Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Multi Carrier PWM Based Harmonic Mitigation of Multi Level PV Inverter with Grid Integration

Present paper tells about the enhanced PWM method for a five level transformer with fewer grids connected and standalone diode clamped multilevel inverter (DCMLI) for reducing the leakage current magnitude. Here we use 2 modified reference waves with improved PWM approach's for producing five level inverter output terminals by gaining the enhanced PWM approach uses single triangle wave. Here to decrease the total harmonic distortion (THD) in the grid current we develop the harmonics to multiple carrier frequencies. The inverter terminals are diminished when the harmonics are shifted to higher order according to the size of the required filter. To synthesize the five level ac output, just 24 power semiconductor switches are required and other six are operated at line frequency so that it diminishes the switching loss and also the system total cost to handle power imbalance among PV sources for every phase of 3-phase inverter to employ a new modulation strategy in its regulation. So the grid connected 5-level multi string inverter gives some interesting results using grid integration.
Auto-calibration of Automotive MIMO Radars Using Simultaneous Localisation and Mapping

This paper presents a new method of automotive MIMO radar self-calibration which uses targets of opportunity embedded in road infrastructure, such as road signs and traffic lights. While conventional offline calibration of a phased array antenna requires accurate knowledge of the positions of calibration targets relative to the radar, such information is not available in a dynamic scenario. To compensate for this, we have developed an estimation procedure based on an extended Kalman filter (EKF) to address the challenge of simultaneous localisation, mapping and calibration. Numerical simulations demonstrate the possibility to decrease the sidelobes level and compensate the steering bias of a MIMO radar with the proposed method.
Generic principles of neighbourhood design : with particular reference to Tehran.

The main aim of this research is to develop a list of principles relevant to the design of neighbourhoods and in particular neighbourhood centres in Tehran. The study embodies two main phases, a literature review and a survey. During the literature review various dimensions of a neighbourhood centre are explored among both 'prescriptive' and 'explanatory' theories in architecture and urban design. Attempts are made to extract the essence and concepts of individual projects among the prescriptive resources, while the investigations of explanatory resources are focused on finding concepts and ideas which are applicable to design. The characteristics of neighbourhood centres are then classified into three categories called functional, perceptual and operational. These categories embody different sections which are separately studied among the literature and design principles are derived out of the writings and presented at the end of each section. Finally, a provisional list of principles is developed as the result of the literature review. The survey is designed to examine the above principles in the real world in two contrasting neighbourhood centres in Tehran. Face-to-face interviews with the residents of the neighbourhoods, together with the observation of cases, comprise the research instruments. The interviews are based on qualitative open-ended questions in order to find out the characteristics of the centres which the residents mostly appreciated or disliked. A checklist is formulated to explore design factors out of the survey responses. The design principles resulting from the survey are then compared with the initial list. The outcome of the research is the final list of generic principles which draws together the results of the literature investigation and the survey. These principles are primarily aimed at those directly engaged in the design of neighbourhood centres such as architects and urban designers. It also includes information for the management body of the centres. For the ease of users the principles are classified into ten sections under the three main categories mentioned above. Along with the above results, there are some secondary findings about the respondents' differences in perception and evaluation of their neighbourhood centre which are presented in the concluding chapter of the thesis. The study intends to decrease the gap between theoretical research and practical work by presenting design principles, extracted from the conceptual theories, that can be easily used by designers. Based on both theoretical and empirical approaches, it also helps to draw attention to the importance of neighbourhood centres in enhancing the quality of day-to-day lives of the city inhabitants.
Opinion Mining Models for Learner Feedback on Massive Open Online Courses

Feedback and rating in MOOC's (Massive Open Online Courses) are critical components in the creation of courses as well as a successful learning process. These data can be evaluated to discover any probable patterns and, depending on the input, the course is likely to be improved. Recent research in sentiment analysis has shown that it may be used to identify emotions. Positive and negative feedback classification can be used as an initial parameter to determine the learner's degree of satisfaction. The major goal of our work includes investigating various machine learning methods for sentiment analysis utilizing existing MOOC course feedback as a case study. The emotions shown in the feedback might give information about the learner's degree of pleasure. Logistic Regression, KNN, Naïve Bayes, Decision Tree and Random Forest were the methods adopted in the research.
A Comprehensive Study of Internet of Things and Digital Business on the Economic Growth and its Impact on Human Resource Management

