Miracle-dz/UniFAR-FTUs huggingface dataset:
Glare Potential Evaluation of Structured PV Glass Based on Gonioreflectometry

Potential glare through reflected sunlight can be a significant hindrance factor for photovoltaic (PV) installations, especially for building-integrated PVs with atypical orientations and tilt angles present. Structured glass surfaces or antireflective (AR) coatings are often used as a solution, however, there is currently no universally recognized method or metric to estimate their impact on glare. This article presents an evaluation of the glare potential of different surface glasses for use in PV modules based on their bidirectional reflectance distribution functions (BRDF). BRDF threshold values for retinal burn damage, flash blindness, and discomfort glare are calculated based on retinal irradiance thresholds from literature. Subsequently, gonioreflectometric measurements on PV minimodules are used to characterize the reflectance profiles of eight different glass surfaces. Results for all measured structured glasses, both satinated and macrotextured, show significant reductions in BRDF compared with smooth glass, largely eliminating the potential for flash blindness as well as discomfort glare at low incidence angles. At high incidence angles, increased potential for discomfort glare as well as forward scattering along the glass surface can be observed. AR coatings, however, are shown to be insufficient to eliminate glare causing flash blindness.
Numerical Analysis of Microfluidic Gold Electrode Array for Dielectrophoretic Characterization of U87 Glioma Cells

Dielectrophoresis have been widely used as rapid and label-free cell characterization and separation method in biology. Integration of an electrode array in a microchannel enabled high throughput yet low-cost alternative to conventional cell separation devices. In this study, we numerically analyzed the performance of a microfluidic gold electrode array to determine dielectrophoretic behavior of U87 glioma cells. We investigated the influence of medium conductivity, applied electric field strength and frequency on the dielectrophoretic spectra of U87 glioma cells.
Insulation Design for a Compact, Medium-Voltage Transformer

The purpose of this work is to develop a compact hatchable, medium-voltage transformer for electric ship power distribution systems. To achieve this, this work proposes a new transformer design capable of operating at 500 kHz and 250 kW and providing 20 kV isolation. The proposed design separates the primary and secondary sides of the transformer with a mica insulation sheet and employs a planar winding arrangement. To distribute the electric field along the mica sheet, a pair of conductive and semiconductive layers are used. The transformer prototype is 30 x 25 x 10 cm3, passes a 30 kV HiPot test, and achieves 99.5% efficiency.
Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits

: Selection indexes represent the real efforts of a breeding program to obtain genetic gains for various significant traits simultaneously. As such, this study selected superior F 5 lines by combined selection for fruit quality using an index based on predicted genotypic values via residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology weighted by agronomic weights. To do so, 97 F 5 papaya lines obtained by the single seed descent method, resulting from the biparental cross between the genotypes JS-12 and Sekati, were evaluated for the main traits related to fruit quality. Results of the analysis of deviance and the genetic parameters indicated that there was genetic variability, indicating possible success in the selection process. Based on the selection index, 29 lines were selected as superior for fruit quality with ‘Formosa’ pattern. The strategy of the selection index combining BLUPs of multiple traits of interest associated with agronomic weights allows maximizing genetic progress while discarding less-promising genotypes.
A CFAR Detection Algorithm for Weibull and Lognormal Clutter Mixture in SAR Images

In this paper, a Constant False Alarm Rate (CFAR) detector based on a Wei bull Lognormal Mixture Model (WLMM) for Synthetic Aperture Radar (SAR) clut-ter images is proposed. In doing this, the clutter parameters of the mixture and the corresponding percentiles are first estimated through the Maximum Likelihood Estimators (MLEs). Then, an adaptive threshold is set to maintain constant the Probability of False Alarm $(P_{F^{\gamma}A})$. The Simulated Annealing (SA) optimization algorithm is used to reduce the searching time in an efficient manner to approximate the global optimum percentiles. Application of the obtained CFAR framework on a real-world image, i.e., MSTAR BTR 60 (Moving and Stationary Target Acquisition and Recognition Bronetransporter), shows that an optimized weighting yields a good detection.
Realtime Determination of Mood in Classroom Environment

