Miracle-dz/UniFAR-FTUs huggingface dataset:
APRICOT: A humAn-comPuteR InteraCtion tool for linking foOd wasTe streams across different semantic resources

In the modern era of data, advanced approaches for extracting information and knowledge from data are required. Moreover, the extracted information needs to be formalised to be usable by information systems. Today, there exist several resources of semantics on food waste, which is a huge environmental problem that need to be fixed as soon as possible. Yet, the problem is that the existing semantic resources are not aligned and therefore needs to be linked. Only in this way, the complementary knowledge from different resources will become of real value. In the paper, an AutoMap algorithm for automated mapping of knowledge on food waste from different semantic resources is presented. By integrating such an algorithm in a web based tool, experts and the general public can get an insight into complex knowledge that is required for inventing new solutions for the food waste valorisation.
A Novel Coupled Inductor Based Active Balancing Technique for Ultracapacitors

A novel active buck-boost equalizer with a coupled inductor is proposed here for balancing ultracapacitors (UC) strings connected in the form of a matrix configuration. As a coupled inductor is used instead of an inductor, the number of switches and inductor windings required for balancing is reduced to N/2 and (N-P), respectively, where N is the number of UC cells to be balanced, and P is the number of parallel strings. The proposed balancing circuit requires only a single pair of complementary pulse width modulation (PWM) signals to balance the entire UCs in the UC strings. Hence, control complexities are significantly less as compared to the existing active voltage balancing methods. The effectiveness of the proposed topology is verified in the MATLAB/simulation platform and validated experimentally.
Exploring the correlation between students' satisfaction and learning outcomes they achieved

The article aims to present modern approaches in the teaching of programming subjects, to evaluate how students perceive them and to assess whether the given methods objectively improve the level of knowledge acquired by students. This research was carried out within the exercises of the subject Operating Systems taught in the bachelor's study of the Department of computers and informatics at the Technical University in Košice, The article will present not only the current way of teaching the subject, but also the development of teaching methodology, which led to the creation of current approaches. For four years, students' satisfaction with the way they were taught was detected through polls, based on which the teaching was constantly modified. The purpose of this article is to verify whether the satisfaction of students was reflected in their study results and achieved better grades than students in the past, whose opinion was not taken into account in teaching.
Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over western Eurasia

Abstract The Eurosiberian Carbonflux project was designed to address the feasibility of inferring the regional carbon balance over Europe and Siberia from a hierarchy of models and atmospheric CO2 measurements over the continent. Such atmospheric CO2 concentrations result from the combination of connective boundary layer dynamics, synoptic events, large-scale transport of CO2, and regional surface fluxes and depend on the variability of these processes in time and space. In this paper we investigate the spatial and temporal variability of the land surface CO2 fluxes derived from the TURC model. This productivity model is driven by satellite NDVI and forced by ECMWF or REMO meteorology. We first present an analysis of recent CO2 flux measurements over temperate and boreal forests, which are used to update the TURC model. A strong linear relationship has been found between maximum hourly CO2fluxes and the mean annual air temperature, showing that boreal biomes have a lower photosynthetic capacity than temperate ones. Then, model input consistency and simulated CO2 flux accuracy are evaluated against local measurements from two sites in Russia. Finally, the spatial and temporal patterns of the daily CO2 fluxes over Eurasia are analysed. We show that, during the growing season (spring and summer), the daily CO2 fluxes display characteristic spatial patterns of positive and negative fluxes at the synoptic scale. These patterns are found to correspond to cloudy areas (areas with low incoming radiation) and to follow the motion of cloud cover areas over the whole domain. As a consequence, we argue that covariations of surface CO2 fluxes and atmospheric transport at the synoptic scale may impact CO2 concentrations over continents and need to be investigated.
A Modified High Gain Boost TNPC Inverter with Neutral Point Balancing for Three-Phase Induction Motor Driven Electric Vehicle

The induction motor with a higher voltage rating is suitable for electric vehicle (EV) applications. The voltage boosting feature is a viable solution to get higher voltage from lower-voltage batteries instead of various series and parallel combinations. In this paper, a modified high gain boost (MHGB) TNPC inverter is proposed for three-phase IM driven EV. The proposed MHGB-TNPC inverter has higher boosting factor without any additional switching arrangements. A modified space vector PWM (SVPWM) control technique utilizing small, medium, and large vectors is adopted here to synthesize output voltage with neutral point voltage balancing and reduction of switching losses. Volt-seconds balance principle is employed to calculate the dwell times for the voltage vectors to be applied. Moreover, a close-loop control is adopted for controlling the inverter output voltage to drive the IM. The proposed scheme is validated through both simulations and experiments.
Map Matching-Based Driving Lane Recognition for Low-Cost Precise Vehicle Positioning on Highways

