Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARATIVE EVALUATION OF THE SENSITIVITY OF MULTI-POLARISED SAR AND OPTICAL DATA FOR VARIOUS LAND COVER CLASSES

The availability of reliable and timely land cover information is the basis to have sound economic planning and resource management for a modern nation like India. In this study, the capabilities of the dual polarimetric Envisat-1 ASAR and Landsat ETM+ data have been investigated for the land cover mapping. A comprehensive evaluation of the sensitivity of the cross-polarized (VH)/like-polarized (VV) ENVISAT-1 ASAR and optical data for various land cover classes has been done and a class separability analysis has been performed under different band combinations. In order to ensure maximum information retrieval, the bands MPDI (Microwave Polarization Difference Index) and NDVI (Normalized Difference Vegetation Index) have also been incorporated in the study. The separability among the class pairs have been analyzed using the Transformed Divergence (TD) procedure while the classification has been carried out using the Maximum Likelihood supervised classifier. The results of sensitivity analysis indicated that the vegetation is highly sensitive to the VH band owing to volume scattering while the built-up class could be more accurately distinguished in the VV band due to the corner reflector effect. The separability analysis further revealed that with the fusion of optical-VH polarised SAR data and the introduction of MPDI band to the multi-polarised SAR data, the separability among various class pairs greatly improved. The Landsat ETM+ and VH backscatter data fused image thus provided the highest classification accuracy of 91.25% with the kappa coefficient of 0.90, thus demonstrating its potential in land cover assessment and monitoring.
Driver Fatigue Detection Using TGAM EEG Signal Processing Module

Driver fatigue and drunk driving are the leading causes of car accidents. To reduce car accidents, researchers suggest using Internet of Things (IoT) technology to detect in-vehicle air quality and alcohol-impaired effects that lead to driving fatigue and driving drunk. Consequently, in this study, we developed an IoT driver fatigue system based on a ThinkGear ASIC Module (TGAM) for capturing the driver’s electroencephalogram (EEG) signals. Additionally, the developed IoT system deployed SGP30 to detect in-vehicle carbon dioxide $\left(\mathrm{CO}_{2}\right)$ and PMS5003 to measure PM10 concentration. Furthermore, we deployed an MQ3 sensor to monitor driver alcohol consumption. The real-time in-vehicle air quality data and EEG data are displayed on a Node-RED webpage. The proposed system for driver fatigue detection also included a Raspberry Pi model B with 8G of RAM.
Building Structure Extraction of Coal Preparation Workshop Based on Scanning Data by 3D Laser Scanner

Architectural design drawings are lacking for coal preparation workshop upgrades due to capacity enhancement. Thus, we developed a method of building structure extraction based on scanning data. A 3D laser scanner was used to acquire scanning data, and a point feature histogram was used as a feature descriptor to achieve a cloud mosaic of different measurement sites. Then, the location and direction of point cloud slices were determined by a point cloud contour. Based on the point cloud slices and the line segment detector straight line extraction method, the line fitting of local point clouds was completed according to minimum variance. Finally, the fitting line was restored to the scanned data, and the building profile structure was extracted through linear optimization. Experiments were carried out with scanning data of a coal preparation workshop in the Zhangcun Coal Mine. The results showed that the proposed method effectively drew the building profile providing a reference for other reverse construction projects.
Research on Cooperative Vehicle Positioning System and Fault Detection Method

In the development of autonomous vehicles, a high-precision vehicle positioning system is critical. The Global navigation satellite system (GNSS) is the most widely used vehicle localization system owing to its advantage and ability to quickly obtain the absolute coordinates of vehicles through satellite positioning. In addition to GPS, other components such as cameras, LiDAR, inertial measurement units (IMU), odometers, and environment feature detection and recognition are used in the design of the localization system. We developed a cooperative vehicle positioning system and a vehicle positioning protection system to ensure the accuracy and reliability of vehicle positioning information. In several scenarios, a single positioning module experienced information interruptions or failures, which prevented autonomous vehicles from obtaining sufficient positioning precision for navigation, thus impacting their vehicle control and safety. Self-driving cars are indispensable. Thus, it is important to ensure the accurate and stable operation of vehicles.
Effect of Mismatch in Frequency of Gate Pulses on Inductor Current Ripple in Coupled Inductor SIDO Boost Converter