Many innovations have occurred with today's technologies, the issue of detecting and analyzing human behavior through image processing techniques has also gained popularity recently. However, studies carried out with image processing in the field of education are not very common and many students' interests in class courses are unknown. Aim of this study is to track students in a classroom via webcam to detect, track their faces and create a system that can detect moments when the teacher cannot determine the condition of the student. For this purpose, a Python application was developed using OpenCV libraries Electrocardiography (ECG) and Galvanic Skin Response (GSR) was observed through monitoring heart rate and GSR. A real-time system was obtained by analyzing our ECG/GSR and heart rate sensor data, which we received in Python, via Arduino. Using the face, heartbeat sensor, ECG, and GSR, students' moods were determined in real time, and by developing this in Arduino, it was able to reflect students' attentional states analyzed in real time with colored LED lights.
Optimal Design of Planar Magnetic for CLLC Resonant Converter

In this paper, a magnetic integrated structure of CLLC resonant converter is proposed. The non-interleaved winding structure design is adopted, and the distance between the primary and secondary winding is increased as a control variable, which effectively increases the leakage inductance of the transformer. Only one magnetic core is used to realize the three magnetic components in the CLLC resonant tank, which solves the problem of too many magnetic components in the CLLC resonant converter. At the same time, the influence of the parasitic capacitance of the winding on the working characteristics of the CLLC resonant converter is introduced, and the factors that affect the parasitic capacitance of the winding are analyzed on this basis. Finally, an experimental prototype with 400 V input, $40V\sim 60V$ output, 1 kW power rating and resonant frequency of 1 MHz is designed. The experimental results verify the correctness and effectiveness of the proposed optimal design method for planar magnetic.
Distributed Back-EMF-based Position Self-Sensing of Fault-Tolerant Permanent Magnet Modular Motor Drives for Electrical Aircraft Propulsion

Permanent magnet (PM) modular motor drives (MMDs) have been recognized as promising candidates for motor drive propulsion units in electrified aircraft due to their high power density, high efficiency, and promising potential for high fault tolerance. A distributed position self-sensing (sensorless) algorithm for modular motor drives is proposed to further enhance the system reliability by eliminating single-point failures attributable to the machine's single shaft-mounted position sensor. A distributed back-EMF state filter with integrated bandwidth-partitioned observer command feedforward (IBP-OCFF) has been developed with virtual electromagnetic isolation features that reject negative impacts on estimated position and speed from both asymmetries inside a module and disturbances from other modules. Simulation and experimental results have demonstrated that a high level of inter-module independence can be achieved in the modular motor drive using the proposed distributed position self-sensing algorithm.
Method for Automated Data Collection for 3D Reconstruction

The paper presents an automated method to guide a human operator during RGB image shot for improving the quality of further 3D reconstruction for low-rise outdoor objects and a use case for method application. The method provides automation as a three-step process: local analysis of images performed during the shooting, global analysis, and recommendations performed after the shooting. Method steps filter out defective images, approximate future 3D-model using Structure-from-Motion (SfM), and map it to human operator trajectory estimation to identify object areas that will have low resolution on the final 3D model, requiring additional shooting. Method structure does not require any sensors except RGB camera and inertial sensors and does not rely on any external backend, which lowers the hardware requirement. The authors implemented the method and use-case as an Android application. The method was evaluated by experiments on outdoor datasets created for this study. The evaluation shows that the local analysis stage is fast enough to perform during the shooting process and that the local analysis stage improves the quality of the final 3D model.
Three-phase Transformerless PV Inverter with Reconfigurable LCL Filter

In this paper, a three-phase transformerless photovoltaic (PV) inverter with a reconfigurable LCL filter is proposed. The reconfigurable filter constructs LCL filter between the converter and the grid with only three inductors which leads to magnetic components reduction by half compared to a conventional LCL filter. Moreover, the reconfigurable filter at high frequencies clamps the common mode (CM) voltage to Neutral and suppresses the leakage current. Therefore, the proposed topology is able to inject high quality grid current with minimum magnetic components and limit the leakage current to meet international standards. The principle of operation is explained by the equivalent circuits. The proposed topology is verified and compared by simulation and experimental results which agree with the theoretical concept.
A Combined 3-D Geometric and Magnetic Modeling Approach of Coils in Air-Cored Resonant Induction Machines

