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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

International Conference on Smart Applications and Sustainability in the artificial intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the artificial intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Image Style Migration Algorithm based on HSV Color Model

Image Style Migration Algorithm based on HSV Color Model

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Cui, Jinxiang Harbin Far East Institute of Technology Department of Artificial Intelligence and Robotics Engineering Harbin China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

Style transfer is a technology that combines the content of an image with the color, texture, contour and other information of other images by using image processing methods. While retaining the original content of the image, it also adds other artistic styles. The most commonly used style transfer technology is deep learning technology. This paper proposes an image style migration algorithm based on HSV color model, which is an improvement on the classical deep learning image style migration algorithm. The algorithm proposed in this paper takes advantage of the intuitiveness and convenience of HSV color model in the representation of color types, and adds the L2 distance between the content image and the H factor in the HSV color model of the migrated image into the loss function. Through experimental verification, this algorithm can achieve the purpose of preserving the color hue of the original content image on the basis of style migration © 2022 IEEE.

关键词： Color

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Improving Sharpness-Aware Minimization by Lookahead 41

Improving Sharpness-Aware Minimization by Lookahead

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Runsheng Zhang, Youzhi Kwok, James T. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong

Sharpness-Aware Minimization (SAM), which performs gradient descent on adversarially perturbed weights, can improve generalization by identifying flatter minima. However, recent studies have shown that SAM may suffer from convergence instability and oscillate around saddle points, resulting in slow convergence and inferior performance. To address this problem, we propose the use of a lookahead mechanism to gather more information about the landscape by looking further ahead, and thus find a better trajectory to converge. By examining the nature of SAM, we simplify the extrapolation procedure, resulting in a more efficient algorithm. Theoretical results show that the proposed method converges to a stationary point and is less prone to saddle points. Experiments on standard benchmark datasets also verify that the proposed method outperforms the SOTAs, and converge more effectively to flat minima. Copyright 2024 by the author(s)

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33rd ACM Web Conference, WWW 2024

作者： Ma, Changyi Yu, Runsheng Zhang, Youzhi Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation CAS Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400701719

The similarity matrix is at the core of similarity search problems. However, incomplete observations are ubiquitous in real scenarios leading to a less accurate similarity matrix. To alleviate this problem, in this paper, based on the key insight that the similarity matrix enjoys both the symmetric and positive semi-definiteness (PSD) properties, we propose a novel similarity matrix calibration method, which is scalable, effective, and sound. Specifically, we establish the PSD property as a constraint for the similarity matrix calibration problem and propose a novel similarity matrix calibration method to estimate the similarity matrix, which approximates the unknown complete ground-truth similarity matrix. To enable a fast optimization process, we further develop a general approximated algorithm that bypasses the computation of singular values. Theoretical analysis ensures stable calibration performance and convergence speed. Extensive experiments of similarity matrix calibration on real-world datasets demonstrate that our proposed method outperforms baseline methods in terms of both accuracy and speed. © 2024 ACM.

关键词： Calibration

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Generating Realistic Sound with Prosthetic Hand: A Reinforcement Learning Approach 46

Generating Realistic Sound with Prosthetic Hand: A Reinforce...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jeong, Taemoon Yamsani, Sankalp Hong, Jooyoung Park, Kyungseo Kim, Joohyung Choi, Sungjoon Korea University Department of Artificial Intelligence Seoul Korea Republic of University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL United States Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798350371499

In this study, we tackle the complex task of enabling prosthetic hands to accurately reproduce sounds, a crucial aspect for distinguishing between different materials through auditory feedback. Sound identification, such as discerning a drywall tap from that on a brick wall, significantly enhances the functionality and user experience of prosthetic devices. However, achieving this level of auditory feedback in prosthetic hands poses considerable challenges. We utilize reinforcement learning (RL) techniques to train prosthetic hands in emulating human-like sound characteristics, focusing on key auditory signals like amplitude and onset timing. Our approach integrates a detailed analysis of these sound attributes to direct the prosthetic hand's movements for the sound generation that mimics natural human actions. We developed a tailored reward function incorporating amplitude, onset strength, and timing criteria to ensure the prosthetic hand's movements align closely with the intended human-like sound output. © 2024 IEEE.

