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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Nanda, Arnav Zoraiz, Mohammad Barua, Dhruva Mohamed, Raghad Duke Pratt School of Engineering Department of Electrical and Computer Engineering Duke Marine Lab United States

ISBN: (纸本)9798350385144

Freediving is a captivating sport that involves diving underwater on a single breath, pushing the boundaries of human capability and exploring the depths of the ocean with minimal equipment. While free diving offers a unique and thrilling underwater experience, it also poses several difficulties. Having been in the water for long periods, many divers can develop hypoxia or decompression sickness. To solve this issue many divers use watches that allow them to monitor the heart rate, oxygen levels, and depth levels. Most of these watches can track and monitor vital signs and warn in case of potential harm or risk to the diver. While these watches are helpful, they are not preventative and many divers have passed away due to oxygen-related issues, decompression sickness, and a myriad of other problems. This project aims to address these problems and improve upon current products produced by Garmin Inc. Duke's EGR102 team's goal is to create an effective data collection and signal analysis algorithm dedicated for efficiently analyzing data regarding different pitches, timbers, and audible tones at varying depths of water. More importantly, the product should be able to stay underwater at 6 meters depth for more than an hour. © 2024 IEEE.

关键词： Oxygen

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Optimization of 5G Antenna Using Machine Learning 5

Optimization of 5G Antenna Using Machine Learning

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassan, Aline Sharanek, Aya Hamzeh, Rayan Serhal, Dina Zantout, Rached American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon Rafik Hariri University Electrical and Computer Engineering Department Mechref Lebanon EFREI Paris EFREI Research Lab Paris France

ISBN: (纸本)9798331519957

This paper presents a machine learning (ML)-based approach to optimize 5G base station patch antennas operating in the 3.3-4.2 GHz range. Traditional antenna design methods are often complex and time-consuming, particularly as the demand for high-speed communication is increasing, motivating the use of ML. Due to the lack of existing data for training the ML models, a custom dataset was created to be used in training and testing of the ML models. The optimized antenna achieved a return loss less than-10 dB and a resonant frequency of 3.5 GHz. An array of the optimized antennas was also designed and analyzed for base station applications. To select the best ML model, the project involved training and testing various models. Random Forest was identified as the best model for the single antenna, while Random Tree performed best for the antenna array. © 2025 IEEE.

关键词： Slot antennas

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CRLB-based Data-driven Covariance Tuning for 5G KF Vehicular Tracking

CRLB-based Data-driven Covariance Tuning for 5G KF Vehicular...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Bader, Qamar Saleh, Sharief Noureldin, Aboelmagd Chalmers University of Technology Department of Electrical Engineering Gothenburg Sweden Queen's University Department of Electrical and Computer Engineering KingstonON Canada Research Lab Gothenburg Sweden Royal Military College of Canada Department of Electrical and Computer Engineering KingstonON Canada

ISBN: (纸本)9798350351255

5G mmWave offers a high-precision positioning solution, functioning effectively in both line-of-sight (LoS) and operable non-line-of-sight (NLoS) conditions. However, in scenarios with complete signal blockage, integrating with motion-based models becomes crucial. This integration is achieved through Bayesian-based estimators, which entail a prediction and a correction stage weighted by their respective covariance matrices. Although covariance matrices of different prediction models have been extensively studied in the literature, the measurement covariance matrix derived from 5G-based position computations remains largely unexplored. In this paper, we propose a measurement covariance matrix tuning scheme based on a data-driven Cramér-Rao lower bound CRLB model. We validate the proposed algorithm within a simple linear Kalman filter (LKF) positioning framework. The methodology was tested in a controlled simulation scenario using a real 24-minute-long vehicular trajectory in a deep-urban environment. The results demonstrate that the developed data-driven model is reliable, maintaining a standard deviation error of less than 7 cm for 95% of the time and less than 0.5 m for 100% of the time relative to the true computed CRLB. The proposed adaptive KF sustains a position error below 30 cm for 99.3% of the time. © 2024 IEEE.