DC-DC converters capable of producing dual DC outputs from a solitary DC input are categorized as Single Input Dual Output (SIDO) converters. These converters exhibit superiority over their Single Input Single Output (SISO) counterparts owing to their reduced component tally and enhanced efficiency. The focus of this paper centers on the examination of the Coupled Inductor Single Input Dual Output (CI-SIDO) boost converter, a modified iteration of the SIDO configuration. The method of shifting the gate pulses of the two MOSFETs with respect to each other is used in CI-SIDO boost converter for reducing inductor current ripples. The range of shift between two gate pulses for which maximum inductor current ripple minimization can be achieved is very specific and narrow. Achieving precise gate pulse shifting for high-frequency converter operation proves challenging due to limitations in both analog and digital circuit implementations. Slight frequency discrepancies between the generated relative gate pulses can lead to notable alterations in inductor current ripples. However, this is not investigated in the literature. This study investigates the notable impact of frequency mismatch on inductor current ripples and evaluates the effectiveness of a gate pulse shifting technique for ripple mitigation. The analysis is valid for different values of duty ratios and coupled inductor parameters. The investigation is validated with MATLAB Simulink and hardware experimentation results.
Analysis and Control of Energy Efficient Welding Machine Test Bed Employing SiC based Power Electronic Converter

This paper proposes the operation, analysis and control of energy efficient welding machine test bed employing Silicon Carbide (SiC) based power electronic converters. The proposed test bed has many advantages as compared to the conventional test bed such as: reduced power losses, size, weight, and absence of heat. A mathematical modelling has been developed for the proposed test bed using SiC based MoSFET and Power Diodes. The steady state and dynamic characteristics of DC arc welding machine has been analyzed for testing as per the IEC 60974 -1 standard. The control algorithm and the switching logic for the test bed have been explored in the form of flow chart and digital logic devices. Extensive Matlab/ Simulink simulations have been carried out for the proposed test bed. The efficacy of test bed has been validated under various operating conditions based on the predetermined values and simulation results. Also, the performance of SiC based test has been compared with the conventional Silicon (Si) based test bed.
Grid Current Based MPPT in Two Stage Photovoltaic Systems

This paper presents a grid-side current control in two stage photovoltaic (PV) systems for achieving maximum power injected into grid (MPIG). In two stage grid integrated photovoltaic systems, maximum power point tracking (MPPT) is employed to control DC-DC converter in maximizing the PV power by sensing photovoltaic parameters. In contrast, the proposed technique maximizes the power injected into the grid while operating in different insolation levels by using grid current control. The proposed method works effectively without knowing PV voltage and PV current and hence the system becomes cost effective as there is no requirement of PV-side sensors. Further, the simulation result demonstrates that the proposed grid-current based MPPT has higher penetration of power into the grid under different insolation conditions.
A Dual Input Single Output Microinverter for Grid Connected PV Application

A flyback based dual input single output microinverter (MI) that can simultaneously operate two PV modules at their respective maximum power points is presented in this paper. Simultaneous operation of the input modules increases the power harvesting from the modules when the modules face significant difference in the environmental conditions, i.e. insolation and ambient temperature. The proposed MI has two stages, (i) flyback based dc-dc stage which operates at high frequency and (ii) grid frequency unfolding stage at the grid side. Due to the connection and operation of the MI, the voltage gain requirement becomes half as compared to the single input MI (SIMI). Further, the power rating of the proposed inverter becomes twice as compared to the SIMI whereas the component count is comparatively lesser as compared to two SIMI connected in parallel. An innovative control strategy is proposed where the output voltages of the dc-dc stage are estimated which reduces the voltage sensor requirement. In addition to this, a current control strategy is proposed where the magnetizing current is roughly estimated and used to have the current control in the flyback stage. Due to the presence of galvanic isolation in the dc-dc stage, there is no direct electrical connection between the input and output side which keeps the leakage current negligible. The viability of the proposed MI and its control strategy are verified through an exhaustive simulation study with the help of a 620 W model built in MATLAB-SIMULINK platform.
Research on ROS-based SLAM Implementation for Indoor Environments

Simultaneous localization and mapping (SLAM) is applicable in robotics and mobile platforms. SLAM approaches empower robots to create intricate maps of their surroundings while concurrently pinpointing their exact location within the dynamic environment in real-time. SLAM approaches differs from general robotics in that they lack a dependable source of odometry, requiring them to adapt to the unpredictable movements of mobile devices. We proposed the use of SLAM approaches in indoor environments in the robot operating system (ROS). The ROS encompasses a range of open-source software libraries and tools. These resources not only support the research discussed in this article but also facilitate the development of robot applications. In ROS, the libraries were selected for developing and integrating diverse robotic components, including sensors, hardware, and algorithms. SLAM approaches with the libraries allows for various visualization and simulation tools, making it an ideal platform for conducting SLAM research and implementation.
Coil Coupling Design for Resonant Inductive Wireless Power Transfer System

