Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust and Optimal Control of Systems With Time Varying Unilateral Constraints Using Non-smooth Transformation

A tracking problem is studied in this paper where a time-varying unilateral constraint and disturbances are present. An extension to the method of non-smooth state transformation is developed. The proposed framework enables synthesis and analysis of control of a rigid body colliding with a time-varying unilateral constraint. The control problem encompasses continuous and discontinuous controllers in the presence of impacts and discontinuous disturbances. This leads to new results in the area of control of systems with impacts and dry friction, a well-known challenging direction in non-smooth mechanics. Major contribution of the paper is in achieving finite-time tracking for a class of unilaterally constrained systems where the system dynamics with impacts are converted into one without impacts. Numerical simulations are shown to demonstrate specific examples of the theoretical development.
On the Fragility of Multivariable Super-Twisting Algorithm for non-Symmetric Uncertain Input Matrix

A non-decoupled MIMO Super-Twisting Algorithm (STA) extension, not requiring the exact knowledge of the system input matrix was recently developed. However, a symmetry assumption had to be made on the uncertain gain matrix. In this paper, we exploit how far from the symmetric case the stability of the multivariable algorithm can be preserved. Some asymmetry bounds are theoretically established showing that for small enough asymmetry, stability can be guaranteed. Moreover, the Lyapunov function used in the analysis, leads to a rather sharp detection of instability, when asymmetry increases. Surprisingly, it also reveals that the actual asymmetry allowance can be quite small suggesting some fragility of the algorithm for the class of uncertainty considered. This motivates the need of new developments for more robust multivariable STA as an interesting open problem.
Graph-theory based analyses of human motor learning: A resting-state I study

Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to investigate brain functional connectivity (FC) changes resulted from interventional studies. Graph-theory based analyses quantity multiple aspects of the brain network by computing different metrics. We used a graph-theory approach to investigate neural plasticity of the human brain due to a novel motor learning protocol that has not been addressed before using rs-fMRI. Thirteen young and healthy participants practiced seven sessions of motor learning exercises. rs-fMRI data were collected before the first and after the 7th session of exercises. We performed ROI-to-ROI analyses along with the computation of different graph metrics on rs-fMRI data after preprocessing steps. The graph networks were constructed using correlation signals originating from 164 parcellated regions (Harvard-Oxford atlas) of the brain. In further analyses, different metrics of the constructed graph were computed and compared between pre- and post-training sessions using proper statistical tests. Thirteen significant regions of interest (Left Dorsal Attention.IPS, Left Angular Gyrus, and Right Postcentral Gyrus, to name a few) were obtained with p-FDR <0.05, which had essential roles in the motor learning procedure.
Smooth Robust Control Applied to Quadrotor Landing

In this work, the problem of autonomous landing of a quadrotor on a vertically oscillating platform is addressed. A robust adaptive altitude control scheme is proposed to deal with the aerodynamic effect called Ground Effect. The robust part of the controller, named Smooth Sliding Controller (SSC), is based on a chattering alleviated smooth control signal. This work presents the first generalization of the SSC, previously designed for linear plants, for the class of nonlinear plants representing vertical dynamics of an aerial vehicle. The stability analysis assumes the vertical velocity is provided. The performance of the proposed method is illustrated by means of numerical simulations and experimental results.
Deterministic Construction of Bipolar Matrices For Compressed Sensing

Compressed sensing refers to the recovery of highdimensional but sparse (or nearly sparse) vectors from a small number of linear measurements. Until now most of the attention has been focused on measurement matrices that consist of real number, or are binary. Relatively less attention has been paid to the design of bipolar matrices, where every element is plus or minus one. Such matrices are preferred in applications such as the design of touchpads for cell phones. Previously the design of bipolar matrices was based on algebraic codes such as the BCH codes, and the methodology was based on satisfying the restricted isometry property (RIP). In the present paper, we adopt a different approach, namely, to start with binary measurement matrices that have uniform column weight (the same number of ones in each column), and show that a simple modification leads to bipolar matrices that satisfy the robust null space property (RNSP). Since RIP implies the RNSP, as shown by the authors in another paper, our approach leads to a far smaller number of bipolar measurements compared to existing methods for the same.
Gradient Based Restart FISTA

