Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-based control of Peristaltic Sortation Machines using Discrete Element Method

This paper presents a novel approach to incorporate detailed Discrete Element Method (DEM) simulation models into a data-based control system applied to the control of a peristaltic singulation and sortation machine (PSM). The bionic principle of peristaltic is often used in nature for locomotion or transporting of goods. For sortation and singulation of parcels, peristaltic movements provide the advantage of faster operation due to the potential parallelization of the singulation and sortation processes and a far more gentle parcel transport. Beside the mechanical design, a major challenge of the PSM design lies in the development of suitable control algorithms. Due to the difficulties to model the complex behaviour of the independent parts physically, the design process appears to be hardly possible and thus a data-based control design based on reinforcement learning (RL) is proposed. Further, a co-simulation which incorporates a detailed DEM simulation is developed. Particularly, by suitably combining the state-of-the-art actor-critic reinforcement learning (ACRL) and a distributed approach using multiple parallel environments, manageable simulation and training times are ensured. The obtained results show the applicability of a DEM model in a co-simulation framework solving the transportation problem of parcels and also the very good performance of the developed RL-based control approach.
Real-time 4-way Intersection Smart Traffic Control System

Since traffic congestion is becoming a regular part of commuters’ life, there is a pressing need for better traffic management. Most current traffic control systems are not sensitive to the current state of the roads being controlled, instead they are fixed, timed traffic signals that do not respond to unpredicted congestion. Solutions have been proposed to solve this problem including creating a large database for each traffic stop and determining the optimal traffic signals for the best vehicle flow based on the statistics collected, which does not react to data outliers. Other solutions suggest installing weight sensors under roads to detect the number of vehicles waiting then setting the duration of the next green light accordingly. This paper proposes an image analysis work flow to analyze the number of waiting vehicles as well as moving vehicles in each arm of a 4-way intersection. Then the collected data is utilized to control the state of the entire intersection to ensure the best traffic flow for all waiting and moving vehicles. Results from this approach yielded an absolute mean error of 0.559 detected representative vehicles with standard deviation of 0.93 on the first dataset and mean absolute error of 0.554 with 1.20 standard deviation for the second dataset. This level of accuracy conformed with the finite state machine control logic of the intersection, moving from one state to the other according to the analyzed images in real-time without causing starvation to any of the intersection arms.
Electromagnetic Characterization of Polycaprolactone electrospun nanofibers filled with Fe3 O4 Nanoparticles

Polycaprolactone electrospun nanofibers filled with magnetic nanoparticles are here presented and proposed to be used in biomedical applications. In order to assure the main properties of this innovative material, the electromagnetic behavior of the pristine and filled with 2, 5 and 10% wt of Fe$_{3}O_{4}$ nanoparticles membranes are here investigated and compared with the bulk material. The electrical permittivity in the frequency range 1MHz-1GHz of the pristine membrane compared with the blank PCL film highlights the effect of the low density induced by the morphological features of the membranes. Moreover, the effect of the filler inclusion is investigated by looking at the AC behavior of the electrical permittivity and conductivity in the same frequency range, in which also the effect of the electrical contact pressure is investigated showing its great influence on the electromagnetic features.
Investigation of DW Spintronic Memristor performance in 2T1M Neuromorphic Synapse

Memristor, the two-terminal memory-resistance device discovered by Chua in 1971, is a promising solution for future processing problems. Its CMOS integration compatibility and large resistance in small size, makes it very successful candidate for large-scale systems like Neural Networks. In last decade, memristors were used in many Neuromorphic Synapses for its advantage of combining processing (dot-product) and memory in same device. There are different materials that can be used to fabricate memristors. In this paper, a comparison between spintronic and TiO2-resistive memristor in two-transistors-one memristor synapse, is introduced. The work was done on Cadence Virtuoso with using Verilog-A for memristor modeling. The comparison reveals that the synaptic implementation with a spintronic memristor is more efficient when high speed is needed. However, the resistive memristor is more adequate due to its lower power dissipation.
An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. One-way Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.
Real-Time Detection Method of Persistent Objects in Radar Imagery with Deep Learning

