Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of the Filter Design on Wide-Bandgap Voltage Source Inverters with Sine Wave Filter for Electrical Drives

State-of-the-art electric drives are mainly equipped with two level three phase voltage source inverters offering an compact setup with good efficiency but electromagnetic interference is an issue especially in sensitive environments. Also bearing currents, excited by common-mode noise, can influence the reliability of the connected machine. To reduce these bearing currents and the inverter's electromagnetic compatibility an output filter can be used. Especially with wide band-gap semiconductors the reduced switching losses offer output filters with low volume. Furthermore, this filter offers several degrees of freedom to optimize the losses of the whole inverter system. Possible filter designs are investigated and compared with each other. A design methodology to find loss optimized filter configurations is presented. By modeling the whole inverter and filter system the losses of the system are calculated for every filter configuration iteratively at a desired operating point. These loss models consider soft switching behavior as well as high frequency losses of the passives to take also configurations with high filter choke current ripple into account.
Systolic Blood Pressure Estimation Using Wearable Radar and Photoplethysmogram Signals

On-body Continuous Wave Radar (CWR) sensors, along with Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors, have been introduced to estimate blood pressure(BP) using time parameters as Pulse Arrival Time (PAT), Pre-ejection Period (PEP) and Pulse Transit Time (PTT) during different postures and exercises tasks. The wavelet transform is applied to the signals to remove the noise components. The on-body CWR sensor and PPG sensor are placed on the sternum and left earlobe, respectively. The time parameters of 43 healthy subjects are collected in the different sitting, standing, and supine postures, and also for 26 healthy subjects in six different exercise tasks as handgrip, light, moderate and heavy bike exercise, and two recoveries. Two different regression methods are used to estimate BP from PTT and the results are compared with the estimated BP from PAT. The best Cumulative Error Percentage (<10mmHg) is 98.2% for 38 subjects, participated in posture tasks and 88.89% for 21 subjects, participated in exercise tasks. The results show the proposed method is promising for estimating BP using PTT obtained from radar and PPG signals.
Advances in Feature Extraction and Modelling for Short Duration Language Identification

This paper presents an overview of the progression of short duration spoken language identification systems and current developments. It reviews different language identification architectures including single level, hierarchical, fully connected, convolutional neural network and bidirectional long-short term memory. The work presented in this paper aims to explore the most effective language identification architecture, frontend and backend for the short duration classification task. Specifically, the use of frequency domain linear prediction features is proposed with bidirectional long short-term memory recurrent neural networks for language identification, which aims to model temporal dependencies between past and future frame based features in short utterances. It shows significant improvements of 26.1% in terms of Cavg compared to state-of-the-art i-vector approach. Evaluations of the LID systems are presented using AP17-OLR language recognition evaluation tasks.
Driver Identification through Multi-state Car Following Modeling

Driver profiling and characterization is one of the key issues that models heterogeneities inter- and/or intradrivers’ behaviors, which forms the basis of many applications such as driver identification, abnormal driving state awareness, personalized driving assistance, etc. This research proposes a driver identification method through multi-state car following modeling, where a GMM (Gaussian Mixture Model) is used to model the internal stochasticity of an individual driver using a mixture of Gaussian kernels, a driver profile is defined to describe the probabilities of a car following behavior being operated by each registered driver, a projection matrix is learned to project a feature vector to a lower dimensional space that has the best discriminative nature for GMM modeling, driver profiling and ID prediction. Experiments are conducted using the naturalistic driving data that are collected on the motorways in Beijing, where a dataset of eight-drivers’ car-following data is generated. Promising results are demonstrated and the performance of driver identification on different aspects are examined.
Simulation and Evaluation of CAVs Behavior in an Isolated Signalized Intersection Equipped with Dynamic Wireless Power Transfer System

This paper proposes an idea of charging eco-driving connected autonomous vehicles (CAVs) which drive smoothly passing signalized intersection above a dynamic wireless power transfer (DWPT) system consisting of segmented coils at the upstream of approaching lanes. Mixed traffic streams with different penetration levels of eco-driving CAVs passing through a DWPT system-equipped signalized intersection are simulated using microscopic traffic simulation software PTV-VISSIM. Energy stored, which is defined as the result of energy transferred to the vehicle battery by DWPT system subtracting energy consumed by the vehicle battery whilst travelling the same distance, is used to evaluate the total impact of eco-driving behavior on energy consumption and charging capacity. The simulation results show that at full CAV penetration rate, there are 62% reduction of average energy consumption of all types of vehicles and 10% increase of average energy stored per battery electric vehicle compared to full HVs case. This indicates that the eco-driving driving behavior has a positive impact on the energy stored powered by the DWPT system designed in this paper.
Blockchain application in renewable energy microgrids: an overview of existing technology towards creating climate - resilient and energy independent communities

