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arXiv 2024年

作者： Khalid, Waqas Yu, Heejung Qadri, Yazdan Ahmad Institute of Industrial Technology Korea University Sejong Korea Republic of Dept. of Elec. & Inform. Eng. Korea University Sejong Korea Republic of Department of Computer Science Yeungnam University Gyeongsan-Si Korea Republic of

Terahertz (THz) communication is expected to be a key technology for future sixth-generation (6G) wireless networks. Furthermore, reconfigurable intelligent surfaces (RIS) have been proposed to modify the wireless propagation environment and enhance system performance. Given the sensitivity to blockages and limited coverage range, RIS is particularly promising for THz communications. Active RIS can overcome the multiplicative fading effect in RIS-aided communications. In this paper, we explore active RIS-assisted THz communications. We formulate the ergodic rate, considering factors associated with active RIS, including active noise and signal amplification, and THz signals, including molecular absorption and beam misalignment. © 2024, CC BY.

关键词： Alignment

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Autonomous Drone Landing on a Moving Naval Base using Vision-Based Robot Control

Autonomous Drone Landing on a Moving Naval Base using Vision...

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elec.rical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Senthil Pon Vignesh Subamanian Suvetha Meenaa Subramanian Padmapriya Muthiah Jalaluddin Mohd Ansari Shajahan Dept. of Elec. and Comm. Engg. SIMATS School of Engineering Saveetha University Chennai India Dept. of Computer Science City College of New York City University of New York New York USA Dept. of Computer Science King Graduate School Monroe College New York USA Dept. of Electrical Engineering City College of New York City University of New York New York USA

This paper proposes a novel technique of an Autonomous Drone landing on top of a moving Naval Vessel Base using Vision-Based Robot Control. To achieve an effective landing a camera is fixed under the drone which captures the images and detects the movement of the landing base. The landing base on top of the vessel is specially designed with a nested tag which is scanned by the fixed camera on the drone from a high altitude. This nested tag is a fiducial marker that is used by the imaging system of the drone as a point of reference in the Naval Vessel Base for precise landing. The controller is designed in the image coordinate frame captured by the imaging system on the drone with the help of the Jacobian Matrix. Whether the vessel remains stationary or moving, it always trembles, especially, when a vessel experiences bad weather at sea, it will be quite challenging for the drone to land on the trembling Naval Vessel Base. These unexpected movements cause the landing tag to disorient from the horizontal view of the captured frames, which makes it difficult to obtain the expected mid-point of the tag. To overcome this complication the translational and rotational motions of the drone are decoupled from the camera using the Virtual Camera Method where the mid-point of the tag is set as the feature point which helps to obtain the expected position during the horizontal plane disorientation of the tag image captured by the camera from the drone. Additionally, Kalman Filter is employed in the imaging system to reduce the observation noise so that precise estimation of the feature point can be achieved.

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General Ramified Recurrence and Polynomial-time Completeness

arXiv

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arXiv 2022年

作者： Danner, Norman Royer, James S. Dept. of Mathematics and Computer Science Wesleyan University MiddletownCT06459 United States Dept. of Elec. Engrg. and Computer Science Syracuse University SyracuseNY13244 United States

We exhibit a sound and complete implicit-complexity formalism for functions feasibly computable by structural recursions over inductively defined data structures. A feasibly computable structural recursion here means that the structural-recursive definition has a run time that is polynomial in the sizes of the representation of the data inputs and where these representations may make use of data sharing. Inductively defined data structures here includes lists and trees. Soundness here means that the programs in the implicit-complexity formalism have feasible run times. Completeness here means that each function computed by a feasible structural recursion has a program in the implicit-complexity formalism. This paper is a follow up on the work of [Dal Lago et al., 2010, Avanzini and Dal Lago, 2018] who focused on the soundness of such formalisms but did not consider the question of completeness. © 2022, CC BY.

关键词： Polynomial approximation

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Information Transfer in Spintronics Networks under Worst Case Uncertain Parameter Errors

Information Transfer in Spintronics Networks under Worst Cas...