The research paper aims to examine the impact of digitalization and IoT technologies on the business world and the ways in which it also impacts the global economy. The paper examines that digital Human Resource Management can increase the productivity and the efficiency of both the HR professionals and the employees of the organisation. The findings of the research also reveals that through digitalisation the HR managers can build better relationships between the employee and the company, can think about the wellbeing of the employees better, and make them feel more valued and appreciated. It can also help in creating a competitive atmosphere in the workplace which can motivate the employees. The research further reveals that the active IoT technologies are increasing in the business world. The articles also analyses the advantages and disadvantages of implementing digitalisation and IoT in the workplace and there exists some requirements that need to be met for the successful implementation of the IoT technology. The research also estimates that in future companies can develop their business by adopting digitalisation. In the paper it is also mentioned that during the pandemic the global economy has decreased and to balance the condition it is important to implement digitalisation in the business world.
Generalized Pareto distribution exploited for ship detection as a model for sea clutter in a Pol-SAR application

Ship remote sensing can be regarded as an essential tool exploited for the purpose of managing maritime traffic. It can contribute promptly in the monitoring network system by increasing ships safety at sea. This work aims to verifier a new model for sea clutter based on analytic distribution. Our objective lies in integrating it to develop a constant false alarm procedure for ship detection. Generalized Pareto distribution was exploited for this objective on Polarimetric-SAR images. We were able in this paper to compute the detector based on the aforementioned distribution and assess the performances related to ship detection capabilities. The set of data processed in this paper was acquired by RADARSAT-2 satellite over a port area of the province of Vancouver located in the coastline region of Canada. It was verified in this paper that the Generalized Pareto distribution is a valid model of sea reflectivity. Second, the derived procedure of ship detection related to this stochastic process achieves a high detection probability (93%) for the HH polarization while showing a small level of false alarm (0.5%).
Fruit Sorting with Amplitude-only Measurements

The food industry is in constant demand of performing and easy to implement Non-Destructive Evaluation NDE techniques. In this paper, we tackle the problem of the automatic inspection of fruits and more specifically, the sorting of healthy and damaged fruits, taking apples and peaches as examples. In a recent work, we have explained how to proceed by combining mm-Wave measurements processed with a 2D-FFT and Machine Learning algorithm. The accuracy reaches at least 80%. Although the 2D-FFT is a real-time processing and thus interesting for an industrial implementation, it requires complex measurements, i.e amplitude and phase, which makes the acquisition system more complex. Here we aim to overcome this difficulty by processing amplitude-only measurements. We make use of an image processing based on the direct conversion of the measured amplitude into images. The images form the dataset for the classifier that we choose as a non-linear SVM with a RBF kernel. The advantage of the SVM is that the computational burden is moved to the training phase where we compute the optimal hyper-parameters C* and ϒ*, while the test is very fast. First, we describe the complete workflow and use a set of apples measured in W-band in Autumn 2019 for validation purpose. We then extend the validation to measurements of peaches conducted over a long time period (summer 2019 and 2020). Finally, we investigate the robustness of the method over frequency while moving to the D-band. For all tests the accuracy is of 100%
2D Matched Filtering with Time-Stretching; Application to Orthogonal Matching Pursuit (OMP)

This paper presents a matched filter which incorporates the time stretching of signals returned from moving targets. This 2D matched filter does not suffer from SNR and resolution degradation due to range migration, and can be used with agile waveforms. The use of this matched filter within the orthogonal matching pursuit (OMP) algorithm, to resolve targets over a large range and velocity span, is demonstrated with random frequency modulated pulses and a random pulse interval.
Utilization of Fog Computing in Task Scheduling and Offloading: Modern Growth and Future Challenges

Recent advancements towards emerging technologies like the Internet of Things (IoT) needs resource-based processing for executing diverse real-time applications. In this IoT environment, the connected devices generate an enormous amount of data and processed in the cloud environment due to its service provisioning nature and device-scalability characteristics. Moreover, the request generated by the IoT devices towards the cloud is not effectually resolved; specifically, monitoring of resource availability, resource management, and task scheduling. The major challenge in the FC environment is task scheduling and resource allocation. The researches on FC is determined to be in its infancy nature where fog infra-structure, application, working environment, taxonomy-based allocations are still known the wider field in the research area. This work provides an extensive survey towards the synthesize of FC in research and industrial community to fulfil the need of users/researchers. Here, FC definitions, computation process, research trends and technical evaluations among the nodes are reviewed extensively. Similarly, the architectures, components are also discussed. The fog components are defined, and the nodes are deployed over the simulation environment. Similarly, the existing research gaps are also analyzed, and the gaps are bridged by examining the deficiency factors related to resource allocation and task scheduling. The limitations of FC with present research works are examined based on the open issues. This paves the way to proceed with future research enhancements over FC paradigm.
Nonlinear Least Squares Estimation for Breathing Monitoring Using FMCW Radars

