Miracle-dz/UniFAR-FTUs huggingface dataset:
Voltage Lift Switched Inductor Double Leg Converter With Extended Duty Ratio for DC Microgrid Application

In a DC microgrid, while integrating the low voltage sources such as Fuel cells and photovoltaics, an efficient DC-DC converter is crucially needed. Contributing to a high voltage whilst being operated on a low duty ratio is among the demanded features of the converter. For high voltage applications, the conventional converters are to be operated beyond the prescribed range of the duty ratio which may result in various detrimental effects such as voltage spikes, increased current ripples, and reduced efficiency. In this paper, a voltage lift switched inductor double leg converter (VLSIDL) has been proposed. This novel converter has the feature of attaining a very high voltage without using any complex circuitry such as transformer, multiple voltage lift circuits, cascaded or parallel structure, etc. This particular converter has three switches that are operated in three modes with two different duty ratios. Double duty control offers flexibility in the selection of duty ratio for a particular output voltage and a significantly high gain is easily attained. The double duty ratio not only provides an extended range but also improves the efficiency and performance of the converter. The steady-state analysis of the proposed converter is done analytically and the same is verified through the hardware prototype.
Anisotropy-based Sensorless Control for Electrical Drives – Part I: Basic Principle

This paper covers the basic principle related to the anisotropy-based1 sensorless control for electrical drives. Several anisotropy-based methods were developed to solve the failure problem of fundamental model based methods in the lower speed, and at zero speed of electric machine. The objective of this paper is to help the reader develop a systematic understanding of this technique that has been developed over the past three decades.
Driver Attention Assistance by Pedestrian/Cyclist Distance Estimation from a Single RGB Image: A CNN-based Semantic Segmentation Approach

Automotive companies are investing a relevant amount of resources for designing autonomous driving systems, driver assistance technologies, and systems for assessing the driver’s attention. In this context, two important applications consist in processing images of the surrounding environment to respectively separate the different objects in the scene (semantic segmentation) and to estimate their distances. In both applications, methods based on Deep Learning (DL) and Convolutional Neural Networks (CNN) are being increasingly used, considering LiDAR scans or RGB images. However, LiDAR scanners require dedicated sensors, high costs, and post-processing algorithms to estimate a dense depth map or a three-dimensional representation of the surrounding environment. Moreover, current methods in the literature based on RGB images do not consider the combination of semantic segmentation and depth estimation for assessing the distances of specific objects in the scene. In this paper, we propose the first method in the literature able to estimate the distances of pedestrians/cyclists from the vehicle by using only an RGB image and CNNs, without the need for any LiDAR scanner or any device designed for the three-dimensional reconstruction of the scene. We evaluated our approach on a public dataset of RGB images captured in an automotive scenario, with results confirming the feasibility of the proposed method.
A Predictive Text System for Medical Recommendations in Telemedicine: A Deep Learning Approach in the Arabic Context

We are currently witnessing an immense proliferation of natural language processing (NLP) applications. Natural language generation (NLG) has emerged from NLP and is now commonly utilized in various applications, including chatting applications. The objective of this paper is to propose a deep learning-based language generation model that simplifies the process of writing medical recommendations for doctors in an Arabic context, to improve service satisfaction and patient-doctor interactions. The developed language generation model is a predictive text system intended for next word prediction in a telemedicine service. Altibbi—a digital platform for telemedicine and teleconsultations services in the Middle East and the North Africa (MENA) region—was utilized as a case study for the textual prediction process. The proposed model was trained using data obtained from Altibbi databases related to medical recommendations, particularly gynecology, dermatology, psychiatric diseases, urology, and internist diseases. Variants of deep learning models were implemented and optimized for next word prediction, based on the unidirectional and bidirectional long short-term memory (LSTM and BiLSTM), the one-dimensional convolutional neural network (CONV1D), and a combination of LSTM and CONV1D (LSTM-CONV1D). The algorithms were trained using two versions of the datasets (i.e., 3-gram and 4-gram representations) and evaluated in terms of their training accuracy and loss, validation accuracy and loss, and testing accuracy per their matching scores. The proposed models’ performances were comparable. CONV1D produced the most promising matching score.
Women engineers’ advancement to management and leadership roles – enabling resources and implications for higher education