The coil coupling structure in wireless power transfer (WPT) system plays a crucial role when power is transferred by magnetic coupling. The coupling coefficient between linked coils and resistive coil losses determine the efficiency and power transfer of WPT system. In this paper, the commonly used coil coupling shapes, including helix and spiral, are compared, and spiral coil structure is developed for EV charging. The inductance characteristics of the coil structure are measured and compared with the theoretically calculated values. The developed spiral coil coupling system is tested for different heights and misalignment. Moreover, the simulations of resonant inductive WPT system with series-series (SS) and LCL-LCL topologies are done in MATLAB/Simulink environment. The LCL-LCL topology aids in mitigating harmonic distortions and improving the power quality of the WPT system, by enabling effective filtering and resonance establishment.
Quadruple Boosting Nine-Level Inverter

This paper outlines unique nine-level inverter that is powered from simply one dc source and is ideal for solar and wind applications. The intended result is created through floating capacitors (FC) and integrating them utilizing a switched-capacitor scheme. This configuration has been recognized due to its sensorless capacitor voltage balancing potential, minimizing control issues in creating a tripolar staircase pattern. Additional features sustainable performance that avoids unintentional capacitor excessive voltage. To control the functioning of the topology, Phase-disposition pulse width modulation (PD-PWM) is employed. Furthermore, proposed topology employs fewer semiconductor devices. A comprehensive contrast of distinct latest nine-level inverter configurations is provided to illustrate the positive aspects of the recommended structure about minimizing the switch count. The successful implementation for the suggested structure is explored using MATLAB/Simulink according to various parametric conditions. The Hardware in loop (HIL) application is utilized to examine the simulated waveforms of suggested configuration.
Impact of Gate Turn-Off Voltage on Body Diode Loss of SiC MOSFET and a Scheme to Improve Light Load Efficiency of SiC Converters

SiC MOSFET’s body diode drop is typically 2-3 times more than Si-based PiN diode of similar rating. This leads to higher losses in the body diode of SiC MOSFET which reduces the converter’s efficiency. The reduction in efficiency becomes significant when the converter operates in discontinuous conduction mode (DCM) during the light load operation. Further, the body diode drop in SiC MOSFET is dependent on the gate turn-off voltage ($V_{EE}$) and it increases as $|V_{EE}|$ increases. Conventionally gate drivers for SiC MOSFET use a high value of $|V_{EE}|$ to prevent Miller turn-on. However, a high value of $|V_{EE}|$ increases the diode losses, which further reduces the efficiency. This manuscript investigates the impact of $V_{EE}$ on the body diode loss in a SiC MOSFET. Furthermore, it proposes a scheme to select an optimum $V_{EE}$ to reduce body diode loss while mitigating the possibility of Miller turn-on. To facilitate the development of the proposed scheme, this manuscript- (i) investigates the dependence of the body diode drop on $V_{EE}$ for SiC MOSFET using device physics-based analysis. Further, it validates the same using static characterisation of commercial SiC MOSFET. (ii) it presents a detailed mathematical analysis to estimate the overshoot in gate-source voltage due to the Miller effect. The proposed scheme is experimentally validated on a buck converter operating in DCM (10% of rated power). Operation with the optimal $V_{EE}$ obtained using the proposed scheme shows a 2.32% improvement in efficiency over conventional designs.
Multilevel Inverters with Reduced Switch Count: A Reality Check

In recent years, industry and academia have shifted the focus of research towards multilevel inverters (MLIs) than conventional two-level inverters for high voltage and high power applications. The recent trend in the topological design has been towards the “reduced switch count” (RSC) based MLI topologies which uses lower number of devices to generate higher number of voltage levels. One of the main benefits of the two-level as well as three-level conventional MLI converters has been the same voltage rating of each component used. The RSC MLI topologies do not retain this property in the name of reducing the component counts which has limited their applications. In this paper, several recently proposed RSC MLI topologies have been analyzed and comparative indices have been proposed. The proposed comparative indices are the ratio of maximum to minimum voltage rating of switches termed as switch diversity factor (SwDF), ratio of maximum to minimum voltage magnitude of sources termed as source diversity factor (SrDF), requirement of different voltage magnitude of switches in RSC topology termed as switch count diversity factor (SCDF). Two different parameters as a function of step voltage have also been used to analyze the different topologies. Several RSC-based topologies with single and multiple sources have been analyzed and a comparative study has been presented. Finally, the recommendation for the improvement of the topological design has been provided.
Application of Process Mining to Production Lines Using Industrial Internet of Things