关键词： Kalman filters

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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In-band full-duplex MIMO PLC systems for relaying networks

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Digital Communications and Networks 2025年第1期11卷 145-159页

作者： Francisco J.Canete Gautham Prasad Lutz Lampe Communications and Signal Processing Lab Telecommunication Research Institute(TELMA)ETS Ingenieria TelecomunicacionUniversidad de MalagaSpain Ofinno RestonVAUSA Department of Electrical and Computer Engineering The University of British ColumbiaCanada

In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long ***,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area *** in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things *** this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying *** present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC *** reach this analysis,we get channel realizations from random network layouts for indoor and outdoor *** adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC *** concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.

关键词： PLC MIMO Relaying networks OFDM Spatial correlation Performance analysis

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Joint Activity Detection and Channel Estimation in MIMO Grant-Free Random Access Networks

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4793-4808页

作者： Yang, Boran Zhang, Xiaoxu Hao, Li Karagiannidis, George K. Southwest Jiaotong University Provincial Key Lab of Information Coding and Transmission Chengdu611756 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

Massive machine communication is predicted to provide widespread and unparalleled connectivity for cellular Internet of Things (IoT) applications via multiple-input multiple-output (MIMO) and grant-free random access (GF-RA) techniques. Compressed sensing (CS) has been widely advocated to support massive connectivity due to the bursty nature of traffic transmission. In this paper, the joint activity detection and channel estimation in MIMO-enabled GF-RA system is formulated as a block single measurement vector (SMV) problem and efficiently addressed by using Bayesian-based CS algorithms. First, the pattern coupled sparse Bayesian learning (PCSBL) and block sparse Bayesian learning (BSBL) algorithms are introduced to solve this problem, where the potential block sparsity properties induced by multi-antenna reception are exploited by assigning the structured hyperpriors. Then, by embedding the Generalized Approximate Message Passing (GAMP) technique into the PCSBL and BSBL frameworks to enable effective approximation of posterior distributions, we propose two computationally efficient Bayesian learning algorithms, i.e., GAMP-PCSBL and GAMP-BSBL. Fortunately, the proposed Bayesian algorithms allow automatic learning of block sparse solutions without requiring noise level and user sparsity ratio as explicit conditions. Simulation results show that the proposed algorithms provide improved performance gains over the standard CS-based methods. © 2002-2012 IEEE.

关键词： Channel estimation

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Device Processing Challenges for Miniaturized Sensing Systems Targeting Biological Fluids

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Biomedical Materials and Devices 2023年第1期1卷 82-98页

作者： Stoukatch, S. Dupont, F. Redouté, J.-M. Microsys Lab Department of Electrical Engineering and Computer Science Liege University Seraing Belgium

This article presents a review of device processing technologies used in the fabrication of biomedical systems, and highlights the requirements of advanced manufacturing technology. We focus on biomedical systems that perform diagnostics of fluidic specimens, with analytes that are in the liquid phase. In the introduction, we define biomedical systems as well as their versatile applications and the essential current trends. The paper gives an overview of the most important biomolecules that typically must be detected or analyzed in several applications. The paper is structured as follows. First, the conventional architecture and construction of a biosensing system is introduced. We provide an overview of the most common biosensing methods that are currently used for the detection of biomolecules and its analysis. We present an overview of reported biochips, and explain the technology of biofunctionalization and detection principles, including their corresponding advantages and disadvantages. Next, we introduce microfluidics as a method for delivery of the specimen to the biochip sensing area. A special focus lies on material requirements and on manufacturing technology for fabricating microfluidic systems, both for niche and mass-scale production segments. We formulate requirements and constraints for integrating the biochips and microfluidic systems. The possible impacts of the conventional microassembly techniques and processing methods on the entire biomedical system and its specific parts are also described. On that basis, we explain the need for alternative microassembly technologies to enable the integration of biochips and microfluidic systems into fully functional systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of

关键词： Alternative microassembly techniques Biological analysis Biomedical system Biosensors

