Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-Switching and Efficient Power Transfer in Capacitive Wireless Systems with LCLC Compensation Networks

This study delineates the conditions for soft-switching in capacitively-coupled resonant converters that are compensated with LCLC matching networks. Such converters' setups are extremely popular in wireless capacitive power transfer (CPT) technology. The detailed analysis explores the intricate relationships between the parameters, operating conditions, and transfer characteristics. It reveals that by design of the compensation networks' parameters according to the highest expected coupling capacitance, then zero-voltage switching (ZVS) conditions are achieved over the entire operation range. The results of the analysis further outline the necessary conditions for zero-current switching (ZCS) at turn off. Consequently, the system maintains soft-switching both at turn on and turn off, for all switches. This provides a significant potential enhancement of the power transfer and processing efficiency, in particular for applications of wireless energy where the operating frequency is very high. The theoretical analysis and predictions have been verified by simulations and experimentally. The simulation platform incorporates a simple and flexible cross-coupled model, also developed in this study, which is used to evaluate the results under various conditions. The experiments have been carried out on a LCLC capacitive-based WPT prototype operated in the MHz range, and examined through several air-gaps up to 120 mm. An excellent agreement has been obtained between the theoretical work, simulations and the experimental evidence.
Local Synchronization of Two DC/AC Converters Via Matching Control

We address the problem of local synchronization of two DC/AC converters connected via a RL line in closed-loop with machine matching control by pursuing a novel approach for the assessment of small-signal stability. Our method is defined using an oblique projection. We use this Lyapunov function to obtain a local stability certificate in the neighborhood of a well-defined set of equilibria characterized by frequency synchronization. To this end, we first reduce the fifth-order DC/AC converter model to a third-order one. Then, we investigate local stability based on Lyapunov's method performed on the linearized and reduced-order model. Moreover, we certify stability via sufficient and fully decentralized conditions that can be satisfied by choosing an appropriate operating point and control gains. Finally, we demonstrate our results using numerical simulations.
3-D-Printed Dielectric Lens With Cone-Shaped Cavity for Axial Ratio Beamwidth Enhancement of Circularly Polarized Patch Antenna

A 3-D-printed dielectric lens is proposed in this paper to widen the 3-dB axial-ratio beamwidth (ARBW) of conventional circularly polarized (CP) patch antenna. The proposed lens is constructed by a solid dielectric cylinder with a cone-shaped air cavity. To illustrate the working principle, the geometric optics method is introduced, then refractions of the CP wave caused by the lens are theoretically analyzed and the related equations of axial ratio (AR) are derived. Besides, the key parameters that affect the ARBW of the CP antenna are adequately investigated, and the general rules are summarized to design it conveniently. To provide two orthogonal modes at center frequency, a dual-feed network is used to drive the patch antenna. To demonstrate, prototypes with and without lens are designed, fabricated, and measured. The experimental results show a good agreement with the simulated ones in terms of returned loss, radiation pattern, gain, and AR. The measured 15-dB impedance bandwidth is 20.4%, the 3-dB AR bandwidth is 17.5%, and the peak gain at main radiated direction is 5.5 dBic. Furthermore, the compared results in measurements show that by using the proposed lens, the 3-dB ARBW of CP antenna can be extended effectively from 82° to 162°, which is highly demanded in global navigation satellite system for reducing multipath interference. Finally, the applicability of the proposed lens is discussed including the available frequency bandwidth, the size of ground plane, and the feasibility for other types of CP patch antennas.
A Millimeter-Wave Reconfigurable On-Chip Coupler with Tunable Power-Dividing Ratios