Process mining has emerged as a powerful solution for analyzing production processes in the manufacturing sector, yet its integration with the Internet of Things (IoT) remains a challenge. Drawing from IoT ontologies and business process context models, we introduced a practical methodology for constructing event logs from industrially gathered IoT data. The study was conducted in a controlled laboratory environment to emulate a discrete production model through an industrial communication and database setup. Employing a systematic process mining framework encompassing data preparation, pre-processing, production process analysis, and interpretation steps, we used industrial controllers and Industrial Internet of Things (IIoT) tools to simulate a sauce production process. The generated data was analyzed using ProM software, fortified by visualization techniques such as the ”log as log skeleton” method, for enhancing process flow comprehension and pinpointing ways for optimization. The study results unveiled a systematic linear structure by employing the Alpha Miner method for process model construction. Notably, the analysis exposes extended duration in the production process, prompting further investigation and refinement. The resultant model provides valuable insights into the actual performance of the process and emphasizes the range of potential process scenarios, making it easier to pinpoint deviations from the ideal model. Through the demonstration of the IIoT data integration, the result significantly contributes to the growing enthusiasm for process-oriented methodologies within the manufacturing sector as well as providing an example approach to transform captured IoT data into event logs via contextualization. Furthermore, the study result provides the groundwork for future research and improvement endeavors to enhance manufacturing practices and outcomes.
Rank-Based Encoder and Decoder for a Genetic Algorithm: An Application to Network Design Problem

We developed a rank-based encoder and decoder as part of a genetic algorithm to solve a supply chain network design problem in a planning horizon spanning multiple periods and complex product structures. The problem requires different components and subassemblies to solve. Encoding network-structure decisions, specifically facility locations and their capacities into a chromosome is relatively simple with the implementation of a rank-based encoder. However, decoding a chromosome into a solution is a non-trivial task as it needs the identification of the network structure and its flow of items moving in the facilities. To effectively solve this, the proposed decoder processed the problem in two stages. In the first stage, the network structure was determined by a ranking procedure in combination with solving an assignment problem, while the network flows were optimized as a minimum-cost flow problem in the second stage. The rank-based decoder generated good-quality solutions within a reasonable amount of time.
Control of Bidirectional Inverter in Mixed Energy EV to Home System

There are multiple conversion blocks to interface grid, solar, and EVs in an EV to home system. In this paper, a system is designed to route energy between various sources and loads in a coordinated and organized manner to maximize energy utilization. The work specifically focuses on the design of the bidirectional inverter that interfaces the utility grid with a DC bus to interface the various sources (EV and Solar) to the utility grid and home load. Role of inverter in all the operating modes are specified. The inverter can work either in standalone mode to power home loads or grid-connected mode to charge the EV or transfer power to the grid. Control strategy for inverter in both the modes is developed along with the plant modelling. An FPGA controller is used to validate the control algorithm. The controller is applied to a lab scale inverter prototype to demonstrate the system performance.
Design of Intelligent Analysis System of Basketball Skilled Movement Based on Data Mining Technology

The collection of skilled movement data during basketball games is of great significance for the decisions in basketball competitions. Therefore, coaches and related staff use a variety of auxiliary tools to collect the data related to the basketball games to collect skilled movement and relevant data. However, it is difficult to obtain the data as it cannot be seen directly. This affects the effective use of basketball data and the formulation of a basketball game plan. In this context, we developed an intelligent analysis system for basketball-skilled movement based on data mining technology. We designed the framework of a real-time tracking and analysis system of basketball tactics, then analyzed the data pre-processing strategy, and proposed the design elements of an intelligent analysis system. We used the Apriori algorithm, clustering algorithm, and data mining algorithm based on the Markov process. The research results are effectively used to train athletes and help coaches make reasonable decisions in games.
Identification of Herbaceous Plants with Electronic Nose using Fuzzy Logic Algorithm

Herbs produce non-woody stems and have many applications in medicine, food, and perfume products. We used an electronic nose system and a fuzzy logic algorithm to identify herbaceous plants in this study. The gases emitted by the plants were analyzed with the MQ sensors. The results were used with a fuzzy logic approach to identify the name of the plant. The performance of the prototype system was evaluated for Oregano, Spearmint, and Peppermint with 38/45 plant samples. The prototype showed an accuracy of 100% for identifying oregano, 73.33% for spearmint, and 80.00% for peppermint. The overall accuracy was 84.44%.
Generative AI: Impactful Considerations to Responsible Data Practices in Business Execution

AI is shaping our society and changing what it means to be human. The future is getting frenetic since the adoption of Generative AI (GenAI) in our everyday life. Humanity is slowly transcending from a society of consumers to creators. We first began with Siri and Alexa; now it is ChatGPT, DALL-E, Stable Diffusion and several other tools powered by Artificial Intelligence (AI) and Machine Learning (ML). Audi as well has started the use of Generative Adversarial Networks (GANs) to get inspiration on wheel design. This paper will focus on: the advantages; the social and cultural impact that GenAI opposes; use cases on how GenAI can enhance innovation and drive business value from the technical prospect; the potential risks that might arise if GenAI is abused and conclude with how technology teams alongside the C-Suite and Board of Directors should take action. These risks include deepfakes, legal confusion, hiring biases and inaccurate chatbots. AI is in the service of humanity and it is a responsibility of ours to treat(/train) it with dignity and fairness.