Little is known about women engineers’ career advancement, the factors that support their progression, or the role that formal education plays in women engineers’ career advancement. Phenomenological analysis of interviews with women engineers in Australia revealed individual, relational and structural influences acting concurrently and interactively that determine, facilitate and constrain their transitions to manager and leader. Building knowledge and skill occurred through combinations of on-the-job learning, and professional development, including formal education. These approaches provided valuable individual and relational resources for women, enabling them to navigate structural obstacles. However, formal education beyond the undergraduate degree was not the primary approach taken by women engineers to support career advancement. Implications for engineering education are discussed.
DOA Estimation with Subarrays via Blind Source Separation Algorithm

In this paper, the direction of arrival (DOA) estimation in terms of weak target is addressed under strong mainlobe jamming. Specifically, a sum-difference beam signal model for multiple subarrays is introduced accounting for target echo and mainlobe jamming. For each subarray, the target and jamming signals are separated from the received sum-difference channel signals via blind source separation (BSS) algorithm. Further, monopulse technique is exploited to estimate DOA of weak target by combining all the separated target signals of each subarray. Finally, the effectiveness and performance of the proposed technique are analyzed via numerical simulations. The results show that the introduced procedure is capable of achieving rejection of the mainlobe jamming, while accurately estimating DOAs of target and jamming.
Mentoring program: women supporting women

Gender equity and inclusion have been relevant topics in the agenda of the companies, organizations and universities. As a university and particularly with engineering, science and technology programs, it is relevant to implement actions that encourage this culture of gender equity in a STEM environment. The main initiatives are related to mentoring programs, which seek to develop and empower women through women themselves. The research aims to identify the factors that influence the impact generated by the mentoring process in the different stakeholders: teachers, researchers and high school students. The mentoring model is composed of three key elements: (a) massive session with life testimonies of engineering and science students, (b) massive session with the testimonies of mentors and mentees, and (c) the mentoring sessions in small groups or one-to-one. The methodology is of a mixed nature and is composed of 4 stages: (1)design of the mentoring program based on the research of literature review and interviews with companies that has experience in mentoring programs, (2) implementation of the mentoring program with high school students and women STEM professors mentors, (3) evaluation of the impact of the program with interviews with mentors, observation, focus groups with mentors and mentees, and surveys, (4) definition of the impact factors and improvement areas of the program. The findings in the different stages allowed to integrate the factors that impact the mentoring process and its involved stakeholders. Some of the factors that influence the impact of the mentoring program are: the role of the mentor and its characteristics, the mentee profile, its commitments and interests, the impact of the life testimony both professional and personal, the type of needs that arise for the mentees and for the mentors, the time of attention, and the interaction.
Design and Validation of a Resonant Multi-Axis Force Sensor for Collaborative Robotics

This paper presents the design and characterization of a new multi-axis force/torque sensor based on resonant technology. This sensor is capable of simultaneously estimating the three force components as well as the three torque components. It is designed for use in collaborative robotics applications and can be integrated into handles or grippers of robotic manipulators to estimate interactions between robots and humans. Compared to the technologies currently in use and based on strain gauges, this system offers many advantages, particularly in terms of both safety and redundancy. This paper describes the design of this sensor and then compares the prototype to a state-of-the-art commercial sensor. Comparisons show that the proposed technology is at least as efficient as the industrial system, although its dimensions remain slightly larger.
Simultaneous Multiprocessing on FPGA-CPU Heterogeneous Chips

Heterogeneous chips integrating different core architectures, including multi-core CPUs and fast-processing FPGAs, are providing a promising solution for running computationally-intensive applications. Schedulers can help assign dataset proportionally to the compute units and hence improve execution performance and/or energy consumption. The emergence of high-level software-defined heterogeneous design tools has made it possible to merge these schedulers into the design environment. In this paper, we introduce and develop two schedulers for parallel execution of applications on heterogeneous platforms. One scheduler is designed to adaptively assign data chunks to CPU cores depending on the throughputs of compute units on an FPGA-CPU chip. This enables balanced execution of an application on the platform. The second scheduler introduced has a simpler design where dataset is processed from two sides by different devices, without requiring the overheads associated with traditional schedulers. The evaluation of these two schedulers using four benchmark applications on a Zynq UltraScale+ ZCU102 device shows that the latter scheduling method can improve throughput by a factor of 1.38. Similar improvement can be observed in energy consumption too.
Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