Air-cored resonant induction machines normally have flat cross-sectional coils because of the air-cored and toothless magnetic structure. Since the precision in the geometry modeling is vital to the inductance-estimation accuracy, a combined geometry modeling and inductance calculation method is proposed. The coil geometry construction process can handle non-circular cross-sections and complex coil shapes, it generates a single- or multi-filament model ready for analytic inductance calculation and coordinates information for 3-D FEA modeling. Using closed-form solutions for the mutual inductance of two straight segments placed at any orientation in 3-D, the filament-level, conductor-level, coil-level and winding-level inductances can be calculated quickly and accurately and avoids numerical singularities so is suitable for incorporating into an optimization program. Good correlation is confirmed in validation against 3-D FEA results on an air-cored resonant induction machine design.
Forex Price Movement Prediction Using Stacking Machine Learning Models

Forex is an attractive choice for investors who admire any making profit challenges in the fluctuating market. But on the other hand, it means investors can lose money at the same time. Many investors look for ways to reduce the risks by finding price movement prediction tools. Therefore, this paper proposes the Stacking Machine Learning Models to predict the future price direction to help investors to decide and plan strategies. We experimented with comparing baseline models to evaluate the accuracy performance. In addition, we improve the accuracy performance using Technical Analysis and Fibonacci Retracements to gain an accuracy of 90%.
Minimal Solvers for Point Cloud Matching with Statistical Deformations

An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.
Smart Street Light Monitoring and Visualization Platform for Campus Management

As a recent trend in urbanization and intelligent technologies, smart lighting systems have been implemented in many major cities to support smart urban environments. This research developed a web application platform for data visualization and lighting device monitoring at Thammasat Uni-versity, Rangsit Campus, Thailand. This implementation provides administrative and operative staff with an all-in-one platform through a convenient interface for monitoring, controlling, and collecting data from area devices and sensors. Platform devel-opment was divided into two sections: back-end application, providing application programming interface (API) endpoints, and front-end application, offering an interface for interacting with on-campus staff. Finally, the web application was deployed on a cloud platform so that responsible persons may access it on any device and acquire data in real time. Given the platform's capabilities, further data analytics may be proposed for building a smarter lighting system.
Comparative Analysis of Harmonic Suppression of Different Types of Active Dampers

With the development of new energy technology, large-scale access of power devices such as inverters to the grid will cause instability of the grid-connected system and harmonic amplification at the point of common coupling (PCC). In order to improve the stability of the grid-connected system and suppress the voltage harmonics, this paper first establishes a mechanism model for the harmonic source and builds a one-port network impedance model of the grid-connected system. For the convenience of analysis, the impedance model is simplified as an equivalent circuit of the RLC in resonant frequency range. Then, aiming at the problem of harmonic suppression, the voltage harmonic amplification coefficient method is proposed to quantitatively analyze the phenomenon of voltage harmonic amplification, and different types of active dampers are added to the equivalent RLC circuit. By analyzing the relationship between different types of active dampers and voltage harmonic amplification coefficient and harmonic suppression effect, the active damper with the best harmonic suppression effect is obtained to adjust the damping characteristics of the whole new energy power generation system and improve the stability of the grid-connected system. Finally, the feasibility of the method is verified by simulation.
loTs and Loop-Mediated Isothermal Amplification- Based Biosensing using Cloud-Enabled Features

Internet-of-Things technology (IoTs) have accelerated biosensor applications in all fields. Loop-mediated isothermal amplification (LAMP)-based biosensor technologies in conjunction with smartphone detection have been adequate to cover the demands of mobile diagnostics. The ease of use, affordability, portability, high sensitivity, flexibility, and specificity demands of point-of-care detection can be achieved by low-cost electronic components, 3-dimensional printing technologies, capturing images of calorimetrically detected readouts made our system a promising approach for real-time point-of-detection in the field. In this study, we implemented a cloud service to our LAMP-based biosensor. We previously performed bacteria detection using colony-based LAMP device and now distributed the optical readouts of the assay using smartphones. We transferred the obtained image and results of the assays through cloud. Our user-friendly interface simplifies the data processing, it directly digitized the readouts and eliminates the need of data interpretation.
Coordinated Control Method of Multi-energy Routers Considering Line Resistance

