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14th International Conference on Information and Communication Technology convergence, ICTC 2023

作者： Lee, Jaeyeol Kim, Tae-Yoon Kim, Jae-Hyun Ajou University Department of Space Survey Information Technology Suwon16499 Korea Republic of Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350313277

The High Altitude Platform Station (HAPS) provides communication services within the stratosphere at an altitude ranging between 20 - 50 km. Utilizing the stable atmospheric conditions of the stratosphere, HAPS offers communication services over extensive areas while facilitating effective communication in line-of-sight (LoS) environments. This paper focuses on the analysis of HAPS cell coverage in a 3GPP standard based cellular communication network where multiple HAPS served as base stations based on the signal-to-interference-plus-noise ratio (SINR). Specifically, the study concentrates on whether user equipment (UE) positioned at the cell edge of the central HAPS maintains a downlink (DL) SINR above a certain threshold. Simulation results show that multiple HAPS 7-cell scenario coverage is achieved at 400 km2 and multiple HAPS one-cell scenario coverage is achieved at 230 km2 © 2023 IEEE.

关键词： HAPS SINR

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Leveraging Machine Learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment 7

Leveraging Machine Learning to Identify Synergistic Drug Com...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sujatha, P. Saravanan, K. Sohail, Mohammed Ali Basi Reddy, A. Dixit, Rohit R. Krishnaiah, Nallam Amrita Vishwa Vidyapeetham Department of AI/ML Guntur Amaravati India R.M.K Engineering College Department of Information Technology Tamil Nadu Tiruvallur District India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Siemens Healthineers NH03755 United States St.Martin's Engineering College Department of Ai&ml Secunderabad Telangana Dhulapally500100 India

ISBN: (纸本)9798350371406

The potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. The feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models. © 2023 IEEE.

关键词： Drug dosage

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Blockchain-Based Smart Contracts for Supply Chain Management: A Strategic Analysis 3

Blockchain-Based Smart Contracts for Supply Chain Management...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santosh, Kathari Dash, Chinmaya Aarif, Mohd Kumar, Yadavalli Suresh Verma, Mrinal Kiranbala, B. Cmr Institute of Technology Department of Mba Bengaluru India St. Claret College Department of Computer Science Bangalore India Global Research Network Noida India Aditya College of Engineering Department of Computer Science & Engineering Andhra Pradesh India Soil School of Business Design Manesar Gurgaon India K.Ramakrishnan College of Engineering Department of Ai and Ds Trichy India

ISBN: (纸本)9798350361186

The supply chain management sector strategic examination of the blockchain relies smart contract technology employs a comprehensive approach, drawing insights from reputable secondary sources to explore performance metrics, transactional flow optimization, consensus mechanisms, and security considerations. The study reveals nuanced performance variations in average throughput and transaction duration, emphasizing the scalability and stability of the blockchain network. The depicted transactional flow showcases enhanced communication and transparency, aligning with strategic goals for supply chain optimization. The introduction of a Proof of Authority (PoA) consensus algorithm underscores the importance of efficient consensus mechanisms for resilience against attacks. By integrating PoA, the proposed blockchain-based smart contracts contribute to a strategic and reliable supply chain management system, enhancing transparency and mitigating potential risks. A robust security analysis demonstrates moderate fault tolerance, high defense mechanisms, and effective security recovery capabilities, aligning with the overarching goals of confidentiality, integrity, and availability of critical supply chain data. Ethereum's Proof of Stake (PoS) mechanism is referenced as an exemplar for improving scalability and security. The comparative analysis highlights the proposed CMBC (Consensus Mechanism with Blockchain) as a standout solution, offering middle-ground high attack diversity, fault tolerance, recovery, and a high attack cost, robust security. CMBC outperforms alternatives, demonstrating superiority in enhancing security and resilience against potential adversarial threats. The proposed CMBC demonstrates notable strengths across key performance indices when compared to Fortified-Chain and SaYoPillow. The proposed method is implemented using python software. The effective standard deviation values, ranging from 9.58 to 25.13, further underscore the system's stability and re

关键词： Smart contract

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Model-Free Control Design Using Incremental Approximate Dynamic Programming and Generalized Extended State Observer

Model-Free Control Design Using Incremental Approximate Dyna...