As an essential component of Intelligent Transportation Systems (ITS), short-term traffic flow prediction is a key step to anticipate traffic congestion. Due to the availability of massive traffic data, data-driven methods with a variety of features have been applied widely to improve the traffic flow prediction. China has the longest total length of expressways in the world and there is significant information recorded when vehicles enter and exit the expressway. In this paper, we collect data at an expressway exit station in Shanghai, split the data according to its originating entry stations and predict the corresponding exit station traffic flow using the multi split traffic flows. First, the original records are collected, preprocessed, split, aggregated and normalized. Second, the Long Short-Term Memory (LSTM) model is applied to learn from the features of the overall flow and split traffic flows to predict the overall exit flow. The baselines are models which only overall flow information is considered. Compared with the baselines, in other models, the split flows according entry stations are also considered for prediction. Finally, the LSTM model is made comparison with the Convolutional LSTM(ConvLSTM), the K-Nearest Neighbor (KNN) model and the Support Vector Regression (SVR) model. When the information of overall flow and 6 split traffic flows is used and step is set to 11 (with 5 minute aggregation), the model prediction performs best. Compared with the best result of LSTM baseline model, the improvement of prediction accuracy is up to 5.48 percent by Mean Absolute Error (MAE).
A Ripple-Free Output Current Interleaved DC/DC Converter Design Algorithm for EV Charging

In this paper, a ripple-free output current interleaved DC/DC converter has been analyzed for Electric vehicle charging stations. Firstly, a ripple-free control strategy able to ensure a theoretically flat output current profile and input voltage ripple minimization at any working conditions is discussed. This strategy drives the active front-end to regulate the DC-link voltage and, at the same time, the interleaved back-end converter to operate in zero ripple working points. Secondarily, a generalized designing algorithm able to consider constraints like AC grid voltage and battery voltage is proposed. Finally, simulations support ripple mitigation capabilities in steady-state and transient conditions for a 12-leg scheme.
Enhancing Space-Time Adaptive Processing Through the Slepian Transform1

A High Dynamic Range Space-Time Adaptive Processing (HDR-STAP) improves cancellation of large interference sources by using a Fast Slepian Transform to capture and cancel more clutter energy than other STAP approaches. STAP’s efficacy relies on its ability to estimate clutter interference across space (antenna elements) and time (pulses) but hits limitations due to interference strength and spectrum spread. In addition to clutter cancellation limits, the target signal can suffer a STAP processing loss. This paper focuses on the signal to clutter enhancement metric to compare performance of different STAP techniques. HDR-STAP projects the estimated clutter covariance matrix onto the Slepian basis functions. A limited number of orthogonal Slepian basis functions are defined using the signals’ sample rate and a selected finite bandwidth. HDR-STAP takes advantage of a Fast Slepian transform (FST) from Karnik, et. all [1], producing a practical implementation. This article demonstrates that HDR-STAP removes more clutter than sample matrix inversion (SMI) and Eigendecomposition STAP while achieving signal losses below those predicted by the Reed, Mallet, and Brennon (RMB) rule.
First experimental results on multi-angle DVB-S based passive ISAR exploiting multipolar data

This work investigates the potentialities of a passive multidimensional ISAR imaging based on DVB-S transmitters of opportunity. Image fusion methods exploiting diversities in both the spatial and polarimetric domains are introduced, aiming at achieving an image with better quality than the images acquired by the individual observation angles and/or polarimetric channels. An analysis using experimental datasets comprising a passive receiver able to collect signals in both the horizontal and vertical polarizations, a cooperative turning ferry, and an Astra satellite has been provided.
DeepPIRATES: A Training-Light PIR-Based Localization Method With High Generalization Ability

Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet Yang et al.) and well handles the complex infrared radiation even in the multi-person case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors’ deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons’ azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.
Leveraging machine learning approaches to estimate the impact of thermostat setpoints on individual household gas consumption

