Journal of Electronics and Electrical Engineering

Analysis of Performance Improvement of Planar Antenna Arrays With Optimized Square Rings for Massive MIMO Applications

Jafar Ramadhan Mohammed, Abdulrazaq A. Khamees — Mon, 08 Apr 2024 00:00:00 +0800

The power pattern of the conventional fully optimized planar arrays can be properly reshaped according to the required user-defined constraints. However, the practical implementation of such fully optimized large planar arrays is complex and expensive. This paper introduces a new and efficient technique that is capable of providing better performance and almost the same power pattern shapes as that of the conventional fully optimized planar arrays by optimally adjusting the element amplitude and phase excitations of the outer-square rings instead of all elements' excitations. The proposed technique starts with a massive fully planar array then divides it into two contiguous sub-planar arrays which are both symmetric about the original array center. The elements excitation amplitudes or phases of the outer sub-planar array are only adjusted to form the desired power pattern shapes, while the amplitudes or phases of the central sub-planar array elements which have usually higher weights than the outer elements are made constants (i.e., they made ones for the case of amplitude-only control and zeros for the phase-only control). The results demonstrate the capability of the proposed planar array to form the required power patterns with far less number of the adjustable elements.

Embedded Cyber-Physical System for Physiological Control of Ventricular Assist Devices

Thu, 21 Mar 2024 00:00:00 +0800

Ventricular Assist Devices (VADs) play a crucial role in both bridging to transplantation and serving as destination therapy for congestive heart failure (CHF) management. This study aims to address the limitations of existing control strategies for VADs, specifically their inability to adapt automatically to hemodynamic changes. It proposes a novel embedded cyber-physical system (CPS) based on real-time data processing, reconfigurable architecture, and communication protocols aligned with Health 4.0 concepts to enhance physiological control over VADs (PC-VAD). The research employs a multi-objective PC-VAD approach within a hybrid cardiovascular simulator. An embedded CPS is introduced to overcome challenges related to differences in controller characteristics between computers and embedded systems. The study assesses the performance of the embedded CPS by comparing it with a computer-based control system. The embedded CPS demonstrates outcomes comparable to the computer-based control system, maintaining mean arterial pressure and cardiac output at physiological levels. Even in the face of variations in ejection fraction, the embedded CPS dynamically adjusts the pump's rotational speed based on simulated clinical conditions. Notably, there is no aortic reflux to the ventricle through the VAD during testing. These findings affirm the satisfactory control performance of the embedded CPS in regulating VADs. The study concludes that the embedded CPS effectively addresses the limitations of current VAD control strategies, exhibiting control performance comparable to computer-based systems. However, further experimentation and in vivo studies are necessary to validate and ensure its applicability in real-world scenarios.

Remote Low-Cost Web-Based Battery Monitoring System and Control Using LoRa Communication Technology

Awang Abdul Hadi Isa, Mohammad Tariq Iqbal — Tue, 19 Mar 2024 00:00:00 +0800

Batteries are a complex electrochemical device that exhibit non-linearity and stochastic behavior which rely upon the operational conditions and environmental factors, making battery monitoring a vital feature throughout its application. This paper introduces a novel web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an integral part of the Internet of Things (IoT). The system is implemented with the ESP32 microcontroller, with an emphasis on affordability in broader applications. The system provides comprehensive real-time online data by integrating a combination of multiple sensors. The proposed system seeks to address the limitations of existing communication technology by utilizing the benefits of LoRa, a technology that facilitates effective long-range, low-energy communication which makes it particularly well-suited for real-time monitoring applications. In addition, a control operation enables users to regulate crucial aspects of batteries, such as their charging and discharging. The research conducted a meticulous experimental evaluation of the proposed system at different operations, and the results successfully aligned with the main objective and aims of the research. The proposed system successfully enables real-time remote monitoring and user control, long-term data visualization through data logging, and assessment of battery conditions. Data logging was introduced to enhance the utilization of future battery evaluation, such as State-of-Charge (SOC), State-of-Health (SOH) and Remaining Useful Life (RUL). As a result, the developed system makes it suitable for many applications requiring effective energy storage solutions, such as renewable energy and Electric Vehicle (EV) applications.

