Miracle-dz/UniFAR-FTUs huggingface dataset:
The Push-Pull Plasma Power Supply - A Combining Technique for Increased Stability

During the operation with a double-electrode Non-thermal Plasma (NTP) reactor with a Gliding Arc Discharge (GAD) there are many technical difficulties [1]. The reactor is the resistance energy receiver with a strongly non-linear characteristics. What is more, high voltages above 10 kV are needed for pre-ionisation during the ignition process, while after ignition, the maintaining voltage is several times less. There is also a need to limit the current immediately for keeping non-thermal conditions in the reactor. A plasma reactor used here is briefly described in the Polish patent [2]. In order to meet the requirements of the reactor's supply, there is a strong suggestion to use a Switched- Mode Power Supply (SMPS), the construction of which combines many interesting features that are not commonly encountered. The push-pull topology is not commonly used in such applications. As a standard, it is used in inverters where the low input voltage is processed. The topology requires a primary winding with special symmetry and the alternating current on the secondary side is obtained by switching the primary current between the two halves of the primary coil by delivering current to the centre tap of the primary coil. In this case, the primary current is switched by IGBT transistors. The control can take place both from the microcontroller and the integrated analogue controller, whose advantage is resistance to radiated disturbances. The power supply can successfully achieve power above 1 kW; however, when working with Micro Gliding Arc Plasma Reactor (MGAPR), such power ranges are not required [3]. In addition, the project is seen to have broad scientific perspectives of development. Improvements of ignition properties were obtained using achievements resulting from work on switching overvoltages. The power supply has a power and frequency regulation. Both of them can work in control feedback loops, but here in the experiment they are not used. The features mentioned are exceptional for the power supply and provide a wide range of possibilities in supplying the nonthermal plasma reactors. There are very wide adjustment properties in the range of 13–26 kHz and it has been proven to be exceptionally efficient in fulfilling its purposes.
Secrecy Fairness Aware NOMA for Untrusted Users

Spectrally-efficient secure non-orthogonal multiple access (NOMA) has recently attained a substantial research interest for fifth generation development. This work explores crucial security issue in NOMA which is stemmed from utilizing the decoding concept of successive interference cancellation. Considering untrusted users, we design a novel secure NOMA transmission protocol to maximize secrecy fairness among users. A new decoding order for two users' NOMA is proposed that provides positive secrecy rate to both users. Observing the objective of maximizing secrecy fairness between users under given power budget constraint, the problem is formulated as minimizing the maximum secrecy outage probability (SOP) between users. In particular, closed-form expressions of SOP for both users are derived to analyze secrecy performance. SOP minimization problems are solved using pseudoconvexity concept, and optimized power allocation (PA) for each user is obtained. Asymptotic expressions of SOPs, and optimal PAs minimizing these approximations are obtained to get deeper insights. Further, globally-optimized power control solution from secrecy fairness perspective is obtained at a low computational complexity and, asymptotic approximation is obtained to gain analytical insights. Numerical results validate the correctness of analysis, and present insights on optimal solutions. Finally, we present insights on global-optimal PA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%, respectively are achieved, compared to fixed PA and individual users' optimal PAs.
Analytical and Numerical Methods to Estimate the Effective Mechanical Properties of Rutherford Cables

Superconducting Nb3Sn Rutherford cables can be used in the accelerator magnets above 10 T regions that cannot be reached with NbTi. These cables are composed of superconducting multifilamentary strands, interstitial epoxy, and insulation materials. To properly design the magnets made from these cables, it is essential to analyze their mechanical behavior. The prerequisite for this is the knowledge of the mechanical performance of Rutherford cables. In large magnets, these cables cannot be modeled with all details but effective properties and homogenization are typically required. In this work, an analytical model and numerical approach for predicting the effective mechanical properties of Rutherford cables are developed. The analytical model is established on a two-step homogenization and mechanical analysis for composite. The effective mechanical properties of the filament area and the transverse Young's modulus of the strand are first determined by means of utilizing the mechanical theory of the unit cell approach. The composite effective mechanical properties of the strand and interstitial epoxy are homogenized in the first step. The second step homogenization derives the effective mechanical properties of the strands epoxy composite and insulation layers. The numerical approach to determine the effective mechanical properties is based on the finite-element analysis. The developed methodologies are used to obtain the effective mechanical properties of two Nb3Sn Rutherford cables. The influence of the insulation thickness and modulus, the strand's modulus, is studied.
SDN Controllers Scalability and Performance Study