In this paper we propose a novel framework for breathing monitoring, based on a simple Nonlinear Least Squares (NLS) estimation, which explores the harmonic structure of the breathing displacement signal. The first part of the study was performed using a controlled target which could be programmed for executing breathing-like movements, thus emulating the chest-wall displacement over time. Finally, the proposed approach was evaluated with real human data, and the results have shown robust and accurate estimation, outperforming previous methods over different scenarios.
A New Multirobot Path Planning With Priority Order Based on the Generalized Voronoi Diagram

This paper proposes a new path planning method called the priority order navigation algorithm (PONA) for multi-robot navigation in a large flat space. The PONA can guarantee collision-free and efficient travel in the space with fixed or/and dynamic obstacles. The priority order of robots is assigned by the user based on the importance degree of the robots’ tasks and the objective is to make the higher priority robot reach its target faster than the lower priority robot. This study uses the generalized Voronoi diagram (GVD) to establish the initial map for PONA and links the navigation points in GVD to plan the path for each robot. Further, we modify the navigation point links to shorten feasible paths for the lower priority robot and its shortest two feasible paths can be switched to each other based on a certain condition to avoid hitting the higher priority robot. The proposed PONA is compared to several benchmark path planning methods, which are the shortest distance algorithm (SDA) and reciprocal orientation algorithm (ROA), in the simulation section and it is found that the PONA can reduce the average length of the trajectory by more than 10% compared with ROA and SDA.
Reverse Engineering Neural Network Folding with Remote FPGA Power Analysis

Specialized hardware accelerators in the form of FPGAs are widely being used for neural network implementations. By that, they also become the target of power analysis attacks that try to reverse engineer the embedded secret information, in the form of model parameters. However, most of these attacks assume rather simple implementations, not realistic frameworks. Layer folding is used in such accelerators to optimize the network under given area constraints with various degrees of parallel and sequential operations. In this paper, we show that folding does mislead existing power side-channel attacks on frameworks such as FINN. We show how we can extract the folding parameters successfully and use that information to subsequently also recover the number of neurons–something not reliably possible without knowing the folding information. Following the methodologies of both profiling side-channel attacks and machine learning, our approach can extract the amount of neurons with 98% accuracy on a test device, compared to 44-79% accuracy based on related work under the same test conditions and datasets. Furthermore, we show how a classifier that is based on regression can detect previously unknown parameters, which has not been shown before. To verify our results under different environmental conditions, we test the target device in a climate chamber under various temperature ranges and still reach accuracies of at least 93%.
Drivable Free Space Detection on High-Resolution Radar Using Convolutional Neural Networks

Drivable free space detection, aiming at detecting collision free drivable area, is an important functionality of Advanced Driver Assistance Systems and Autonomous Vehicles. Although camera based approaches are well established, it is difficult to provide reliability in bad weather conditions. Therefore, radars are often chosen over LiDAR as preferred orthogonal redundant sensors, due to their low cost. Recent advancements in frequency modulated continuous wave (FMCW) radar technology have enabled measurement of thousands of detections in one cycle, leading to an imaging capability of a radar scan. Many radar-based methods determine free space, which is not collision free, from an accumulated grid map. However, as of yet, no approaches have attempted to detect drivable free space directly in one measurement cycle. Nevertheless, this task is difficult due to spatially sparse and noisy detections. This work shows that by formulating the problem as two-class semantic segmentation, we can detect drivable free space directly from a single radar scan. With the introduction of high view encoder and decoder blocks, our network design can consider global and local context. In addition, the lightweight architecture enables training on a very small dataset. As no public dataset is available, we show the effectiveness of our approach quantitatively on our test set by 0.6353 mean Intersection over Union and qualitatively on real scenarios.
PreCFAR Gridmaps for Automotive Radar

For higher levels of driving automation, a vehicle has to perceive the static environment in addition to other road users to ensure safe and reliable operation. This paper presents a radar-based method utilizing a lower data level than typical peak detection point clouds (e.g. Constant False Alarm Rate (CFAR) detection) to address some challenges in static environment perception. Using dense, image-like radar data overcomes CFAR's disadvantage of masking on extended objects as well as the suppression of low reflection amplitude targets. The improved performance of the proposed technique is demonstrated in a direct comparison to a CFAR approach based on real-world experiments.
Full-Band Millimeter Wave Waveguide Magic Tees and Power Dividers for Manufacturing Ability