This paper presents a millimeter-wave on-chip tunable coupler with tunable power dividing ratios and constant phase responses. Composed by two coupled-lines, two capacitors and two series-connected varactors, the proposed tunable coupler offers wideband frequency responses. Theoretical analysis for wideband operation is provided with design parameters. For demonstration, a millimeter-wave tunable coupler is implemented in a standard 0.13-μm SiGe (Bi) CMOS technology and measured through an on-wafer probing system. From 25 to 31 GHz, the proposed tunable coupler shows a power-dividing ratio tuned from 0 to 5 dB, while maintaining an in-band return loss of better than 10 dB and an output isolation of 20 dB, simultaneously. The phase imbalance is better than ±4° with a measured insertion loss of 1.6 dB across the entire tuning range.
Evaluating the Observability of Network Security Monitoring Strategies With TOMATO

Monitoring systems for malicious behavior increasingly requires aggregating and analyzing data from various sources, such as network flows, host logs, and end-point monitoring platforms. However, there’s currently a lack of metrics and methodologies to compute the observability and efficiency of a security monitoring strategy. This manuscript introduces TOMATO (Threat Observability & Monitoring Assessment Tool), which is a platform to evaluate the effectiveness of a security monitoring strategy by exploring both the number of known adversarial techniques that can be detected within a network, along with evaluating the number of false-positives produced by the monitoring strategy. The output produces both an observability score and efficiency score of a set of deployed monitoring techniques, which are evaluated based on the data from the environment, and simulated attacks generated from MITRE ATT&CK. The proposed approach is then integrated into an ELK stack and evaluated on real SCADA devices within the WSU Smart City Testbed.
Feedback for nonlinear system identification

Motivated by neuronal models from neuroscience, we consider the system identification of simple feedback structures whose behaviors include nonlinear phenomena such as excitability, limit-cycles and chaos. We show that output feedback is sufficient to solve the identification problem in a two-step procedure. First, the nonlinear static characteristic of the system is extracted, and second, using a feedback linearizing law, a mildly nonlinear system with an approximately-finite memory is identified. In an ideal setting, the second step boils down to the identification of a LTI system. To illustrate the method in a realistic setting, we present numerical simulations of the identification of two classical systems that fit the assumed model structure.
Robust, Low-Loss RCIGCT Technology and MV Applications

The combination of the robust low-loss RC-IGCT technology with the modular multi-level converter (MMC) technology is a key enabler for high-power MV applications from 50 to 500 MV A. The RCIGCT technology covers cost-efficiently phase currents in the range from 1000Arms up to 3500 Arms. Future bi-mode BGCT technologies will enable even higher currents without any parallel connection up to 5000 Arms. The ceramic presspack housing of the RCIGCT in combination with the IGCT clamp network allows a unique and safe plasma containment in all fault cases. Future high power MV application like pumped hydropower, MVDC collection grids for renewables, interties for distribution grids and future MVDC distribution backbones can be served at leading costs and efficiency levels. The low-loss RCIGCT technology is inherently a future option at higher voltage levels, i.e. in HVDC applications.
Evaluation of 2D Negative-Capacitance FETs for Low-Voltage SRAM Applications

In this work, we comprehensively evaluate and analyze the stability and performance of 6T SRAM cells using 2D MFIS-type negative capacitance FETs (2D-NCFETs) based on the IRDS 2030 node with 10-nm gate length. Our results indicate that 2D-NCFETs possess better RSNM than the 2D-FET counterpart under low supply voltages. Our study also shows that 2D-NCFETs have better WSNM except for $\mathrm{V}_{\mathrm{DD}} =0.2\mathrm{V}$ due to the existence of hysteresis loop in write curve during write operation. By using write-assist circuits or back-gating techniques, we demonstrate that the WSNM of 2D-NCFETs can be significantly improved. We further analyze the performance of read and write operations, and 2D-NCFETs have been found to possess better performance than 2D-FETs.
5G-RANGE Project Field Trial

