Miracle-dz/UniFAR-FTUs huggingface dataset:
VirtualTablet: Extending Movable Surfaces with Touch Interaction

Immersive output and effortless input are two core aspects of a virtual reality (VR) experience. We transfer ubiquitous touch interaction with haptic feedback into a virtual environment (VE). The movable and cheap real world object supplies an accurate touch detection equal to a ray-casting interaction with a controller. Moreover, the virtual tablet extends the functionality of a real world tablet. Additional information is displayed in mid-air around the touchable area and the tablet can be turned over to interact with both sides. It allows easy to learn and precise system interaction and can even augment the established touch metaphor with new paradigms.
C-RAN Employing xRAN Functional Split: Complexity Analysis for 5G NR Remote Radio Unit

Fronthaul (FH) bandwidth in cloud radio access network (C-RAN) can be significantly reduced with an appropriate functional split by offloading more signal processing functionalities to the remote radio unit (RRU). However, this not only reduces the acclaimed centralization benefits but also increases the complexity of the RRU. Considering the practical aspects such as power consumption, cost, size and weight, it is often desirable to make RRUs as simple, yet efficient, as possible. In this paper, we analyze the complexity of the RRU with 5G New Radio (NR) considering functional split 7.2 as recently standardized split by the xRAN Forum. C-RAN with 5G NR allows to support a wide range of scenarios and use-case specific requirements. In addition, we compare suitability in terms of efficiency and flexibility of the RRU being implemented using either field programmable gate array (FPGA) or general purpose processor (GPP), e.g., x86 considering their computational requirements. Based on the complexity analysis, we calculate the required number of the FPGA or GPP to support the complexity of the RRU. We show that FPGA is more feasible option compared to x86 in terms of power consumption, particularly for rooftop-mounted RRU.
Gesture Commands for Controlling High-Level UAV Behavior

In this paper, an accelerometer and gyroscope are used to sense gesture commands, which are then classified using a logistic regression model. Seven gestures were chosen and mapped to specific behaviors that a fixed wing unmanned air vehicle could accomplish. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. The system was trained to recognize the seven gestures and then tested in a hardware-in-the-loop simulation. The system was able to identify all gestures with an overall accuracy of 90% and with five of the seven gestures being accurately identified at least 94% of the time. Each of the behaviors associated with the gestures was tested in simulation and the ability to dynamically switch between behaviors was proven. The results show that the system can be used as a natural interface to assist an operator in directing an unmanned air vehicle’s behaviors.
Raster Scan Multi-Frequency Near-Field Antenna Characterization

The paper deals with the multi-frequency characterization of antennas by using Near-Field measurements collected over one optimized grid common to all the frequencies. In this way the frequency measurements are carried out with a single scan. The approach provides a drastic reduction of the number of measurement points and uses a raster scan grid to take advantage of the classical continuous scan or of the new scanning capabilities shown in [6].
Controller Design Using Backstepping Algorithm for Fixed-Wing UAV with Thrust Vectoring System

This paper describes the design method of nonlinear flight controller for a fixed-wing UAV with a thrust vectoring system (TVS) using the backstepping method. The flight dynamics of the UAV exhibits strong nonlinear coupling behavior between its translational and rotational motion. The backstepping algorithm has been successfully applied to controller design for such a nonlinear system. However, the main idea of the method is to use some of the state variables as virtual controls that need ungeneratable forces by the UAV. To overcome this problem, we use the TVS that can generate thrust in an arbitrary direction. Numerical simulation is performed to confirm the effectiveness of the proposed control method for a fixed-wing UAV with the TVS.
Continuous Monitoring meets Synchronous Transmissions and In-Network Aggregation

Continuously monitoring sensor readings is an important building block for many IoT applications. The literature offers resourceful methods that minimize the amount of communication required for continuous monitoring, where Geometric Monitoring (GM) is one of the most generally applicable ones. However, GM has unique communication requirements that require specialized network protocols to unlock the full potential of the algorithm. In this work, we show how application and protocol co-design can improve the real-life performance of GM, making it an application of practical value for real IoT deployments. We orchestrate the communication of GM to utilize the properties of a state-of-the-art wireless protocol (Crystal) that relies on synchronous transmissions and is designed for aperiodic traffic, as needed by GM. We bridge the existing gap between the capabilities of the protocol and the requirements of GM, especially in the case of periods of heavy communication. We do so by introducing an in-network aggregation technique relying on latent opportunities for aggregation that we exploit in Crystal's design, allowing us to reliably monitor duplicate-sensitive aggregate functions, such as sum, average or variance. Our results from testbed experiments with a publicly available dataset show that the combination of GM and Crystal results in a very small duty-cycle, a 2.2x - 3.2x improvement compared to the baseline and up to 10x compared to previous work. We also show that our in-network aggregation technique reduces the duty-cycle by up to 1.38x.
A Nonlinear Modal-Based Framework for Low Computational Cost Optimal Control of 3D Very Flexible Structures

