Miracle-dz/UniFAR-FTUs huggingface dataset:
Remarks on the geometry of the feedback loop

Youla parametrization of stabilizing controllers is a fundamental result of control theory. It provides a theoretical and practical tool that renders LFT loops into model matching framework. Based on the geometric techniques introduced in our previous work we have provided a controller blending rule that defines a controller semigroup structure based only on the knowledge of the plant and a single stabilizing controller and we have introduced a novel, geometry based parametrization. In this paper we investigate the possibility to construct stabilizing fractions of the given stabilizing controller relative to the blending rule. We also extend our geometry based framework to the LFT loops. Our main goal is to show that every controller which stabilizes the interior loop also stabilizes the LFT loop. Contrary to the expectations, this problem is far from being trivial.
Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking

The Internet is constantly growing in size and becoming more complex. The field of networking is thus continuously progressing to cope with this monumental growth of network traffic. While approaches such as Software Defined Networking (SDN) can provide a centralized control mechanism for network traffic measurement, control, and prediction, still the amount of data received by the SDN controller is huge. To process that data, it has recently been suggested to use Machine Learning (ML). In this paper, we review existing proposal for using ML in an SDN context for traffic measurement (specifically, classification) and traffic prediction. We will especially focus on approaches that use Deep learning (DL) in traffic prediction, which seems to have been mostly untapped by existing surveys. Furthermore, we discuss remaining challenges and suggest future research directions.
A Simple Necessary and Sufficient Condition of Asymptotic Stability for Scalar Fornasini-Marchesini Models

A simple and straightforward sufficient and necessary condition is given to check the asymptotic stability of 2D scalar Fornasini-Marchesini models. The condition only looks at the absolute value of the constants of the model and hence make it very practical to derive simple examples. A possible extension to the matrix case is discussed notably when the matrices commute.
Reflectionless Wideband Bandpass Filter Designed With Multilayered Microstrip Vertical Transition

In this paper, a symmetrical wideband bandpass filter (BPF) with reflectionless behavior is presented via multi-layered microstrip vertical transition. By employing two resistively-terminated microstrip lines printed on the top and bottom layers of this proposed multilayered BPF, ultra-broad input/output reflectionless capabilities along with wideband filtering performance intrinsic to this circuit structure can be achieved. The operational principle of this designed BPF is theoretically discussed. For experimental-validation purposes, a multilayered filter prototype is simulated and fabricated. It exhibits a measured wideband filtering response centered at 1.91 GHz and power-matching levels better than 19.27 dB from 0 to 3.75 GHz.
A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms

Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the system identification problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse bilinear forms, in which the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the identification of such bilinear forms. Experimental results highlight the good behavior of the proposed solution.
Robust Output-Feedback Prescribed-Time Stabilization of a Class of Nonlinear Strict-Feedback-Like Systems

While prior results on prescribed-time stabilization (i.e., convergence within a fixed prescribed time interval irrespective of the initial state) consider a chain of integrators with uncertainties matched with the control input (i.e., normal form), we consider here a general class of nonlinear strict-feedback-like systems with nonlinear state-dependent uncertain functions allowed throughout the system dynamics. Furthermore, we address the output-feedback problem and show that a dynamic observer and controller can be designed based on our dual dynamic high gain scaling based design methodology along with a novel temporal transformation and form of the scaling dynamics with temporal forcing terms to achieve both state estimation and regulation in the prescribed time interval.
MPPDS: Multilevel Privacy-Preserving Data Sharing in a Collaborative eHealth System

