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Mediterranean Electrotechnical Conference (MELECON)

作者： Luigi Borzi’ Alessandro Manoni Alessandro Zampogna Fernanda Irrera Antonio Suppa Gabriella Olmo Dept. Control and Computer Engineering Politecnico di Torino Turin Italy Dept. Information Engineering Electronics and Telecommunication Sapienza University Rome Italy Dept. Human Neurosciences Sapienza University Rome Italy IRCCS NEUROMED Institute Pozzilli Italy

ISBN: (数字)9781665442800

ISBN: (纸本)9781665442817

Wearable sensors represent a valuable means for monitoring motion signs and symptoms of Parkinson’s disease (PD). In this paper, we explore the potential of a single inertial sensor to yield information correlated to the patient’s subjective perception of axial motion impairment during daily activities. This latter is expressed using as a relevant metric the sum of MDS-UPDRS items 2.11-2.13. Methods: thirty-one patients with PD were enrolled in this study, and asked to perform a timed-up-and-go test while wearing an inertial sensor on their thigh. Several time- and frequency-domain features were extracted from the inertial signals. They were fed to a random forest regression model for the prediction of the axial impairment metric. The model was optimized using 10-fold cross-validation and performance were assessed using leave-one-subject-out test. Results: Pearson correlation coefficient with the addressed metric of 0.76 (0.86) and mean absolute error of 1.70 (1.52) were obtained in patients under (not under) dopaminergic therapy. Moreover, moderate to strong correlations were found between the predicted score and some important disease progression, axial impairment, and motor performance metrics. Conclusion: a single wearable inertial sensor may be used for assessing motor disabilities of patients with PD.

关键词： Measurement Correlation Inertial sensors Thigh Predictive models Feature extraction Biomedical monitoring

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A Multi-Level HXRI-Based Approach for XR-Based Surgical Training

A Multi-Level HXRI-Based Approach for XR-Based Surgical Trai...

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IEEE International Conference on systems, Man and Cybernetics

作者： Avinash Gupta J. Cecil Miguel Pirela-Cruz Department of Industrial and Systems Enterprise Engineering University of Illinois Urbana-Champaign Dept. of Computer Science Center for Cyber-Physical Systems Oklahoma State University Stillwater USA Creighton University Dignity Regional Medical Center Chandler USA

In the past decade, Extended Reality (XR), through rapid technological advancement, has managed to make modest changes in surgical training approaches. Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-based simulators have been adopted for training in various surgical domains such as brain, eye, laparoscopic, and orthopedic surgery among others. However, there is a lack of effort in exploring Human Extended Reality Interaction (HXRI)-based concepts to design effective training environments that corroborate the actual training procedure. In this paper, an HXRI-based multi-level approach has been proposed for the creation of XR-based training environments. The multi-level approach takes advantage of both Virtual and Mixed Reality to create a holistic training environment while eliminating the disadvantage arising from using stand-alone VR and MR-based training environments. The approach was based on the results of an exhaustive study in which medical personnel interacted with VR and MR environments to train in an orthopedic surgical procedure. The approach, design, and development of the XR environments, the study, and the results from the study which underscore the importance of such a multi-level approach are presented in the paper.

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Improving super-resolution performance using meta-attention layers

arXiv

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arXiv 2021年

作者： Aquilina, Matthew Galea, Christian Abela, John Camilleri, Kenneth P. Farrugia, Reuben A. Dept. of Communications & Computer Engineering Faculty of ICT University of Malta UM Msida Malta Malta Deanery of Molecular Genetic & Population Health Sciences University of Edinburgh Scotland Edinburgh United Kingdom Dept. of Communications & Computer Engineering Faculty of ICT UM Dept. of Computer Information Systems Faculty of ICT UM Dept. of Systems & Control Engineering Faculty of Engineering UM

Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations. Additional information (metadata) describing the degradation process (such as the blur kernel applied, compression level, etc.) can guide networks to super-resolve LR images with higher fidelity to the original source. Previous attempts at informing SR networks with degradation parameters have indeed been able to improve performance in a number of scenarios. However, due to the fully-convolutional nature of many SR networks, most of these metadata fusion methods either require a complete architectural change, or necessitate the addition of significant extra complexity. Thus, these approaches are difficult to introduce into arbitrary SR networks without considerable design alterations. In this paper, we introduce meta-attention, a simple mechanism which allows any SR CNN to exploit the information available in relevant degradation parameters. The mechanism functions by translating the metadata into a channel attention vector, which in turn selectively modulates the network’s feature maps. Incorporating meta-attention into SR networks is straightforward, as it requires no specific type of architecture to function correctly. Extensive testing has shown that meta-attention can consistently improve the pixel-level accuracy of state-of-the-art (SOTA) networks when provided with relevant degradation metadata. Despite average memory/runtime overheads of less than ≈2.6%/0.025 seconds for the datasets and models considered, meta-attention improves the performance for both PSNR and SSIM;for PSNR, the gain on blurred/downsampled (×4) images is of 0.2969 dB (on average) and 0.3320 dB for SOTA general and f

