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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer 2

Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Prusty, Sashikanta Dash, Sujit Kumar Prusty, Sushree Gayatri Priyadarsini Dalai, Sasanka Sekhar Tripathy, Nrusingha Nayak, Subrat Kumar Department of Computer Science & Engineering Bhubaneswar751030 India Department of Electrical & Electronics Engineering Bhubaneswar751030 India

ISBN: (纸本)9798350344882

IoT and cloud computing have a significant impact on healthcare services, particularly when it comes to disease prediction in smart cities. Big data about breast cancer (BC) can be sent through IoT devices (digital sensors, etc.) and stored in the cloud. As a result, these huge data are used to improve BC prediction accuracy in a cloud setting. Thus, BC-based cloud IoT is thought to be a significant issue that smart city stakeholders in health care must deal with. In smart cities, cloud computing helps patients predict the presence of BC anywhere and at any time by using advanced machine learning (ML) techniques. The main goal of this study is to demonstrate the effectiveness of a secure Internet of Things and cloud computing in the identification of BC at our home. Thus, we proposed an ICM (IoT_Cloud-based ML) method that describes the workflow of how a user can predict BC disease at any place and at any time. And finally, we designed performance metrics for all three models and found logistic regression model (LR) outperforms with higher accuracy of 83% than DT at 72% and NB at 80%. However, all these experiments have been carried out using Python 3.8.3 programming software on the Jupyter 6.4.3 Notebook platform in Windows 10 operating system. © 2023 IEEE.

关键词： Cloud computing

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Robust Coarse-to-fine 3D-Target-Localization Algorithm for Underwater-IoT-Based Networks: Design and Performance Evaluation under Uncertain Multi-Parameters

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IEEE Internet of Things Journal 2025年第13期12卷 25119-25135页

作者： Xian, Jiangfeng Ma, Junling Mei, Xiaojun Wu, Huafeng Saeed, Nasir Han, Dezhi Marino, Mario Donato Li, Kuan-Ching Shanghai Maritime University Institute of Logistics Science and Engineering Shanghai201306 China Shanghai Maritime University Merchant Marine College Shanghai201306 China University of the United Arab Emirates Department of Electrical and Communication Engineering Al Ain15551 United Arab Emirates Shanghai Maritime University College of Information Engineering Shanghai201306 China Oslo Metropolitan University Department of Computer Science Pilestredet 35 Oslo0166 Norway Providence University Department of Computer Science and Information Engineering Taiwan

Underwater Acoustic Internet of Things Networks (UAIoTNs) can furnish excellent technical support and information services for applications involving marine observation and detection, marine disaster prevention and mitigation, and maritime search and rescue, in which accurate positioning information is the fundamental requirement. The combination of high dynamics and complexity of the ocean environment to the high latency and narrowband of underwater acoustic communication are complex challenges in UAIoTNs. Due to these facts, this work investigates the received signal strength (RSS)-based three-dimensional (3D) target localization in UAIoTNs taking into account the absorption effect, uncertain transmission power (UTP), and a time-varying Path Loss Exponent (PLE). Through Taylor’s first-order expansion and certain approximations, we envision the underwater stratified acoustic propagation localization challenge as an Alternating Non-negative Constrained Least Squares (ANCLS) framework. To address the challenges posed by unknown multi-parameters, a robust coarse-to-fine localization algorithm (RCFLA) is proposed. At first, the coarse localization phase utilizes the Active Set Method (ASM), while the subsequent fine localization one employs the improved Broyden-Fletcher-Goldfarb-Sanno (BFGS) trust region method to enhance convergence towards the global optimal solution. The iterative process refines the underwater target location, UTP, and PLE, using the ASM-derived rough solution as the initial estimate. Analysis of computational complexity and derivation of the Cramér-Rao Lower Bound (CRLB) with stratified propagation and absorption effect demonstrates the superiority of RCFLA. Furthermore, Lyapunov’s second stability theorem is used to prove the stability of the RCFLA and presents a complete proof of global convergence. Numerical simulation and experimental results validate the algorithm’s optimal localization accuracy across various scenarios, showing reduced overh

关键词： Cramer-Rao bounds

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Advancing Functional Near-Infrared Spectroscopy (fNIRS) with Transformers: A PRISMA-driven systematic review

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IEEE Access 2025年 13卷 97794-97817页

作者： Abdollahpour, Neda Kakaraparthi, Abhinav Sai Valluru, Chakradhar Artan, N. Sertac New York Institute of Technology Department of Electrical and Computer Engineering New York NY United States New York Institute of Technology Department of Computer Science New York NY United States