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Human-Inspired Anticipatory Motion Planning for Robotic Manipulators in Contact Tasks

Human-Inspired Anticipatory Motion Planning for Robotic Mani...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Slim, Malak Daher, Naseem Elhajj, Imad H. American University of Beirut Vision and Robotics Lab Electrical and Computer Engineering Department Beirut Lebanon

ISBN: (纸本)9781665481090

In this work, we present a novel solution aimed at improving robotic manipulators' performance in contact tasks. Inspired by the human motor control system, which relies on a feedforward mechanism to anticipate and plan movements based on the physical properties of the target environment, our approach plans the robot's motion during the reaching phase, prior to contact. To validate our approach, we conducted experiments using the KUKA youBot arm in two distinct environments, represented by soft and hard materials. Results showed that the robot exhibited compliant behavior, with an average reduction of 71% in overshoot, 60% in rise-time, and 68% steady-state error of the force control response during contact. © 2024 IEEE.

关键词： Motion planning

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Dyna-ESN: Efficient Deep Reinforcement Learning for Partially Observable Dynamic Spectrum Access

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IEEE Transactions on Wireless Communications 2024年第6期24卷 4517-4531页

作者： Chang, Hao-Hsuan Mohammadi, Nima Safavinejad, Ramin Yi, Yang Liu, Lingjia Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburgVA United States Standards and Mobility Innovation Lab of Samsung Research America United States

This paper focuses on advancing reinforcement learning for challenging environments characterized by partial observability and non-stationarity, such as dynamic spectrum access (DSA). In the literature, the Deep Recurrent Q-Network was introduced to capitalize on the inherent temporal correlations present in DSA. Nevertheless, its practicality is still questionable due to sample inefficiency and slow convergence. We introduce Dyna-ESN, leveraging both model-based and model-free methods by employing Reservoir Computing for generative modeling. Specifically, we utilize Echo State Networks (ESNs) to synthesize samples for enhancing the sample efficiency of a model-free Deep Echo State Q-network, enabling effective operation of agent given limited genuine relevant samples obtained through interaction with environment. To mitigate potential adverse effects of synthetic samples, an evaluation algorithm guides the sample selection process, ensuring reliability. A sample augmentation technique is also introduced to allow agents to collect adequate samples despite controlling the sensing rate and duration of secondary transmissions. Our analysis explores trade-offs between data evaluation and sample efficiency, as well as the bias-variance trade-off of the model, identifying optimal design parameters. Evaluating the performance of Dyna-ESN in DSA scenarios demonstrates its performance benefits over existing methods, paving the way for more efficient and effective techniques in complex dynamic environments. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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MODELGUARD: Information-Theoretic Defense Against Model Extraction Attacks 33

MODELGUARD: Information-Theoretic Defense Against Model Extr...

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33rd USENIX Security Symposium, USENIX Security 2024

作者： Tang, Minxue Dai, Anna DiValentin, Louis Ding, Aolin Hass, Amin Gong, Neil Zhenqiang Chen, Yiran Li, Hai Department of Electrical and Computer Engineering Duke University United States Cyber Security Lab Accenture

ISBN: (纸本)9781939133441

Malicious utilization of a query interface can compromise the confidentiality of ML-as-a-Service (MLaaS) systems via model extraction attacks. Previous studies have proposed to perturb the predictions of the MLaaS system as a defense against model extraction attacks. However, existing prediction perturbation methods suffer from a poor privacy-utility balance and cannot effectively defend against the latest adaptive model extraction attacks. In this paper, we propose a novel prediction perturbation defense named MODELGUARD, which aims at defending against adaptive model extraction attacks while maintaining a high utility of the protected system. We develop a general optimization problem that considers different kinds of model extraction attacks, and MODELGUARD provides an information-theoretic defense to efficiently solve the optimization problem and achieve resistance against adaptive attacks. Experiments show that MODELGUARD attains significantly better defensive performance against adaptive attacks with less loss of utility compared to previous defenses. © USENIX Security Symposium *** rights reserved.

关键词： Differential privacy

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