Providing broadband Internet access in remote areas have been a challenge to any terrestrial wireless network. The high cost of spectrum license and the limited coverage of current technologies have imposed a prohibitive operational cost for remote areas wireless networks. 5G-RANGE project proposes an innovative physical layer (PHY) and medium access control layer (MAC) layers to opportunistically exploit TV white spaces (TVWS) while providing high throughput in a large coverage area. This paper presents the first field test results based on this new technology. The field trial was performed with the 5G-RANGE project transceiver, implemented for real-time operation over a software-defined radio (SDR) platform. The 2x2 multiple-input multiple-output (MIMO) Generalized Frequency Division Multiplexing (GFDM) system operates in 700 MHz using two adjacent Ultra High Frequency (UHF) TV channels. The landscape in the region of the field trial is predominantly composed of hills. The main goal of the field test was to determine the throughput and coverage that the 5G-RANGE transceiver can achieve under real conditions. The results show that a high data rate can be achieved with low bit error rate (BER) at distances up to 50 km distant from the base station (BS).
Relay Selection for Wireless Cooperative Networks using Adaptive Q-learning Approach

Relay selection is an effective method to improve the system performance of co-operative communication, and thus has received significant attention. In this paper, by assuming that the instantaneous channel state information (CSI) is unknown at the source and relays, we propose a Q-learning (QL) based on relay se-lection method, which can select the relays with maximum cumulative reward to obtain the maximum throughput of the cooperative networks. Besides, Boltzmann learning rule is adopted to achieve well counterpoise between action exploration and exploitation. Simulation results show that the proposed QL algorithm can select the optimal relay adaptively and improve the system performance significantly in comparison with random selection algorithm. Furthermore, it can be found that as the number of relay nodes increases, the QL algorithm can still adaptively select the optimal relay without increasing the computational load.
Spontaneous Emotion Detection by Combined Learned and Fixed Descriptors

Spontaneous vs posed (SVP) problem refers to the capability of a computer vision system to analyze a facial image and to recognize if the facial behavior is due to a genuine emotion or is posed via voluntary control. In this paper we propose a method for automatic identification of the spontaneous behavior. From a technical point of view, the contribution is the usage into this application of learned convolutional filters, although the database used is not of adequate size to train an end-to-end deep network. Secondly, from an evaluation point of view, our method reaches the best performance for such a scenario, where separation between genuine and posed face expression is sought. Thirdly, the numerical evaluation is thoroughly discussed in the context of heavily imbalanced dataset.
FTIR-Spectroscopic and Electrical Analysis of Thermally Stressed Liquid Dielectrics

Mineral oils are employed as liquid dielectric insulators in many high voltage transformers. It is recognised that the dielectric properties of these oils degrade over time with continued exposure to high voltages and thermal stress. This breakdown can be monitored using FTIR spectroscopy or electrical techniques. In this work the mineral oils have been thermally stressed by exposure to 120°C over a prolonged time period with changes to the electrical breakdown voltage and FTIR spectra measured at frequent intervals. Different samples of oil were prepared with some exposed to Kraft paper, copper or a combination of the two. The ratio of oil: copper: Kraft paper was consistent with that commonly found in transformers. Results show that the growth of the carboxylic acid peak can be correlated with a total acid number (TAN) through a calibration process which is less time consuming, won’t overestimate TAN and is an alternative to titration.
Cost-constrained Handoff in Next Generation Heterogeneous Wireless Networks

The future 5G networks are emerging with an integrated network architecture that will incorporate various radio access technologies (RAT) and radio frequency bands, e.g. millimeter wave (mmWave), microwave and ISM band in a single network to offer various services such as voice, video, TV etc. To avail these services, user equipment will also be equipped with multi-connectivity support over different RAT and frequency bands. However, due to mobility, user needs to switch to BS that may/may not be operating in the same RAT and/or same frequency bands. As each RAT has different handoff mechanism and each frequency bands have different nature of propagation, especially in mmWave bands, it becomes necessary for the user end (UE) device to determine the optimal BS to switch to, from among the candidate BS in its proximity. In this work-in-progress article, we study handoff policy in such a multi-RAT and multi-band 5G network environment using Markov Decision Process (MDP) to reach an optimal decision. The optimal policy is determined by taking into account bandwidth available at the BS, channel condition between the UE and BS and user’s maximum budget. The efficacy of the proposed scheme is evaluated using numerical results. The ultimate goal of the paper is to design an optimal handoff scheme when users are mobile and have multiple BSs in its proximity that can be either mmWave BS, or traditional microwave BS or a Wi-Fi access points, however, the complete solution will be presented in extended version of this paper.
Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines

This paper presents a Non-Line-Of-Sight (NLOS) identification approach based on machine learning algorithms for ultra wide band positioning systems. The identification of NLOS conditions is crucial for positioning using trilateration as NLOS introduces positive biases in the calculated distances. The proposed method is based on the classification of the Channel Impulse Responses using Fisher’s Linear Discriminant and Support Vector Machines (SVM). The proposed approach has been validated by measurements in both an anechoic chamber where known reflections and obstacles are introduced and in a basement corridor as real environment scenario with more than 500 and 700 measured data sets for training, respectively. Results show an average identification accuracy of 92% for the case using SVM in the anechoic chamber and almost 100% for Fisher’s discriminant combined with SVM for the corridor scenario.
Improving Redundancy and Safety of UTM by Leveraging Multiple Uass

Reliability is one key challenge facing safe widespread integration of UASs within uncontrolled airspaces. We apply system engineering methods to airspace management to explore architectures and operational concepts which can compensate for partial failures and bridge the reliability gap. Our exploration leads to an operational approach which leverages multiple UASs to achieve redundancies. We employ simulation to demonstrate the benefits of operating single-file platoons over fixed routes, in the context of the NASA UAS Traffic Management (UTM). We show that it is possible to simultaneously reduce impact of localization errors, achieve better resilience under degraded communications, gracefully remove from airspace UAS compromised by cyber attacks, improve conflict management and increase airspace capacity. Our multi-agent airspace simulation improves realism using Firmware In the Loop (FITL), Intent Control Loop (ICL) and of execution contingencies as concurrent high priority activities.UTM, Systems Engineering, Contingencies, Platoons, Denied GPS, Degraded Communications, Cyber Attacks, Simulation, Firmware Modeling, Intent Modeling
A 6-DOF Telexistence Drone Controlled by a Head Mounted Display

Recently, a new form of telexistence is achieved by recording images with cameras on an unmanned aerial vehicle (UAV) and displaying them to the user via a head mounted display (HMD). A key problem here is how to provide a free and natural mechanism for the user to control the viewpoint and watch a scene. To this end, we propose an improved rate-control method with an adaptive origin update (AOU) scheme. Without the aid of any auxiliary equipment, our scheme handles the self-centering problem. In addition, we present a full 6-DOF viewpoint control method to manipulate the motion of a stereo camera, and we build a real prototype to realize this by utilizing a pan-tilt-zoom (PTZ) which not only provides 2-DOF to the camera but also compensates the jittering motion of the UAV to record more stable image streams.
An Auditing Framework for Vulnerability Analysis of IoT System

Introduction of IoT is a big step towards the convergence of physical and virtual world as everyday objects are connected to the internet nowadays. But due to its diversity and resource constraint nature, the security of these devices in the real world has become a major challenge. Although a number of security frameworks have been suggested to ensure the security of IoT devices, frameworks for auditing this security are rare. We propose an open-source framework to audit the security of IoT devices covering hardware, firmware and communication vulnerabilities. Using existing open-source tools, we formulate a modular approach towards the implementation of the proposed framework. Standout features in the suggested framework are its modular design, extensibility, scalability, tools integration and primarily autonomous nature. The principal focus of the framework is to automate the process of auditing. The paper further mentions some tools that can be incorporated in different modules of the framework. Finally, we validate the feasibility of our framework by auditing an IoT device using proposed toolchain.
WearIoT - Wearable Iot Human Emergency System