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging modality in various domains including cognitive neuroscience, brain-computer interfaces (BCIs), clinical diagnostics, mental workload assessment, and neurorehabilitation. Despite its growing applications, processing fNIRS signals presents unique challenges. The advent of deep learning, particularly transformers, has taken on an increasingly pivotal role in addressing these complexities. As transformers have revolutionized multiple fields, their application in fNIRS signal processing has gained significant traction recently. To support ongoing research, this study systematically reviews the advancements and applications of transformer models in fNIRS research, following PRISMA guidelines. A comprehensive analysis was conducted on 30 studies (N = 895), offering a structured evaluation of current trends, methodologies and outcomes. This review paper highlights transformer architectures in fNIRS-based studies, with a particular focus on advanced modifications in key stages such as the embedding and attention mechanisms. Multimodal studies analyzing fNIRS with other modalities, such as Electroencephalography (EEG), using transformers are also reviewed. Finally, the paper examines open-access and private datasets used in the literature and offers guidelines for future research, identifying open problems that warrant further exploration. © 2013 IEEE.

关键词： brain-computer interface (BCI) deep learning emotion recognition Functional near infrared spectroscopy (fNIRS) mental arithmetic motor imagery systematic review transformer architecture transformer models

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An Integrated Scenario-Based Testing and Explanation Framework for Autonomous Vehicles 2

An Integrated Scenario-Based Testing and Explanation Framewo...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Goss, Quentin Clay Pate, W. Akbas, Mustafa Ilhan Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL United States

ISBN: (纸本)9798350307733

The validation and verification approaches for autonomous vehicles have commonly employed scenario-based testing in various environments. Simulation platforms play a key role in the execution and evaluation of test scenarios, particularly for validating perception, decision-making, and actions in various traffic scenarios. It is imperative to establish a systematic validation methodology and use a formal specification for these scenarios, which significantly contributes to standardization, repeatability, and verification processes. In this paper, we propose an integrated scenario-based testing and explanation framework for autonomous vehicles that is modular, reusable, and available. Our proposed framework compartmentalizes testing processes into interchangeable and extendable components. In addition, we identify bottlenecks and suggest improvements to the testing process while also implementing solutions, followed by examples of experiments. This approach also provides the ability to explain autonomous vehicle scenarios by adapting state-of-the-art explainable artificial intelligence to the framework. © 2024 IEEE.

关键词： Autonomous vehicles

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Corona Inception and Breakdown in High-Altitude, Low-Pressure Air under Negative DC Voltage for Aircraft Electrification Applications

Corona Inception and Breakdown in High-Altitude, Low-Pressur...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Kalakonda, Sai Pavan Hamidieh, Mohammad Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

The achievement of the net-zero emission goal is considered contingent upon the implementation of transportation electrification. Presently, all-electric commercial vehicles are restricted to land and water transport. The prospect of complete electrification of aircraft, particularly the plausible realization of all-electric aircraft (AEA), is acknowledged as being imminent. Within the electric power system (EPS) designs proposed for AEA, medium DC voltage systems in the order of up to a few tens of kilovolts are employed. Challenges manifest in the management of such voltages within a low-pressure environment. One of the emerging challenges pertains to the partial discharge (PD) behavior under DC stress, a phenomenon acknowledged to exhibit pressure- and field-dependent variations. The importance of studying the dynamics of pre-breakdown and breakdown phenomena in such environments is underscored by the fact that air serves as a crucial insulator for the EPS in AEA. Accordingly, this experimental study aims to comprehend the behavior of air subjected to high negative DC voltages and offer insights into the PD onset dynamics, specifically corona inception and breakdown. This investigation utilizes a needle-to-plane electrode configuration, examining its performance under a standard range of aviation-specific pressures across varying air gaps. The findings of this study contribute to the development of reliable electrical insulation systems tailored to withstand the demanding conditions encountered during aircraft operation. © 2024 IEEE.