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IEEE Conference on Decision and Control

作者： A. A. Rompokos F. C. Langbein E. Jonckheere Dept. of Elec. Eng. Univ. of Southern California Los Angeles CA School of Computer Science and Informatics Cardiff University UK

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

A novel quantum landscape optimization with respect to bias field control inputs is developed with the goal of achieving optimal transfer fidelity subject to robustness against bias field, spin couplings and other uncertainties. This objective is achieved by minimization of a convex combination of fidelity error and worst-case perturbation of fidelity error under directional perturbation of uncertain parameters. The novelty is that the end-point perturbations of the parameters are points of a random uniform sampling of the sphere centered at the nominal values of the parameters. This reveals that the previously developed perfect state transfer with zero sensitivity solution keeps high fidelity and robustness under large rather than differential perturbations.

关键词： Robust control Couplings Sensitivity Uncertainty Perturbation methods Minimization Robustness

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A study on dummy signal processing of targets for the self-propelled artillery

A study on dummy signal processing of targets for the self-p...

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International Symposium on elec.rical and elec.ronics engineering (ISEE)

作者： Tung Nguyen-Thanh Chau Dang-Tran Ngoc Thuong Le-Tien University of Technology - HCMUT Academy of Military Science and Technology HCM City Vietnam Academy of Military Science and Technology HoChiMinh City Vietnam Elec. ELectronics Engineering Dept. University of Technology HCMUT VietNam National University VNU

The self-propelled artillery is one of the weapons with high mobility and very effective in supporting infantry to master battlefields. It is equipped with a radar to capture targets and control weapons to destroy targets. Normaly, the cabin of the vehicle is too small, therefore the commander cannot monitor the crew's actions to improve the effectiveness of the practical training. In this paper, the authors have conducted a research on pseudo-signal generation for self-propelled artillery radars then transmitting them to computers as a basis step for the improving research and training systems. To carry out the above research, the authors conducted a practical survey of signal parameters on the real equipment then combined them with the technical documents to analyze and selec. signals to be transmitted to the experiment computer. Study about methods for simulating the pseudo-signal generation of targets, signal transceiver and display technology on self-propelled artillery radar screen through programming of the STM32-F411 microcontroller and the application of Lora technology (Long Range Radio) to transmit and receive signals. The research results through hard and software experiments have shown as a basis step for evaluating the effectiveness of the design.

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Robust Charging Solutions for 400V EV Batteries: Implementing a Quasi Single-Stage Current-Fed Resonant Converter

Robust Charging Solutions for 400V EV Batteries: Implementin...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Muhammad Furqan Waqar Uddin Kamran Zeb Muhammad Ashar Ayaz Irfan Khan Muhammad Khalid School of Electrical Engineering and Computer Science (SEECS) National University of Science and Technology (NUST) Islamabad Pakistan Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia IRC-SES KFUPM Dhahran Saudi Arabia Dept. of Electrical and Computer Engneering Texas A and M University College Station TX USA Dept. of Elec. and Comp. Engineering Texas A and M University College Station TX USA Electrical Engineering Department KFUPM and IRC-SES KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This work presents a high-performance AC-DC quasi single-stage current-fed resonant converter based elec.ric vehicles (EVs) charger. A non-linear control technique is applied which has two main objectives i.e; Power factor correction (PFC) and regulation of DC output voltage. For this purpose, super twisting sliding mode control (ST-SMC) is suggested in this study for 400V batteries of EVs. To reduce switching losses and improve efficiency, the secondary side switch shows turn OFF with zero voltage switching (ZVS) while the primary ones exhibit ZVS at turn ON instant. Hardware-in-loop (HIL) experimental validation based on the Delfino F28369D dual-core microcontroller is used to verify the performance of the controller and converter simulated on PSIM and MATLAB/Simulink.

关键词： Microcontrollers Switching loss Switches Resonant converters Zero voltage switching Regulation Batteries Safety Voltage control Sliding mode control

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Implementation of an Efficient IoT Enabled Automated Paralysis Healthcare System 8

Implementation of an Efficient IoT Enabled Automated Paralys...

