Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Improvement of Direct Inductive Power Transfer Systems Using Adaptive Model Predictive-Based Phase Shift Control

In this paper, an adaptive model predictive (AMP)-based phase shift control is proposed to improve the dynamic performance of inductive power transfer (IPT) systems. The proposed control is inherited from the conventional model predictive control (MPC) with the prominent characteristics of time-domain design and easy implementation, while incorporating an adaptive mechanism for the step change of the control input (phase shift angle) to ensure both accuracy and fast transient performance. The step change of the phase shift angle is automatically magnified for fast dynamic tracking and dwindled for tracking accuracy at steady states. The settling time of the dynamics can be precisely designed based on the equations of the proposed control. Both simulation and experiments validate the dynamic improvement of the proposed AMP-based phase shift control over the conventional proportional-integral (PI)-based phase shift control. The advantages of adopting adaptive step change over fixed step change for the phase shift angle are also revealed by empirical verifications.
Low-cost 3D Printed Circularly Polarized Lens Antenna for 5.9 GHz V2X Applications

This paper presents design and realization of a circularly polarized antenna consisting of a linearly polarized patch antenna and a 3D printed lens, at the same time performing the functions of wave collimator and a polarizer. The antenna is dedicated for 802.11p systems, as a part of road infrastructure, with operation bandwidth 5.85 - 5.925 GHz. Its realised gain and axial ratio at center frequency 5.9 GHz are 14.3 dBi and 2.17 dB respectively. The lens provides approximately 6% bandwidth with axial ratio below 3 dB. The proposed antenna is easy to design and fabricate and can be realized with the use of low-cost materials.
Battery Aging Behavior Evaluation Under Variable and Constant Temperatures with Real Loading Profiles

Studying and analyzing battery aging behavior is crucial in battery health prognostic and management. This paper conducts novel and comprehensive experiments to evaluate battery aging under variable external stresses, including different dynamic load profiles and variable environmental temperatures. Respond analysis in the time and frequency domain is performed to account for the different aging rates under different current loadings, where the statistic calculation and fast Fourier transform are used for the analysis. The empirical model is used to fit the fade curve for the comparisons between constant and variable temperatures. The capacity decrease and internal resistance increase are extracted to evaluate capacity and power fade, respectively. The experimental results show that the urban dynamic operating conditions help to prolong the service life compared to the constant current aging case. In contrast, the aging under the highway profile accelerates the aging process. Although the average temperature is the same as under constant temperature conditions, variable temperature conditions accelerate battery aging.
STABLE SOLUTION OF TIME DOMAIN INTEGRAL EQUATION METHODS USING QUADRATIC B-SPLINE TEMPORAL BASIS FUNCTIONS*

A method is described to provide a radiographic silver halide material by coating on at least one side of a support, covered with a hydrophobic subbing layer comprising as a latex copolymer vinylidene chloride, methylacrylate and itaconic acid, following hydrophilic layers: at least one gelatinous dye containing layer comprising one or more dyes, at least one silver halide emulsion layer, at least one protective antistress layer, and optionally an afterlayer, characterized in that said hydrophilic layers have a swelling ratio of not more than 200% and in that said hydrophilic layers are coated simultaneously by the slide-hopper coating or by the slide-hopper curtain coating technique.
Layout Hotspot Pattern Clustering Using a Density-based Approach

Since the number of hotspot patterns detected on a layout using machine learning technique is very large, it takes designers a lot of time to classify these hotspot patterns for subsequent modification. These hotspot patterns are diverse and complex in shape. Therefore, we propose a density-based hotspot pattern clustering approach to classify these hotspot patterns into groups, which extracts the density feature of hotspot patterns while considering the shifted and distorted polygons on hotspot patterns. Experimental results show that our approach can classify the hotspot patterns more efficiently than SIFT method with similar results in each group.
A High Frequency Coupled Inductor Design for High Power Density DC-DC Converters

Coupled inductors has been extensively adopted in DC-DC converters due to their distinctive features, such as the flux cancelation ability, enhanced performance, higher power density and lower weight compared to their non-coupled counterparts. The fast-switching speed of the wide bandgap power electronics demands high frequency, high density, and more efficient passive components, especially the inductors. In this paper, we present a high frequency coupled inductor design using parallel air gaps and copper foil windings with the commercial off-the-shelf planar magnetic cores. The proposed inductor is prototyped and demonstrated in a four-phase interleaved, 50 kW boost converter. The developed inductor prototype has a volume less than 0.5 L and a resonant frequency over 7 MHz with much lower ac resistance compared to a printed circuit board (PCB) based inductor.
An Automated and Fine- Tuned Image Detection and Classification System for Plant Leaf Diseases