Given the world’s current climate change challenge and residential gas consumption being a major end-use of energy, people more than ever need to minimize their household’s energy footprint. Personalised, actionable advice can give people tips on which actions they can take to reduce residential energy usage, such as lowering the thermostat temperature. For this advice to be relevant it is important to understand the quantitative impact of thermostat setpoints on daily gas usage for each individual household. In this article, this impact is estimated by comparing three machine learning approaches.Linear regression, deep learning and gradient boosting machine are applied to a multi-dimensional time series dataset for 300 Dutch households. The three approaches are compared based on three metrics: root mean square error (RMSE), explainability and scalability. The results of the best model (gradient boosting machine) are explained using a technique called SHapley Additive exPlanations (SHAP). This interpretation method can quantify the contribution of all inputs, among which thermostat setpoints, to the daily gas usage prediction of the model for different individual households.This article adds to the current state of the art by focusing on the impact of influenceable thermostat setpoints, as opposed to less actionable factors such as house size, insulation status of the house and weather. By applying SHAP, the personal impact and differences between individual households are estimated, in contrast to only learning trends. Moreover, a machine learning model, trained on a representative dataset, is applicable at scale to other households for estimating a personal, quantified impact of setpoint choices.
Current Trajectory-Based Fault Detection and Fault Tolerant Control for Three-phase Induction Drives

In this paper, a simple and fast open-phase fault (OPF) detection and fault tolerant control method is proposed for three-phase induction machine. Instead of using integrators or low pass filter, this method utilizes the phase current magnitude and trajectory to detect open phase fault in three-phase motor drive. This significantly increases the speed of fault detection, allowing an OPF to be detected and compensated within several sampling cycles, which is advantageous for safety-critical applications. In addition, a double synchronous reference frame PI (DSRF-PI) controller method is adopted as the fault tolerant controller. Compared to previous methods that utilizes feedforward voltage compensation, the DSRF-PI does not require prior knowledge on the machine parameters and operating condition, and therefore is easier to implement. The proposed fault detection and fault tolerant control method are simulated using MATLAB Simulink simulation where the results confirms the high speed and robustness of the proposed methods.
A Systematic Review on NOMA Variants for 5G and Beyond

Over the last few years, interference has been a major hurdle for successfully implementing various end-user applications in the fifth-generation (5G) of wireless networks. During this era, several communication protocols and standards have been developed and used by the community. However, interference persists, keeping given quality of service (QoS) provision to end-users for different 5G applications. To mitigate the issues mentioned above, in this paper, we present an in-depth survey of state-of-the-art non-orthogonal multiple access (NOMA) variants having power and code domains as the backbone for interference mitigation, resource allocations, and QoS management in the 5G environment. These are future smart communication and supported by device-to-device (D2D), cooperative communication (CC), multiple-input and multiple-output (MIMO), and heterogeneous networks (HetNets). From the existing literature, it has been observed that NOMA can resolve most of the issues in the existing proposals to provide contention-based grant-free transmissions between different devices. The key differences between the orthogonal multiple access (OMA) and NOMA in 5G are also discussed in detail. Moreover, several open issues and research challenges of NOMA-based applications are analyzed. Finally, a comparative analysis of different existing proposals is also discussed to provide deep insights to the readers.
Statistical analysis of execution time profile for temporal validation of a distributed hard real-time system

This paper addresses modelling and assessing of worst-case execution times of tasks in a distributed real-time system. The presented case study is based on a stress test system for power semiconductors.Typical distributed systems involve inherent inter-dependencies that have to be handled with minimized delays. Studying the computing task’s timing behavior becomes necessary to understand the timing violations. Modelling the temporal behaviour of the system using probabilistic Worst-Case Execution Time (WCET) analysis aids to overcome the timing faults.This paper focuses on (1) the study of the temporal behaviour through the execution time profile of the distributed hard real-time system; (2) the statistical analysis by performing probability distribution modelling on the measured data. Measurement-based probabilistic timing analysis is an emerging and reliable method used to arrive at industry quality estimates. This method is used here in the paper to carry out temporal validation of the real-time computing tasks on our case study.
Fault-tolerant Permanent Storage for Container-based Fog Architectures

Container-based architectures are widely used for cloud computing and can have an important role in the implementation of fog computing infrastructures. However, there are some crucial dependability aspects that must be addressed to make containerization suitable for critical fog applications, e.g., in automation and robotics. This paper discusses challenges in applying containerization at the fog layer, and focuses on one of those challenges: provision of fault-tolerant permanent storage. The paper also presents a container-based fog architecture utilizing so-called storage containers, which combine built-in fault-tolerance mechanisms of containers with a distributed consensus protocol to achieve data consistency.