关键词： Image reconstruction

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Channel Estimation for UAV-based mmWave Massive MIMO Communications with Beam Squint

Channel Estimation for UAV-based mmWave Massive MIMO Communi...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

The incorporation of UAVs in 5G and envisioned 6G wireless communication systems is considered for many applications and use-cases, either as part of the infrastructure, providing coverage and connectivity (e.g., during unforeseen and rare events) or as an end-user, e.g., in remote sensing, real-time monitoring and surveillance, to name a few. From the perspective of the physical layer and the involved signal processing algorithms, the transmission environment between the UAVs and the ground communication devices, along with the utilisation of massive MIMO in the mmWave spectrum, require new channel estimation algorithms to support the required physical layer functionality. In this paper, the problem of channel estimation in a multi-user, UAV-based mmWave massive MIMO system is considered in view of the so-called beam squint effect as well as the time-varying nature of the involved channels due to mobility. The proposed approach takes advantage of the low-rank channel matrix and solves a minimisation problem via ADMM, leading to a low complexity, iterative algorithm. The performance of the proposed algorithm is evaluated via simulations and its efficacy is demonstrated over other algorithms from the relevant literature.

关键词： Wireless communication Upper bound Surveillance Channel estimation Signal processing algorithms Massive MIMO Physical layer

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Modeling the effect of surface roughness for screen-printed silver ink on flexible substrates

Modeling the effect of surface roughness for screen-printed ...

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Electronic Components and Technology Conference (ECTC)

作者： Mohamed Y. Abdelatty Ashraf Umar Gurvinder S. Khinda Mohammed Alhendi Mark D. Poliks Dept. of Systems Science and Industrial Engineering State University of New York at Binghamton Binghamton NY USA Dept. of Electrical and Computer Engineering State University of New York at Binghamton Binghamton NY USA

The role of surface roughness of printed RF components becomes more crucial as the frequency increases. Thus, modeling surface roughness for printed RF components is fundamental for accurate results. At multi-GHz range, the skin dept. becomes comparable to the root-means-square height of the rough conductor surface. Failure to model the increased attenuation caused by conductor surface roughness can result in losses past the design margin and cause severe signal integrity problems. In this paper, we build a model to simulate the surface roughness of microstrip-lines; the model is based on real surface roughness measurements. We focus on theoretically modeling of screen-printed silver ink microstrip lines on polyimide substrate. The main objective is building a model to simulate surface roughness of printed RF component based on experimental measurements and study its effect on RF performance. In this way, it can be used for different inks printed using different additive manufacturing techniques. We used the modified Morgan and Hammerstad equation and the compound model to determine frequency dependent values for effective layer thickness and conductivity for a screen-printed silver ink trace. The results were compared to the experimental measurements. A very good agreement is observed between S21 using our model and the experimental measurements. An RMSE of 13.1% is achieved between experimental measurement and the simulated data.

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Modeling the effect of trace profiles on the RF performance of additively manufactured microstrip transmission lines on polyimide substrates

Modeling the effect of trace profiles on the RF performance ...

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Electronic Components and Technology Conference (ECTC)

作者： Ashraf Umar Mohamed. Y. Abdelatty Gurvinder S. Khinda Mohammed Alhendi Mark. D. Poliks Dept. of Electrical and Computer Engineering State University of New York at Binghamton Binghamton NY USA Dept. of Systems Science and Industrial Engineering State University of New York at Binghamton Binghamton NY USA

The trace profile and thickness of a conductor have a significant effect on its Radio Frequency (RF) performance. Additively manufactured traces in particular, have a non-uniform thickness/profile. The thickness of the trace at the edges of a printed conductor is normally less than that at the center of the trace. At high frequencies, these edge thicknesses are less than the skin dept. of the conductor thereby leading to increased losses along the line. It is therefore important to accurately model the printed transmission lines so as to have a better understanding of how these factors affect the RF performance of the printed traces. This paper models the effect of trace profiles on the RF performance of a screen printed microstrip transmission line using (ACI FE3124) a silver ink on 5 mil thick Kapton. The profile of a sample transmission line is obtained using the Keyence laser microscope. This profile is then broken down into various sections. The different profile types are then simulated using ANSYS® High Frequency Structure Simulator (HFSS) from 10 MHz to 40 GHz. RF parameters for the different trace profiles are then compared. It is observed that traces with overall thicknesses less than the skin dept. result in higher insertion losses while varying transmission line widths due to printing imperfections affects the trace impedances.