Plant leaf disease detection is the process of discovering and diagnosing diseases that damage the plant leaves. This can be done using a variety of approaches including visual inspection, laboratory tests, computer vision techniques etc. Plant leaf diseases must be detected and categorize the cause in order to take corresponding counter measures to manage and control them for healthy and achieve high-yielding crops. In this contemporary Deep Learning era traditional object detection methods have become obsolete due to limitations such as the need for manual crafted features, lack of robustness, and inability to handle large datasets. Deep learning-based approaches are more robust, can account for differences in brightness, interference, and perspective, and can automatically learn features from enormous datasets. These techniques are practical because they can efficiently process large datasets in a brief time frame. Several research studies have developed expert and automatic disease detection methods. Most of this research are limited to one or two plant species and a few disease types. In this research study, we presented a computer vision approach applying the state-of-the-art YOLO algorithm with our own dataset consisting of 8 different classes of pant leaf diseases caused by fungi, bacteria, viruses and pest. Our system achieved promising results and effectively predicted the diseases with the bounding boxes and class probabilities. Overall, this research paper provides a solution for plant disease detection using YOLOv5 and contributes to the development of automated agricultural management systems.
Closed-Loop Stability Analysis of Digitally Current Mode Controlled Three-level Buck Converter using A Simplified Discrete-Time Modeling Framework

Primary challenge in a digitally controlled three-level buck converter (3LBC) is to identify suitable modeling, analysis, and design techniques, keeping in mind simplicity and accuracy, in presence of sampling delay and additional flying capacitor dynamics. Using a discrete-time (DT) framework, this paper develops a generic full-order large-signal model, which is accurate and takes into account the structural change of the statespace model during the flying capacitor reconfiguration. However, owing to the mathematical complexity of full-order model, this paper also develops a generic and simple reduced-order largesignal DT model, which is as accurate as the full-order model. Accuracy of the reduced-order model is compared with the fullorder model, both in large-signal and small-signal scenarios in SIMPLIS environment. A mixed-signal peak current-mode control with active balancing of flying capacitor voltage is proposed. Using the small-signal DT model, analytical stability criteria are developed for inductor current dynamics, flying capacitor voltage dynamics and closed-loop converter dynamics. Analytical stability criteria are verified using SIMPLIS simulation and experimental results through a 12 V to 1 V, 500 kHz, 3LBC hardware prototype. The proposed modeling framework will be helpful for stability analysis and direct digital control design.
Magnetic Design of a 4.16 kV/1 MW Medium Voltage PV Plus Storage Solid State Transformer (PVS-SST)

This paper discusses the magnetic design for a modular 3 phase/ 9 module 4.16 kV/1 MW PVS-SST. The magnetic system is a critical part of the PVS-SST determining the galvanic insulation, power density and high-power transfer. The magnetic subsystem includes a 222-kW medium frequency transformer (MFT), two external inductors equally split to both side of the MFT. The electrical insulation system is tested at 12 kV ac peak voltage. In addition, the partial discharge (PD) test is conducted at 7.5 kV ac peak voltage to ensure the PD-free operation. The magnetic module is applied in a dual active bridge (DAB) converter with 200 kW rated power. The peak efficiency of the DAB converter is 99.53%, and the efficiency at 200 kW is 98.85%. Finally, the PVS-SST was successfully verified with medium voltage grid-tied test.
Energy-efficient beacon listening scheme for periodic vital signal monitoring

A new energy-efficient beacon listening scheme with a medium access control protocol is proposed for periodic vital signal monitoring in a wireless body area network system. The conventional system receives every beacon frame to adjust the protocol context; however, the proposed scheme receives only the necessary beacon frames. As a result, power consumption decreases by reducing idle listening times. The simulation results demonstrate that the proposed scheme reduces power consumption of the beacon mode to almost that of the non-beacon mode. Compared with the normal beacon mode, it reduces the power consumption by 25% when the traffic model is assumed to be electrocardiogram (ECG). This is suitable for devices monitoring periodic data such as an ECG application.
Multimodal Person Verification based on audio-visual fusion

Face and voice are two of the most popular biometric features commonly used for identity verification. It has been found that there are occasions that challenge unimodal authentication systems, and related studies have demon-strated that a simple combination of these two modalities can yield more powerful recognition systems. Based on this, this paper explores feature fusion for bimodal authentication strategies and introduces attention mechanisms for data enhancement. The recognition effectiveness of the three sys-tems of unimodal and bimodal recognition is verified on the SpeakingFaces dataset, and finally the average error rate of the bimodal recognition system is lower than that of the unimodal system, which proves that the proposed system can significantly improve the performance of the model. In the future, the fusion of fractional layers will be tried and compare with the model in this paper to find the best bimodal fusion method.
Causal Models in the Social Sciences.