With the development of smart healthcare services, there is a growing demand for healthcare environments in which a patient can be collaboratively treated by multiple healthcare providers. To enable such collaborative treatments, it is essential to have a collaborative eHealth system that is capable of storing and managing large-scale health data generated by multiple healthcare providers as well as patients themselves in one place. However, this system may raise serious privacy concerns because of unauthorized access to sensitive health data of a data owner by other users. Usually, such privacy concerns can be resolved using attribute-based encryption (ABE), which provides fine-grained data access control mechanisms. Through ABE, in which access control is expressed by a set of attributes, the sensitive data of a data owner can be selectively shared with a set of data users by encrypting it with a set of attributes. However, with the growing popularity of smart healthcare services, which are enabled by analyzing large-scale health data collected from diverse populations, a binary access control policy akin to this is not sufficient for supporting the ever-growing request for sharing health data. Hence, in this paper, we develop a novel collaborative eHealth system that supports <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula>ultilevel <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>rivacy-<inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>reserving <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>ata <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>haring, MPPDS. In MPPDS, a data owner is able to share his or her health data with various data users within a collaborative eHealth system, under different levels of privacy protections. Specifically, our approach leverages local differential privacy to preserve the privacy of data owners in the process of data sharing and relies on ABE to support multilevel privacy guarantee mechanisms depending on the degree of trust between data owners and data users. We also present a method to effectively estimate aggregate statistics based on the collection of health data shared by different data owners under MPPDS. Experimental results with real and synthetic data sets demonstrate that the proposed MPPDS scheme enables more trusted data users to compute more accurate aggregate statistics than less trusted data users in a collaborative eHealth system.
A Collaborative Filtering Approach Based on Naïve Bayes Classifier

Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users’ and items’ information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction’s accuracy in some datasets.
Surface Plasmonic Resonance Sensor for Wider Range of Low Refractive Index Detection

A D-shaped photonic crystal fiber (PCF) based plasmonic sensor for detection of a wider range of low refractive indexes operating at near to mid-infrared wavelengths is presented in this paper. Using finite element method (FEM) on the basis of y-polarized transverse electric (TE) mode TEy, wavelength sensitivity, amplitude sensitivity and resolution of the proposed sensor have been investigated numerically. Plasmonic material Gold (Au) and sensing layer are placed outside the fiber for attaining practically feasible detection mechanism. A thin layer of titanium dioxide (TiO2) is employed as an adhesive layer in order to attach the plasmonic material with silica glass. In the sensing range of 1.00 to 1.37, maximum sensitivities of 20000 nm/RIU and 811 refractive index unit (RIU)−1 are obtained with resolutions of $5\times 10^{-6}$ and $1.43\times 10^{-5}$ RIU using wavelength and amplitude interrogation method, respectively. Therefore, it would be a suitable candidate for environmental monitoring, pharmaceutical inspection, organic chemical sensing, and other analytes detection.
Anisotropy-Based Bounded Real Lemma for Multiplicative Noise Systems: the Finite Horizon Case

In this paper, the advanced analysis of linear discrete time varying multiplicative noise system in terms of anisotropy-based theory is considered. Accurate formulae for anisotropic norm of such system with mutually independent multiplicative noises and additive input disturbances are given. There two types of anisotropy-based bounded real lemmas were derived, the first one in terms of backward-time difference Riccati equations, the another one in terms of inequalities.
Deep Learning Models for Tuberculosis Detection from Chest X-ray Images

This paper explores the usefulness of transfer learning on medical imaging for tuberculosis detection. We show an improved method for transfer learning over the regular method of using ImageNet weights. We also discover that the low-level features from ImageNet weights are not useful for imaging tasks for modalities like X-rays and also propose a new method for obtaining low level features by training the models in a multiclass multilabel scenario. This results in an improved performance in the classification of tuberculosis as opposed to training from a randomly initialized settings. In other words, we have proposed a better way for training in a data constrained setting such as the healthcare sector.
The Effects of Immersion on Harm-inducing Factors in Virtual Slot Machines

Slot machines are one of the most played games by pathological gamblers. New technologies, e.g. immersive Virtual Reality (VR), offer more possibilities to exploit erroneous beliefs in the context of gambling. However, the risk potential of VR-based gambling has not been researched, yet. A higher immersion might increase harmful aspects, thus making VR realizations more dangerous. Measuring harm-inducing factors reveals the risk potential of virtual gambling. In a user study, we analyze a slot machine realized as a desktop 3D and as an immersive VR version. Both versions are compared in respect to effects on dissociation, urge to gamble, dark flow, and illusion of control. Our study shows significantly higher values of dissociation, dark flow, and urge to gamble in the VR version. Presence significantly correlates with all measured harm-inducing factors. We demonstrate that VR-based gambling has a higher risk potential. This creates the importance of regulating VR-based gambling.
Data Driven Control: An Offset Free Approach