As more and more new energy devices are connected to the power grid, the energy Internet with energy routers as nodes is an effective way to accept new energy access, which can realize the two-way flow of energy and effective control of power. In this paper, considering the influence of line resistance when multiple energy routers need mutual power to improve the stability of system operation, it analyzes the coordinated control strategy of the designed multi-energy routers running in an ideal case and the influence of line resistance on the effect of coordinated control strategy. Then improves the control strategy in order to eliminate the influence of the line resistance, and finally achieves the maximum utilization rate of new energy and the highest reliability of load power supply when multi-energy routers operate cooperatively. Aiming at the coordinated control strategy designed in this paper, the simulation verification is given at the end.
Development and Field-Testing of a Non-intrusive Classroom Attention Tracking System (NiCATS) for Tracking Student Attention in CS Classrooms

This Research to Practice Full Paper presents our Non-intrusive Classroom Attention Tracking System (NiCATS) and discusses the data collected through it. Academic instructors and institutions desire the ability to accurately and autonomously measure students' attentiveness in the classroom. Generally, college departments use unreliable direct communication from students, observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. It has been widely reported that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, mini-lectures, case studies, or videos. Tracking these “drops” in attention can be crucial for the accurate timing of these change-ups in activities. This allows for maximal attention and a greater amount of deeply learned material. Autonomously collected data can also be used either in real-time or post-hoc to alter the design and presentation of lectures. Keeping track of student attention is vital to having confidence in delivering material. Even if lectures do not break up presentation slides with attention-raising activities, they can still show more important information during periods of high attention and less important information during periods of low attention. This area of research has applications both in in-person classrooms and online learning environments. The long-term goals of this research can prove invaluable for large in-person classrooms or classrooms where students’ faces are obscured, such as behind computer monitors.
Steady-State Analysis of Power Converters using the Enhanced State Vector Algorithm

Based on the state vector algorithm, an accurate and efficient method is proposed to find the steady-state characteristics of power converters. The convergence to steady-state is accelerated using an adaptive order selection method and simplifying the Taylor series. The proposed approach includes an optimized searching algorithm to determine the switching time of the uncontrolled switches. This method's main advantage is its ability to find the steady-state characteristic of converters with complex structures and uncontrolled switches while maintaining the required accuracy and speed. The method is applied to several examples, and to verify the results and superiority of the method, they are compared with PLECS steady-state analyzer, conventional, and augmented state vector results.
Remaining useful life of bearings based on temporal features and support vector regression

Prognostics and health management (PHM) play a significant role in increasing system reliability, safety and reducing maintenance costs, particularly in critical sectors (aeronautical, military, nuclear, aerospace, etc.). In the present paper, a prognostic method for a bearing component is proposed based on temporal features and support vector regression which are extracted from vibration signals and reduced by principal component analysis (PCA) approach, by selecting the highest monotonicity level features. Then, support vector regression is presented to predict the remaining useful life of the considered component using the fusion health indicator. The obtained results are promising in terms of performance and the effectiveness of the proposed method through the use of two different experimental datasets.
A Vision System With 1-inch 17-Mpixel 1000-fps Block-Controlled Coded-Exposure Stacked-CMOS Image Sensor for Computational Imaging and Adaptive Dynamic Range Control

This study introduces a vision system that can acquire images at high speeds and high resolutions. Image sensors are used not only in digital still cameras but also in various applications that require capturing wide luminance differences beyond human perception. For example, fast, high-resolution object recognition, and motion tracking in automatic driving systems are essential, particularly in dark tunnels or the mid-summer sunshine. However, the resolution, frame rate, pixel size, and dynamic range should be traded off to achieve a high performance in capturing moving objects with a high contrast. We have developed a high-speed vision system with a readout operation of 1000 fps, resolution of 4K ${\times }\,4\text{K}$ , dynamic range of 110 dB, and fine pixels of $2.7~{\mu }\text{m}$ . These characteristics were achieved using several technologies such as 1) coded exposure (CE), which divides the image plane into smaller blocks and controls the exposure time of each block individually, 2) arrangement of analog-to-digital converters in parallel for each block, and 3) three-dimensional wafer stacking, which enables high-density integration of circuits and pixels. The proposed system can be applied in CE-based computational imaging in addition to high-dynamic-range applications for handling both the dark and bright areas in a scene.