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Residual-driven Fuzzy C-Means Clustering for Image Segmentation

IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 876-889页

作者： Cong Wang Witold Pedrycz ZhiWu Li MengChu Zhou Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China the Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071and also with the Institute of Systems EngineeringMacao University of Science and TechnologyMacaoChina Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in *** propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of *** on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown ***,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image *** experiments on synthetic,medical,and real-world images are *** results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

关键词： Fuzzy C-Means image segmentation mixed or unknown noise residual-driven weighted regularization

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Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

Majority Voting-based Consensus Mechanism for UAVs Decision ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Riya Kakkar Rajesh Gupta Mohammad S. Obaidiat Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing China The Amity University Noida Uttar Pradesh India

The revolutionary and rapid development of Information and Communication technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput.

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Dual-mode Circular Microstrip Patch Antenna for Airborne Applications

Dual-mode Circular Microstrip Patch Antenna for Airborne App...

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European Conference on Antennas and Propagation, EuCAP

作者： Zubair Akhter Rana M. Bilal Atif Shamim Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

A dual-mode, dual-band 2.4/ 5.2 GHz circular microstrip patch antenna for airborne application is presented. Initially, a reference circular patch antenna is designed on Rogers© 5880 substrate for dual-mode (TM11 and TM01) performance with a single feed location. The bandwidth (BW) of the reference antenna is found to be 33 MHz and 155 MHz at 2.4 GHz and 5.2 GHz bands respectively. Later, the BW of the reference antenna at both the frequency band is enhanced with the help of proximity patches placed around the periphery of the reference/driven patch. It is found that the BW of the antenna at both the bands is enhanced by 2.5% at 2.4 GHz and 3.9 % at the 5.2 GHz band and similar improvements in their gains are also observed. The proposed antenna is fabricated and tested in an anechoic chamber for its impedance bandwidth and radiation pattern performance. A close match in simulated and measured performance of the antenna is achieved and an appropriate comparison of measured quantities is well illustrated in the paper.

关键词： Antenna measurements Anechoic chambers Three-dimensional displays Patch antennas Dual band Microstrip antennas Bandwidth

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Cyclodextrin-modified layered double hydroxide thin-film nanocomposite desalination membrane for boron removal

Cyclodextrin-modified layered double hydroxide thin-film nan...

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2023 AIChE Annual Meeting

作者： Ee, Liang Ying Zhao, Qipeng Gao, Jie Lim, Chit Kai Xue, Kai Chin, Sze Yuet Li, Sam Fong Yau Chung, Tai-Shung Chen, Shing Bor Department of Chemical and Biomolecular Engineering College of Design and Engineering National University of Singapore 4 Engineering Drive 4 Singapore117585 Singapore Department of Chemistry College of Humanities and Sciences National University of Singapore 3 Science Drive 3 Singapore117543 Singapore Chemical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Centre of High Field NMR Spectroscopy and Imaging School of Physical and Mathematical Sciences College of Science Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore NUS Environmental Research Institute National University of Singapore T-Lab Building 5A Engineering Drive 1 Singapore117411 Singapore Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

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Multi-task Hierarchical Adversarial Inverse Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chen, Jiayu Tamboli, Dipesh Lan, Tian Aggarwal, Vaneet School of Industrial Engineering Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering George Washington University WashingtonDC20052 United States Department of Computer Science and AI Initiative King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots. Existing MIL algorithms suffer from low data efficiency and poor performance on complex long-horizontal tasks. We develop Multi-task Hierarchical Adversarial Inverse Reinforcement Learning (MH-AIRL) to learn hierarchically-structured multi-task policies, which is more beneficial for compositional tasks with long horizons and has higher expert data efficiency through identifying and transferring reusable basic skills across tasks. To realize this, MH-AIRL effectively synthesizes context-based multi-task learning, AIRL (an IL approach), and hierarchical policy learning. Further, MH-AIRL can be adopted to demonstrations without the task or skill annotations (i.e., state-action pairs only) which are more accessible in practice. Theoretical justifications are provided for each module of MH-AIRL, and evaluations on challenging multi-task settings demonstrate superior performance and transferability of the multitask policies learned with MH-AIRL as compared to SOTA MIL baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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A Generalized Pointing Error Model for FSO Links with Fixed-Wing UAVs for 6G: Analysis and Trajectory Optimization

arXiv

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arXiv 2024年

作者： Moon, Hyung-Joo Chae, Chan-Byoung Wong, Kai-Kit Alouini, Mohamed-Slim The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Yonsei Frontier Lab. Yonsei University Seoul03722 Korea Republic of The Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in unmanned aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV's flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Utilizing our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models. © 2024, CC BY.

关键词： Energy efficiency

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On the security of authenticated key agreement schemes for e-healthcare

On the security of authenticated key agreement schemes for e...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Kasi, Abhijit Obaidat, Mohammad S. Rewal, Purva Mishra, Dheerendra Hsiao, Kuei-Fang Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications Bhopal462003 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

With the rapid development of Internet and communication technology and the advances in devices' computing power, people can avail the services of e-healthcare with ease. To guarantee the authorized access of data in e-healthcare services, numerous authentication schemes have been proposed. However, the analysis of existing protocols along with efficiency is required before their adoption. To understand the key security challenges in existing design and relation to efficiency, we present a case study on recent authentication schemes for e-healthcare. For our study, we considered Chen and Chen (2021) authentication scheme for e-health, Dharminder et al. (2021) chaotic map-based authentication scheme, and Wu et al. (2021) authentication scheme for e-healthcare. We perform cryptanalysis to demonstrate the security vulnerabilities and to understand the key factor behind the security failure. We also analyze the performance of recent designs and present a discussion on their limitations and advantages. We also demonstrate comparative analysis in terms of execution cost and security attributes to find their execution efficiency in response to security. © 2022 IEEE.

关键词： Efficiency

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COVIDX: computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

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engineering Applications of Artificial Intelligence 2024年 135卷

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Kucner, Tomasz Piotr Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle's hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA's Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2024 The Authors

关键词： Autonomous vehicles

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gaith Rjoub Saidul Islam Jamal Bentahar Mohammed Amin Almaiah Rana Alrawashdeh Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi UAE King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decisionmaking efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： Intelligent automation Navigation Decision making Ecosystems Smart homes Reinforcement learning Transformers

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