This work presents a data driven control strategy able to track a set point without steady state error. The control sequence is computed as an affine combination of past control signals, which belong to a set of past closed loop trajectories stored in a process historian database. This affine combination is computed so that the variance of the tracking error is minimized. It is shown that offset free control (zero mean tracking error) is achieved under the assumption that the underlying dynamics are linear and the closed loop trajectories of the database are in turn offset free. That is, the proposed strategy inherits the offset free tracking capability of the stored past closed loop trajectories. No prior or subsequent knowledge about the process dynamics is required. The procedure to build the database is to store only the best trajectories that meet a design criteria, chosen from a series of iteratively tuned controllers. In this way the proposed controller will learn how to obtain a well tuned control in spite of the different operating conditions.
On Robustness of Equilibria in Dynamical Transportation Networks

With growing traffic demands, transportation networks become more and more congested and prone to disruptions. In this paper, we study how different perturbations affect the free flow equilibria, i.e., equilibria where no congestion effects are present, in transportation networks. More specifically, if and when the equilibrium of the perturbed dynamics is still in free flow. A generalized cell transmission model models the dynamics of the transportation network, and the perturbations considered are perturbations in the exogenous inflows, flow capacity drops, and perturbations in the routing, i.e., when drivers deviate from their normal route preferences. The paper also shows that social optimum assignments in traffic networks, may not be the most robust ones.
Unconstrained Robust Adaptive Beamforming with Improved Capon Estimator

In this paper, an improved Capon estimator is introduced, and reserves the characteristics of signal space after filtering the noise. More precise power estimation for both noise and signal from different directions can be obtained. Then, the covariance matrices of both interference-plus-noise and signal are reconstructed. With the estimated noise power and the covariance matrices, we propose 4 similar algorithms. Different from the previous methods, the proposed algorithms avoid the robust conditions in the constraints, even to solve a new convex optimization problem which would increase the amount of computation significantly. Simulation results show that the improved Capon estimator achieves better spatial resolution than existing methods, and the performance of the proposed robust adaptive beamforming is almost close to the optimal value across a wide range of signal to interference and noise ratio.
Evaluation on Probe-positioning Errors in Near-field Holographic Phase-less Measurement

The influence of the probe-positioning errors on planar near-field holographic phase-less measurement is displayed in this paper. Compared with the traditional near-field measurement, the near-field phase-less measurement diminishes the requirement of the stable and accurate phase. However, probe-positioning errors in phase-less near field measurement will still make the far field calculation results inaccuracy. As for the phase retrieve, different from other techniques, such as iterative Fourier technique (IFT), digital holographic reconstructed technique only needs to measure the amplitude of the near field once, which reduces the measurement time by half. The probe-positioning errors are set through introducing random deviation from the ideal grids along the scanning axes. The simulation results reveal that the near-field holographic phase-less measurement is less sensitive to the normal (z-axis) errors than the traditional near-field measurement.
Design and Implementation of Signal Processing Unit for Two-Way Relay Node in MIMO-SDM-PNC System

This paper investigates design and implementation of the signal processing unit for the relay node in a two-way relay multiple-input multiple-output spatial division multiplexing (MIMO-SDM) system using physical-layer network coding (PNC), reffered to as MIMO-SDM-PNC. Based on Field-programmable gate array (FPGA) platform, two processing architectures for zero-forcing (ZF) and minimum mean square error (MMSE) detector are proposed for the relay node. Using the standard pipe-lining and the parallel computing methodologies, a novel architecture is developed in order to achieve low latency and low-area occupation for FPGA implementation. The proposed architecture has been composed in Verilog language and synthesized on the ISE tool for Xilinx FPGA Virtex 7. Experimental results demonstrate that the proposed design offers high performance in terms of low latency and high throughput.
Adaptive Robust Cubature Kalman Filter for Power System Dynamic State Estimation Against Outliers

This paper develops an adaptive robust cubature Kalman filter (ARCKF) that is able to mitigate the adverse effects of the innovation and observation outliers while filtering out the system and measurement noises. To develop the ARCKF dynamic state estimator, a batch-mode regression form in the framework of cubature Kalman filter is first established by processing the predicted state and measurement data information simultaneously. Subsequently, based on the regression form, the outliers can be detected and downweighted by the robust projection statistics approach. Then, the adverse effects of innovation and observation outliers can be effectively suppressed by the generalized maximum likelihood (GM)-type estimator utilizing the iteratively reweighted least squares approach. Finally, an adaptive strategy is developed to adjust the state estimation error covariance matrix under different conditions. Extensive simulation results obtained from the IEEE New England 10-machine 39-bus test system under various operating conditions demonstrate the effectiveness and robustness of the proposed method, which is able to track the transients of power system in a more reliable way than the conventional cubature Kalman filter (CKF) and the unscented Kalman filter (UKF).
Classifying Autism Spectrum Disorder Based on Scanpaths and Saliency

