Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Converter Topologies and Their Derivations and Interrelationships

Canonical switching cell is known as the basic building block of dc-dc buck, boost, buck-boost, Ćuk converters, voltage/current source inverters (VSI/CSI) as well as multilevel converters (MLCs). This article reveals the existence of another basic switching cell as a singular structure of the canonical switching cell. The proposed new basic cell can constitute single-ended primary-inductor converter (SEPIC), Zeta converters, and Z-source converters. Each of the basic switching cells has the simplest form with two complementary switches, one inductor and one capacitor. An approach to construct a dc-dc converter by connecting source/load to the basic switching cells’ terminals is demonstrated. Some existing topologies of dc-dc converters are correlated following the proposed approach. Further, this article reviews Z-source/quasi-Z-source (qZS) converters, universal converters, and multiple-source converters to uncover some general approaches for topology manipulating. Z-source/qZS converters and the emerging classes of universal converters are used as examples to demonstrate the incorporation of their switches. Multiple source converters are fabricated by combining basic switching cells, in both the voltage source and current source form, using the duality principle. With the ever-increasing research efforts on converter topologies, it is hoped that this article will improve the understanding of the existing converter relationships and inspire insights for new topological developments and applications.
Compact Metamaterial Antenna for WLAN Applications

In this paper, the metamaterial technique of miniaturization was applied on the ground of 3.3 GHz conventional patch antenna in order to conceive a new antenna that resonate at 2.45 GHz suitable for WLAN applications. Two symmetric metamaterial (MTM) square unit cells based on complementary split ring resonator (CSRR) were etched on the ground plane of the antenna. The conception was done with FR-4 dielectric as a substrate, its permittivity is 4.4 and its loss tangent is 0.0025, and cooper annulled as metal for radiating element and the ground. The simulation was done by CST solver. The new conceived 2.45 GHz metamaterial antenna is 45.7% smaller than 2.45 GHz conventional patch antenna and has gain of 2.33 dB and band width of 103 MHz.
So-EAGlove: VR Haptic Glove Rendering Softness Sensation With Force-Tunable Electrostatic Adhesive Brakes

Haptic gloves allow the players to feel the virtual world more realistically by providing force feedback. At present, haptic gloves are mainly driven by conventional motors, and for them, being lightweight, low cost, low in power consumption, and intrinsically safe to operators are challenging. Here, we designed a haptic glove (So-EAGlove) integrating a flexible electrostatic adhesive brake to resist human fingers with a tunable braking force and render a softness sensation. This glove weighs only 51 g, but is capable of simulating objects in a large range of Young's modulus from 540 Pa to 5.4 MPa. The electrostatic adhesive brake costs only approximately 2.43 mW during operation, i.e., more than three days powered by a small button battery. We built a feedforward control model and evaluated its performance. Experimental results show that this glove can generate an accurate force to follow the force-displacement profile of the corresponding real objects. The error is less than 7%, barely noticeable by the subjects. The subjective tests also demonstrate that little statistical difference exists between the real objects and the virtual objects for the subjects.
Design and Characterization of 10-Elements MIMO Antenna With Improved Isolation and Radiation Characteristics for mm-Wave 5G Applications

A ten-elements MIMO antenna with improved isolation and reduction of X-polar radiation (XPR) in millimeter (mm) wave region of n258 (24.25-27.5 GHz) is presented. The individual antenna element consists of stub loaded coplanar waveguide (CPW) structure with elliptical shaped radiator. Elliptical shaped Defected ground structure (DGS) has been incorporated at ground to reduce the mutual coupling effect between the adjacent antenna pairs and the minimum isolation obtained 35 dB. The backside of the substrate has a metallic plane with defects which is not connected to the coplanar ground of the antenna and it acts as a reflector to improve the radiation characteristics by lowering of XPR as well as improving the co-pol to X-pol isolation up to 20 dB and 28 dB in the boresight direction in both the XZ and YZ planes respectively. The diversity performances in terms of Envelope Correlation Coefficient (ECC), Diversity Gain (DG) and Channel Capacity Loss (CCL) are obtained as $0.1\times 10^{-6}$ , 10 dB and 0.005 bps/Hz respectively which are within the acceptable limits. Mean effective gain (MEG) values with respect to the excited port 2 and port 3 is identical and approximately −3 dB and the multiplexing efficiency of the proposed MIMO antenna is satisfactory over the bandwidth. The group delay is investigated and found to be almost constant with a variation of 1.2 ns thus indicating linear phase characteristic of the antenna. The proposed structure is compared with other mm-wave MIMO antennas and found to be advantageous. A prototype of the proposed 10-elements MIMO is fabricated, and the measured results resemble with the simulated results.
Evaluation of the GPM IMERG half-hourly final precipitation product in the quantification of rainfall erosivity in central Italy

The NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) product is one of the few gridded precipitation products available at a sub-hourly temporal scale. This high temporal resolution is required in many hydrological applications, including the estimation of the rainfall-runoff erosivity factor R, used in the USLE and RUSLE soil erosion prediction models. In this study, through a comparison with data measured at two rain gauges in central Italy, the limits and potential of the GPM-IMERG product on O.t-degree resolution in providing estimates of the R-factor are highlighted. Results indicate that the product can provide accurate estimates in areas relatively homogeneous from an orographic and climatic point of view. On the other hand, it fails to identify the gradients of both precipitation and erosivity that characterize more complex areas, such as those close to the mountain ranges.
A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing

Property preserving encryption techniques have significantly advanced the utility of encrypted data in various data outsourcing settings (e.g., the cloud). However, while preserving certain properties (e.g., the prefixes or order of the data) in the encrypted data, such encryption schemes are typically limited to specific data types (e.g., prefix-preserved IP addresses) or applications (e.g., range queries over order-preserved data), and highly vulnerable to the emerging inference attacks which may greatly limit their applications in practice. In this paper, to the best of our knowledge, we make the first attempt to generalize the prefix preserving encryption via prefix-aware encoding that is not only applicable to more general data types (e.g., geo-locations, market basket data, DNA sequences, numerical data and timestamps) but also secure against the inference attacks. Furthermore, we present a generalized multi-view outsourcing framework that generates multiple indistinguishable data views in which one view fully preserves the utility for data analysis, and its accurate analysis result can be obliviously retrieved. Given any specified privacy leakage bound, the computation and communication overheads are minimized to effectively defend against different inference attacks. We empirically evaluate the performance of our outsourcing framework against two common inference attacks on two different real datasets: the check-in location dataset and network traffic dataset, respectively. The experimental results demonstrate that our proposed framework preserves both privacy (with bounded leakage and indistinguishability of data views) and utility (with 100 percent analysis accuracy).
Perceptions of the tertiary learning environment: is mathematics worth the effort?

The decline in enrolments and interest in advanced mathematics studies is of growing concern internationally. Previous research suggests that a range of factors can influence students' academic decisions. The focus of the paper is on one of these potential sources of influence— students' perceptions of the tertiary mathematics learning environment. Data from two large-scale surveys (N = 1883) and from a smaller number of interviews (N = 71) with students enrolled in tertiary mathematics courses at five Australian universities are presented and discussed. Collectively, the survey results and the interview data reveal considerable variations in the quality of the teaching and student support available in different mathematics departments. Students' comments were constructive and offered valuable ideas for improving the existing situation, retaining current students and attracting others to mathematics.
Independent Current Control With Differential Feedforward for Three-Phase Boost PFC Rectifier in Wide AC Input Frequency Application

Independent current control of the three-phase boost power factor correction (PFC) rectifier with differential feedforward is proposed for wide ac input frequency application. When conventional independent current control with proportional controller (P-controller) is used for digital-controlled three-phase boost PFC rectifier, the time delay of digital control brings phase leading of input currents. Such a phase leading is proportional to the ac input frequency. To achieve high power factor in wide ac input frequency application, differential feedforward is introduced to the input current control in the proposed control strategy to suppress the phase leading of input currents. The analysis and the design of the proposed control strategy are illustrated and a 2-kW experimental prototype is built to verify the analysis results. Experimental results show that the power factor of the three-phase boost PFC rectifier is improved significantly within wide ac input frequency range, e.g., 360–800 Hz. The proposed control strategy achieves satisfied performance when the ac input frequency varies.
Robust State-Feedback $\mathbf{H}_{\infty}$ Control of Quadrotor

This paper presents an $\mathbf{H}_{\infty}$ control design for quadrotors to enhance robustness against unmodeled disturbances. In optimal control, the algebraic Riccati equation (ARE) frequently arises in topics like Linear Quadratic Regulator (LQR) or $\mathbf{H}_{\infty}$ control. The $\mathbf{H}_{\infty}$ control minimizes the maximal gain between the disturbance input and the control output, which can be solved by acquiring the solution of ARE while finding the minimal $\mathbf{H}_{\infty}$ norm of the corresponding transfer function. Thus, a simple bisection algorithm is also proposed for computing the stabilizing ARE solution of the $\mathbf{H}_{\infty}$ control, which combines a state-of-the-art algorithm for the ARE called the structure-preserving doubling algorithm (SDA).
Applications of Machine Learning in Healthcare: An Overview