This paper proposes a map matching-based driving lane recognition system for low-cost precise vehicle positioning on highways. The proposed method finds the position where the road boundaries detected by a LIDAR sensor are best matched with the road boundaries of the precise digital map and then recognizes the driving lane based on the position. To improve the limitations of the existing Chamfer matching method, this paper proposes the following two methods. The first is a method of generating one candidate position for each lane by using the lane information of the map and the left and right lane offsets obtained from the front camera. Second is a simple matching method that converts the LIDAR and map data into arrays and compares only the lateral distances to measure the matching score. In addition, this paper proposes a method for maintaining sufficient road boundary information in every frame by applying a temporal accumulation to the LIDAR data. In the experiment, the proposed method was quantitatively evaluated using a database acquired from sensors mounted on a real vehicle on highways. The experimental results show that the driving lane recognition rate is 99.56%, the lateral position error is 0.49m, and the processing time is 6.4ms. These results prove that the proposed system provides sufficient accuracy and reliability even in the presence of GPS position errors in various highway environments.
Brain-Mobility-Interface based on Deep Learning Techniques for Classifying EEG Signals into Control Commands

This paper proposes an interface that enables users to mentally control a personal mobility robot, PMR. The user interface is named as brain-mobility-interface, BMI. In the BMI, EEG signals of a user are measured and fed as inputs. From the EEG signals, the brain state and face direction of the user, indicating the intention for PMR control, are estimated. For this purpose, two control command classifiers based on deep neural networks, DNNs, are applied to the BMI. As the output, the EEG signals are classified into control commands depending on the estimated user’s intentions. The control commands are composed of linear and angular velocities of the PMR. Through the network training, the estimation performance of both the classifiers is increased to more than 99 [%]. In the control experiment, furthermore, we show that the classification performance of the BMI is enough for a user to control the PMR as intended with only the mental commands.
Theoretical Analysis and Verification of Electron-Bombardment-Induced Photoconductivity in Vacuum Flat-Panel  Detectors

Vacuum flat-panel detectors (VFPDs) using a cold cathode and photoconductor have important applications in large-area photodetectors; however, the mechanism for achieving high photoresponsivity must be further explored. In this article, theoretical analysis was performed to examine the electron-bombardment-induced photoconductivity (EBIPC) effect based on the equivalent circuit model. Formulas for photo/dark current were derived, which indicated that carrier multiplication was mainly due to energetic electron bombardment. The theoretical formulas also revealed the carrier multiplication mode inside the photoconductor of VFPDs and the relationship between the responsivity and device parameters. To verify the theoretical results, VFPDs with a ZnS photoconductor and ZnO nanowire cold cathode were studied. The responsivity initially increased and then decreased by increasing the photoconductor thickness. In addition, a broad dynamic range was achieved with a linear dynamic range of 106.02 dB, which was attributed to the efficient collection of carriers induced by EBIPC at the optimized thickness. Our theoretical results were validated by the experimental results and can provide guidance for developing VFPDs based on EBPIC.
Automatic Toolpath Pattern Recommendation for Various Industrial Applications based on Deep Learning*

Automatic toolpath generation (ATG) systems are a class of robotic systems aimed at generating customized patterns of robotic trajectories and toolpaths to automate several industrial processes such as polishing, deburring, masking, etc. ATG systems are especially valuable in automating industrial processes that require high precision, are repetitive or labor intensive. ATG systems face challenges when CAD data for the manufacturing object/workpiece is either not available or inaccurate. We have developed an adaptive ATG system that can generate the robotic tool path based on scan data for both contact and non-contact manufacturing processes. There are total five tool path patterns designed for our ATG systems, including zigzag tool path, spiral tool path, meridian tool path, contour tool path and boundary tool path. Since the workpiece/coupon differs in shapes and sizes, the choice of a specific tool path pattern or a combination of several patters for the given process is a critical criterion for optimal manufacturing results. In this paper, we present our tool path pattern recommendation methods based on deep learning neural networks in Tensorflow. 3D point cloud segmentation and classification are deployed to identify the object features. Moreover, transfer learning is used in order to enhance performance and save training time. This collective decision making can be implemented either on the computing edge or cloud.
EyeLoc: Smartphone Vision-Enabled Plug-n-Play Indoor Localization in Large Shopping Malls