Citizens and stakeholders are increasingly associating in so-called renewable energy communities and are participating in the energy transition by investing in, producing, selling and distributing renewable energy. Such communities usually use photovoltaic systems on the rooftops of public and private buildings that are connected to a microgrid. Those energy system models represent an electrical network that is decentralized in which energy consumers also become producers (prosumers) and management of the grid is done by users. Optimization and trading of surplus energy produced in microgrids could be done via blockchain technology. On one hand, those innovations could offer a solution for adaptation and mitigation of climate change, and on the other hand include economic development, creation of new jobs, cheaper energy, and energy security. The present paper gives an overview of existing technology and its usage on cases in the world but is focusing on implementation in the EU and Croatian markets.
Control of a three-phase series resonant converter based solid state transformer

This paper describes a new reactive current control technique for a high-power bidirectional series-resonant isolated DC-DC converter, namely solid state transformer. The proposed technique based on the experimental measurement of the three-phase medium frequency transformer. The proposed method enables to select the optimal reactive current operating point to achieve the maximum efficiency. A 17 kW three-phase medium frequency transformer prototype has been designed and measured. The laboratory prototype of the solid state transformer based on a series resonant converter has been realized in order to verify the proposed control technique.
Multiobjective Evolutionary Algorithm based on Fast Elite Sampling Strategy and Difference-based Local Search for VRPTW

This paper addresses the vehicle routing problem with time windows (VRPTW), aiming to reduce the vehicles number and to minimize the time-wasting during the delivery process caused by early arrival. For solving VRPTW, a multiobjective evolutionary algorithm based on fast elite sampling strategy and difference-based local search (MOEAFESS/DLS) is proposed. The special strategy in MOEAFESS/DLS is fast elite sampling strategy (FESS) which consists of two parts. First, using Pareto dominating and dominated relationship-based fitness function (PDDR-FF) to evaluate individuals, which can easily select nondominated individuals and the individuals which have larger domination area. Then, mixing with the sampling strategy of vector evaluated genetic algorithm (VEGA) can achieve fast convergence and sufficient diversity. The evolution according to FESS will be used as a global search strategy for MOEA-FESS/DLS. In addition, a local search based on differences between individuals called difference-based local search (DLS) is used in MOEA-FESS/DLS. In this way, the individuals with poor performance in the population generated by global search are guided to move closer to those who perform better. So it can further enhance the search ability of MOEA-FESS/DLS. Experimental results on Solomon benchmark demonstrate that the proposed MOEA-FESS/DLS is effective, which the performance outperforms NSGA-II, SPEA2 and MOEA/D.
Predictive-based Modulation Schemes for the Hybrid Modular Multilevel Converter

In this paper, two predictive modulation schemes are derived for a hybrid modular multilevel converter. The modulation scheme considers the electric model of both full and half bridge cells to predict the capacitor voltages in the next sampling period. The performance of both modulators is assessed through simulations and experimental results for different modulation indices.
Online Tracking of Canonical Brain Network Activation and Behavioral Prediction Using Bayesian Filtering

Online inference of the activation of a group of brain regions or networks may prove useful for developing adaptive and closed-loop brain-machine (BMI) systems. In this paper, we applied a two-stage Block Sparse Bayesian Learning (BSBL-2S) method to electroencephalographic (EEG) data for online tracking of the relative evidence of activation of groups of cortical regions of interest (ROIs) comprising the nodes of two brain networks. Bayes factors (BF) are presented as measures of relative evidence for brain network activity. A second Kalman filter is used to predict behavior based on Bayes factors. The method is evaluated with simulated and real EEG data. EEG data was simulated by smoothly modulating the relative activations of canonical task positive network (TPN) and default mode networks (DMN). The calculated BFs (TPN/DMN) identified and tracked the changes in relative TPN and DMN network activation and accurately predicted simulated behavioral measures (reaction times to experiment events). The method was applied to EEG data from 8 subjects performing a lane-keeping simulated driving task in which the subjects were instructed to quickly respond to random perturbations of their vehicle’s lane position. The estimated BFs showed significant linear and quadratic predictive relationships with response time (RT), with poorest task performance assocated with decreasing BF (increased DMN activation, relative to TPN). These results suggest that online estimation of BF for cortical network activation could be useful in the development of neuroscientifically grounded adaptive BMI systems.
Numerical Studies and Validation of Experimental Pentacene Transistor Characteristics

This paper present a numerical validation of experimental results of Bottom Gate/Bottom Contact Pentacene transistor using SILVACO ATLAS simulator. We investigated the impact of different gate dielectric thickness on transistor performance and we estimated threshold voltage Vth, current ratio Ion/Ioff and saturation mobility μ values. We found that gate dielectric thickness is proportional to Vth and inversely proportional to μ.
Model Predictive Path Integral Control for Car Driving with Autogenerated Cost map Based on Prior Map and Camera Image

