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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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Assessing Interaction Mechanics in Extended Reality for Control engineering Applications 32

Assessing Interaction Mechanics in Extended Reality for Cont...

引用

32nd International Conference on software, Telecommunications and computer Networks, SoftCOM 2024

作者： Remenyi, Armin Alsaleh, Saleh Tepljakov, Aleksei Tallinn University of Technology Dept. of Software Science Tallinn Estonia Tallinn University of Technology Dept. of Computer Systems Tallinn Estonia

ISBN: (纸本)9789532901382

This research paper delves into the evaluation of human-machine interactions within the realm of Extended Reality (XR) applications, focusing on the integration of Digital Twins (DT) for control systems. The research explores multiple XR interaction methods and assesses their efficacy in enhancing user experience and operational efficiency in industrial settings, specifically in control systems. The study employs advanced tools such as the Microsoft HoloLens 2 headset, Unreal Engine 5, and the Mixed Reality Toolkit (MRTK) to develop and test XR interaction methods. Data collection and analysis involves usability testing using the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX) to measure cognitive, physical and temporal demands on users. The findings highlight the most effective XR interaction strategies, providing valuable insight for future research and practical implementations in intelligent manufacturing environments. © 2024 University of Split, FESB.

关键词： NASA

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal 8

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

引用

8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Ince, Cemile Ince, Kenan Hanbay, Davut Inönü University Computer Engineering Dept Malatya Turkey Inönü University Software Engineering Dept. Malatya Turkey

ISBN: (纸本)9798331531492

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version. © 2024 IEEE.

关键词： Pixels

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Design of a Compact 2-Bit PIN Diode Reflection Type Phase Shifter using Radial Stub

Design of a Compact 2-Bit PIN Diode Reflection Type Phase Sh...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Bilawal, Farah Babaeian, Fatemeh Karmakar, Nemai Chandra Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (纸本)9798350379693

The goal of this research is to create an optimized hybrid coupler structure for a PIN diode reflection type phase shifter (RTPS) operating at microwave frequencies. To date, no phase shifter has been reported with the proposed technique, compactness, minimum phase error and balanced amplitude. Another limitation of designing such structures at such high frequency range, is that the 50Ω impedance lines' width approaches its length for given substrate thickness, that is undesirable. This study introduces a new method of developing a module at 100 Ω and translating it back to 50 Ω at the input and output terminals. Moreover, the proposed design is highly resilient to manufacturing constraints owing to the addition of radial stubs instead of lumped components, as reflective loads. Its performance with respect to its bandwidth coverage is better with 15% bandwidth enhancement, insertion loss around 3dB, and reflection co-efficient at all ports is better than -15dB over the whole operating bandwidth. The simulated and measured results validate the proposed design effectively. © 2024 IEEE.

关键词： Bandwidth

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A Novel Dual Channel Isolated Current Source Gate Driver for Fast Switching MOSFETs 50

A Novel Dual Channel Isolated Current Source Gate Driver for...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Elahi, Asjad Youssef, Mohamed Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology OshawaON Canada

ISBN: (纸本)9781665464543

A new dual-channel discontinuous mode isolated current source gate driver (ICSGD) is proposed in this paper. The proposed gate driver can provide two isolated gate drive signals for two power MOSFETs in circuits where these switches are in sync. The proposed gate driver can achieve very fast turn on and turn off transitions, due to its current mode nature. In addition, this technique can achieve partial gate energy recovery by recovering a fraction of the RMS (CV2) energy, which is typically dissipated over the gate drive resistors in voltage source gate drive (VSGD) techniques. Thus, provides better efficiency. The proposed circuit consists of six transistors and a small pulse transformer to allow swift operation over a wide range of duty cycles. Current through the transformer is discontinuous to avoid conduction losses due to circulating currents such as in some resonant gate drivers (RGD). Consequently, the proposed ICSGD provides superior performance over its VSGD counterparts, especially in high-frequency (HF) operation. The design is implemented in both PSIM and Typhoon HIL (hardware in the loop) platforms. The model is verified with a match of results from PSIM and Typhoon. The PSIM & HIL results show that the ICSGD achieves a minimum of 20% efficiency improvement. © 2024 IEEE.

关键词： High frequency transformers

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A software Framework Based on JUnit for Automated software Testing in computer Science Courses 54

A Software Framework Based on JUnit for Automated Software T...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

引用

5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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Polarimetric Image Fusion Strategies for Improving Target Shape and Contrast in Remote Sensing Applications 21

Polarimetric Image Fusion Strategies for Improving Target Sh...

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Electro-Optical and Infrared systems: Technology and Applications XXI 2024

作者： Ratliff, Bradley M. Tyo, J. Scott Taylor, Michael Prikkel, Connor Dept. Electrical & Computer Engineering Dayton University DaytonOH United States Electrical & Computer Systems Engineering Dept. Monash University Melbourne Australia

ISBN: (纸本)9781510681088

Images from passive imaging polarimeters are often displayed in terms of their intensity (s0), degree of linear polarization (DoLP), and angle of polarization (AOP). The AOP and DoLP together generally provide information about shape and surface quality of imaged objects;however, the information conveyed in these images are often difficult to interpret, especially to an analyst unfamiliar with polarization phenomenology. Furthermore, it can be challenging to train machine learning approaches with direct polarimetric information due to the limited availability of application-specific data. Augmenting s0 images with polarimetric information in the form of contrast and shape enhancement thus allows for the data to remain in the domain of traditional EO-IR intensity imagery that is more familiar for both humans and trained networks. In this work we explore several strategies for effectively fusing polarimetric information into s0 imagery to improve target shape and contrast for both human and algorithmic consumption, with a particular focus on remote sensing and target detection applications. © 2024 SPIE.

关键词： Image fusion

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Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging 5

Artificial Intelligence (AI) for Improving Performance at th...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Nour, Samar M. Faculty of Engineering Dept. Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350381030

Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated. © 2023 IEEE.

关键词： Medical imaging

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