Indoor localization is becoming an emerging requirement in many large shopping malls. Existing indoor localization systems, however, require exhausted system bootstraps and calibration phases. The huge sunk cost usually hinders practical deployment of the indoor localization systems in large shopping malls. In contrast, we observe that floor-plan images of large shopping malls, which highlight the positions of many shops, are widely available in Google Maps, Gaode Maps, Baidu Maps, etc. According to several observed shops, people can localize themselves (called self-localization). However, due to the requirements of geometric sense and space transformation, not all people get used to this way. In this article, we propose EyeLoc, which uses smartphone vision to enable accurate self-localization on a floor-plan image. EyeLoc addresses several challenges, including developing a ubiquitous smartphone vision system, extracting efficient vision clues, and achieving robust measurement error mitigation. We implement EyeLoc in Android and evaluate its performance in emulated environment, two large shopping malls and a semioutdoor large Outlets. The results show that the 90-percentile errors of localization and heading direction are 5.97 m and 20° in 70 000 m2 malls.
Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT

As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G—using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.
A free-rotating gripper for grasping or rotating an object

Automation of display and disposal products is strongly required at convenience stores. However, it is difficult for robots to correct the posture of fallen products and to grasp and transport products in any posture. In this paper, we developed robot hand with free rotation gripper which can switch grasp or rotate products easily for efficient work. It has rotating plate at the end of the robot hand and two grip modes. They are “Grasp mode” and “Rotation mode”. Grasp mode prevents free rotation of gripped object, and Rotation mode can change the posture of gripped object. The posture of object is determined by the relationship between the grip position and the position of the center of gravity. With this robot hand, performance evaluation experiment was conducted; the result confirmed that the robot hand was able to grip cork block, convenience store’s rice ball and sandwich in Grasp mode and Rotation mode, and gripper behavior was changed by the grip mode.
An Innovative Framework for Supporting Remote Sensing in Image Processing Systems via Deep Transfer Learning

In this work, we propose a method based on Deep-Learning and Convolutional Neural Network (CNN) ensemble fine-tuning for the task of remote sensing imagery registration and processing. Our method is based on the CNN transfer learning technique that allows the use of large-scale models that are already pre-trained on big general datasets and fine-tunes them for a particular application area. This approach can significantly decrease the needed size of the training set, for cases where such big training datasets are not available, and improve the quality of classification using a larger CNN or an ensemble of CNNs. This paper addresses the challenges encountered at each stage of the proposed pipeline. For image registration, objects of predefined type are detected, such as roads with hardcover and buildings using a CNN ensemble. Also, a CNN ensemble is used to detect undesirable structures in the image, such as clouds or rocks on the agricultural fields. Our image segmentation method can be used for image matching and fusion. To test our approach, we use an annotated dataset from the Kaggle contest "Dstl Satellite Imagery Feature Detection," UC Merced Land Use Dataset, and a custom annotated dataset of remote sensing imagery of agricultural areas.
Towards Personalized Hybrid Recommender System using Average Visit Intervals

The overload of online data creates a problem in filtering information that is appropriate to a user, which is why many companies and websites utilize recommendation systems. Finding products and contents that match users’ interests has become crucial for online content-providers, and users are always in search for content that suit their preferences. It is becoming more and more difficult for users to find the content they are looking for with the rate of content increasing in the world, thus the importance of recommender systems has increased more than ever. Yet, recommendation systems have not been perfected and shows much room for research and development. This paper therefore presents a new hybrid method, integrating a parallel collaborative and content-based filtering system with a ranking system based on a user profile with the addition of a time variable to represent the time sensitive characteristic of content and interest. The evaluation of our approach shows promising results in the cinematographic field and indicates further potential for development.
Power Flow Control of Grid-Connected Single-Source Multilevel Converter with DC-Link Voltage Control