Persistent object detection in radar imagery becomes harder if the results are expected before the next image arrives to the digitizer card. This requires a clear commitment between the hit rate, the false contact rate and a time restriction in order to get real-time results taking one full revolution of the radar as the basic unit. The conventional algorithms use CFAR techniques and obtain acceptable results, but with a high false contact rate, especially in near-shore radar imagery, which contain ground clutter portions of the images. This work presents the first results of the analysis to the solutions to this problem by applying Deep Learning. This research proposes the use of convolutional neural networks Faster R-CNN on radar imagery. The developed methods are applied using a methodology. The purpose of this research is to provide methods and techniques to improve the detection of persistent objects, thus having a positive impact in the maritime control and surveillance operations.
Fault Detection for Uncertain Delta Operator Systems with Limited Communication Based on T-S Fuzzy Model

A fault detection filter for uncertain delta operator systems based on T-S fuzzy model is designed. The phenomena of limited communication, packet dropouts, time delay and the uncertainty in the system are considered simultaneously in filter design. Then, by constructing the Lyapunov functional in delta domain, sufficient conditions for the augmented system with asymptotical stability and H∞ performance are proposed. Furthermore, the desired filter parameters can be obtained via linear matrix inequalities (LMIs). Finally, simulation results certify the effectiveness of the presented method.
A Modular Real Time Simulation Solution for More Electric Aircraft Complex Architecture

This paper presents a Multi FPGA- based solution for More Electric Aircraft power systems real-time simulation. The proposed solution allows control and protection devices to be designed and tested in virtual power system before they can be implemented in a physical system. To demonstrate the effectiveness of the multi-FPGA solution, a model approximately representing the Boeing 787 More Electric Aircraft is considered. The B787 power system represents a challenge for any real time simulator. It includes a considerable number of fast and slow switching frequency converters, an extensive number of circuit breakers and very short cables that complicates the power decoupling task.Four complete feeders of the B787 power system are simulated using Dual-Kintex-7 FPGA boards. The controllers and mechanical parts of the Motorized Turbo Compressors are compiled, and real time simulated using a 3.5 GHz Intel processor. The performance of this overall Processor-In-The-Loop integration (PIL) is validated based on two criteria: 1) Evaluation of the results accuracy compared to the reference offline simulation. 2) Evaluation of the numerical stability of the simulation under steady state and fault conditions.
Harmonic Imaging Improves Delineation of Human Carotid Plaque Features by ARFI Variance of Acceleration

Stroke is often caused by carotid plaque rupture, with rupture potential indicated by plaque structure and composition. Delineating carotid plaque components that confer rupture risk is vital to stoke prevention. We have previously shown that ARFI Log Variance of Acceleration, log(VoA), discriminates intraplaque hemorrhage (IPH) from lipid-rich necrotic core (LRNC) and collagen (COL) from calcium (CAL) in vivo in humans, while ARFI peak displacement does not. We hypothesize that log(VoA) performance will be further improved by harmonic tracking of ARF-induced displacement. In a pilot clinical study of six carotid plaques imaged in vivo in patients undergoing carotid endarterectomy (CEA), plaque component contrast-to-noise ratio (CNR) achieved by harmonic log(VoA) was compared to that achieved by tracking at fundamental low (4.0 MHz, FL) and high (8.89 MHz, FH) frequencies with validation by spatially-matched histology derived from the extracted plaque specimens. Over all examined plaques, harmonic and both fundamental low and high log(VoA) distributions statistically differentiated plaque components (Wilcoxon, $\mathrm{p} < 0.01$, panel iv). Harmonic log(VoA) achieved the highest CNR between COL and LRNC, which is relevant to fibrous cap delineation, but FH tracking yielded the highest CNR between LRNC and IPH and between COL and CAL. These results suggest that combined harmonic and FH tracking could improve ARFI log(VoA) characterization of carotid plaque components that are correlated to rupture.
A Time-efficient and High-performance FPGA-based Continuous Floating-point Matrix Computing Accelerating Architecture for Control System

