Miracle-dz/UniFAR-FTUs huggingface dataset:
Prototype of Wearable Robot with Tactile Sensor Measurable Contact Force Distribution with User

To improve safety of wearable assist robots, the authors make a prototype wearable robot suitable for the contact force distribution feedback control system. Because wearable assist robots contact the user and apply force directly, ensuring safety of the user is most important. In order to improve safety from the aspect of control, a contact force distribution feedback control system has been researched. The system controls contact force between the user and a robot directly. Although the system validated in principal with a simple robot model on simulation, validation with an actual robot has not done yet. There are no wearable robots measurable the contact force distribution with user. In order to measure the contact force distribution for the control system, two points: measurement reproducibility and measurement as distribution, are necessary. In this paper, a prototype robot that satisfies the two points is made. The robot is confirmed to have the two points necessary for the system by measurement experiment.
Analytical Prediction of Iron-Core Losses in Flux-Modulated Permanent-Magnet Synchronous Machines

In this paper, an analytical approach allowing the prediction of iron-core losses in flux-modulated permanent-magnet synchronous machines. According to the Maxwell–Fourier method (viz., the multi-layer model) and Cauchy’s product theorem, the magnetic field distribution is determined in different parts of machine by considering the iron permeability on the basis of solving both Poisson’s and Laplace’s equations. Next to the dc bias flux density and the minor hysteresis loops, which are taken into account, the Bertotti’s model and the flux variation locus are employed to calculate the iron-core losses under load and no-load conditions in post-processing from the magnetic field analytical calculation. Finally, in order to validate the proposed analytical method, the results are verified by the comparison with the finite-element method. The comparisons show good results of the proposed model.
Application of Finite-Time Stabilization to Position Control of Quadcopters

This paper discusses an application of finite-time stabilization methods to the control of quadcopters. The finite-time stabilization methods guarantee the finite-time convergence to the equilibrium state and exhibit robustness property with respect to disturbances and modeling errors. The application of the finite-time stabilization to the control of UAV is expected to be effective. This paper shows an application of a finite-time stabilization to quadcopters and investigates the effectiveness of the stabilization via the physics simulation based on ROS and Gazebo. The performance of the finite-time stabilization method is compared with those of an exponentially stabilizing feedback controller.
A Deep Convolutional Encoder-Decoder Network for Page Segmentation of Historical Handwritten Documents Into Text Zones

Recent research activity for page segmentation and pixel-labeling problems focuses strongly on deep Neural Network architectures. In this paper, we present a Convolutional Encoder-Decoder based method for the segmentation of historical handwritten images into distinct text zones. This is achieved by labeling each pixel of the image to one of the predefined classes (main body, comments, decorations, periphery, background). Traditional methods make use of prior knowledge of documents and rely on data-oriented features and experimental rules. We propose a method using Convolutional Encoder-Decoder pairs and we show that deep architectures fit properly to our problem. Experiments on different public datasets demonstrate the effectiveness of the proposed method that outperforms previous techniques in many cases.
Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation

The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity.
Administration de composants logiciels pour systèmes sans fil

Une des grandes tendances dans les systemes sans fil est de concevoir leur logiciel comme un assemblage de composants. Les composants sont interconnectes grâce a leurs interfaces, tout en cachant leur implementation afin d'augmenter leur reutilisabilite et permettre leur deploiement par des tiers. Le deploiement s'effectue sur des dispositifs tres varies tels que des telephones mobiles, des PDA, des boitiers de television numerique, etc. Compte tenu du fait que les environnements de deploiement sont differents des environnements de developpement, des comportements anormaux et/ou des dysfonctionnements se produisent, ce qui, par consequent, met en evidence le besoin crucial d'un systeme d'administration. Pour repondre a cette problematique, nous avons concu une architecture pour l'observation et le controle a distance du comportement des composants logiciels sans fil. Le comportement est decrit par des machines a etats UML 2 qui sont directement executees par les composants et repliquees sur le systeme d'administration. Des politiques d'administration basees sur les etats des composants peuvent alors etre definies et appliquees. Notre proposition a ete evaluee et mise en oeuvre dans la technologie WMX (Wireless Management eXtensions), qui permet, a la fois, le developpement dans les systemes sans fil de composants logiciels administrables et la construction de gestionnaires bases sur le comportement dans le systeme d'administration.
Carbon Based Electrode for Wearable Biosignal Monitoring Patch