Model Predictive Path Integral (MPPI) algorithm is an efficient approach to control an autonomous car. However, it requires a cost map. Preparing a cost map in advance is a task that can be completed only for a static closed unchanging environment, but impossible for real city conditions where the environment is dynamic. In this work, we present a method for automatic creation of the cost map based on map data and image from a camera mounted on a self-driving car. The described approach was tested on the Kia Soul car.
Impacts of the Detuning of Compensation Inductances to the Performance of a Double-Sided LC-Compensated CPT System

This paper investigates the detuning of the compensation inductances in a double-sided LC circuit and its impact to the performance of a CPT system. It reveals that the variation of the inductance would result in an asymmetric frequency property, including the magnitude of the output current and output voltage. The impacts can be summarized in two major parts. First, the phase angle of the input current is affected, which relates to the soft-switching condition and the system power transfer efficiency. Second, the output-side current and voltage magnitude can be affected, which determines the output power. A prototype is implemented to validate the theoretical analysis. The plate size is 300mm×150mm, and it can transfer 368.5W power through 4mm glass layer with the efficiency of 91.8%. For the frequency property, the experimental results and the theoretical analysis match well in a wide frequency range. In addition, the experiments show that both the constant current and constant voltage working modes are achieved with a relatively small load regulation ratio.
Performance Analysis of Buffer-Aided Hybrid NOMA/OMA in Cooperative Uplink System

This paper investigates a cooperative uplink system where two users wish to send messages to a base station with the help of a buffer-aided relay. Transmission modes in terms of both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) are considered. For the considered system, the probability for the user-to-relay channels to successfully perform NOMA is first theoretically derived in two scenarios of dynamic and fixed power controls (PCs) at users. Then, an efficient buffer-aided hybrid NOMA/OMA based mode selection (MS) scheme is proposed, which adaptively switches between the NOMA and OMA transmission modes according to the instantaneous strength of wireless channels and the buffer state. The state transmission matrix probabilities of the corresponding Markov chain is also derived, and the performance of the proposed hybrid NOMA/OMA scheme is analyzed with closed-form expressions, in terms of sum throughput, outage probability, diversity gain, and average packet delay. For both the dynamic and fixed PCs, the proposed scheme is proved to achieve a diversity gain of two when the buffer size is not smaller than three, which means that fixed PC will not lead to a loss of diversity gain.
Dominance in Visual Space of ASD Children Using Multi-Robot Joint Attention Integrated Distributed Imitation System

The focus of this research is to study: 1) the distribution of joint attention of autistic (ASD) children in their left and right visual space, 2) the corresponding dominance of either left or right hemisphere of brain using electroencephalography (EEG), and 3) the relationship between the dominance of visual space and hemisphere of brain. The proposed multi-robot system includes sequential and simultaneous actuations of robots dealing with single and multi-robot communication. Results indicate that most of the autistic children initiate joint attention from right to left vision space. Similarly, for imitation, robot at right visual space is imitated and focused more. These findings are also supported by the results obtained from the dominance of brain and number of eye contacts. Eight participants, five males and three females, participated in this research. Six participants belong to minimal while two belong to mild case on autism spectrum. Minimal cases of autism indicated proper dominance of either left or right visual space and hence the respective dominance of hemispheres of brain too. While mild cases of autism showed balanced performance in visual space but distinguishable dominance of hemispheres of brain. The overall average followed imitation accuracy for robot at right visual space is 83.49 % while for robot at left visual space is 61.53 %. Similarly, overall average eye contacts with robot at right visual space are 16.47 and for robot at left visual space are 14.95 per minute. Power spectral density (PSD) values of EEG data also indicate the dominance of right hemisphere of brain. F value is 2.93, F-critical value 4.60, and p-value is 0.108.
Exploring the Efficiency of OpenCL Pipe for Hiding Memory Latency on Cloud FPGAs

OpenCL programming ability combined with OpenCL High-Level Synthesis (OpenCL-HLS) tools have made tremendous improvements in the reconfigurable computing field. FPGAs inherent pipelined parallelism capability provides not only faster execution times but also power-efficient solutions when executing massively parallel applications. A major execution bottleneck affecting FPGA performance is the high number of memory stalls exposed to pipelined data-path that hinders the benefits of data-path customization.This paper explores the efficiency of “OpenCL Pipe” to hide memory access latency on cloud FPGAs by decoupling memory access from computation. The Pipe semantic is leveraged to split OpenCL kernels into “read”, “compute” and “write back” sub-kernels which work concurrently to overlap the computation of current threads with the memory access of future threads. For evaluation, we use a mix of seven massively parallel high-performance applications from the Rodinia suite vs. 3.1. All our tests are conducted on the Xilinx VU9FP FPGA platform of Amazon cloud-based AWS EC2 F1 instance. On average, we observe 5.2x speedup with a 2.2x increase in memory bandwidth utilization with about 2.5x increase in FPGA resource utilization over the baseline synthesis (Xilinx OpenCL-HLS). 11This work has been funded and supported by the Xilinx University Program (XUP).
Application of Artificial Intelligence on a Symptom Diagnostic Platform for Telemedicine A Pilot Case Study