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8th IEEE International Conference on engineering Technologies and Applied sciences, ICETAS 2023

作者： Annepu, Visalakshi Jameel Al-Tamimi, Adnan Naji Aljibori, H.S.S. Challa, Naga Raju Bagadi, Kalapraveen Mohammed, M.N. Abdullah, Oday I. Alfiras, M. Abbas Helmi, Rabab Alayham School of Comp. Science & Eng. VIT-AP University Amaravati India College of Technical Engineering AI-Farahidi University Baghdad Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Bapatla Engineering College Dept. of Elec. and Comm. Eng. Baptla India College of Engineering Gulf University Mechanical Engineering Department Sanad26489 Bahrain University of Baghdad Dept of Energy Engineering Iraq College of Engineering Gulf University Mechanical Engineering Dept Sanad26489 Bahrain Hamburg University of Technology Institute of Laser and Systems Technologies Hamburg Germany College of Engineering Gulf University Electrical and Electronic Engineering Department Sanad26489 Bahrain School of Graduate Studies Management and Science University Shah Alam Selangor Malaysia

ISBN: (纸本)9798350327090

This system aims to provide comprehensive care to individuals with paralysis, including symptom monitoring, medical treatment, and rehabilitation. The IoT-based system comprises interconnected devices, such as sensors, wearables, and mobile apps that communicate with one another and with healthcare providers. The system can track patients' vital signs, activity levels, and medication adherence, and provide immediate feedback to healthcare providers. Additionally, the system can aid with rehabilitation exercises, send reminders, and gather data on progress to enhance treatment outcomes. The system's intelligent analytics can also assist healthcare providers in detecting patterns in patient behavior and tailoring treatment plans more effectively. Overall, an IoT-based paralysis healthcare system has the potential to transform healthcare delivery by offering personalized, real-time care and monitoring to individuals with paralysis. © 2023 IEEE.

关键词： Internet of things

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Development of an Ultra-Sensitive Magnetic-Based Biosensor;a Simulation Study

Development of an Ultra-Sensitive Magnetic-Based Biosensor;a...

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elec.rical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Khalid Yahya Abbas Ali Husseini Onur Dirican Hani Attar Mahmoud Aldababsa Mohamed Hafez Dept of Electrical Engineering Sabratah University Regdalen Libya Dept of Elec. and Electr. Engineering Istanbul gelisim University Istanbul Turkey Life science and biomed. center Istanbul Gelisim University Istanbul Turkey dept. of Pathology Laboratory Techniques Istanbul Gelisim University Istanbul Turkey Dept of Energy engineering Zarqa university Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Dept of Elec. and Electr. Engineering Nisantasi University Istanbul Turkey Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

This study presents an advanced magnetic biosensor design incorporating an L-shaped ferromagnetic core with UL dimensions and an air gap replaced by highly porous aluminum or copper foam later-filled biological samples containing high-permeability ferromagnetic nanoparticles. The sensor detects specific biological molecules through magnetic field interactions. The system's elec.rical parameters were methodically optimized for enhanced performance. The research investigated the impact of various materials on the air gap's magnetic properties and assessed the relationships between permeability, output-induced voltage, input voltage, and input frequency. Findings indicate that using materials with higher magnetic permeability, such as Magnetite (Fe304) or Cobalt ferrite (CoFe2O4) ferrofluids, considerably improved the biosensor's performance by optimizing magnetic coupling between primary and secondary windings. This innovative magnetic biosensor holds potential for diverse applications, including medical diagnostics, environmental monitoring, and industrial process control. The study offers valuable insights into magnetic biosensor design and optimization, facilitating heightened sensitivity and selec.ivity in detecting target molecules.