We fabricated a novel patch type flexible dry electrode for long-term bio signal monitoring by mixing carbon nanofibers (CNFs) in biocompatible-elastomer (MED6015). The CNFs are high conductivity, low price and easy dispersed in elastomer uniformly related with CNTs (Carbon nanotubes). The fabricated CNFs electrodes are coated with sticky elastomer (MG7-9850) to attach on skins without any other adhesive. The mixing ratio of CNFs and elastomer is diverse to compare electrical and mechanical characteristics. ECG (Electrocardiography) signal quality, sweat effects and motion effects are evaluated and the fabricated electrode was shown good quality of signals and tolerant from both effects. Also the electrode was shown to be biocompatible from biocompatibility test. These electrodes could be used for the long-term measurement of bio potential monitoring and wearable devices.
How intelligent is the SmartGrid in Ecuador?

Electricity is an essential component in modern life and the economy, it has promoted the development of so-called industrial era over an extended period of time, and actual information era; however organisms such as WEC1, WWAP2 and ONU SDG3 consider the automation of the current power system model as an unsustainable energy system model for the 21st century because renewable energy sources alone will not be sufficient to meet the large increase of the energy demand and fulfill it in 2030. We are in the knowledge era, and it has said that “artificial intelligence is the new electricity” [1]. This sets enormous challenges for implementation of a new model for the infrastructure, the acquisition and use of the smart electricity grid (Smart Grid) in Ecuador where power is required to be affordable, reliable and sustainable. It describes the current situation, and presents the status of implementation of this concept, to then be able to structure the foundations of a model based on artificial intelligence, with participation of each one of its actors, presenting the opportunity to carry the electricity power sector and the Ecuadorian society, a new era of development, sustainability, energy independence, efficiency and high availability.
Zero-forcing Precoding for the MU-MISO Downlink: How Many Antennas Do We Need?

In this study, we consider zero-forcing (ZF) precoding under uncorrelated Rayleigh channels with both perfect channel state information (CSI) and pilot-based CSI in a multi-user multiple-input single-output (MU-MISO) system. Using perfect CSI, we first demonstrate that the needed number of the transmit antennas for ZF precoder to achieve a certain percentage of the maximal achievable sum-rate monotonically decreases with the increase of the transmit signal-to-noise ratio (SNR). In addition, the analysis of the needed transmit antennas shows that an upper bound of the required number of antennas for easy calculation only relates to the number of users and the target percentage. For completeness of the research, the pilot-based case is analyzed to examine that how pilots will affect the aforementioned results. Finally, the concept of massive MIMO for practical deployment is discussed via simulation results.
Experimental Characterization of Millimeter-Wave Indoor Propagation Channels at 28 GHz

The increasing requirement for the mobile data traffic accelerates the research of millimeter-wave (mm-wave) for future wireless systems. Accurate characterization of the mm-wave propagation channel is fundamental and essential for the system design and performance evaluation. In this paper, we conducted measurement campaigns in various indoor scenarios, including classroom, office, and hall scenarios, at the frequency bands of 27–29 GHz. The spatial channel characteristics were recorded by using a large-scale uniform circular array. A high-resolution parameter estimation algorithm was applied to estimate the mm-wave spherical propagation parameters, i.e., the azimuth angle, elevation angle, delay, source distance, and complex amplitude of multipath components. With the same measurement system, the channel parameters including decay factor, delay spread, angular spread, and line of sight power ratio are investigated thoroughly in individual indoor scenarios and compared in different indoor scenarios. Furthermore, the impact of the furniture richness level and indoor geometry on the propagation parameters are also investigated.
Fast Sparse Bayesian Channel Estimation for Wideband mmWave Systems

In this paper, we propose a fast channel estimation algorithm for wideband millimeter wave (mmWave) massive MIMO systems, where the hybrid precoding architectures are adopted. Stimulated by the joint sparsity of different subcarriers, a distributed compressed sensing-based strategy is presented to reduce the required pilot overhead. Based on a hierarchical channel model, a fast sparse Bayesian learning method, which can drastically release the relaxed evidence low bound, is designed to accelerate the convergence rate. Simulation results verify that the proposed algorithm is capable of achieving substantially higher estimation accuracy and convergence rate as compared to other existing Bayesian channel estimation strategies.
Using of the Software System for Finding Solutions on the Basis of Game-Theoretic Methods for Teaching Students in the Magistracy in the Direction «Applied Mathematics and Informatics»