Academic Education in the Era of Generative Artificial Intelligence

Maryam Vafadar, Ali Moradi Amani — Thu, 22 Feb 2024 00:00:00 +0800

This paper provides a technical review of the Generative AI technology and its challenges and opportunities in the education sector. Generative Artificial Intelligence (GAI), presented in some tools such as ChatGPT, has been continuously penetrating our normal lives. It has also attracted several research efforts, from both academia and industry researchers, to solve real-world problems in different applications such as finance and health. Generative AI is indeed a type of Artificial Intelligence that can efficiently "create" a wide variety of information in the form of images, videos, audio, text, and 3D models. Therefore, they can facilitate data analysis and visualization, and enhance personalized and adaptive learning in the education sector. Although instructors and teachers will not be substituted by Generative AI robots completely, education and academic delivery of courses are expected to experience a revolution in the presence of GAI. Similar to other new technologies, serious potential challenges and opportunities are expected in employing GAI in the education sector.

A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems

Wei He, M. Tariq Iqbal — Mon, 19 Feb 2024 00:00:00 +0800

Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.

Evaluation of a HVDC Interconnection to Improve the Voltage Stability of the Nigerian Transmission Network

Omowumi G. Olasunkanmi, Andrew R. Barron, Alvin Orbaek White, Grazia Todeschini — Mon, 22 Jan 2024 00:00:00 +0800

The Nigerian transmission network is prone to outages and low reliability, and both issues are currently impacting the population's quality of life and hindering economic development. This situation is expected to worsen in the future, due to the increase in forecasted demand. This paper assesses the stability of the Nigerian transmission network under existing grid conditions, and determines the impact of High Voltage Direct Current (HVDC) on voltage stability. The PV and QV analysis methods were carried out using the DIgSILENT PowerFactory software package. Two weak buses were identified, and an HVDC model was built and integrated into the existing network at these locations. The results demonstrated that the HVDC interconnector positively impacts system stability by improving the voltage profile and the reactive power margin (RPM).

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Yaw O. M. Sekyere, Francis B. Effah, Philip Y. Okyere — Fri, 12 Jan 2024 00:00:00 +0800

The constant rise in energy demand and concerns about climate change have led to increased penetration of renewable energy sources (RES). Maintaining active power balance between generation and demand in power systems with significant penetration of these highly variable and intermittent renewable sources requires an efficient load frequency control (LFC) strategy. One such strategy that has gained the attention of researchers is optimal tuning of PID controllers of LFC using metaheuristic method. This paper presents a PSO variant for optimal tuning of PID controllers for load frequency control of power system integrated with renewable energy resources. The proposed PID tuning technique is tested on a two-area power system commonly used in the literature. Seven scenarios have been used to validate the effectiveness of the proposed Load Frequency Control. For more realistic evaluation, governor dead band and communication time delays have been incorporated in the test system in one of the scenarios. Simulation results obtained when compared with those of three well-known PID-tuning metaheuristic algorithms produced shorter settling time and smaller frequency and tie line power deviations.

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

Yaw O. M. Sekyere, Francis B. Effah, Philip Y. Okyere — Tue, 09 Jan 2024 00:00:00 +0800

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

Forecasting-Aided State Estimation in Power Systems During Normal Load Variations Using Iterated Square-Root Cubature Kalman Filter

