Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability Evaluation of Brain Computer Interfaces: Analysis of Methods and Tools

Usability includes concepts such as satisfaction, efficiency and effectiveness. This may be partly the result of functionality, but they are mainly defined by the user's interaction with the system. The Brain-Computer Interface-BCI provides a direct communication path between the brain and the external world by translating signals from brain activities into codes or commands. The purpose of usability of BCI is that the interfaces can be understood, learned, used and be attractive to user, under specific conditions of use. For usability evaluation of BCI different methods (Questionnaires, Interviews, neurophysiological measures) and tools (VAS, QUIS, SUS, electroencephalography - EEG, functional near-infrared spectroscopy - fNIRS), magnetic encephalography - MEG) have been proposed. The evaluations are generally based on traditional methods (Questionnaires or Interviews), obtaining subjective answers without the certainty of the precision. In this paper an analysis of the usability evaluation methods of the BCI is presented.
Crosstalk Noise Characterization Between Spoof SPP Transmission Line and Differential Microstrip Lines

In this paper, the crosstalk noise between spoof surface plasmon polariton (SPP) transmission line (TL) and conventional differential microstrip (MS) lines is investigated. Due to the strong field confinement of spoof SPP TL, as well as the mode mismatching between the spoof SPP TL and conventional MS lines, the transmitting electromagnetic energy of spoof SPP TL can hardly be coupled to adjacent differential MS lines, which helps reduce the crosstalk noise between them effectively. The full-wave simulated results show that the crosstalk noise suppression is much better than the MS line. In addition, the time domain simulated results illustrate that both differential mode and common mode noises of adjacent differential pairs are significant reduced when spatially designed spoof SPP TL is utilized.
Fully Distributed Event-Based Protocols for Lipschitz Nonlinear Multi-Agent Systems

This article addresses the cooperative control problem of multi-agent systems with Lipschitz nonlinearities. In order to achieve consensus, we design a distributed adaptive event-triggered protocol for these participators. The proposed protocol with mixed event-triggered mechanism can be implemented by each agent in a fully distributed fashion and guarantees that the consensus errors and the adaptive gains are uniform ultimate bounded. We also derive explicitly the relationship between the error bound and the parameters of the protocol. Further, the protocol can avoid the Zeno behavior while reducing the operating frequency of the sensor and controller. The problem of leader-follower consensus is also considered.
A Re-Identification Strategy Using Machine Learning that Exploits Better Side Data

Data on people’s daily activities are being collected as big data and then mined for corporate and public purposes. However, concern about privacy is the major obstacle to using such data. Although data anonymisation can mitigate the privacy risk, researchers have shown that people can often be re-identified by linking the anonymised data with other data (i.e. side data). Though re-identification has been improved by the use of machine learning, two problems remain: the unavailability of data and the inappropriateness of the side data. We have developed a re-identification strategy using machine learning in which anonymised data are linked to a different type of side data that are easy to obtain and for which the represented person is easy to identify. We tested our strategy on the re-identification of the owners of 78 anonymous Twitter accounts by using information on résumés as side data. We linked the Twitter posts and résumés by estimating the profiles of the account owners and matching them with those described in the résumés. We were able to link roughly 50% of the accounts to their owners when all tweets were used and roughly 20% of the accounts when only the latest 63 tweets were used. The proposed strategy would help people be better aware of the privacy risks of using personal data and hopefully lead to the implementation of improved protection measures.
Up-Down and Left-Right by the Heart Transcriptome

Myocardium transcriptomes of mouse left and right atria and ventricles were profiled separately to identify the differences that might be responsible for the distinct functional roles of the four heart chambers. In total, 16,886 distinct unigenes have been quantified in all 16 samples collected from four adult male mice from the same litter. 15.76% of the quantified genes on the left and 16.5% on the right exhibited differential expression between the corresponding atrium and ventricle of the same side, while 5.8% in atria and 1.2% in ventricles were differently expressed between the left and the right. Beyond the differentially expressed genes, the study revealed distinct expression control and coordination of ion channels and genes within the cardiac muscle contraction, oxidative phosphorylation, glycolysis/glucogenesis, calcium and adrenergic signaling pathways. Interestingly, while expression of Ank2 (encoding ankyrin-B) oscillates in phase with all its binding partners in the left ventricle, the percentage of synergistically expressed partners of Ank2 is 15% and 37% in the left and right atria and 74% in the right ventricle. The analysis revealed also the high interventricular synchrony of the expression of ion channels.
Low Saturation Onset MOS Transistor: an Equivalent Network