To implement the Strategy of scientific and technological development of the Russian Federation on the transition to advanced digital, intelligent production technologies, robotic systems, new materials and methods for designing, creating systems for processing large amounts of data, machine learning and artificial intelligence, it is necessary to train highly qualified personnel in the field of development mathematical basis and software for modern and future intelligent systems for various applications, including for the educational process. Training of such specialists is organized at the Department of Applied Mathematics of the National Research University “MPEI” (NRU “MPEI”) in the direction “Applied Mathematics and Informatics”. The paper presents the experience of application within the discipline “Methods and software tools for decision support” of a software system for finding solutions in conflict situations on the basis of game-theoretic methods for teaching students of NRU “MPEI” at the Master's program at the Department of Applied Mathematics in the direction “Applied Mathematics and Informatics”.
Research and Control Strategy of Air-conditioning Load Model Based on Demand Response

Air-conditioning load as an important part of the peak load, its reasonable regulation can effectively improve system efficiency. The air-conditioning load is a typical thermostatically controlled load, and its thermal storage characteristics make it an excellent demand response resource with advantages of fast response speed and low cost. In order to achieve a reasonable dispatch for air-conditioning resources, based on the third-order air-conditioning model, aggregate the air-conditioning load using the Monte Carlo simulation method. On the premise of satisfying user comfort, a method of evaluating the adjustable capacity of air-conditioning is proposed by controlling the working temperature range of air-conditioning. The simulation results prove the feasibility of the method and provide a quantitative basis for the planning of day-ahead scheduling when the air-conditioning load participates in the demand response.
Stability Analysis and Control of Two-Stage Three-Phase Photovoltaic Grid-Connected System with Phase-Locked Loop

This paper presents a small-signal model of two-stage three-phase photovoltaic grid-connected system including phase-locked loop (PLL). Using the eigenvalue analysis, the dominant oscillation mode of the system under weak grid conditions is determined and the results show that MPPT control parameters have relatively little impact on system stability. Based on the interaction between PLL and inner current loop, an additional control method is proposed to suppress the dominant oscillation mode. It is further proved by eigenvalue analysis and time domain simulation that the proposed method is correct. Then the lower limit values of the additional control coefficient under different PLL gains are given.
Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution

Camera arrays provide spatial and angular information within a single snapshot. With refocusing methods, focal planes can be altered after exposure. In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays. In our method, the disparity is first estimated. Then, the unfocused region (bokeh) is rendered by using a depth-based anisotropic filter. Finally, the refocused image is produced by a reconstruction-based superresolution approach where the bokeh image is used as a regularization term. Our method can selectively refocus images with focused region being superresolved and bokeh being esthetically rendered. Our method also enables postadjustment of depth of field. We conduct experiments on both public and self-developed datasets. Our method achieves superior visual performance with acceptable computational cost as compared to the other state-of-the-art methods.
GPU-Accelerated Matrix-Free 3D Ultrasound Reconstruction for Nondestructive Testing

In this paper, we propose a matrix-free 3D ultrasonic reconstruction scheme based on the Fast Iterative Shrinkage-Thresholding algorithm (FISTA) implemented on a GPU. The matrix-free implementation allows to reconstruct images even for problem sizes that would be intractable when explicitly calculating the matrix. However, due to the matrix-free implementation, additional steps are necessary to estimate the stepsize parameter required by FISTA, since the optimal stepsize depends on the largest singular value of the operator matrix, which in the matrix-free version is unavailable and cannot be built due to its size. The estimation is performed based on a priori knowledge of the model. We compare our method to 3D SAFT and OMP images of volumetric ultrasound measurement data of a steel specimen to show how FISTA leads to sharper images facilitating sizing and locating of defects within a specimen.
Stochastic Power Control Policies for Battery-Operated Wireless Power Transfer

Radio frequency wireless power transfer (RF-WPT) is an emerging technology that enables transferring energy from an energy access point (E-AP) to multiple energy receivers (E-Rs), in a wireless manner. In this paper, we focus on the problem of WPT from a battery-operated E-AP to multiple E-Rs. To save the battery life of the E-AP and at the same time, provide the required quality of service (QoS) for the E-Rs, we aim at finding a proper power transfer policy that minimizes the average transmitted power of the E-AP while providing the required received power of each E-R. We formulate this problem with a stochastic optimisation problem, which is non-convex and has no closed-form expression for the objective function. First, we focus on the special case of single E-R, and derived a closed-form expression for the optimal solution which requires the CSI distribution. Next, for the general case of multiple E-Rs, we propose a Min Drift Plus Penalty (MDPP) algorithm which does not require any information on the CSI distribution. We prove that the proposed algorithm attains a near-optimal solution within a guaranteed gap to the optimal solution. Numerical results demonstrate the significant performance of the proposed solutions.
A 23-GHz Low-Phase-Noise Digital Bang–Bang PLL for Fast Triangular and Sawtooth Chirp Modulation