Teena Johnson, Sofia Banu, Tukaram Moger — Mon, 08 Jan 2024 00:00:00 +0800

The main aim is to estimating the voltage profile at all the buses in the system before the arrival of next set of hybrid measurements from field. The effectiveness of the algorithm ISCKF during load variations is evaluated with respect to already implemented Kalman filter approaches for this application. This includes the sudden changes in loads which occurring in practical power systems. The utilization of an iterated square-root cubature Kalman filter (ISCKF) for power system forecasting-aided state estimation (FASE) is being studied during normal load variations. Its implementation involves "Newton-Gauss iterative method being embedded into the square-root cubature Kalman filter (SCKF)" at the measurement update step of Kalman filter. The square-root factor of error covariance matrices is calculated by utilizing QR decomposition to avoid losing of positive definiteness of the matrix. The estimation is carried out utilizing hybrid measurements from remote terminal units and phasor measurement units. The state vector is forecasted using the proposed method in the interval period between two measurement arrivals from the devices. Thereby, caters to state estimation of the voltages at buses in the system even when the measurements are unavailable. The efficacy of the proposed algorithm to FASE is evaluated for IEEE 30-bus system and Northern Region Power Grid (NRPG) 246-bus system. The simulation results show that the proposed ISCKF outperforms the CKF by significant improvement in accuracy of forecasting-aided state estimation. ISCKF will be able to give results before the next set of hybrid data arrives (expected from an estimation algorithm). Therefore, the proposed estimation algorithm is applicable for real-time practical application, with respect to large power systems as well.

Role of Phonon Scattering on the Transport and Performance of an N-Channel Monolayer Black Phosphorus Transistor

Khairul Alam — Mon, 08 Jan 2024 00:00:00 +0800

We investigate the influence of phonon scattering on the transport properties and performance metrics of a monolayer n-channel black phosphorus transistor within a four-band tight binding Hamiltonian, employing a recursive Green's function algorithm and Buttiker probe scattering model. Our analysis reveals that electron-phonon scattering significantly degrades the on-state current, while its effects in the subthreshold region are found to be negligible. Further examination identiﬁes optical phonons as the primary contributors to the degradation of on-state current, with acoustic phonons playing a less prominent role. The ballisticity of the device declines from 42% to 24% when transitioning from solely acoustic phonon scattering to the combined inﬂuence of acoustic and optical phonons. Expanding the placement of Buttiker probes from beneath the gate region to cover the entire path from source to drain results in a further 48% reduction in on-state current. The on-state current exhibits a parabolic relationship with the inverse Kelvin temperature. To quantify the effects of phonon scattering on device performance, we assess the key parameters, transconductance and unity current gain frequency. Phonon scattering is observed to severely impact both the parameters. The on-state transconductance declines from its ballistic value of 24.9 mS/µm to 3.99 mS/µm when both acoustic and optical phonons are concurrently active. Similarly, the unity current gain frequency decreases from 1.18 to 0.2 THz due to phonon scattering. Additionally, our analysis reveals that approximately 7–9% of the total power dissipated within the device is attributed to phonon scattering effects, while the remainder is released through thermalization in the device's contacts. Phonon scattering is shown to induce both lattice cooling and heating, depending on the presence or absence of potential barriers. When a potential barrier exists in the channel, electrons injected from the source experience lattice cooling before the barrier region and lattice heating after crossing the barrier. Including the source and drain contact resistances in our model unveils that achieving a contact resistance value of approximately 100 Ω-µm is crucial for the effective functioning of black phosphorus devices.

EchoTrace: A 2D Echocardiography Deep Learning Approach for Left Ventricular Ejection Fraction Prediction

Anup Kumar Paul, Yakub Sadlil Bhuiyan — Thu, 04 Jan 2024 00:00:00 +0800

A key indicator in the diagnosis, prognosis, and management of individuals with heart failure (HF) is the left ventricular ejection fraction (EF). The amount of blood that is forced out of the left ventricle with each contraction provides important details on how well the heart can circulate oxygen-rich blood across the human body. Echocardiography has long been the most commonly used imaging method for determining LVEF due to its availability and cost-effectiveness. This paper makes use of the EchoNet-Dynamic dataset, which has left ventricle coordination data. An organized data preprocessing pipeline is created to extract frames along with coordinates. The suggested model architecture incorporates pre-trained transfer learning models that are optimal for the task of localizing the left ventricle boundaries. By predicting coordinates with CNN regression-type models, we showed how a novel volume tracing method could be used to localize the left ventricle boundary and perhaps mitigate the drawbacks of segmentation-based methods. Based on predetermined thresholds, we divided the ejection fraction (EF) values into "normal," "mild," and "abnormal" categories to detect the patient's heart condition. The analysis revealed a high degree of sensitivity for the "normal" and "abnormal" classes but was lower in the "mild" class. We obtained a confusion matrix accuracy of 77%.