Matrix computing is one of the most important linear algebra modes that is broadly used in both scientific and engineering applications. Currently, there is still a lot of space for the optimization of continuous matrix computing accelerating. In this study, we first present two memory access optimization schemes which significantly minimize the I/O time and the total delay. Then, we extend the data accuracy of continuous matrix computing from double-precision to single-precision and half-precision floating-point data, which can enhance data diversity and improve computing performance. The experiments show that the I/O time is reduced by 40% after coarse-grained parallel optimization. Moreover, the I/O time is almost completely hidden by the calculation time after fine-grained data flow optimization. The accelerator achieves a maximum frequency of180 Mhz with 128 PEs and performs 184.3 GFLOPS for half-precision floating-point data. Our design is more outstanding in time-efficient and application scope comparing with state-of-the-art FPGA-based structures.
Increasing Thrust Force on 2-4 Phase Resonant Electrostatic Induction Motors Through Stacking

This paper investigates the performance of resonant electrostatic induction motors in multi-layer stacking, in which several sets of films are stacked to increase thrust force output and decrease required inductance. A mathematical analysis of this matter is performed, based on the previously deducted equations on resonant voltage and thrust force, which are used to model how these parameters are expected to scale with the number of layers. The analysis has revealed that thrust force should increase linearly with the number of layers, while coil size should decrease. A set of experiments is also performed in order to evaluate the model. The results have shown agreement with the predictions, suggesting this method can be used to achieve the expected gains.
Synchronous Imaging of Multiple Slices Using Higher-Order Bessel Beams and a Spherical Lens

Higher-order Bessel-like optical beams can be created through off-axis coupling of light into a multimode fiber. Thanks to the aberration that is inherent in spherical surfaces, through directing the Bessel-like beam comprising multiple concentric rings onto a low-cost lens, we created multiple foci at different depths. Moreover, scanning of the optical fiber with a piezoelectric actuator in the lateral direction, we were able to acquire data from different targets located at different planes. After coupling back into the fiber, the reflected portions of light from different targets could be spatially differentiated and directed to different photo-detection units. The proposed architecture is particularly appealing for laser scanning endoscopy applications, as it does not require additional passive or active optical components for generating Bessel beams (axicons or spatial light modulators), therefore offering both miniaturized realization and high optical transmission. We’ve further demonstrated the tunability of foci separation via altering fiber-coupling conditions with tilt and translation stages. Overall, the proposed scanning method has paved the way for addressing multiple slices at single lateral scan, thus offering improved speed for imaging or ablation applications.
Thunderbird Add-On “SmartMail” for More Convenient E-Mail Writing

In this paper we introduce “SmartMail”, an add- on for Mozilla’s e-mail client Thunderbird. It helps to increase productivity of users, especially the ones with international and multi-lingual e-mail recipients. We conducted a usability study which shows a significant acceleration of time spent writing e-mails as well as a reduced error rate for the chosen language and gender. After filling a questionnaire based on the System Usability Scale, the majority of test users rate the user experience satisfactory. Half of them even prefer using Thunderbird with this add-on over their own e-mail client.
Improved microbubble (MB) Localisation Using Particle Detecting algorithm: Evaluation of Algorithm Performance for Different Beamforming Methods

The performance of image analysis techniques (particle detection) on contrast enhanced ultrasound (CEUS) images could be enhanced by using it in combination with the right beamformer (BF). The current study investigates the best performing combination of a particle detecting algorithm (Kanoulas et al. 2019) with four beamformers (BFs), classical and adaptive. In a series of in silico experiments, adjacent MBs are placed in distances comparable to the lateral resolution limit, the CEUS images of the MBs were simulated in FieldII, and finally beamformed with the four methods. The images were processed with the MB detection algorithm and the results were evaluated by the true detections (TD), missed MBs, spurious detections, and localisation uncertainty (LU). For the smallest distances all methods deteriorate but the MV methods provided 4–12% more TD. For the intermediate distances the TD were comparable for all BFs but the adaptive methods provided lower LU. When a set of evaluation metrics is used, the adaptive methods provide marginally but systematically improved results which suggests that, under the appropriate imaging conditions, they could be used to enhance vessel mapping.
Machine Learning Model for Multiclass Lesion Diagnoses