This paper describes a 23-GHz digital bang–bang phase-locked loop (PLL) fabricated in 65-nm CMOS for millimeter-wave frequency-modulated continuous-wave radars. The presented circuit aims to generate a fast sawtooth chirp signal that grants significant advantages with respect to the more conventional triangular waveform. Such a signal, however, features a very large bandwidth that requires the adoption of a two-point injection scheme. This paper, after intuitively discussing how the nonlinearity of the digitally controlled oscillator affects the accuracy of frequency modulation, presents a novel automatic pre-distortion engine, operating fully in background, which linearizes the tuning characteristic. The 19.7-mA fractional-N PLL having an rms jitter of 213 fs and an in-band fractional spur of −58 dBc is capable of synthesizing fast chirps with 173-MHz/ $\mu \text{s}$ maximum slope and an idle time of less than 200 ns after an abrupt frequency step with no over or undershoot.
Appliance Control by TDoA-based Localization and Gesture Recognition

Smart home is one of the major Internet of Things (IoT) applications. When realizing a smart home, designing an intuitive interaction for appliance control has become a crucial issue. A control intention to an appliance comprises two main meanings, identification and control command. But, previous works seldom deliver these two meanings at the same time. To fully convey the control intention, this paper proposes a scheme of intuitive appliance control that tracks and recognizes hand moving trajectory and hand gestures respectively by exploiting acoustic information. We utilize a 3D positioning method based on time difference of arrival (TDoA) of acoustic signals to detect the trajectory of a hand movement for appliance identification and design a gesture recognition method by Doppler effect for control command. We validate the effectiveness of the proposed scheme via real-life experiments. The results show that average locating errors to the targets are 5.24° and 10.35° in the horizontal and vertical planes respectively and the accuracy of gesture recognition is 96.66%. Since human's eyesight is horizontal to the ground, locating an object in the vertical view is error-prone. We also provide a scroll gesture to assist the appliance selection.
PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine

Principal component analysis (PCA) is one of the crucial dimensionality reduction (DR) techniques in which the original features are transformed into lower dimensional space. Though the PCA space has orthogonal principal components (PC), it does not provide a real reduction of dimensionality in terms of the original features (variables), as all features including irrelevant and redundant features are required to define a single PC. It is necessary to remove such type of features by using feature subset selection (FSS) for better generalization performance. The principal intent of this paper is to introduce a PCA-based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach that is able to handle weighted classification problem. The PF-FELM is mainly categorized into four subsystems: PCA, FSS, fuzzification, and classification. FSS selects an optimal feature order that is based on maximum occurrences within the various filter-based ranking algorithms. The simulation results are computed and compared by using sequential forward search strategy for clinical datasets. As a case study, an uncertain real-time application of plant disease and nutrient deficiency classification is considered, helping remote farmers as expert advice. For validation of the proposed PF-FELM, statistical and hypothetical tests are evaluated by using correlation measure and paired t-test. With the results, it is inferred that PF-FELM provides 12.17% improved generalization performance as compared to P-FELM (without FSS) by reducing 13.94% features.
A Novel Method for Solving Multi-Attribute Decision Making by Using Expected Value Under Interval Type-2 Fuzzy Environment

Interval type-2 fuzzy set (IT2FS) is a very helpful tool to deal with Multi-Attribute Decision-Making (MADM) problem in real world. In this paper, a novel method is developed to solve MADM problem under IT2FS environment. Firstly, three kinds of interval type-2 fuzzy set (IT2FS) average expectation operator formulas are put forward, and a new expected value formula of interval type-2 fuzzy sets (IT2FSs) is proposed by combining the three average expectation operators in interval type-2 fuzzy environment. Then, fuzzy factor, hesitant factor and interval factor of IT2FS are put forward to measure uncertain information of IT2FS. Next, a new cross-entropy formula is proposed to measure uncertain relationship between IT2FSs, and a new comprehensive cross-entropy formula of IT2FS is put forward based on cross-entropy formula and total probability formula to reflect influence relationship among alternatives in decision-making. Meanwhile, we put forward a novel entropy formula to measure the uncertainty of IT2FSs themselves. On the other hand, an optimal linear programming model is constructed with the expected value, comprehensive cross-entropy and entropy. Finally, an example analysis is utilized to show the feasibility and effectiveness of the proposed method.