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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Purboseno, Sentot Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Agricultural Engineering Yogyakarta55283 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and a flood embankment structure, which is also connected to three different rivers: Barito River, Mangkatif River, and Kapuas Mumpung River. To quantify flood risk assessments in the area, we perform a hydrodynamic analysis using the unsteady flow method via HEC-RAS software version 4.1.0. We use geometrical profiles from topography measurements and the outer boundary of the Dadahup DIR as input for the hydrologic model. Our results estimate the maximum surface water level from both cross and longitudinal sections in the channels and find that several points near the flood embankment area are prone to flood events. Our modeling approach provides a preliminary assessment for the local government to formulate a flood mitigation plan and policy in the area. © 2021 IEEE.

关键词： Rivers

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Platform for Evanescent Wave Sensor Fabrication Based on 3D-Printed Fiber Optic Holder

Platform for Evanescent Wave Sensor Fabrication Based on 3D-...

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Optical Fiber Sensors (LAWOFS), Latin American Workshop on

作者： Afonso Henrique Henschel Baptista Pedro Bertemes-Filho Aleksander Paterno Raquel Bettega Jose Pedro Mansueto Serbena Nathalia Prediger Ricardo Kamikawachi Department of Electrical Engineering Center for Science and Technology - Santa Catarina State University – CCT-UDESC Joinville Brazil Group of Organic Optoelectronic Devices (GOOD) Federal University of Parana – UFPR Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology - Parana – UTFPR Curitiba Brazil

ISBN: (数字)9786589532026

ISBN: (纸本)9798350362725

This paper describes the low-cost manufacturing process of an evanescent wave fiber sensor platform that allows the etching of the fiber in hydrofluoric acid with the proposed 3D-printed fiber holder in an acid resistant material. The fiber holder is practical for allowing several processing procedures during its use and sensor fabrication, allowing sensor optical and electrical operation. In this work, some aspects of its characterization are presented in the optical domain showing the excitation of surface plasmons in the visible range, and also indication of its electrical operation of the thin-film as electrode.

关键词： Optical fiber polarization Optical fiber sensors Gold Optical polarization Biomedical optical imaging Optical device fabrication Optical films

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Statistical 3D Device Simulation of Full Fluctuations of Gate-All-Around Silicon Nanosheet MOSFETs at Sub-3-nm Technology Nodes

Statistical 3D Device Simulation of Full Fluctuations of Gat...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Kola, Sekhar Reddy Li, Yiming Chen, Chieh-Yang Chuang, Min-Hui National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

We for the first time study characteristic fluctuation of gate-all-around silicon nanosheet MOSFETs induced by random dopants fluctuation (RDF), interface trap fluctuation (ITF), and work function fluctuation (WKF), and their combinations. The explored devices affected by the WKF and its combination result in sizeable variability. Because of enigmatical interactions among the aforementioned random factors, overestimated variabilities occur if we only consider one random source each time, compared with the statistical 3D device simulation for full RDF, ITF and WKF at the same time. Notably, 10.78% and 103% overestimations are found for the threshold voltage and off-state current fluctuations. © 2022 IEEE.

关键词： Nanosheets

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Economics of Semantic Communication in Metaverse: An Auction Approach

Economics of Semantic Communication in Metaverse: An Auction...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Zi Qin Liew Hongyang Du Wei Yang Bryan Lim Zehui Xiong Dusit Niyato Han Yu Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Metaverse provides embodied artificial-reality experience to the users in the virtual spaces. Many innovative and creative services such as virtual conference and tourism have been realized in the digital twins maintained by the virtual service providers (VSP) in Metaverse. Digital twins are digital copies of the physical world constructed virtually by the VSPs using real-world data. For a realistic experience, VSPs need to collect data that is up-to-date and relevant to their services. In this paper, we propose an incentive design framework to support the data trading between VSPs and edge devices. In the auction model, we model the valuation of data by considering data relatedness and data freshness. In our model, the semantic communication model is used to filter the relevant data, and the age of information (AoI) metric is used to assess the data freshness. Results show that by considering the data freshness, our mechanism helps to increase the average update frequency so that the VSPs obtain fresh data for construction of digital twins. Our model ensures the desired properties of individual rationality, incentive compatibility, and budget balance.

关键词： Measurement Metaverse Data integrity Semantics Streaming media Information filters Data models

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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on Informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Rudi Nirwantono Faisal Asadi Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment.

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