Cancer detection is one of the most important research fields in the area of intelligent computing. Skin lesion diagnosis is a challenging topic, and several models have experimented on different datasets. Researchers proposed classification models that classify the lesion type if it is malignant or benign. The aim of this research is to propose a multiclass machine learning model that detect the lesion diagnosis rather than its type. The used dataset was retrieved from the International Skin Imaging Collaboration datasets archive since it is a benchmark that has thousands of dermoscopic images of different diagnoses. Melanoma, Nevus, and Seborrheic keratosis were the used lesion diagnosis. The proposed model consists of sequential phases, that start with the filtering and end with the classification. Kernel Support Vector Machine and Random Forest were the classifiers of the proposed model and their performance was measured by the KFold cross-validation accuracy.
Nonlinear Dynamics Modeling of the Vibrating Single-element Transducer for 3D Ultrasound Image Reconstruction using Recurrent Neural Network

In our previous work, 3D high-frequency ultrasound imaging using a vibrating single-element transducer driven by a tubular piezoelectric actuator was proved to be feasible. Considering the inoperability of imaging opaque phantom using imaging system containing optical positioning module (PSD), the acquisition of the transducer trajectory is a crucial problem for image reconstruction. The vibrating transducer is regarded as a base-excited cantilever with nonlinear effects. However, the accuracy of the established nonlinear system model declines due to the change of the working environment. In this work, recurrent neural network (RNN) is adopted to identify the nonlinear dynamics model of the cantilevered transducer to predict the accurate trajectory of the transducer for volume reconstruction.
Real-time and Freehand Multimodal Imaging: Combining White Light Endoscopy with All-Optical Ultrasound

Minimally invasive surgery offers significant benefits over open surgery in terms of patient recovery, complication rates, and cost. Accurate visualisation is key for successful interventions; however, no single imaging modality offers sufficient resolution, penetration, and soft-tissue contrast to adequately monitor interventional treatment. Consequently, multimodal interventional imaging is intensively investigated. All-optical ultrasound (AOUS) imaging is an emerging modality where light is used to both generate and detect ultrasound. Using fibre-optics, highly miniaturised imaging probes can be fabricated that yield high-quality pulse-echo images and are readily integrated into minimally invasive interventional instruments. In this work, we present the integration of a miniature (diameter: 800 µm), highly directional AOUS imaging probe into a commercially available white light urethroscope, and demonstrate the first real-time, 3D multimodal imaging combining AOUS and white light endoscopy. Through the addition of an electromagnetic tracker, the position and pose of the instrument could be continuously recorded. This facilitated accurate real-time registration of the imaging modalities, as well as freehand operation of the instrument. In addition, the freehand imaging paradigm allowed for “piece-wise” scanning where the instrument was retracted and repositioned without recalibration. The presented imaging probe and system could significantly improve the quality of image guidance during interventional surgery.
Modal Analysis of Voltage-source-based and Current-source-based Parallel Grid-supporting Inverters

In an AC microgrid, the grid-supporting converter can operate either as a current source or a voltage source. By establishing the state-space models of parallel inverters for different operating modes, this paper conducts the stability assessment of droop-controlled grid-supporting inverters, and compares parallel inverters operating as voltage sources and current sources in AC MGs. Detailed analyses of participation factors, damping ratios, and oscillation frequencies are also performed for the given inverter operation. Furthermore, the effect of key control parameters on the system stability are analyzed. Finally, analytical results are further verified through simulation studies.