Fast gradient methods (FGM) are very popular in the field of large scale convex optimization problems. Recently, it has been shown that restart strategies can guarantee global linear convergence for non-strongly convex optimization problems if a quadratic functional growth condition is satisfied [1], [2]. In this context, a novel restart FGM algorithm with global linear convergence is proposed in this paper. The main advantages of the algorithm with respect to other linearly convergent restart FGM algorithms are its simplicity and that it does not require prior knowledge of the optimal value of the objective function or of the quadratic functional growth parameter. We present some numerical simulations that illustrate the performance of the algorithm.
Design of a Low SWaP Dual-Band Radiometer for UAS Remote Sensing Application

a new design of a compact radiometer intended for uav application is presented. radiometers in spaceborne systems tend to be affected by clouds and atmospheric noise. the design of this airborne sensor is explained and it is divided into three phases: 1) a new dual-band antenna array and l-band receiver; 2) the design of the ka-band receiver and testing of the array; 3) the implementation and testing of the low frequency system on a controlled field. the microstrip patch array and slot array designs are presented. the l-band array gain is 14.337 db, and the beamwidth is 30 degrees, and the ka-band array gain is 12.47 db. the designs of the l-band and ka-band receiver are explained, and results of the first prototype of the l-band receiver are similar to results obtained by spaceborne sensors.
Restoration of Out-of-Focus Fluorescence Microscopy Images Using Learning-Based Depth-Variant Deconvolution

Image quality is degraded in the out-of-focus region because of the depth-variant (DV) point spread function (DV-PSF) of a fluorescence microscope. Either non-blind or blind deconvolution for restoration results in limited improvement. In this work, we propose a two-step learning-based DV deconvolution (LB-DVD) to restore the out-of-focus image. In the first step, DV-PSF is predicted by a defocus level prediction convolutional neural network (DelpNet). In the second step, the extracted DV-PSF is used for DV deconvolution. To our knowledge, LB-DVD is proposed and demonstrated for the first time. DelpNet achieves an accuracy of 98.2% for predicting defocus levels of image patches ($84 \times 84$ pixels). The subsequent DV deconvolution gives rise to good performance in peak signal-to-noise ratios and structural similarity index, which are improved by up to 6.6 dB and 11%, respectively, before and after the deconvolution. As for a wide-field image, there exist different DV-PSFs within the two-dimensional fluorescence image due to the surface undulation. An overlapping weighting patch-wise LB-DVD is used in image montage to eliminate patch boundary artifacts. As a result, our LB-DVD shows the feasibility and promise to be applied to typical fluorescence microscopy in practical applications.
Identification of Anomalous Users in Twitter based on User Behaviour using Artificial Neural Networks

Increased number of users in Online Social Networks(OSN) such as Facebook, Twitter, Instagram etc has led to privacy and security concerns. In this paper, we propose a behavioral based risk assessment method which will help to identity Social Bots and Compromised Accounts in Twitter by leveraging the Neural Networks. Our solution can detect unusual accounts based on the users’ writing style, such as, time of tweeting, language used for writing tweet, frequency of tweeting, etc. This way, if the writing style of the user changes from what is considered to be normal, we flag the user as risky and the OSN admin can formulate strategies to overcome the same. Experiments were performed on the real world dataset extracted from twitter. Results show tremendous accuracy in identifying Social Bots and that only few tweets are required to identify whether an account has been compromised or not.
Beta Wave Activity Analysis Of EEG During Mental Painting Reflects Influence Of Artistic Expertise

In this study, Differences in beta wave activities of multichannel EEG signals related to artists and non-artists during mental painting were analyzed. The Davies-Bouldin Index was used to evaluate the quality of the feature space. It was found that SMR (13-15Hz), low beta (15-18Hz) and high beta (18-30Hz) wave activities were significantly higher for artists as compared to non-artists during the mental painting. The most discrimination between the EEG signals of the two groups was also observed in relative low beta power at Fp1. Finally, the EEG signals of the two groups were classified using the selected feature and a SVM classifier with an average detection accuracy of 81±18.53%. The increased beta wave activities for visual artists reflect their strengthened attention due to training in the visual arts. The obtained results may be used for designing a neurofeedback training protocol to improve artistic abilities of novice visual artists. In this approach, the increase in relative low beta power at Fp1 was proposed as the neurofeedback training protocol for enhancing mental painting performance.
Analysis of Homogeneous Waveguides via the Meshless Radial Basis Function-Generated-Finite-Difference Method