In this work a four-transistor network is presented which is almost equivalent to a single transistor with lower saturation onset voltage. Output characteristics are analyzed and the reduction on saturation onset voltage is assessed. This network is adequate to replace output transistors in cascode mirrors, so that the low output conductance is achieved with much less prejudice to output voltage swing.
Design and Optimization of Permanent Magnet Assisted Synchronous Reluctance Motor for Better Torque Performance

For an optimal design of synchronous reluctance motor of permanent magnet assisted synchronous reluctance motor (PMA-SynRM), the significant challenge are rotor topology and the choice of numerous design variables. A novel rotor structure of PMA-SynRM is proposed to achieve that the reluctance torque and the magnetic torque reach a maximum value at a close current phase angle by adjusting the position of the permanent magnet and the structure of flux barrier. Meanwhile, an asymmetrical rotor flux barrier and flat tips are adopted to reduce the torque ripple. Then, combined with the Taguchi method, the sequential subspace optimization method (SSOM) is implemented for proposed PMA-SynRM by using the differential evolution algorithm and pareto evaluation. Finally, the finite element analysis (FEA) is carried out to validate the technical advantages of proposed PMA-SynRM topology and design methods.
Live Demonstration: Low Power Embedded System for Event-Driven Hand Gesture Recognition

This demonstration presents a low power embedded system to classify hand movements. The surface ElectroMyo-Graphic (sEMG) signals acquired from the forearm are preprocessed using the Average Threshold Crossing (ATC) event-driven technique, which heavily reduces hardware complexity and power consumption. The quasi-digital output is sent to an ultra low power microcontroller, which implements a fully-connected Neural Network (NN). A small Arduino-based tank is used to demonstrate the real-time behavior of the system and to show the correctness of the predicted gestures1.
Non-uniform Partitioning for Collaborative Execution on Heterogeneous Architectures

Since the demand for computing power increases, new architectures arise to obtain better performance. An important class of integrated devices is heterogeneous architectures, which join different specialized hardware into a single chip, composing a System on Chip - SoC. Within this context, effectively splitting tasks between the different architectures is primal to obtain efficiency and performance. In this work, we evaluate two heterogeneous architectures: one composed of a general-purpose CPU and a graphics processing unit (GPU) integrated into a single chip (AMD Kaveri SoC), and another composed by a general-purpose CPU and a Field Programmable Gate Array (FPGA) integrated into a single chip (Intel Arria 10 SoC). We investigate how data partitioning affects the performance of each device in a collaborative execution through the decomposition of the data domain. As a case study, we apply the technique in the well-known Lattice Boltzmann Method (LBM), analyzing the performance of five kernels in both architectures. Our experimental results show that non-uniform partitioning improves LBM kernels performance by up to 11.40% and 15.15% on AMD Kaveri and Intel Arria 10, respectively.
A Novel Data Association Algorithm For Ghost Elimination In Passive Radar Systems

In this paper, a novel data association algorithm is developed for detecting and localizing multiple targets. The fusion of the measurements involving angle-of-arrival (AoA) and time-of-arrival (ToA) generated by the passive sensors is accomplished effectively. The ghost problem faced during this fusion is solved by clustering these measurements and assigning scores to each of them. Score assignment is performed using AoA values and hyperbola intersections generated by ToA values. In addition, entropy is used for eliminating ghost clusters more efficiently. Then, clusters which have the highest scores are used to estimate target positions by applying maximum likelihood estimation. This algorithm is tested with different number of targets and different noise levels.
Using Perfect Codes in Relay Aided Networks: A Security Analysis

Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.
$F_{0}$ estimation of speech using $L_{1}$-norm Sparse LP

Linear Prediction (LP) analysis is currently used in speech processing that is based on $L_{2}$-norm optimization. Nowadays, big data and data mining are paid deep attention, as a result, sparse estimation is strongly focused. Sparse estimation-based LP analysis has been proposed that is based on $L_{0}$-norm or $L_{1}$-norm optimization. We have already proposed Time-Varying Complex AR (TV-CAR) speech analysis for an analytic signal. These are based on $L_{2}$-norm optimization and we have evaluated the $L_{2}$-norm optimization method on speech processing such as $F_{0}$ estimation of speech, speech enhancement, HMM speech recognition. Complex residual resulting from inverse filtering for analysis signal is applied for $F_{0}$ estimation since the residual provides fewer components of formant structure. In this paper, the $L_{1}$-norm based LP method is evaluated on $F_{0}$ estimation using the IRAPT (Instantaneous Robust Algorithm for Pitch Tracking) method in which the LP residual is used.
A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