Causal models are formal theories stating the relationships between precisely defined variables, and have become an indispensable tool of the social scientist. This collection of articles is a course book on the causal modeling approach to theory construction and data analysis. H. M. Blalock, Jr. summarizes the then-current developments in causal model utilization in sociology, political science, economics, and other disciplines. This book provides a comprehensive multidisciplinary picture of the work on causal models. It seeks to address the problem of measurement in the social sciences and to link theory and research through the development of causal models.Organized into five sections (Simple Recursive Models, Path Analysis, Simultaneous Equations Techniques, The Causal Approach to Measurement Error, and Other Complications), this volume contains twenty-seven articles (eight of which were specially commissioned). Each section begins with an introduction explaining the concepts to be covered in the section and links them to the larger subject. It provides a general overview of the theory and application of causal modeling.Blalock argues for the development of theoretical models that can be operationalized and provide verifiable predictions. Many of the discussions of this subject that occur in other literature are too technical for most social scientists and other scholars who lack a strong background in mathematics. This book attempts to integrate a few of the less technical papers written by econometricians such as Koopmans, Wold, Strotz, and Fisher with discussions of causal approaches in the social and biological sciences. This classic text by Blalock is a valuable source of material for those interested in the issue of measurement in the social sciences and the construction of mathematical models.
Cyclone Intensity Estimation using Infrared Satellite Imagery

Tropical Cyclone is one of the most frequently occurring natural disaster in India. Roughly speaking, every year there are five to six occurrences of tropical cyclones forming, with a ratio of 4:1 between the coastal regions of the Bay of Bengal on the East coast and the Arabian Sea on the West coast. For a long time, experts have attempted to develop a method for analyzing and estimating strength or severity of a Tropical Cyclone (TC) real time. The lack of a method that is universally accepted and consistent for estimating intensity results causes an inherent bias in the estimation process, and the most commonly utilized technique is a manual algorithm that relies on cloud images obtained from satellites. Tropical cyclones, have the potential to generate a multitude of severe weather conditions, including storm's, extremely powerful winds, and lightning strikes. Tropical Cyclones lead to the risk of both human fatalities and substantial property damage. Hence, bias in this field has greater risks. As a part of this study, we have contributed a deep-learning-based state of the art model to aid in estimating strength or severity of a Tropical Cyclone (TC) real time. The model uses imagery captured using infrared technology from INSAT-3D & INSAT-3DR Satellites. The main aim of this study is to estimate cyclone intensity with reduced bias. This work has been done over the Indian Ocean Basin using Mercator projection images for Asian Sector from the India's first telecommunications satellite system, INSAT Satellite. Additionally, we have also supplied this model with a visualization portal that has dedicated pages to promote open-source and contextual information for end users.
A Filtering Dipole Antenna Design with Bandwidth Enhancement for 5G

The present study proposes a dipole filtering antenna with wide-band characteristics for communication. The filtering is achieved by employing parasitic elements and wide-band characteristic is obtained by reshaping the dipole and using specially designed balun structure. One radiation null is optimized at the higher frequency band limit for sharp transition from pass band to stop band via two half-rectangular ring resonators. The antenna operates between 2.6 and 5 GHz and has maximum realized gain of 8.39 dBi. The |S11| is less than -10 dB in the operating frequency band. The simulation results and evolution of the design procedure are presented.
Grinding Trajectory Planning Strategy of Bone Robot based on Archimedes Curve

The current robots used in epiphyseal plate cutting are primarily used for passive positioning, which means they cannot achieve autonomous positioning of the robot for grinding. Furthermore, because they only rely on the doctor's hand-eye coordination to detect the boundary of the bone bridge, the intraoperative real-time grinding accuracy is severely lacking. The grinding trajectory planning strategy of bone robot based on Archimedes curve is suggested as a solution to this issue. The trajectory planning of the end of the bone grinding robot is done utilizing the quintic polynomial interpolation in conjunction with the Archimedes curve principle, taking into account a real epiphyseal plate grinding scenario. Comparing the experiments with sawtooth trajectory planning, it is discovered that the generated trajectory motion parameters reach the target point more smoothly for the method proposed in this paper, and the effective interval reduction of joint angle is about 0.323 rad, the effective interval reduction of joint velocity is about 0.176 rad/s, and the effective interval reduction of joint acceleration is about 0.401 rad/s2, This improves the dynamic performance of the robot.
Implementation of 3-bit asynchronous binary up-down counter using Electro-Optic Effect based Mach-Zehnder Interferometers