The radial basis function-generated-finite-difference (RBF-FD) method is applied to the analysis of homogenous waveguides. To this end, the Helmholtz equation and the boundary conditions are collocated on the waveguide cross section. At each collocation node, derivatives are locally approximated by the RBF-FD formulas based on the polyharmonic splines supplemented with high-degree polynomials. As a result, a sparse matrix eigenvalue problem is obtained, which allows cutoff wavenumbers and axial fields to be calculated. To illustrate the accuracy of the method, we consider a semicircular and an eccentric circular waveguide.
Exploiting Asymmetric Errors for LDPC Decoding Optimization on 3D NAND Flash Memory

By stacking layers vertically, the adoption of 3D NAND has significantly increased the capacity for storage systems. The complex structure of 3D NAND introduces more errors than planer flash. To address the reliability issue, low-density parity-check (LDPC) code with a strong error correction capability is now widely applied on 3D NAND flash memory. However, LDPC has long decoding latency when the raw bit error rates (RBER) are high. This is because it needs fine-grained soft sensing between voltage states to iteratively decode the raw data. Multiple sensing voltages are applied on flash cell array to gain necessary information for decoding. In this article, a new sensing level placement scheme with reduced number of sensing levels is proposed. The basic idea for the placement scheme is motivated by three asymmetric error characteristics of flash memory: the asymmetric errors between different states, the asymmetric errors caused by voltage left-shifts and right-shifts and asymmetric errors among layers in a 3D NAND flash block. With awareness of these three types of error characteristics, reduced number of sensing levels are placed to achieve reduced read latency for LDPC decoding while maintaining the error correction capability of LDPC. Experiment analysis shows that the proposed scheme achieves significant performance improvement.
An Auto-Adaptive Approach Towards Subject-Independent Motor Imagery BCI

Brain-Computer Interfaces (BCIs) are aimed to help people suffering from severe disabilities by controlling an external device via brain activities. The major purpose of a BCI system is to decode the ongoing brain activities and translates them into output commands. EEG-based BCIs have been receiving a lot of attention due to their non-invasive nature. One of the major problems of BCI systems is their need for long training and calibration sessions. Subsequently, when a new subject is introduced to the BCI system, it takes a long time for the system to adapt the model parameters. In this study, an auto-adaptive BCI is proposed to address these issues. The method is based on an auto-adaptive calibration scheme which combines EEG data space adaptation, a weighting scheme, and a trial removal strategy. The proposed BCI framework reduces the effect of non-stationarities in EEG signals which affects session to session and subject to subject performance via building a model which adapts continuously to changes in brain signals. The proposed method was evaluated on a publicly available dataset. The results showed that the average classification accuracy increased from 75.8% to 86.92% by using the proposed method.
Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes’ objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.
Overcoming Accessibility Barriers for People with Severe Vision Impairment in Web-based Learning Environments: A Literature Review

This Research Full Paper presents a literature review about overcoming accessibility barriers for People with Severe vision impairment in Web-based Learning Environments.People with vision impairment face several barriers while interacting with web-based environments.Among them, one can cite the lack of suitability of these environments for the use of screen readers, e.g. if the description of the environment elements are not readable by the screen reader.These barriers are also present in web-based learning environments. In this work we present, through a literature review, the most common accessibility barriers faced by people with vision impairment while interacting with web-based learning environments. In addition, we propose solutions that can mitigate this problem, allowing broad access to distance education environments and a better learning experience with them.
A Simplified Method for Quick Calculation of Corona I–V Characteristics of Unipolar Transmission-Line Configurations