Individuals suffering from autism spectrum disorder (ASD) demonstrate viewing patterns that are often different from those exhibited by control subjects, especially in the context of emotional or social stimuli. Previous studies with such content typically relied on precise hand-labelling of the regions of interest in displayed images, thus limiting the transferability of performed analyses onto new, unlabelled images. In contrast to this, we propose an approach that classifies the viewing behaviour of individuals as likely associated with either ASD or typical development in a fully automatic fashion, relying on scanpath features and analytically predicted saliency. Our analysis further demonstrates that gaze data for images with multiple faces have a substantially higher discriminative power than other image groups.
Implementation of a lower-limb model for monitoring exercises in rehabilitation

The aim of this paper is to propose a biomechanical lower-limb model envisaged to monitor how well a person carries out rehabilitation exercises. The analysis of the human movement provides quantitative information on the quality of the execution of exercises. Inertial Measurement Units can be used to assess the movement performance. The implemented system obtains the joint angles by means of an extended Kalman filter, and in order to improve the measurements, some kinematic constraints are considered. Preliminary results are very promising, providing information about the quality of the movement compared to the expected one. This fact can help any patient to learn how to do correctly the exercises. It is even more important with older adults that normally have trouble in properly performing rehabilitative routines.
Mars Surface Mineral Abundance Estimation Using THEMIS and TES Images

In our earlier paper, we have implemented an automated atmospheric compensation algorithm for Thermal Emission Imaging System (THEMIS) images. Moreover, we also implemented a pansharpening approach to fusing THEMIS and Thermal Emission Spectrometer (TES) images. Our objective in this paper is to investigate the performance of mineral abundance estimation using the original TES images and pansharpened TES images. To achieve the above objective, we focus on the Ares Vallis area on Mars, which has been well studied in the literature. Comparing our preliminary results with published results in the literature, we observed that the general trend is similar, but some concentration estimates are different. Due to lack of ground truth, it is hard to judge which results are better at this point. Nevertheless, the results are encouraging and more studies are needed in the future.
Partial Discharge Defect Recognition in Power Transformer using Random Forest

Partial Discharge (PD) diagnostic become more important for high voltage (HV) equipment condition monitoring. PD phenomenon in power transformer could indicate insulation aging or degradation, which in long term could reduce the integrity of the insulation and leading to transformer failure. High accuracy of recognition rate for different PD defect is necessary for a successful PD diagnostic. This paper presents Random Forest (RF) method for PD defect recognition in power transformer. RF is one of supervised learning algorithm in machine learning. RF known as an ensemble classifier build using many decision trees. The majority vote of each three will determine the PD type. There are three defects used in this paper, protrusion, floating metal, and void. A commercial PD measurement system and detecting impedance was used to record the phase resolved partial discharge (PRPD) patterns of different defects. 8 PD statistical features extracted from PRPD patterns to identify each defect. To calculate the accuracy of RF method, different amount of PD features was use for recognition and then compare with other methods.
The theoretical study of dielectric properties of water using the modified Onsager-Kirkwood-Fröhlich theory

In the work, we present the new method for calculation dielectric properties for Water in the wide temperature range. We use the modified Onsager-Kirkwood-Fröhlich theory, which makes it possible to calculate the polarization characteristics without using the static permittivity. We assumed that the dielectric is an isotropic medium consisting of molecules that do not interact with each other and are located in molecular electric fields. It allows us to use the average cosine of the angle between local electric field vectors and the dipole moment vectors of molecules in a medium as the average measure of the local orientation of the dipoles. This approach also makes it possible to equate the value of the energy of the interaction of dipoles with molecular fields to the value of the internal interaction energy if the energy of the interaction of the dipoles with external fields used for the experimental determination of the value of static permittivity is much less than the energy of the interaction. The calculated values of the dipole moment agree with the modern quantum mechanical calculations, and the results of calculating the values of permittivity are in practical agreement with experiment in the wide range of123.15K-573.15K. The method allows to calculate the static permittivity of water in various aggregate states: ice Ih in the range from 123K to melting point, supercooled water in the range from 238K to melting point, water at the saturation line from the melting point to the precritical region.