关键词： Reinforcement learning

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Multi-mode input shapers for eliminating residual vibrations

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JMST Advances 2024年第3期6卷 247-256页

作者： Kang, Chul-Goo Kang, Brian Byunghyun Department of Mechanical Engineering Konkuk University Seoul Korea Department of Artificial Intelligence and Robotics Sejong University Seoul Korea

When an abrupt input is applied to engineering systems, residual vibrations generally occur. These residual vibrations degrade operational performance, reduce productivity, and lower control accuracy; therefore, they must be eliminated as quickly as possible. Recently, input shaping control methods, which eliminate residual vibrations by appropriately shaping the command input, have received significant attention as a means to control these vibrations. Traditional input shaping control primarily targets systems with a single vibration mode, and even when addressing two vibration modes, it has not systematically classified and handled multi-mode input shapers. In this paper, we systematically classify multi-mode vibrations and design multi-mode input shapers capable of eliminating multi-mode vibrations at once for each category. Additionally, we propose performance indices to evaluate the performance of multi-mode input shapers in cases with multiple vibration modes.

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Detection of Alzheimer's Disease using Deep Learning: An Optimized Approach 6

Detection of Alzheimer's Disease using Deep Learning: An Opt...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Ahmed, Gulnaz Joo Er, Meng Zikria, Shahid Sadiq Fareed, Muhammad Institute of Artificial Intelligence and Marine Robotics Dalian Maritime University Dalian China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou China

ISBN: (纸本)9798350371253

This paper suggests a new CNN that requires just a few parameters to diagnose AD and is perfect for training on smaller datasets. Compared to existing state-of-the-art models, the proposed model uses a more optimized technique to identify the early stages of Alzheimer's disease. The image is sharpened and normalized as part of the preparation phase to help the model learn more quickly and succinctly. We used a bespoke CNN model that makes precise predictions with minimum parameters. Five Convolutional layers and three fully connected layers, which have equally learned samples from each class, make up the proposed model. The approach was compared using a variety of accuracy metrics, such as accuracy, AUC, precision, recall, and Fl-Score, as well as categorical cross-entropy loss, self, and other comparisons. To accurately forecast the method, several trials were carried out. The values for the evaluation measures are as follows: accuracy, AUC, precision, recall, fl-score, and loss are 99.95%, 99.99%, 99.97%, 99.95%, and 0.0016 respectively. According to simulation results, the proposed model performs better than existing cutting-edge models in all evaluation metrics. © 2023 IEEE.

关键词： Medical imaging

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Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

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Computers, Materials & Continua 2024年第8期80卷 1889-1905页

作者： Abdulraqeb Alhammadi Zaid Ahmed Shamsan Arijit De Center for Artificial Intelligence and Robotics(CAIRO) Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Electrical Engineering College of EngineeringImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Information Engineering Electronics and TelecommunicationsSapienza UniversityRome00185Italy

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation *** complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational *** localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online *** networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user *** networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to *** article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user *** proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s *** operates in both online and offline *** the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s *** results unequivocally showcase the superior performance of the proposed model when compared to other existing models and *** proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing ***,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

关键词： Localization positioning Bayesian fingerprinting received signal strength(RSS)

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Design and Calibration of Dual Sensor: Towards Simultaneous Instrument Tracking and Deformable Shape Sensing in Computer-Integrated Surgery 46

Design and Calibration of Dual Sensor: Towards Simultaneous ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jeon, Sangseo Hnin, Khaing Thandar Jung, Yong Gi Lim, Sunghwan Korea Institute of Science and Technology Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Seoul02792 Korea Republic of Sungkyunkwan University School of Medicine Samsung Medical Center Department of Otorhinolaryngology - Head and Neck Surgery Seoul06351 Korea Republic of

ISBN: (纸本)9798350371499

We introduce a novel integration of vision sensors in the form of a dual sensor system. The dual sensor system combines three distinct units: an optical localizer and two RGB-D sensors. This system is uniquely capable of concurrently providing real-time 3D shape information for deformable objects and accurate pose tracking data for medical instruments. This capability positions the dual sensor with immense potential for application in image-guided surgical navigation, robotic systems, and robot-assisted automation for surgical procedures involving deformable target objects. This paper provides an overview of the design, fabrication and a one-time calibration methodology of the dual sensor. An experiment was performed to assess the calibration accuracy of the dual sensor. Experimental results indicated that the dual sensor system delivers a level of accuracy that renders it suitable for deployment within the envisioned scope of *** relevance - The presented dual sensor provides real-time shape scanning of intra-operatively deforming objects and instrument tracking capabilities simultaneously, contributing to successful outcomes of diverse computer-integrated surgeries dealing with non-rigid surgical targets such as breast, face, and incised skin area © 2024 IEEE.

关键词： Robotic surgery

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