This article is concerned with the calculation of the corona current–voltage characteristics of a unipolar high-voltage direct current (HVDC) transmission line with a single conductor stretched above the ground plane. In light of justified assumptions, the corona charge density around the conductor surface is obtained using an iterative procedure. This makes it possible to calculate the corona current for a given applied voltage. It is interesting to note that the per-unit charge density around the conductor surface with reference to its value at the gap axis has the same distribution irrespective of the conductor radius and height above the ground plane as well as of the applied voltage. Along this axis, Deutsch’s assumption is waived and the charge density is calculated as being dependent on the conductor radius, conductor height, onset voltage, and applied voltage. A curve-fitting polynomial is made for the per-unit distribution of charge density around the conductor surface. Determination of the surface charge density at the gap axis and the polynomial describing the per-unit charge density distribution represent the basis of a proposed simplified method for quick calculation of the $I$ – $V$ characteristics, irrespective of the conductor radius and height above the ground plane. The corona current–voltage characteristics, as calculated by the proposed simplified approach, are compared with those calculated before and with those measured in different laboratories. The computation time is drastically reduced when using the proposed simplified method.
Dispersive Quasi-Normal Mode (DQNM) Expansion in Open and Periodic Nanophotonic Structures

This paper reviews recent developments in our modal expansion technique for the scattering problem of unbounded electromagnetic structures with highly dispersive media. The technique makes use of Dispersive Quasi-Normal Modes (DQNMs), also known as natural modes of photonic structures, obtained by solving spectral problems associated to Maxwell’s equations. The final expansion formula, based on a simple version of Keldysh’s theorem, reveals the contributions of eigenmodes onto the scattered field, allowing us to understand the scattering properties of arbitrary shaped nanophotonic structures (bounded or unbounded), where permeability and permittivity can be dispersive, anisotropic, and even possibly nonreciprocal. This provides complete physical insights to the spectral characteristics of given structures as well as a transparent interpretation of numerical results. We demonstrate this modal analysis on a 2-D model of diffraction grating, made of a periodic slit array etched in a free-standing silver membrane.
Antenna Selection in Massive MIMO Based on Greedy Algorithms

As wireless services proliferate, the demand for available spectrum also grows. As a result, spectral efficiency is still an issue being addressed by many researchers aiming at improving the quality of service to a growing number of users. Massive multiple-input multiple-output (MIMO) has been presented as an attractive technology for the next wireless systems since it can alleviate the expected spectral shortage. Nevertheless, such a technique requires a dedicated chain of radio frequency (RF) components for each antenna element which result in high costs at base station (BS) side. To reduce the number of RF chains, we propose several transmit antenna selection schemes aiming at minimizing the mean square reception error and also reducing the transmission power which is one of the main contributions of our work. The proposed strategies are inspired by the matching pursuit technique and its quantized version, named matching pursuit with generalized bit planes. The presented results show that reliable reception can be accomplished with low computationally intensive algorithms for antenna selection.
Heterogeneous Parallelization for Object Detection and Tracking in UAVs

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV’s resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.
Current sensorless control of bidirectional converters under mixed conduction mode

This paper investigates the current sensorless control of bidirectional converters operating both in continuous (CCM) and discontinuous conduction mode (DCM). Often the current control loop is the inner part of a nested control system. For a specific application of such a nested structure, we design two extended state observers (ESO) for the voltage and current control loops, respectively. Our specific application is a power filter (ripple eliminator) connected to a DC bus. For the outer voltage loop, the ESO is to estimate the DC bus disturbance; whereas for the inner current loop, the inductor current and aggregate disturbance caused by plant uncertainty are estimated by another ESO. The controllers are designed based on the active disturbance rejection control (ADRC) principle. Since no current sensors are necessary, the cost and size of the converter can be reduced without sacrificing performance. To improve the efficiency, the converter works under DCM near zero-current crossings. Some modifications to the observer are proposed to guarantee seamless transitions between the two modes. The proposed algorithm is verified by simulation of a 1kW bidirectional converter.
Adaptive Tracking Control via Immersion and Invariance: An (i)ISS Perspective

This paper studies the immersion and invariance (I&I)-based adaptive tracking problem of nonlinear systems with linear and nonlinear parameterizations. For a linear parameterization of the unknown parameters, a new strict Lyapunov function construction method is first presented for I&I adaptive control systems using the notion of integral inputto-state stability (iISS). This then motivates us to develop a new adaptive I&I tracking control scheme for a class of nonlinearly parameterized systems. Under an ISS small-gain condition, a strict Lyapunov function can be explicitly constructed to show that both global asymptotic tracking and estimation can be achieved uniformly.