In this paper, a full-band millimeter wave waveguide magic tee design that is friendly to computer numerical controlled (CNC) machining, manufacturing assembly and industrial standard production processes is presented. Traditional magic tee designs either barely cover the full waveguide bandwidth or are difficult to fabricate with consistent electrical performance. An improved magic tee junction utilizes a simple post in the middle of the junction and stepped waveguide transformers to provide the mechanical mate with standard waveguide openings and flanges. The dimensions of the magic tee junction are EM simulated and optimized by considering the variations of the manufacturing process for optimal performance and high reproducibility. An E-band magic tee and a V-band four-way power divider are presented to demonstrate the improved designs. The newly developed magic tee and power divider have shown good performance for full band coverage with high production yields in manufacturing environments.
Combination of MUSIC inversion algorithm with SKP decomposition for forest height estimation in a Tomography SAR application

This research work treats of an application of Tomography-SAR (Synthetic Aperture Radar) which aims to compute forest height. Tomo-SAR is a method based on the acquisition of N SAR images of the same zone obtained at different altitudes to estimate the reflectivity map of the studied area. The proposed algorithm is build upon the combination between the MUSIC (Multiple Signal Classification) inversion and Sum of Kronecker Product (SKP) decomposition. The LIDAR (Laser Detection and Ranging) dataset of the study area was used as ground truth for the validation of the procedure. Results of the proposed inversion are verified qualitatively and quantitatively, by comparing the estimated vegetation with the digital surface model extracted from the LIDAR dataset. After analyzing the obtained results, we conclude that the height of the forests estimated by the proposed inversion presents a bias compared to the LIDAR data which converges towards an overall underestimation of the vegetal structures, which means that we need to extract and combine other parameters from the SKP decomposition to enhance the generated DSM.
ENERGY OPERATION NETWORK PROMOTION FOR MODELS OF INFORMAL EDUCATION

This study aims to compile teaching materials designed for students to gain hands-on experience in energy conservation and carbon reduction, also to create maneuverable teaching tools to simulate renewable energy operation. In order to achieve the set teaching goals, meetings of experts were summoned to verify and revise related literature as groundwork for the development of teaching tools. To complete our teaching materials, we digitized and posted them on education network under energy conservation and carbon reduction so students can learn and assemble teaching tools online as well. As a result of the integration of classroom instruction and the education computer network in information sharing among the students have become prevalent. The results revealed the following: 1. the hands-on experience obtained on energy topics at the teacher's training camp helps to raise the energy consciousness of the college students. 2. the attitude, subjective norm and conscious behavior control of the college students will affect their behavioral intention on energy conservation and carbon reduction. 3. Hands-on experience with maneuverable teaching tools helps the elementary school students with their energy perception. 4. Learning from experience rather than knowledge from academic instruction tends to attract more learning interest from the students.
An Ultra-Wideband Microstrip-to-WR15 Waveguide Transition for MMIC Applications

A Microstrip-to-WR15 Waveguide from 50GHz to 75 GHz is proposed. It is based on a metal enclosure and a double-layer PCB. The PCB contains a Klopfenstein-Taper as the first transition between the microstrip and a Substrate Integrated Wave (SIW) with optimized matching. A coupling aperture on the bottom side of the PCB acts as the second transition from the SIW to the waveguide. For matching the coupling aperture to the WR15 waveguide, a three-stepped-impedance converter is milled into the metal enclosure. The measured reflection coefficient is below −10 dB, and the measured transmission is better than −2.5 dB in the entire band. The advantages are easy manufacturing, tolerance insensitivity, and ultra-high bandwidth. Thus, it offers a wide range of applications for mm-Wave sensors.
Information Technology Competency Scale applied to the Hospitality Industry

This article aims to validate an instrument that can measure Information Technology in the hospitality industry. The main study focused on the hotel industry and it was conducted with 216 employees of two different hotel groups in Brazil and Portugal. An instrument developed by Tippins & Sohi (2003) was translated into Portuguese and adapted for the hospitality industry. Later, an exploratory factor analysis was made in order to verify the psychometric properties of the version translated and adapted. In addition, a short-form scale version was developed. The results indicated that the scale could be reduced to nine items without compromising psychometric properties of the full-length scales. Finally, it has been found that the scale was capable of measuring differences in IT competency between groups. The results show that managers have greater IT competency than employees. Regarding the different hotel groups was noticed that they have significantly equal means for IT operations and IT knowledge. Therefore, hotels which could improve these two factors might be the ones which will be able to differentiate themselves and create competitive advantage. Theoretical and practical implications of the study are also discussed.