A nonlinear modal-based reduced-order model, equipped with an efficient adjoint-sensitivity analysis, is presented as a low computational cost framework for optimal control of very flexible structures, with particular focus on efficiently computing finite rotations. Multiple shooting is shown to improve convergence of a highly nonlinear problem when compared to the single shooting case, with optimisation further accelerated via parallelisation, which suggests the presented approach may be employed for real-time control of very flexible structures.
Automated Parameter Extraction and SPICE Model Modification For Gate Enclosed MOSFETs Simulation

This work focuses in using experimental measurements to extract the aspect ratio (W/L) for Gate-Enclosed or Annular MOSFETs. All measurements and calculations are performed with an automated virtual instrumentation (VI) environment developed in LabView. The VI was capable of extracting threshold voltage and low field mobility, needed for W/L calculation. An extraction procedure for the body effect factor is also presented. For validation purposes, extracted parameters were compared with those provided by the foundry (MOSIS) using traditional rectangular transistors. The extracted W/L was validated by comparing to Giraldo's experimental method. The SPICE BSIM 3 model parameters provided by the foundry is then modified using the extracted parameters including the aspect ratio. With this modification better simulation results were obtained. They were compared to the experimental measurements for an annular MOSFET drain voltage vs drain current characteristic curves at different gate voltages, showing an improvement of 58% when compared to the original non modified SPICE model parameters.
Autonomous Line Maze Solver Using Artificial Intelligence

Artificial intelligence is steadily growing, especially nowadays. It starts to represent a necessity for various technological processes such as the automation of operations in various factories, certain robots that can make decisions without human help, cars that can drive alone. In this paper we present an application of artificial intelligence, namely a problem that is common in the field of robotics: navigating a robot in a labyrinth without human interaction, using the left hand rule and dead -end filling. The line maze solver using these algorithms will be able to drive through a labyrinth that has a solution and more precisely a “disk”. The robot uses a Raspberry Pi 3 as a “brain”, where with the Python 2 programming environment we have managed to implement these algorithms to solve our problem.
Bioimpedance Spectroscopy in Subjects with Auricular Vagus Nerve Stimulation

In this paper, single side body impedance spectroscopy is performed between wrist and ankle in response to auricular transcutaneous electrical vagus stimulation, to obtain the Cole parameters Ro, R∞ and fc. Following a resting period of 5 minutes in supine position, left ear vagus nerve was stimulated for 3 minutes and BIS performed every 10 seconds, on 8 healthy subjects with age: 23,6±1,5 years, height: 175±8,3 cm; weight: 69,5±18,7 kg and BMI: 22,6±5,5 kg/cm2. Cole circles moved to the right (inreased Ro and R∞) with stimulation while the centers shifted upwards. Intracellular and extracellular resistances and the capacitance Cm all increased at the onset of vagus stimulation; resulting in a decrease of 3,4±1.33% in fc. Changes in Ri mainly indicate water shift between cells and extracellular fluid; physiological changes occurred on cell membrane. fc independent of the participants’ anthropomorphic factors could be used as an indicator to vagus nerve stimulation, without further normalization. This preliminary study was realized with a limited number of participants; however, to reach statistically meaningful results measurements will be performed over a larger sample size.
Control of Switched Boolean Control Networks by State Feedback

Control of infectious diseases using bacteriophage therapy is regaining interests in modern medicine and systems biology as an alternative treatment for antibiotic-resistant bacteria. A key issue is to control the phage's replication process: indeed, phage may switch either towards lytic state or lysogeny state during its reproduction due to some conditions. However, only lytic state is relevant for bacteria elimination. In this paper, we model switching replication dynamics of the $\lambda$ phage, i.e., a class of bacteriophage, as a switched Boolean control network (SBCN) and we address the problem of designing all possible switching signal-dependent state feedback controllers to stabilize the phage system at lytic developmental pathway. An algebraic state-space representation method is adopted to determine all possible families of reachable sets. Necessary and sufficient conditions for the stabilization of SBCN to a given equilibrium point under arbitrary switching signals are presented, where the control input is switching signal-dependent. Moreover, all possible switching signal-dependent state feedback controllers are obtained based on all complete families of reachable sets. Results are relevant for both the development of SBCN theory and for practical applications. At last, the example of bacteriophage $\lambda$ is analyzed to show the effectiveness of the main results.
PV-to-Bus DPP Architecture based on Unidirectional DC-DC Converter for Photovoltaic Application