With a growing demand on better healthcare quality in the society, exploring possibilities of digital health as an aid in medical consultation would be a choice. In this study, we present an Artificial Intelligence Diagnostic Platform and demonstrate its features in diseases diagnoses based on patients’ self-reported symptoms and physical examination by physician. We included 36 patients who had used the platform as a pilot study and 22 valid records were analyzed. In comparison with human doctors, its differential diagnoses showed 100% coverage of the diagnosis made by the human doctor in each and every patient case. It is proven that Envive AI Diagnosis system accurately diagnosed the patients based on symptoms and physical exams. The findings revealed the potential of introducing digital analytics in consultation, especially in primary care settings in the future.
The Performance of Local-Learning Based Clustering Feature Selection Method on the Diagnosis of Parkinson’s Disease Using Structural MRI

The neurodegenerative diseases are modelled by the deformation of the brain neurons. In the detection of neurodegenerative diseases including Parkinson’s Disease (PD), the Three-Dimensional Magnetic Resonance Imaging (3D-MRI) has been utilized, recently. In this paper, by using a Voxel-Based Morphometry (VBM) method, the morphological alterations between the Structural MRI (sMRI) data of 40PD and 40 Healthy Controls (HCs) have been determined. By using the structural alterations between the PD patients and HC and two sample t-test method, the 3D Gray Matter (GM) and White Matter (WM) tissue masks are obtained separately for two different hypotheses, t-contrast and f-contrast. The Feature Selection and Kernel Learning for Local Learning-based Clustering (LLCFS) method is used to rank the features and a Fisher criterion algorithm is utilized to determine the number of the top-ranked features. The selected features are classified by using two classification approaches, namely Support Vector Machines (SVM) and Naive Bayes (NB). The results indicate that the classification performances of both NB and SVM methods with f-contrast outperform that with t-contrast for all GM, WM, and the concatenation of GM and WM tissue volumes. Additionally, the classification performance of SVM is higher than that of NB for all GM, WM, and the combination of GM and WM tissues. The highest area under curve results are obtained as 75.63%, 85.00%, and 90.00% for GM, WM, and the concatenation of them, respectively.
A New Supervisor Synthesis Approach for Discrete-Event Systems With Infinite Behavior

<inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula><italic>closure</italic> (together with <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula><italic>controllability</italic>) plays an important role in the supervisor synthesis for liveness specifications imposed on discrete-event systems (DESs) with infinite behavior. However, it is not closed under arbitrary unions, and thus there does not exist the supremal <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula>closed (and <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula>controllable) sublanguage. In this paper, we first develop an algorithm to compute a subautomaton of the automaton representing an arbitrarily given <inline-formula> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula>-language by deleting all <italic>bad loops</italic> (which lead to un-closedness of the given language) in the automaton, and show that the obtained subautomaton represents an <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula>closed sublanguage and is maximal from the perspective of an automaton transition graph. With this algorithm, we propose a new approach to construct a supervisor synthesizing an <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula>language even if it is not <inline-formula> <tex-math notation="LaTeX">$\omega -$ </tex-math></inline-formula>closed. Furthermore, we prove that, compared with the supervisor computed by the Thistle’s supervisor synthesis approach, the supervisor synthesized by our approach is often more permissive. Examples are presented for illustration.
Improvement of Grid Current Quality for Droop-Controlled Grid-Connected Inverters under Distorted Grid Conditions

Grid-connected inverters based on droop control have been widely used in distributed generation (DG) systems, as they can ensure smooth transition between grid-connected mode and islanded mode. However, the traditional active power-frequency (P-ω) and reactive power-amplitude (Q-E) droop control method is vulnerable to the grid voltage distortion, because of the following two reasons. Firstly, the inverter under droop control behaves like a controlled voltage source connected in parallel with utility grid, and the grid current is not directly controlled. Secondly, the line impedance interlinking the inverter and the grid is usually very small, so a slight distortion in grid voltage can lead to serious distortion in grid current. To suppress grid current distortion, this paper proposes to employ harmonic virtual impedance (HVI), which is effective only at harmonic frequency, to increase the equivalent impedance between inverter and grid. Compared with the existing control methods, the proposed HVI control scheme does not need to detect grid voltage and it is easy to be implemented. Comprehensive simulation and experimental results are provided to validate the effectiveness of the proposed method.