Software Defined Networks (SDN) is a networking approach that decouples the intelligent control plane from networking devices and establishes a separate entity called ”controller” that rule the behaviour of the data plane on physical networking devices. Due to the rapid evolution and growth of SDN controllers in the market, this paper aims to present an extensive study on performance and scalability of different open source SDN controllers available in the existing literature. This work covers previous studies and expands them with updated information and official benchmarking methodologies. The study provides a framework based on the standards recommended by the IETF (Internet Engineering Task Force) and it will serve as a guideline to the SDN community to benchmark different SDN controllers.
System Response of Permanent Magnet Synchronous Motor Drive Based on SiC Power Transistor

This paper explores the system response of motor drive systems using wide band gap silicon carbide (SiC) transistors. A SiC power transistor-based voltage source inverter with field-oriented control is established by using MATLAB/Simulink for permanent magnetic synchronous motor. Based on simulation results, the findings are that increasing switching frequency significantly improves dynamic response of speed and that the total harmonic distortion of the three-phase current can also be improved.
Trajectory Design of Laser-Powered Multi-Drone Enabled Data Collection System for Smart Cities

This paper considers a multi-drone enabled data collection system for smart cities, where there are two kinds of drones, i.e., Low Altitude Platforms (LAPs) and a High Altitude Platform (HAP). In the proposed system, the LAPs perform data collection tasks for smart cities and the solar-powered HAP provides energy to the LAPs using wireless laser beams. We aim to minimize the total laser charging energy of the HAP, by jointly optimizing the LAPs' trajectory and the laser charging duration for each LAP, subject to the energy capacity constraints of the LAPs. This problem is formulated as a mixed-integer and non- convex Drones Traveling Problem (DTP), which is a combinatorial optimization problem and NP-hard. We propose an efficient and novel search algorithm named Drones Traveling Algorithm (DTA) to obtain a near-optimal solution. Simulation results show that DTA can deal with the large-scale DTP (i.e., more than 400 data collection points) efficiently. Moreover, the DTA only uses 5 iterations to obtain the near-optimal solution whereas the normal Genetic Algorithm needs nearly 10000 iterations and still fails to obtain an acceptable solution.
A Wearable Real-Time CMOS Dosimeter With Integrated Zero-Bias Floating Gate Sensor and an 861-nW 18-Bit Energy-Resolution Scalable Time-Based Radiation to Digital Converter

Radiation, being invisible and odorless, has become a major concern in modern-day healthcare, mining, security, and nuclear applications that require professionals to work in environments involving radioactive materials, often with only elementary training. Furthermore, the scenario is worsened by the absence of a real-time, accumulative radiation dosimeter of small/wearable form factor that can provide direct digital output for long-term continuous monitoring. In this article, we present the first monolithically integrated CMOS wearable radiation dosimeter, consisting a floating-gate (FG) resistive sensor with a sensitivity of 14 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> /rad, and a time-domain resistance-to-digital-converter (RDC) that functions as an 18-bit voltage-controlled oscillator (VCO)-based ADC, consuming 861 nJ for 10-ms active read time (which translates to 861-nW at 1 S/s), with 3.29-pJ/conversion step energy efficiency. The proposed time-based RDC exhibits programmable energy-resolution scalability by controlling the measurement time (unlike traditional voltage/current-mode ADCs) and achieves 6-bit better resolution than the state-of-the-art VCO-based ADCs at low frequency (conversion time ≈10 ms). The implemented integrated dosimeter achieves 8-bit better resolution, 7.5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> lower power, and 40<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> better sensitivity (up to 10-mrad dose) than the current CMOS FG dosimeters.
Noise Suppression of Distributed Acoustic Sensing Based on f-x Deconvolution and Wavelet Transform

A noise suppression method based on f-x deconvolution and the wavelet transform is proposed for eliminating jump edges in the phase noise of distributed acoustic sensing. Experimental results show that the proposed method has good performance in terms of enhancing the quality of seismic waves. It ensures relatively stable phase sensitivity and improves the signal-to-noise ratio, making the characteristics of seismic wave signals more obvious.
Performance Comparison of Joint Correlation and Improved Energy Detection for Spectrum Sensing