Differential power processing (DPP)architecture as a promising technology is proposed now to solve the mismatch issues among photovoltaic (PV)cells. In the current study, DPP converters use the bidirectional DC-DC converters, which requires at least two active switches to achieve energy extraction and energy injection. This paper proposes a PV-to-bus DPP architecture based on unidirectional flyback converter that reduces the total number of switches by half reducing the costs significantly. Furthermore, flyback converters are working in the discontinuous conduction mode (DCM).
Nonlinear Hierarchical Model Predictive Control for the Energy Management of a Hybrid Electric Vehicle

In this paper a hierarchical model predictive control framework for the energy management of a hybrid electric vehicle is extended by replacing the supervisory linear model predictive controller with a nonlinear model predictive controller. The nonlinear system dynamics of a parallel hybrid electric vehicle are incorporated into a nonlinear optimal control problem which is solved at each time instance. The discretization of the continuous time system dynamics is realized via direct multiple shooting. The low-level linear model predictive controller considers the actuator dynamics for optimal tracking of the driver's torque demand while the high-level controller considers the slow dynamics of the vehicle and the battery. The proposed strategy is validated in simulation with the worldwide harmonized light vehicles test cycle and compared to a hierarchical model predictive control framework with linearized system dynamics.
4D Antennas Design and Technology – the State of the Art

The 4D (four-dimensional) antenna array introduces time as an additional degree of freedom in antenna beamforming. Periodical on/off switching applied to each element of the antenna array leads to generation of sidebands which are suitable for multi-channel transmission and smart beamforming. This paper provides an overview of the state of the art in the area of 4D antennas with focus on technical limitations and achievable performance.
Decentralized Control Barrier Functions for Coupled Multi-Agent Systems under Signal Temporal Logic Tasks

We study the problem of controlling multi-agent systems under a set of signal temporal logic tasks. Signal temporal logic is a formalism that is used to express time and space constraints for dynamical systems. Recent methods to solve the control synthesis problem for single-agent systems under signal temporal logic tasks are, however, subject to a high computational complexity. Methods for multi-agent systems scale at least linearly with the number of agents and induce even higher computational burdens. We propose a computationally-efficient control strategy to solve the multi-agent control synthesis problem that results in a robust satisfaction of a set of signal temporal logic tasks. In particular, a decentralized feedback control law is proposed that is based on time-varying control barrier functions. The obtained control law is discontinuous and formal guarantees are provided by nonsmooth analysis. Simulations show the efficacy of the presented method.
Simple Modular Equalization System with Reduced Switch Count and Constant Current Characteristics for Series-Connected EDLC Modules

This paper proposes a modular equalization system integrating module- and cell-level voltage equalizers for energy storage systems consisting of series-connected multiple energy storage modules, each comprising multiple cells connected in series. Phase-shift switched capacitor converters (PS-SCCs) and tapped-inductor-based resonant voltage multiplier (TI-RVM) are employed as module- and cell-level equalizers, respectively. Since two kinds of equalizers are integrated with sharing active switches, the proposed system can halve the switch count compared to conventional modular systems, hence achieving the reduced circuit complexity and cost. In addition, equalization currents of both the PS-SCC and TI-RVM can be constant, regardless of voltage imbalance conditions, offering intrinsic current protection capability at both the module and cell levels. An experimental equalization test was performed using the prototype for three electric double-layer capacitor (EDLC) modules, each containing six cells connected in series. Module and cell voltages were equalized at different rates, demonstrating the equalization performance.
Factorized Carry Lookahead Adders

New factorized carry lookahead adders corresponding to the regular carry lookahead adder (RCLA) architecture viz. the factorized regular carry lookahead adder (FRCLA), and the block carry lookahead adder (BCLA) architecture viz. the factorized block carry lookahead adder (FBCLA) are presented. The idea behind the proposed factorized carry lookahead adders is discussed and example implementations are provided. The RCLA, BCLA, FRCLA and FBCLA were realized using the gates of a 32/28nm CMOS standard digital cell library. The results show that the proposed FRCLA achieves an average reduction in the power-delay product i.e., energy by 7.85% for 32- and 64-bit additions compared to the best among the rest.
Polymer-Based 3-D Printed Ku-Band Steerable Phased-Array Antenna Subsystem

This paper introduces the first fully 3-D printed tunable microwave subsystem, consisting of 26 circuit elements. Here, a polymer-based 3-D printed Ku-band 4-element steerable phased-array antenna with fully integrated beam-forming network is demonstrated. Polyjet was adopted for fabricating the main body of the subsystem, as it is capable of producing a geometrically complex structure with high resolution over a large volume. Low-cost fused deposition modeling was chosen to manufacture the dielectric inserts and brackets for the phase shifters. The measured radiation pattern revealed that the phased-array antenna subsystem has total beam steering angles of 54° and 52° at 15 GHz and 17 GHz, respectively. Excellent input return loss behavior was observed across the optimum operational frequency range of 15 to 17 GHz, with a worst-case measured return loss of 12.9 dB. This work clearly shows the potential of using 3-D printing technologies for manufacturing fully integrated subsystems with complex geometric features.
Recommendations for Mobile Apps Based on the HITS Algorithm Combined With Association Rules