The evolution of technologies gives rise to new concepts in many areas. Health problems are increasing as the world population. Global aging is increasing as well as care costs are also rising. It is increasingly necessary to improve precocious disease detection and medical intervention. Wearable monitoring systems, along with wireless communications, form the basis of an emerging class of sensor networks. These information technologies enable the early detection of abnormal conditions and help in their prevention. The goal is to create one of these systems composed by a network of sensors that is implemented in a jacket through conductive wires with connected sensors. In contact with the human body it has the function of doing several readings, such as body temperature, heartbeat, among others. Another goal is to detect user falls. The detection of falls is increasingly important for the user, as it is a situation that can endanger peoples health. For the development of this concept, Mobile Communications and the Global Positioning System are used. The first is a technology that allows to create emergency calls in response to system alarms, the second indicates the geographical location. To complement the system there is an online platform that registers the position of the user as well as his data. There is also an alert area in which the user can check his alarming values. In case of emergency the system contacts the emergency services or in special cases help can be obtained through an UAV.
Investigation of Various Hybrid Acoustic Modeling Units via a Multitask Learning and Deep Neural Network Technique for LVCSR of the Low-Resource Language, Amharic

Multitask learning (MTL) is helpful for improving the performance of related tasks when the training dataset is limited and sparse, especially for low-resource languages. Amharic is a low-resource language and suffers from the problems of training data scarcity, sparsity, and unevenness. Consequently, fundamental acoustic units-based speech recognizers perform worse compared with the speech recognizers of technologically favored languages. This paper presents the results of our contributions to the use of various hybrid acoustic modeling units for the Amharic language. The fundamental acoustic units, namely, syllable, phone, and rounded phone units-based deep neural network (DNN) models have been developed. Various hybrid acoustic units have been investigated by jointly training the fundamental acoustic units via the MTL technique. Those hybrid units and the fundamental units are discussed and compared. The experimental results demonstrate that all the fundamental units-based DNN models outperform the Gaussian mixture models (GMM) with relative performance improvements of 14.14%–23.31%. All the hybrid units outperform the fundamental acoustic units with relative performance improvements of 1.33%–4.27%. The syllable and phone units exhibit higher performance under sufficient and limited training datasets, respectively. All the hybrid units are useful with both sufficient and limited training datasets and outperformed the fundamental units. Overall, our results show that DNN is an effective acoustic modeling technique for the Amharic language. The context-dependent (CD) syllable is the more suitable unit if a sufficient training corpus is available and the accuracy of the recognizer is prioritized. The CD phone is a superior unit if the available training dataset is limited and realizes the highest accuracy and fast recognition speed. The hybrid acoustic units perform the best under both sufficient and limited training datasets and achieve the highest accuracy.
The Effect of Audio and Visual Modality Based CPR Skill Training with Haptics Feedback in VR

The hypothesis of this study is to verify the sensory dominance with the combinations of three sensory modalities (Audio-Haptics (AH), Visual-Haptics (VH), Audio-Visual-Haptics (AVH)) using Virtual Reality (VR) based Cardiopulmonary Resuscitation (CPR) simulator. To test this hypothesis three experiments with three different groups of participants were conducted with the above three modes of combinations. Finally, three groups were tested for their CPR performance on an unknown linear chest stiffness of mannequin-based CPR simulator and their performance score was compared. The % mean and standard deviation of the performance score (p-value: 0.00006) in the testing phase for group A-AH, B-VH, and C-AVH is $77.95\%\pm 8.27\%, 89.47\%\pm 6.19\%, \ \text{and} \ 91.73\%\pm 3.14\%$ respectively. The results show that the group who trained with the three sensory modalities have better performance than that of the other two groups. Our future work is to incorporate rescue breathing in the CPR training simulator for better skill training.