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International Conference on Unmanned aircraft Systems (ICUAS)

作者： Juyoung Kim Hanna Lee Youngjun Lee Jongho Park Youdan Kim Department of Aerospace Engineering Seoul National University Seoul Republic of Korea Department of Military Digital Convergence and the Department of AI Convergence Network Ajou University Suwon Republic of Korea

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

A model-free control strategy is proposed which minimizes the cost function consisting of decision and ob-servation. Real-time control can be achieved for unknown systems without relying on any pre-trained policies and model information. By estimating various external disturbances and model uncertainties to stably achieve the control goal, robust-ness can be improved. The numerical simulations demonstrate the effectiveness of the proposed method in controlling the acceleration of missiles.

关键词： Adaptation models Uncertainty Control design Atmospheric modeling Observers Numerical simulation Robustness

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High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

High-Capacity, A Secret Video Sharing Using DM-Stego and Gen...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Logeshwari, R. Velswamy, Rajasekar Subhashini, R. Karunakaran, V. Srm Institute of Technology Department of Network and Communication Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Dept. of CSE-AI and Ml Coimbatore India

ISBN: (纸本)9798350300857

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application. © 2023 IEEE.

关键词： Genetic algorithms

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Deep Reinforcement Learning based SAR Image Pre-Processing Algorithm with Finite Buffer LEO Satellite networks 13

Deep Reinforcement Learning based SAR Image Pre-Processing A...

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13th International Conference on Information and Communication Technology convergence, ICTC 2022

作者： Kim, Tae-Yoon Kim, Kyeongrok Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Satellite System 1 Team Hanwha Systems Yongin17121 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

As space technology advances, launching low Earth orbit (LEO) satellites become easier and LEO satellites are being used in various fields. In particular, LEO synthetic aperture radar (SAR) system is in the spotlight with many advantages, e.g., regardless of weather condition, 24 hour operation. SAR system can be used in various fields such as object detection and disaster observation. However, SAR image has speckling noise, so image pre-processing must be required. There are many researches on the SAR image processing, however, few publications are considering a buffer status. Therefore, in this paper, we suggest the optimal SAR image pre-processing in LEO SAR satellites and a ground station with finite buffer based on deep reinforcement learning (DRL). As a result of DRL simulation, while changing the buffer size of the LEO SAR satellites, efficiency of buffer was improved by selecting the optimal filter size according to the state of the buffer. © 2022 IEEE.

关键词： Synthetic aperture radar

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale ai Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (Lai) models to improve semantic representation and compression capabilities. However, the substantial computational demands of Lai models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from Lai models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of Lai models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable ai. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Personalized HRTF Estimation Based on One-to-Many Neural network Architecture 153

Personalized HRTF Estimation Based on One-to-Many Neural Net...

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153rd Audio engineering Society Convention 2022

作者： Lee, Geon Woo Kim, Hong Kook Chun, Chan Jun Jeon, Kwang Myung Gwangju61005 Korea Republic of Gwangju61005 Korea Republic of Department of Computer Engineering Chosun University Gwangju61452 Korea Republic of AI Convergence Technology Lab. IntFlow Inc. Gwangju61472 Korea Republic of

ISBN: (纸本)9781713870401

In this paper, we propose a one-to-many neural network (NN)-based model to estimate a personalized head-related transfer function (HRTF). The proposed model comprises a feature representation module and an estimation module. The feature representation module provides a deep feature associated with anthropometric measurement data for a given sound direction. The estimation module is mainly constructed using a bi-directional long short-term memory layer with feature vectors from multiple directions, which results in estimated HRTFs simultaneously for all the multiple directions. The performance of the proposed personalized HRTF estimation method is evaluated using the Center for Image Processing and Integrated Computing (CIPIC) database. Experiments show that the proposed personalized HRTF estimation method reduces root mean square error and log spectral distance by 0.89 and 0.45 dB, respectively, compared to the conventional NN-based method. © 2022 AES New York 2022 - 153rd Audio engineering Society Convention 2022. All rights reserved.

关键词： Mean square error

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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