Spectrum sensing is a important feature in cognitive radios, wherein the idea is to detect the presence of the incumbent on a licensed spectrum. It is known that the improved energy detector (IED)- which calculates the sample norm of an optimally chosen order in the received observations - outperforms the conventional energy detector (ED). The relatively recently proposed joint correlation and energy detector (CED) combines the sample energy and the first order correlation values in the received observations, and it is known that when the combining is carried out optimally, it outperforms ED. We present a comparative performance study of CED and IED, for different incumbent signal models, following the Neyman-Pearson framework. We show that the CED consistently outperforms IED across the Gaussian, sinusoidal and constant signal models. Similar to what was reported earlier, the IED is observed to outperform ED only for low values of probability of false-alarm. Hence, this study establishes the applicability of CED for spectrum sensing, as opposed to IED and ED.
Hand Gesture Based Quadcopter Control Using Image Processing And Adaptive Machine Learning

Accurately recognizing gesture in real time is a complex problem to solve. It can be addressed to some extent using colored gloves and markers, but models so built do not generalize well. Another option is to utilize boundary based gesture recognition, but these are more complex to build and prediction is time consuming. Through this work we try to resolve both these issues by construction of a straightforward machine learning architecture which accurately implements gesture recognition in real time and can also be utilized to build a generalized model. The solution we have developed is based on the premise that an architecture which can contextually recognize gestures and can also adaptively identify variations to the same can be used for solving problems ranging from body language detection, translating sign language to devising an intuitive method for controlling UAVs (Unmanned Aerial Vehicles).
Switched Concurrent Learning Adaptive Control of Switched Systems With Nonlinear Matched Uncertainties

This paper investigates the model reference adaptive control (MRAC) problem of switched systems with nonlinear matched uncertainties based on mode-dependent average dwell time method. A novel switched MRAC scheme is proposed, which simultaneously uses both current and recorded data from the switched system, to study the convergence of the state tracking error and the weight error without depending on the persistency of excitation. It is critical to establish the relationship among the nonlinear matched uncertainties, the switched concurrent learning(CL) algorithm, the average dwell time of each subsystem, and the convergence domains of the state tracking error and the weight error. First, we find a class of switching signals characterized by mode-dependent average dwell time conditions such that the switched reference model is bounded-input bounded-state stable. Second, in the case of time-invariant nonlinear uncertainties, we design a switched CL adaptive law that ensures that the tracking error converges to zero, and the adaptive weights exponentially converge to their ideal values. Subsequently, in the case of nonlinear time-varying uncertainties, we estimate the convergence rates of the state tracking error and the weight error of each subsystem outside a ball, and thus obtain sufficient conditions that ensure the convergence of the trajectories of the state tracking error and the weight error to a ball centered at the origin with a given radius. Finally, the proposed approach is applied to an electro-hydraulic system, and its effectiveness is demonstrated.
Chipless RFID: A Low-cost Consumer Electronics in the Retail marketplace for Moving Item Detection

A low-cost consumer electronics using chipless Radio Frequency Identification (RFID) for detecting various items in the retail marketplace is presented. The scenario comprises a smart shopping trolley, where the tag is attached to various reflective and absorptive items which are moving along a defined path. The detection of moving tags is based on a commercial Ultra Wide Band (UWB) low-cost reader and the robust detection technique which separates the tag back-scattered signal from multiple interfering received signals. The experimental results show that the use of the proposed technique is able to detect the tag signal, when the tag is moving at a constant relative velocity of 0.5 m/s (approximately) even in a noisy environment.
Edge-Based Microservices Architecture for Internet of Things: Mobility Analysis Case Study

In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.
LTE as a Service: Leveraging NFV for Realising Dynamic 5G Network Slicing

Advances in virtualisation technology have reached the mobile networking domain. Network Functions Virtualisation Management and Orchestration (NFV MANO) as proposed by ETSI and realised via Opensource MANO (OSM) allows sharing or partitioning a fairly generic pool of hardware into virtual compute, network and storage resources among differentiated services. A direct consequence of this is a dramatic reduction in CAPEX/OPEX, but also the possibility of instantaneously deploy network services across one or several Mobile Network Operators' (MNO) infrastructure. This work demonstrates how the upcoming fifth generation (5G) of mobile communications envisions NFV MANO for instantiating network services, including Core Network components, and wireless SDN controllers for enforcing end-to-end QoS policies via network slices that span from the radio access segment to the backend packet network.
Coverage Area Performance for Multiple Interfering UAVs