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A Precise and Rapid Early Pregnancy Test: Development of a Novel and Fully Automated Electrochemical Point-of-Care Biosensor for Human Urine Samples

SSRN

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SSRN 2022年

作者： Yuksel, Mustafa Luo, Wei McCloy, Brad Mills, Jason Kayaharman, Muhammed Yeow, John T.W. Dept. of Systems Design Engineering University of Waterloo ON Canada SannTek Labs Inc WaterlooON Canada Dept. of Electrical and Computer Engineering University of Waterloo ON Canada Waterloo Institute for Nanotechnology University of Waterloo ON Canada

Human chorionic gonadotropin (hCG), a glycoprotein hormone secreted from the placenta, is an important biomarker for pregnancy. In this study, we designed a precise, rapid and fully automatic device with an electrochemical point-of-care biosensor capable of quantitative hCG detection from human urine samples for early pregnancy detection. Gold and Ag/AgCl electrodes, whose structure with optimum isopotential region and current density, were simulated using COMSOL Multiphysics® software and custom-made from Flex Medical. The sensing surface was fabricated with DSP self-assembled monolayers (SAMs) and covalently immobilized anti-hCG-beta antibody. The detection method involved a sandwich assay using anti-hCG alpha-HRP. Based on an automated agitation design implemented in our device, the surface reaction rate is significantly improved comparing to routinely performed sandwich assays, and therefore a rapid detection of very low concentration can be achieved. Electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA) measurements were used to characterize the immobilization of the antibodies and to determine the sensor activities respectively. The sensors exhibited a detection limit of 2.17 mIU/ml with a detection range of 0 to 100 mIU/ml in agreement with clinical hCG levels. They responded very well to hCG, but not to luteinizing hormone (LH), which has a high degree of cross-reactivity with hCG. The results showed that the immunosensor has high specificity, good reproducibility, and long-term stability for the detection of hCG in urine samples. © 2022, The Authors. All rights reserved.

关键词： Antibodies

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Salp Swarm Algorithm-Based Nonlinear Robust control of Magnetic Levitation System Using Feedback Linearization Approach 2020

Salp Swarm Algorithm-Based Nonlinear Robust Control of Magne...

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3rd International Conference on Electronics, Communications and control engineering, ICECC 2020

作者： Ismael, Omar Y. Qasim, Mohammed Noaman, Mohanad N. Kurniawan, Aditya Systems and Control Engineering Dept. Ninevah University Ninevah Mosul Iraq Mechatronics Engineering Dept. Politeknik Kota Malang Malang Indonesia

ISBN: (纸本)9781450374996

This paper presents a robust nonlinear controller design for a magnetic levitation system (MLS). The feedback linearization method is utilized to transform the nonlinear model of MLS into the controller form. The controller is then designed and its parameters are optimized by the Salp Swarm Algorithm (SSA). Extensive MATLAB simulations are performed to evaluate the performance of the proposed controller as well as to compare it with PID and LQR controllers which are optimized by the SSA as well. Obtained results demonstrate that the proposed controller successfully tracks different kinds of reference signals (step, sine, and square) even in the presence of the system parameter perturbations and outperforms the PID and LQR controllers. © 2020 ACM.

关键词： controllers

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Analyzing Evacuated Tube-Based Domestic Electric/Solar Water Heater with an Aluminum Reflector to control Electrical Consumption

SSRN

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SSRN 2023年

作者： Sabry, Ahmad H. Al-Dujaili, Ayad Q. Oleiwi, Bushra Kadhim Alkubaisi, Yaser Mahmood Humaidi, Amjad J. Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Control and Systems Engineering University of Technology-Iraq Baghdad Iraq Department of Sustainability Management Dubai Academic Health Corporation Dubai4545 United Arab Emirates Control and Systems Eng. Dept. University of Technology Baghdad Iraq University Tenaga National Malaysia

The second largest consumer of domestic energy is electric water heaters (EWH). Using hybrid Electric/Solar Water Heaters (ESWHs) in place of EWHs is a practical energy-saving alternative. The effectiveness and optimization of various solar water heater technologies have received a lot of attention, but hybrid electric/solar water heater (ESWH) systems have received less attention. However, there is a lack of ESWH systems improving their performance towards more energy savings. This work develops a hybrid ESWH system using an Aluminum (AL) reflector sheet underneath the system’s evacuated tubes and discusses the improvements in electrical energy savings experimentally. Five scenarios have been applied for four days for each season of Baghdad-Iraq from July 2020 to July 2121. The energy of all the selected days is found to be significantly higher when the water heater working on electrical power only, while the energy is lower when the water heater working on hybrid solar/electrical power. The result obtained showed a great savings approaches to (337) kWh yearly as compared with EWHs, which promises a significant reduction of grid electricity demand and greenhouse gas emissions. © 2023, The Authors. All rights reserved.

关键词： Water heaters

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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