Traditional research techniques do not work well in the dynamic environment of big data in the healthcare industry, which is characterized by enormous numbers, complexity, and speeds. Methods that can effectively estimate models across massive datasets of medical usage data, health findings, private computer data, and records from many other sources are critically needed. The goal of machine learning (ML), a branch of artificial intelligence (AI), is to increase the efficiency and precision of healthcare. Applications of machine learning (ML) are significantly changing the healthcare industry. Big data and improved computing power present an opportunity to apply ML algorithms to benefit healthcare. The healthcare curriculum has to incorporate a variety of ML-related topics so that medical personnel may successfully direct and analyze research in this field. Machine learning will revolutionize nearly every aspect of the healthcare profession as it stands today. To understand the various healthcare applications of this technology, it is necessary to examine the research that has been done in this area. This enumerative article provides a thorough review of machine learning in healthcare, covering its terminology, basic concepts, and present and potential uses.
On Projection of Safe Operation for Grid-Following Inverters - Grid Parameter Estimation

This work investigates the projection of safe operation for grid-following inverters using a reference model. This work applies recursive-least square and model reference adaptive estimation techniques to estimate the unknown grid parameters. The estimated grid parameters are used in a dynamic reference model to project the safe operation of the inverter. This work also demonstrates that the controller nonlinearity and parameter variations can cause unsafe operations, such as unexpected instability at low power levels, namely hidden mode of instability. This instability issue in the nominal operating range may occur for given control parameters in a weak-grid condition. The dynamic reference model for PQ-controlled inverters is applied to analyze this instability issue using the estimated grid parameters. Both adaptive identification techniques can estimate the unknown parameters accurately, and the dynamic reference model can safeguard inverters from an unsafe operation, i.e., hidden instability, by examining the incoming new power set-points before engaging them to the local controller. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a grid emulator.
How water affects growing plants: an experimental set-up based on 2D vision and on-the-edge sensing

Determining water stress in plants and how this affects their growth and yield quality is not an easy task. This is usually done by monitoring the soil's water stress and by manual measurements or visual inspection to detect any premature sign of wilting. However, these methods are unreliable and still require human intervention on the field. The proposed instrumented field is a step stone towards automated crop growth monitoring by means of low-cost equipment. The tested crops are string beans and tomatoes. The experiment was aimed at determining their water stress according to specific water conditions set beforehand on each field's row. Data acquired during the testing period include soil temperature and moisture, air temperature and humidity, and daily color images taken every 15 min. Preliminary analysis conducted on images by means of traditional image processing techniques allowed analysis of fruits and leaves health and water status
Wind Turbine Control in Partial Load Operation by Using the Optimization Algorithms from the Deep Learning Techniques*

The most common operation of wind turbines is in the case of under-rated wind speed, i.e., when the wind speed cannot reach the rated wind speed. In this situation, the control system has to bring the machine to an operating point that makes possible the maximum power extraction. To this end, many control strategies and control approaches are available. Two of them are the Optimal Torque Control (OTC) and the Hill-Climb Search Control (HCS). On the other hand, machine learning activities have signifi-cantly improved several optimization algorithms in recent years, which can also be used with HCS. In the present work, some of these algorithms are selected and investigated with the goal of being used with HCS combined with OTC for a joint goal of power extraction maximization and rotor inertia reduction in very large wind turbines. The interest is set on the evaluation of the algorithms for the real-time control implementation.
Preprocessing FMRI Data In SPM12

Functional magnetic resonance imaging (fMRI) has ended up the most famous technique for imaging brain functions. Currently, there is a giant range of software packages for the analysis of fMRI data, such as AFNI, SPM, Free Surfer, FSL, fMRIPrep and Workbench. In this work, we propose an fMRI preprocessing on SPM applied to ABIDE II database and we evaluate this processing by applying an MLG.
Fast Learning-Based 3-D Lidar Localization with Multiscale Feature Recursive Matching for Autonomous Driving