With the increasing popularity of intelligent devices, the mobile apps market has exploded. Due to a large number of candidate app services, it has become very difficult for users to choose the mobile apps that he/she wants to install. Therefore, it is crucial to improve users’ experience and make personalized recommendations. In some cases, the traditional recommendation methods can be convenient, but they still have some shortcomings, resulting in inaccurate recommendations in general. To address this issue, this paper proposes a method for mobile app recommendations that are based on the Hyperlink-Induced Topic Search (HITS) algorithm combined with association rules. This method integrates the authority and hub scores into the candidate applications through the download and rating information, and it not only considers the importance of mobile apps in association rules but also takes the reliability factor of users into account. Experiments with the Huawei application market datasets show that the proposed method significantly improves the recommendation accuracies compared with the traditional methods.
Brain Activity in Virtual Reality: Assessing Signal Quality of High-Resolution EEG While Using Head-Mounted Displays

Biometric measures such as the electroencephalogram (EEG) promise to become viable alternatives to subjective questionnaire ratings for the evaluation of psychophysical effects associated with Virtual Reality (VR) systems, as they provide objective and continuous measurements without breaking the exposure. The extent to which the EEG signal can be disturbed by the presence of VR systems, however, has been barely investigated. This study outlines how to evaluate the compatibility of a given EEG-VR setup on the example of two commercial head-mounted displays (HMDs), the Oculus Rift and the HTC Vive Pro. We use a novel experimental protocol to compare the spectral composition between conditions with and without an HMD present during an eyes-open vs. eyes-closed task. We found general artifacts at the line hum of 50 Hz, and additional HMD refresh rate artifacts (90 Hz) for the Oculus rift exclusively. Frequency components typically most interesting to non-invasive EEG research and applications $(< 50\ \text{Hz})$, however, remained largely unaffected. We observed similar topographies of visually-induced modulation of alpha band power for both HMD conditions in all subjects. Hence, the study introduces a necessary validation test for HMDs in combination with EEG and further promotes EEG as a potential biometric measurement method for psychophysical effects in VR systems.
Cybersickness Analysis with EEG Using Deep Learning Algorithms

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms in previous studies. In this work, we apply and compare Deep Neural Network (DNN) and Convolutional Neural Network (CNN) deep learning algorithms for objective cy-bersickness measurement from EEG data. We also propose a data preprocessing for learning and signal quality weights allowing us to achieve high performance while learning EEG data with the deep learning algorithms. Besides, we analyze video characteristics where cybersickness occurs by examining the 360 video stream segments causing cybersickness in the experiments. Finally, we draw common patterns that cause cybersickness.
A Beamforming Algorithm Based on Maximum Likelihood of a Complex Gaussian Distribution With Time-Varying Variances for Robust Speech Recognition

This letter addresses acoustic beamforming for robust speech recognition. Beamforming has been demonstrated to be one of the most effective approaches for robust recognition of distant speech using multi-microphones. In this letter, we derive a beamforming method, which we refer to as the “maximum-likelihood distortionless response (MLDR)” beamformer, based on the maximum-likelihood estimation (MLE) of a linear filter, with a distortionless constraint on the steering direction, assuming that the optimal beamformer outputs in the time-frequency domain follow a zero-mean complex Gaussian distribution with time-varying variances. By optimizing the beamformer output variances as well as the filter alternately with iterative update rules, and also by using the moving average of output powers at adjacent frames for robust estimation of an output variance, the MLDR beamformer may minimize the power of a relatively accurate noise component at the output, which resulted in better recognition performance than conventional beamformers. In addition, it can be further improved by initializing the output variances to averaged powers of a neural-network(NN)-masked input signal to estimate target speech powers, which achieved even better performance than compared beamformers exploiting trained NNs.
Nonlinear Model Predictive Control to Aid Cooperative Localization

This paper proposes a nonlinear model predictive control (NMPC) scheme to tackle the problem of localization and path planning of a group of unmanned aerial vehicles (UAVs) in global positioning system (GPS) denied environments. It is assumed that the UAVs can cooperate by sharing information among themselves. It is also assumed that the area under consideration contains some landmarks with known locations. The NMPC computes the optimal control inputs for the vehicles such that the vehicles cooperate to transit from a source location to a destination while choosing a path that will cover enough landmarks for localization. An Extended Kalman Filter (EKF) is used to estimate the vehicle positions using only relative bearing measurements. The efficacy of the proposed method was evaluated through numerical simulations, and the results are discussed.