The key to the integration of unmanned aircrafts in the national airspace is to prevent them from colliding with the other traffics in the airspace. However, it is a great challenge to generate a safe, stable and robust collision-free path for unmanned aircraft system (UAS), a.k. a. unmanned aircraft vehicle (UAV), in real time, if the airspace is highly dynamics and heterogenous (i.e. thronged with aircrafts with different motion states). Based on the dynamic artificial potential field (DAPF) algorithm, this paper provides advisories on how to generate a real-time reactive collision-free path for unmanned aircraft vehicles flying in a dynamic airspace, aiming to ensure the flight safety and minimize the impact on surrounding traffic. Firstly, the safety distance was defined as a variable threshold, which scaled adaptively according to the relative motion states of the surrounding obstacles and the performance of the own UAV. Moreover, the forces of the potential field were improved, such that their magnitudes could be adjusted automatically according to the threat levels of the surrounding obstacles. The threat level of an obstacle depends on the relative position, speed and flight trend between the UAV and the obstacle. In addition, the repulsive force along the relative position of the traditional artificial potential field (APF) was retained, and a steering force was added to change the flight direction of the UAV, aiming to speed up the collision avoidance. Furthermore, the attractive force was modified to help the UAV return to the planned path quickly and stably. After that, the capacity were determined to ensure the feasible and practical path planning for the UAV. Finally, the proposed UAV path-planning method was proved effective, safe, stable and adaptive through simulations.
Aging of transformer insulation of experimental transformers and laboratory models with different moisture contents: Part II — moisture distribution and aging kinetics

Part II of the paper presents the results of the experimental data processing presented in Part I dedicated to distribution of water content in the standard Kraft paper along the height of HV windings during aging of experimental transformers and comparisons of paper aging rates of experimental transformers and laboratory models. The dependence of aging constant (pre-exponential factor of Arrhenius equation) on the moisture content of cellulose insulation is introduced. The developed functional dependence is obtained by detailed processing of the data — hot-spot temperature, moisture and degree of polymerization of cellulose insulation during 460 days of aging experiment of two transformers on load, with conductor insulation moisture of ca. 1 % and ca. 4 % and 300 days of aging experiment on corresponding laboratory models. Based on the results shown in this paper, aging curve, i.e. change of degree of polymerization during transformer operation insulated with Kraft paper and inhibited mineral oil was proposed, including varying water contents during aging. Using this equation it is possible to evaluate aging rate of solid insulation, starting from known hot-spot temperature and moisture content in cellulose insulation. Good comparison of aging curves of experimental transformers and laboratory models, i.e. measured and calculated values of degree of polymerization of paper to existing literature findings was obtained.
Optimization Design of In-wheel Motor Based on Halbach (Ce,Nd)FeB Magnet

(Ce,Nd)FeB permanent magnet is cheaper than NdFeB. For the lack of (Ce,Nd)FeB performance magnet, a Halbach rotor structure is proposed. In this paper, the high temperature B-H curve of (Ce,Nd)FeB is deduced. Several kinds of PM shapes and PM materials are compared in this paper. And two-segment Halbach array is optimized to improve the performance of motor. The temperature field of outer rotor permanent magnet in-wheel motor is analyzed bases on typical driving cycle. The method of multi-objective estimate is used to select the optimal structure.
Selective Control Algorithm for N-Phase Switching Power Pole of 4-Leg Interlinking Converter in AC/DC Hybrid Microgrid

This paper proposes a selective control algorithm to reduce switching losses of N-phase switching pole of 3-phase 4-leg interlinking converter (ILC) in AC/DC hybrid microgrid. Unbalanced load connected to 3-phase 4-leg ILC causes unbalanced voltage condition and this problems can be complemented by N-phase switching pole of 4-leg ILC. However, if the switching pole on N is operated constantly regardless of control decision due to unbalanced load conditions, switching loss on the N-phase switching pole will cause power loss in the entire topology. To compensate this problem, selective control algorithm considering voltage unbalance factor (VUF) for N-phase switching pole in ILC is proposed. Proposed algorithm decides switching operation of N-phase switching pole depending on unbalanced load voltage. When occurring unbalanced load condition, voltage unbalance factor can be reduced by proposed control algorithm. At the same time, the current flowing through the N-phase switch pole and switching losses can be reduced.
A Stochastic-Programming-Based Coupled Constraint Equilibrium Problem Under PV Uncertainty in Day-Ahead Energy Market

In this paper, as for a microgrid system, we establish a game-theoretic framework for modeling the strategic behavior of the energy aggregators (microgrid components) that are connected to solar energy resources. Considering the prediction uncertainty of the solar energy, we model the profit function of each aggregator (market player) with a two-stage stochastic structure. Then, according to the power flow equation, a coupled constraint equilibrium is defined, and some properties are studied. To compute the specific equilibrium, we recast the problem by its Karush–Kuhn–Tucker (KKT) conditions and then reformulate the problem. The numerical simulation illustrates the coupled strategies of the market players and the existence of the coupled constraint equilibrium.
Analysis of a New Switched Flux Permanent Magnet Coupler with Synchronous Rotating Adjuster