关键词： Ferrites Ferrofluid Magnetic cores Permeability Magnetic analysis Biosensors Cobalt

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Towards on-device face recognition in body-worn cameras

arXiv

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arXiv 2021年

作者： Almadan, Ali Rattani, Ajita Dept. of Elec. Eng. and Computer Science Wichita State University Wichita United States

Face recognition technology related to recognizing identities is widely adopted in intelligence gathering, law enforcement, surveillance, and consumer applications. Recently, this technology has been ported to smartphones and body-worn cameras (BWC). Face recognition technology in body-worn cameras is used for surveillance, situational awareness, and keeping the officer safe. Only a handful of academic studies exist in face recognition using the body-worn camera. A recent study has assembled BWCFace facial image dataset acquired using a body-worn camera and evaluated the ResNet-50 model for face identification. However, for real-time inference in resource constraint body-worn cameras and privacy concerns involving facial images, on-device face recognition is required. To this end, this study evaluates lightweight MobileNet-V2, EfficientNetB0, LightCNN-9 and LightCNN-29 models for face identification using body-worn camera. Experiments are performed on a publicly available BWCface dataset. The real-time inference is evaluated on three mobile devices. The comparative analysis is done with heavy-weight VGG-16 and ResNet-50 models along with six hand-crafted features to evaluate the trade-off between the performance and model size. Experimental results suggest the difference in maximum rank-1 accuracy of lightweight LightCNN-29 over best-performing ResNet-50 is 1.85% and the reduction in model parameters is 23.49M. Most of the deep models obtained similar performances at rank-5 and rank-10. The inference time of LightCNNs is 2.1x faster than other models on mobile devices. The least performance difference of 14% is noted between LightCNN-29 and Local Phase Quantization (LPQ) descriptor at rank-1. In most of the experimental settings, lightweight LightCNN models offered the best trade-off between accuracy and the model size in comparison to most of the models. © 2021, CC BY.

关键词： Cameras

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Cardiovascular Disease Prediction Using Machine Learning Algorithms 8

Cardiovascular Disease Prediction Using Machine Learning Alg...

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8th IEEE International Conference on engineering Technologies and Applied sciences, ICETAS 2023

作者： Bagadi, Kalapraveen Annepu, Visalakshi Jameel Al-Tamimi, Adnan Naji Challa, Naga Raju Aljibori, H.S.S. Mohammed, M.N. Abdullah, Oday I. Abbas Helmi, Rabab Alayham Alfiras, M. School of Electronics Enineering VIT-AP University Amaravati India School of Comp. Science & Eng. VIT-AP University Amaravati India College of Technical Engineering Al-Farahidi University Baghdad Iraq Bapatla Engineering College Dept. of Elec. and Comm. Eng. Baptla India College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Engineering Gulf University Mechanical Engineering Department Sanad26489 Bahrain University of Baghdad Dept of Energy Engineering Iraq College of Engineering Gulf University Mechanical Engineering Dept Sanad26489 Bahrain Hamburg University of Technology Institute of Laser and Systems Technologies Hamburg Germany School of Graduate Studies Management and Science University Selangor Shah Alam Malaysia College of Engineering Gulf University Electrical and Electronic Engineering Department Sanad26489 Bahrain

ISBN: (纸本)9798350327090

The fact that cardiovascular disease (CVD) is a major cause of death worldwide highlights the significance of accurate prediction for successful preventative and treatment measures. Machine learning algorithms, which use the analysis of vast patient data to reveal hidden patterns and risk variables, have recently come to light as potential techniques for CVD prediction. This study intends to analyse how machine learning (ML) techniques are used in CVD prediction and evaluate how well they perform in comparison to conventional approaches. The study makes use of a sizable patient cohort's medical history, demographic data, and clinical factors in a complete dataset. Predictive models are built using a variety of machine learning algorithms, including support vector machine (SVM), gradient boosting, K-nearest neighbours, naive Bayes classifier, and logistic regression. To find the most important factors influencing CVD risk, feature selec.ion techniques are used. Metrics like accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve are used to assess how well the machine learning models work. The outcomes are contrasted with well-known risk prediction models and clinical guidelines in order to assess the added value of machine learning methods. This work intends to improve CVD prediction capabilities and offer useful insights for better risk assessment and management strategies by utilizing the power of machine learning. © 2023 IEEE.

关键词： Forecasting

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