In photonic communication systems, Electrooptic effect-based Mach-Zehnder Interferometer is a crucial component that significantly determines the achievable data transmission rate. Recent developments in manufacturing and processing methods have provided a renewed focus for research on optical switching. Optical logic gates, Flip-flops, latches, and other digital electronic systems are the essential components of next-generation high-speed communication networks for advancement in technology of computers, communication systems, combinational and sequential circuits and can be used as fundamental building blocks for large integrated circuits. The present work shows the development of a T flip flop and a 3-bit asynchronous binary up-down counter using a minimum number of MZI switches. This approach ensures low optical cost in the performance of all proposed devices. MATLAB software has been used for the realization of devices. The simulation result has been validated by the relevant devices standard results.
A Beamforming Network for 5G/6G Multibeam Antennas Using the PCB Technology

This paper presents the design of a 4×4 Blass matrix for enabling beamforming operations in a four-element uniform linear antenna array (ULAA) operating at 3.5 GHz, corresponding to the first frequency band reserved for the forthcoming fifth and sixth generation (5G/6G) systems. To obtain a simple and inexpensive device, the proposed feeding network, which provides to the ULAA beam pointing capabilities towards four different directions, is entirely implemented using printed circuit board technology. The design procedure is realized adopting an extended iterative mathematical framework accounting for losses control and providing the pointing angles and the matrix coefficients. The performance of the conceived architecture is numerically investigated through full-wave simulations. The versatility, low losses, and low price of the developed microstrip network makes it suitable for both 5G/6G air/terrestrial femtocell base stations and Internet of Things (IoT) cluster-head sensors/actuators.
An Implementation of Dowell model with Neural Network to Foil Winding Transformer

This research investigates the accuracy of Dowell model (DM) and builds an accurate and practically useful semi-analytical method for leakage inductance modeling of foil winding transformers. As a widely used model for AC resistance and leakage inductance, DM is well known for its high applicability and high accuracy compared to the other modeling methods. However, it is found that transformers with certain geometrical conditions are used in most works of literature for implementing DM. Although some analyses about the modeling accuracy were conducted, the applicability of DM for some geometry is still unclear. Therefore, in this research, the accuracy of the modeling is analyzed, focusing on the frequency and geometry dependency of foil winding. Some results show the modeling error of DM has frequency dependency. Furthermore, modeling using DM with Artificial Neural Network (ANN) is implemented to achieve more practically usable modeling. As a result, the utility could be proved with accurate modeling results of the transformer samples. Some advantages are also discussed to show more possibility of developing this method.
Calculation of Cable Electrical Parameters Considering Proximity Effect

In this paper, the electrical parameters of the power cables considering proximity effect are calculated. The conductor subdivision method is used to calculate the conductor layer impedance. The earth impedance parameter considering horizontal stratified soil is calculated, and the broadband model of cable is established. Finally, the influence on the cable parameters when some conditions are changed is analyzed.
Active Power Decoupling Strategy for Single Phase AC Charging Using a Dual Inverter Drive

The dual inverter drivetrain topology allows for the reuse of drivetrain components for charging, eliminating the need for an onboard charger. However, during single phase charging, this topology suffers from an inherent second harmonic ripple in the battery current which doubles its peak value, leading to higher losses and increased heat production in the batteries. An active power decoupling strategy is presented here, that eliminates this ripple by the addition of passive elements and a new control strategy. No additional active components are required, other than a pair of relays to connect the passive elements during charging. The strategy is validated experimentally for a 240V/32A/7.68kW charging system.
Novel Low-loss Coaxial Slot Array Based on Gap Waveguide Technology for E-band Automotive Radar Applications

This paper presents a novel ultra-thin slotted array antenna for automotive radar applications operating at E-band. A low-loss air-filled coaxial waveguide transmission line based on multilayer waveguide (MLW) technology is formed by vertically stacking three unconnected thin metal plates. The proposed antenna element consists of two series-fed columns of 6 slots which are combined with a power divider. In order to prevent any possible field leakage due to the air gaps between the layers, a periodic textured structure is used in the top and bottom layers. The desired patterns are fabricated by using chemical etching technique which is a fast and low-cost manufacturing process suitable for mass-production. The design, numerical simulations and experimental validation of the proposed antenna are presented in the frequency band from 76 to 81 GHz. The measurement results show a realized gain of around 15 dBi and a reflection coefficient better than -10 dB in the entire band of interest. The proposed coaxial MLW slot array antenna provides the advantages of low-loss, low-profile, low-cost and mass-production capability at millimeter-wave frequencies.