Unmanned aerial vehicles (UAVs) have the capability of supplementing as well as improvising terrestrial cellular communications as aerial base stations to improve network coverage. This puts significance on the effective deployment of multiple UAVs while maximizing the coverage area in presence of co- channel interference generated by these UAVs. To this end, it is important to determine the parameters that affects the coverage area performance. In this paper, we investigate the effect of co-channel interference on the effective coverage of UAV-based small cells (USCs) deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell edge. We propose a coordinated multi- UAV strategy to evaluate the trade-off between the UAV separation distance and the overall coverage area assuming symmetric placement of UAVs at a common optimal altitude to ensure minimum transmit power. Numerical results unveil that the number of UAVs and the separation distance between them should be carefully designed to achieve the optimal coverage area performance.
Samiksha: Low Cost Field Monitoring System

Samiksha is cost-effective, easy to use field monitoring system. It is developed for poor Indian farmers. It takes advantage of gondola based balloon system in order to do the survey. It is equipped with small cannon which fires plastic dots on animals who are trying to enter the field. It enables the user to monitor area under consideration using a camera attached to it. The system can be controlled remotely using simple smart-phone in one out of three modes namely manual, semi-automatic and extended-automatic. Development cost is less than $25, which is further going to be reduced by fifty percent in next version of the project when we will go for mass production. In this paper we will focus on Samiksha-1 which is first prototype. We will compare its performance with existing cheapest drone present in India with respect to cost and service time.
Self-Protecting HTS Current Lead-Demonstration of a New Technology

A straight forward solution to reduce the heat load of Current Leads to superconducting magnets operating at 4 K is the use of high temperature superconductors (HTS) thereby eliminating the ohmic heating at the range below 50–77 K. The heat input from the current leads is then determined by heat conduction in the HTS section only, leading to some factor 10 reduction in the heat load in comparison to fully normal conducting conventional leads. Modern HTS conductors demonstrate extremely high current density and further decrease of the current lead heat load is limited not by the material properties but by the requirement of robustness and reliable protection of the leads in the case of cryogenic failures in order to avoid lead burnout. While accelerator magnets are designed for a fast dump at quench events within a fraction of a second, larger stored energy magnets like detector magnets may have discharge times of tens of seconds or even more. In this case the HTS part of the lead has to withstand the ohmic heating for a relatively long time and this requirement then determines the heat input at operation conditions. A new idea to reduce the heat load while keeping full reliability of the HTS leads is using a thermo-mechanical switch that is connected electrically in parallel to the HTS lead section. In the case of a quench in the HTS lead section, by using the differential thermal expansion between materials, the switch turns on and the overheated section is bypassed by the high cross-section of the conductive part of the switch. We wish to use this new technology for the first time for the BabyIAXO detector magnet. In this paper the design and test of the first and unique Self-Protecting HTS Current Lead with a current rating of 800 A is presented.
STT-MRAM Co-design Deep Learning Model for IoT Applications

Machine/deep learning is one of key technologies to enhance the effectiveness of applications (e.g., vision and pattern recognition) for the Internet of Things (IoTs). Researchers strive to design an efficient structure using machine/deep learning in software-aided methods to accelerate computation. However, software-aided methods would be restricted in hardware resources such as capability of memory access. Memory, such as Static Random Access Memory (SRAM) and Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), is critical component to effect the speed of data computation or access on applications. In this paper, we propose a hardware-aided model, STT-MRAM co-design deep learning model, to speed up such as vision recognition for the domain of IoTs, in which a STT-MRAM control circuit is designed to efficiently access data from memories and a software-based CPU is applied in a FPGA to process vision recognition. Notably, STT-MRAM is an emerging memory in lower power consumption, higher intelligence and non-volatile, which are critical for the design of IoT platform.
ANN Based Human Emotion Recognition Using Discriminative Facial Patches

Face reflects the emotions in mind and hence by analyzing facial expressions one can read the emotions of others. Emotion recognition is a challenging task. Inspired by the sophisticated human nervous system, the Artificial Neural Network (ANN) is an efficient machine learning tool to handle this kind of difficult problems. Hence in this paper, an efficient ANN based human emotion recognition system using the features of discriminative facial patches is proposed. First of all, the facial landmark points are detected from the facial expression images and some active patches are considered. Then, dimensionality reduction is performed to obtain the discriminative facial patches and the features are extracted from these patches. Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) features are extracted as the feature vectors. The experiments on JAFEE datasets are made to recognize the six universal expressions (anger, fear, disgust, happiness, sadness and surprise). Finally, the extracted features are classified using both Support Vector Machine (SVM) and ANN classifiers. Results show that recognition of human expressions with ANN using HOG features has a better recognition rate compared to that using appearance features.