department Ankara Turkey

ISBN: (纸本)9781665407762

This study covers almost the ultimate set of binary-classification performance instruments derived from four dimensions of a confusion matrix, namely true positives/negatives and false positives/negatives and enhances their representation by establishing a meaningful interpretation of the dimensions. A common textual formatting scheme is provided to improve the readability and comprehensibility of performance instruments' representation. A compact dashboard (named 'TasKar', the abbreviation of 'Tasnif Karnesi', 'Classification Report' in Turkish) is developed and provided online to calculate and visualize a total of 52 performance instruments (27 measures, 23 metrics, and 2 indicators) by entering confusion matrix elements only. Taking parametric, variant, and recently proposed instruments the number covered becomes 65. Despite the limited approaches in confusion matrix visualization in the literature, three new graphics were devised to visualize true/false positive/negative rates (TPR, FPR, TNR, FNR), positive/negative predictive values (PPV, NPV), and false discovery/omission rates (FDR, FOR) performance metrics. It is expected that the proposed method and tool will be used by researchers in computation, interpretation, and standardized representation of classification performance as well as by teachers and students in machine learning education. © 2021 IEEE.

关键词： Visualization

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Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines

Comparative Evaluation between Accelerated RISC- V and ARM A...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Vasileios Christofas Petros Amanatidis Dimitris Karampatzakis Thomas Lagkas Sotirios K. Goudos Konstantinos E. Psannis Panagiotis Sarigiannidis Department of Computer Science International Hellenic University Kavala Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application.

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Evaluating the Effects of Mobility Restrictions during COVID-19 on Land Surface Temperature with GIS and Satellite Data

Evaluating the Effects of Mobility Restrictions during COVID...

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International Conference on informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Ika Dewi Sartika Saimima Muhammad Khaerudin M. Hadi Prayitno Prio Kustanto Ismaniah Ismaniah Ajif Yunizar Pratama Yusuf Arman Andrian Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Law Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Industrial Engineering Universitas Bhayangkara Jakarta Raya Jakarta Indonesia

In order to analyze specific occurrences, such as the COVID-19 pandemic’s effects, experts from a variety of professions might work together. The majority of study, such as those on economics, health, spread projections, and similar topics, focuses on just one area of the field. Therefore, several multidisciplinary teams need to collaborate in analyzing the occurring phenomena. The method employed in this research involves using Remote Sensing and Geographic Information Systems (RS-GIS) to observe the impact of the pandemic phenomenon on environmental conditions, specifically surface temperature warming in Bekasi Regency, Indonesia. This is due to its impact not only on health but also on other aspects. This study converts Landsat 7 and Landsat 8 images using (RS-GIS) technologies to extract thermal sensors (Band 10 and Band 11). The test results indicate that the temperature rises annually, but that it falls during the COVID-19 pandemic.

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Hierarchical Reinforcement Learning-based Mapless Navigation with Predictive Exploration Worthiness

Hierarchical Reinforcement Learning-based Mapless Navigation...

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IEEE International Conference on Mechatronics and Automation

作者： Yan Gao Ze Ji Jing Wu Changyun Wei Raphael Grech School of Engineering Cardiff University Cardiff United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom College of Mechanical and Electrical Engineering Hohai University Changzhou China Spirent Communications Paignton United Kingdom

Hierarchical reinforcement learning (HRL) is a promising approach for complex mapless navigation tasks by decomposing the task into a hierarchy of subtasks. However, selecting appropriate subgoals is challenging. Existing methods predominantly rely on sensory inputs, which may contain inadequate information or excessive redundancy. Inspired by the cognitive processes underpinning human navigation, our aim is to enable the robot to leverage both ‘intrinsic and extrinsic factors’ to make informed decisions regarding subgoal selection. In this work, we propose a novel HRL-based mapless navigation framework. Specifically, we introduce a predictive module, named Predictive Exploration Worthiness (PEW), into the high-level (HL) decision-making policy. The hypothesis is that the worthiness of an area for further exploration is related to obstacle spatial distribution, such as the area of free space and the distribution of obstacles. The PEW is introduced as a compact representation for obstacle spatial distribution. Additionally, to incorporate ‘intrinsic factors’ in the subgoal selection process, a penalty element is introduced in the HL reward function, allowing the robot to take into account the capabilities of the low-level policy when selecting subgoals. Our method exhibits significant improvements in success rate when tested in unseen environments.

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Safety Assessment of Vehicle Characteristics Variations in Autonomous Driving Systems

arXiv

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arXiv 2023年

作者： Pan, Qi Wang, Tiexin Arcaini, Paolo Yue, Tao Ali, Shaukat The School of Computer Science and Engineering Beihang University Beijing China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Institute of Informatics Tokyo Japan Simula Research Laboratory Oslo Metropolitan University Oslo Norway

Autonomous driving systems (ADSs) must be sufficiently tested to ensure their safety. Though various ADS testing methods have shown promising results, they are limited to a fixed set of vehicle characteristics settings (VCSs). The impact of variations in vehicle characteristics (e.g., mass, tire friction) on the safety of ADSs has not been sufficiently and systematically studied. Such variations are often due to wear and tear, production errors, etc., which may lead to unexpected driving behaviours of ADSs. To this end, in this paper, we propose a method, named SAFEVAR, to systematically find minimum variations to the original vehicle characteristics setting, which affect the safety of the ADS deployed on the vehicle. To evaluate the effectiveness of SAFEVAR, we employed two ADSs and conducted experiments with two driving simulators. Results show that SAFEVAR, equipped with NSGA-II, generates more critical VCSs that put the vehicle into unsafe situations, as compared with two baseline algorithms: Random Search and a mutation-based fuzzer. We also identified critical vehicle characteristics and reported to which extent varying their settings put the ADS vehicles in unsafe situations. © 2023, CC BY-NC-ND.

关键词： Autonomous vehicles

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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

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computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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