关键词： Partial discharges

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EQUIVARIANT SHAPE-CONDITIONED GENERATION OF 3D MOLECULES FOR LIGAND-BASED DRUG DESIGN 11

EQUIVARIANT SHAPE-CONDITIONED GENERATION OF 3D MOLECULES FOR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Adams, Keir Coley, Connor W. Department of Chemical Engineering MIT United States Department of Electrical Engineering and Computer Science MIT United States

Shape-based virtual screening is widely used in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D graph structures compared to known ligands. 3D deep generative models can potentially automate this exploration of shape-conditioned 3D chemical space;however, no existing models can reliably generate geometrically realistic drug-like molecules in conformations with a specific shape. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molecular shape and variationally encoding chemical identity. We ensure local geometric and chemical validity of generated molecules by using autoregressive fragment-based generation with heuristic bonding geometries, allowing the model to prioritize the scoring of rotatable bonds to best align the growing conformation to the target shape. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization, demonstrating its utility over virtual screening of enumerated libraries. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Ligands

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Tunable multicolor luminescence in vanadates from yttrium to indium with enhanced luminous efficiency and stability for its application in WLEDs and indoor photovoltaics

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Nano Research 2023年第8期16卷 11486-11494页

作者： Yaoyang Zhang Jing Mao Peifen Zhu Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouri 65211USA

The preparation of high-efficiency phosphor is the key to the construction of white light-emitting diode(WLED)devices and their application in indoor *** with YVO_(4),InVO_(4)is not suitable as the host material of lanthanide ions because of its strong ***,the work focused on combining the broadband emission from InVO_(4)and the red luminescence from YVO_(4):Eu^(3+)to obtain enhanced and stable multicolor *** band structure,density of state,and optical properties were studied by density functional *** spectral configuration of YVO_(4):In^(3+)/Eu^(3+)with(112)surface appears to be broadening and redshifts with increasing layer *** the In^(3+)concentration is 3.5 mol%,the YVO_(4):30%Eu^(3+)/In^(3+)emits the strongest *** Judd-Ofelt parameterΩ2 of YVO_(4):In^(3+)/Eu^(3+)increases with increaing In^(3+)concentration,indicating that the symmetry *** adjusting In^(3+)/Eu^(3+)contents,the YVO_(4):In^(3+)/Eu^(3+)not only can emit white light with a color rendering index of 95,but also can be used as high-efficiency red phosphor to build WLED devices with blue emitting N/Tb codoped carbon quantum dots(CQDs-N:Tb^(3+))and green emitting MOF:Tb^(3+)(MOF=metal organic framework),for which the color rendering index can also reach 95 and the color temperature is 5549 *** manufactured WLED devices were further used to excite the silicon solar cell and make it show good photoelectric characteristics.

关键词： luminescence enhancement multicolor stability density functional theory(DFT)calculation white light-emitting diodes(WLEDs) indoor photovoltaics

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes 2

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Mahdy, Nevien A. Elnokrashy, Ahmed F. Hammad, D.A. Mohamed, Wael A. Benha University Benha Faculty of Engineering Electrical Engineering Department Benha Egypt Nile University Faculty of Information Technology and Computer Science Computer Science Department Giza Egypt Benha University Benha Faculty of Engineering Basic Engineering Sciences Department Benha Egypt

ISBN: (纸本)9798350362633

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D. © 2024 IEEE.

关键词： Classification Deep learning Glucose prediction Hypoglycemia Type 1 diabetes

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Detection of Hardware Trojans in IoT Devices using mmWave RADAR 14

Detection of Hardware Trojans in IoT Devices using mmWave RA...

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14th IEEE International Conference on RFID Technology and Applications, RFID-TA 2024

作者： Swan, Rachel Bhullar, Harkiran Kaur Rojas-Nastrucci, Eduardo A. Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9798331540227

The rapid advancement of Internet of Things (IoT) devices has transformed multiple industries. However, the extensive integration of IoT technology has introduced significant security challenges. The physical-layer (PHY) security of IoT devices is vulnerable to malicious hardware modifications, commonly known as hardware Trojans (HTs). Given the unpredictable nature of HTs, there is a need for countermeasures such as radio-frequency fingerprint-based identification methods. This paper presents a novel solution for detecting HTs embedded in wireless devices using millimeter Wave (mmWave) RADAR measurements far outside the operating frequency range of the radar. HT detection using RADAR is demonstrated using a commercial-of-the-shelf mm-wave RADAR, which transmits and receives reflected signals from a passive wireless module designed with a WiFi system-on-chip (ESP8285). The module also includes a custom-made IEEE 802.11 WiFi system-on-chip with signal conditioning components and a hardware Trojan that disrupts the communication link by shorting the antenna. Decision Tree-based model machine-learning approaches are used to successfully identify the presence of HTs from data provided by RADAR measurements, demonstrating their usefulness for large-scale analyses and providing a reliable solution to PHY security challenges. To the best of the authors’ knowledge, this is the first time the concept of using RADAR techniques for hardware trojan detection is presented and validated. © 2024 IEEE.

关键词： computer viruses

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