To solve the problem of complex adjuster and permanent magnets (PMs) demagnetization at high temperature, a new type of adjustable permanent magnet coupler (PMC) with synchronous rotating adjuster is proposed, whose torque can be adjusted by turning a switched flux angles between the adjuster and PMs along the axial direction. The structural feature and working principle of the PMC are illustrated. The distribution of static magnetic field and the air-gap flux at different switched flux angles are obtained. The torque curves are calculated in transient field by using the 3-D finite-element analysis method. To predict the torque characteristics of the PMC, a new analysis method is proposed which uses the relationship between Maxwell suction and the torque. The test results show that analytical prediction can agree the test data well at low torque/slip.
Pancreatic beta cell based optical biosensor and system for continuous glucose monitoring

A bio-electronics system that measures cytosolic [Ca2+] in vitro in the INS-1E pancreatic β-cells using a 430 nm excitation light with the excitation wavelength of the probe being at 435 nm was successfully implemented and tested. We transfected the INS-1E cells with a plasmid that is responsible for the FRET based [Ca2+] protein sensor complex, that emit light at both 485 nm and 535 nm. The F10 cell based biosensor and its working principle are described with protein structure homology modeling. The efficacy of absorption angle independent filters atop the photo-detector is demonstrated with a 1163% improvement in SNR. Oxygenation of cells alongside stimulant circulation is possible with the aid of a magnetic stirrer. The system successfully monitors glucose continuously with 2.5 mM stimulation. Continuously glucose monitoring with periodic 2.5 mM glucose stimulation and binary 11 mM glucose vs. KRBH were successful showing clear repeatable ratio changes.
A Subjectivity-Aware Algorithm for Label Aggregation in Crowdsourcing

Crowdsourcing has already attracted a wide attention in the field of machine learning and its related fields. A large amount of labeled data can be obtained quickly and cheaply on crowdsourcing platforms. To deal with the problem that labels collected from crowds are usually noisy due to the low accuracy of non-expert online workers, we use quality control methods to improve the qualities of crowd data. Unfortunately, current quality control methods only consider the instance difficulty or the worker reliability to account for the variety of labels to the same instance, and these methods did not take subjectivity of workers into consideration which also effects the responses. In this paper, we present a novel subjectivity-aware algorithm for label aggregation, which also model the difficulty of instances and reliability of workers as latent parameters. This method is an EM-like algorithm, which not only infers the ground truth of the instances, but also simultaneously estimates the latent parameters. Experimental results on real-world datasets show that our method outperforms the state-of-the-art ground truth inference algorithms.
A Comparative Performance Study of Electrocardiogram-Based Human Identity Recognition

With more and more Internet of Things based healthcare systems being developed in hospitals, community clinics and individual homes for medical and healthcare applications, issues of information security have become increasingly critical. Entity identification is a basic and important security mechanism for access control of an information system. As a kind of must-have physiological signals in most Internet of Things based healthcare systems, electrocardiogram (ECG) has its potential as a type of biometric traits for human identity recognition. This work focuses on the recognition performance analysis with ECG signals acquired from healthy subjects and those with cardiovascular diseases. The stability of ECG based identity recognition was also analyzed with signals captured within one month interval. Experimental results indicate that a better recognition accuracy for healthy subjects has been achieved compared to elderly unhealthy subjects. As ECG signals collected from subjects with cardiovascular diseases change more dramatically over time compared to those from healthy ones, more ECG signals from different session of recording are needed to improve the identification accuracy. With ECG records from different session of recording being supplied in the model training process, accuracy rates at 95.62% and 98.14% were achieved for unhealthy and healthy subjects, respectively.
Evaluating Array DBMS Compression Techniques for Big Environmental Datasets

Earth remote sensing imagery come from satellites, unmanned aerial vehicles, airplanes, and other sources. National agencies, commercial companies, and individuals across the globe collect enormous amounts of such imagery daily. Array DBMS are one of the prominent tools to manage and process large volumes of geospatial imagery. Recently we presented ChronosDB — innovative geospatial array DBMS that outperforms SciDB by up to 75× on average. SciDB is the only freely available distributed array DBMS to date. Unlike SciDB, ChronosDB does not require importing files into an internal DBMS format and works with imagery “in situ”: directly in their native file formats. This is one of the many virtues of ChronosDB. In this paper, we investigate the impact of data compression on the performance of array processing operations. We compress the data with diverse methods and explore compression impact on the processing speed. We thoroughly compare the performance on source and compressed data in ChronosDB and SciDB on real-world data on computer clusters in Microsoft Azure Cloud.