In this paper, the closed-loop control architecture for a single-source multilevel inverter (MLI) is presented in order to regulate the dc-link voltage and power transfer to the grid. The MLI topology consists of a diode-capacitor voltage multiplier circuit to obtain several self-balanced capacitors voltages. The adopted control technique allows the decoupled control of active and reactive power injected to the grid at almost unity power factor and reduced total harmonic distortion. The proposed MLI serves a dual purpose, i.e., boosting the input voltage with a high boost ratio and inverting action with multilevel output AC voltage. A 500 W laboratory prototype of the proposed converter was built with the closed-loop control. The performance of the grid-tied MLI was investigated in different real time scenarios and the experimental results are presented to demonstrate the performance of the closed-loop control architecture.
State Reference Computation for PMSM Implemented with Single-Precision Floating Point Datatype

Typically, electrical drives with permanent magnet synchronous machine (PMSM) require a separate reference current generation to map a torque request to optimal phase currents. However, many algorithms are based on simplifications and therefore do not lead to an operation with minimum losses and maximum torque. Others require the usage of double-precision algorithmics to obtain accurate results. This paper proposes an algorithm for reference current computation considering the maximum-torque-per-ampere and the maximum-torque-per-volt criterion, a potentially variable DC link voltage and resistive voltage drops. The algorithm is based on the steady-state PMSM equations. It is able to handle saliency and fluctuating motor parameters, e.g. due to magnetic saturation. It fulfills the requirements for a usage in a hard real-time application and in comparison to other approaches it works with single-precision floating point datatype and is therefore specially suited for cost-effective drives.
Large Intelligent Surface-Assisted Nonorthogonal Multiple Access for 6G Networks: Performance Analysis

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted nonorthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> NOMA users is assisted by an LIS comprising <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> reflective elements (REs). First, we derive the probability density function (PDF) of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for <inline-formula> <tex-math notation="LaTeX">$M = 1$ </tex-math></inline-formula> and large <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> values (<inline-formula> <tex-math notation="LaTeX">$M > 10$ </tex-math></inline-formula>) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for PEP in high signal-to-noise ratio regime, asymptotic diversity order, and tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.
Influence of Carbon on pBTI Degradation in GaN-on-Si E-Mode MOSc-HEMT

In this article, threshold-voltage V<sub>TH</sub> instabilities under positive gate voltage stress V<sub>GStress</sub> in GaN-on-Si devices are thoroughly investigated. Measurement-stress-measurement pBTI technique using ultrafast V<sub>G</sub> ramp was applied in this study. PBTI transients performed at different V<sub>GStress</sub> and several temperatures highlight the influence of two trap populations, one being related to Al<sub>2</sub>O<sub>3</sub> gate oxide defects and the other one to C<sub>N</sub> acceptors in GaN lattice. Both trap populations are located close to the Al<sub>2</sub>O<sub>3</sub>/GaN interface and lead to V<sub>TH</sub> instabilities via two different underlying mechanisms simulated by TCAD. PBTI transients obtained under several dc and ac stress conditions have also been modeled using capture emission time (CET) maps and allowed the identification of the two trap populations. Analysis of the temperature-dependent CET maps gives an activation energy of 0.8–0.9 eV related to C<sub>N</sub> traps and an energy range between 0.7 and 1.5 eV ascribed to Al<sub>2</sub>O<sub>3</sub> defects above the GaN conduction band energy. This study provides a better understanding of the underlying physical mechanisms, leading to BTI degradation in GaN-HEMT technologies.
Open Switch Fault- DCM Mode Analysis of Neutral Point Clamped Inverter

Neutral Point Clamped (NPC) Multilevel Inverters (MLIs) find tremendous application in high and medium power and is proposed to be used in Electric Vehicles (EV). Compact size than other MLIs and advantages like low voltage stress on switches, high power quality, high DC bus voltage, reduced DC link current and low size phase conductors than 2-level inverters emphasis the use of NPC for EV applications. Due to the presence of large number of switches, switch faults are unavoidable during the operating lifetime of MLIs. The operation of NPC during open switch faults of inner switches are different and severe than outer switches open circuit faults. The shift of NPC from CCM to DCM mode of operation with inner switch fault causes the presence of abnormality in the pole voltage. In this paper, a voltage equation for analyzing the pole voltage during single inner switch open circuit fault of NPC inverter is proposed. The equation attests the occurrence of abnormal pole voltage during fault. The equation is verified by simulation and experimental results.