Robust localization is crucial for autonomous driving. In this study, we proposed a fast lidar (light detection and ranging) localization architecture that uses the multiscale local features of point clouds to select keypoints in a point cloud frame. We also designed a method called multiscale recursion space compression block (MRSCB) to generate reference points. Next, we used a deep neural network to learn the feature similarity between keypoints and reference points. By calculating the generated point pairs, the model can determine the location of point cloud frames in pre-built point cloud maps. Overall, the design of the MRSCB allowed our model to reduce hardware usage and thus improved the runtime speed. In conclusion, the experimental results obtained with a public autonomous driving data set called KITTI indicated that our method outperformed the five state-of-the-art lidar localization methods in terms of not only localization accuracy but also runtime speed reduction.
Stealthy Local Covert Attacks on Cyber-Physical Systems

In cyber-physical systems (CPS), the networked structure not only enhances the accessibility of data, but also introduces the risk of cyber attacks. The stealthy attacks that are undetected by the monitoring system, are the most dangerous type of cyber attacks. In this article, we introduce a new attack strategy to call attention to the problem of stealthy cyber attacks on CPS. The proposed attack scenario does not require access to all control input signals and sensor output signals, so it needs fewer disruption resources and is called a local covert attack. We show that the local covert attack can be made completely stealthy by applying the decoupling technique or by combining the basic idea of a covert attack and a zero-dynamics attack. Simulation results are provided to demonstrate the stealthiness of the proposed local covert attacks.
SCIENTIA MEDICA

Aims: there is increasing awareness that for effective patient care we need more than only randomized controlled trials with groups of participants and that care-fully collected single case ( N = 1) data have several important advantages over traditional group-level studies. With the advance of technology, collecting relevant data from a single case is becoming easier by the day, and this offers tremendous opportunities for understanding how behaviors displayed by an individual can be influenced by one or several key variables. For example, how pain experienced influences the amount of time spent on physical exercise. Method: using publicly available observational single case data, five models are compared: a classical ordinary least squares (OLS) linear regression model; a dynamic regression model (DRM); a two-level random-intercepts model (2LRI); a continuous covariate first-order autoregressive correlation model (CAR1); and an ordinary least squares model with time trend (OLST). These models are com- pared in terms of overall model fit statistics, estimates of the relation between physical activity (response variable of interest) and pain (covariate of interest), and residual statistics. Results: 2LRI outperforms all other models on both overall model fit and resi- dual statistics, and provides covariate estimates that are in between the relative extremes provided by other models. CAR1 and OLST demonstrate an almost identical performance and one that is substantially better than OLS – which performs worst – and DRM. Conclusion: for observational single case data, DRM, CAR1, OLST, and 2LRI account for the serial correlation that is typically present in single case data in somewhat different ways under somewhat different assumptions, and all perform better than OLS. Implications of these findings for observational
Study and predictions of emergency rescue in the Doubs department, France

Saving a few minutes in the rescue of a person having an infactus or for a drowning can save lives. The means to save this time are therefore actively sought by the teams in charge of emergency rescue, such as the fire department in France. Part of the answer lies in the fact that some of these accidents have a predictive character: people swim outdoors especially in summer, and when it is hot and the air is polluted, there is an increased risk of discomfort and respiratory distress. The aim of this article is to describe how to experimentally implement an operational predictive tool for emergency rescue. We will study various models, and present the results obtained by the best of them, whose scores effectively allow the solution to be considered operational.
Gender Differences in Connecting Veterans to Care Through the Veterans Crisis Line

Background: Suicide is a major public health crisis within the US military veteran community, with distinct gender differences in suicide risk and behavior. The Veterans Crisis Line (VCL) is a component of the Veterans Health Administration (VHA) suicide prevention program; through VCL, veterans may be referred to a VHA Suicide Prevention Coordinator (SPC) to arrange follow-up care. Research shows that engagement with an SPC is a strong protective factor in reducing veteran suicide risk. Methods: We evaluated SPC referral acceptance and assessed correlates of SPC referral decline using VCL administrative data for contacts: (1) made between January 1, 2018, through December 31, 2019; (2) by veterans contacting VCL on their own behalf; (3) with gender identified; and (4) current thoughts of suicide. Then, among a subsample of 200 veterans, evenly distributed by gender, we examined data from call synopsis notes to identify reasons given for veterans declining an SPC referral. Results: We found it was generally callers rated as lower risk by responders, and callers with loneliness as a reason for contacting VCL, who more frequently declined the SPC referral. An analysis of reasons given for declining SPC referral found that concerns with/about Veterans Affairs care were a key concern, particularly among women veterans articulating specific negative care experiences. Other reasons for referral decline included managing the veteran’s needs directly on the call or via alternative resources. Conclusion: The VCL as an intervention provides an important opportunity to re-engage vulnerable veterans into care, one that may be more challenging for women veterans.