Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG Can Be Used to Measure Embodiment When Controlling a Walking Self-Avatar

It has recently been shown that inducing the ownership illusion and then manipulating the movements of one's self-avatar can lead to compensatory motor control strategies in gait rehabilitation. In order to maximize this effect, there is a need for a method that measures, and monitors embodiment levels of participants immersed in VR to induce and maintain a strong ownership illusion. The objective of this study was to propose a novel approach to measuring embodiment by presenting visual feedback that conflicts with motor control to embodied subjects. Twenty healthy participants were recruited. During experimentations, participants wore an EEG cap and motion capture markers, with an avatar displayed in a HMD from a first-person perspective. They were cued to either perform, watch or imagine a single step forward or to initiate walking on the treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). Results show that subjective levels of embodiment correlate strongly with the difference in $\mu$ - ERS power over the motor and pre-motor cortex between the modified and non-modified feedback trials.
Resilient and private consensus in multi-agent systems

We expound recent results introduced in [1] and consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. By investigating the Differentially Private MSR (DP-MSR) algorithm in [1], we present preliminary results on the accuracy of the algorithm in terms of a priori knowledge of some parameters of the faulty nodes. The results are illustrated via simulations.
Less conservative state feedback design conditions for switched Takagi-Sugeno fuzzy systems

Relaxed conditions for switched Takagi-Sugeno fuzzy systems under state feedback are proposed. The result explores two strategies to decrease the linear matrix inequalities (LMIs) conservatism. Properties of membership are explored to add slack matrix variables into the LMI formulation and a standard right hand side LMI relaxation procedure is extended for switched TS fuzzy systems. Numerical examples illustrate the efficiency of the presented results.
Learning Probabilistic Models for Safe Predictive Control in Unknown Environments

Researchers rely increasingly on tools from machine learning to improve the performance of control algorithms on real world tasks and enable robots to operate for long periods of time without intervention. Many of these algorithms require a model for the dynamics of the robot. In particular, researchers designing methods for safe learning control often rely on an upper bound on model error to make guarantees about the worst-case closed-loop performance of their algorithm. There are different options for how to learn such a model of the robot dynamics. We study probabilistic models for use in the context of stochastic model predictive control. Two popular choices for learning the robot dynamics are Gaussian Process (GP) regression and various forms of local linear regression. In this paper, we present a study comparing GPs with a particular form of local linear regression for learning robot dynamics with the aim of guaranteeing safety when a robot operates in novel conditions. We show results based on experimental data from a 900 kg ground robot using vision for localisation.
Miniaturized and Wideband Lumped Filter Using LTCC Integrated Package

A miniaturized multilayer lumped-element bandpass filter (BPF) based on low-temperature co-fired ceramic (LTCC) with a central frequency of 50MHz is proposed. The filter is constructed with high-Q spiral octagonal inductors and vertically-interdigital-capacitors (VICs). The overall size of the proposed BPF is only 26 mm × 15 mm. Meanwhile, the BPF achieved a wideband fractional bandwidth (FBW) of 30% and a low in-band insertion loss of 0.88 dB as well. This is of great significance for the communication application of very-high-frequency (VHF) band.
Fault-Tolerant Adaptive Neural Control of Multi-UAVs Against Actuator Faults

This paper is concerned with the fault-tolerant cooperative control (FTCC) problem of multiple unmanned aerial vehicles (multi-UAVs) in the communication network. By exploiting neural network (NN) to approximate the nonlinear terms existing in the highly nonlinear multi-UAVs system, a distributed neural adaptive control scheme is proposed when only a subset of follower UAVs has access to the leader UAV’s states. To solve the problem of “explosion of complexity” in traditional backstepping architecture and reduce the number of online updating parameters of NN, dynamic surface control (DSC) and minimal learning parameter techniques are employed to reduce the computational complexity. Furthermore, by combining graph theory and Lyapunov approach, it is proved that velocities and altitudes of all follower UAVs can track the velocity and altitude of the leader UAV. Finally, simulation results are presented to verify the effectiveness of the proposed control scheme.
Self-Testing Analog Spiking Neuron Circuit

Hardware-implemented neural networks are foreseen to play an increasing role in numerous applications. In this paper, we address the problem of post-manufacturing test and self-test of hardware-implemented neural networks. In particular, we propose a self-testable version of a spiking neuron circuit. The self-test wrapper is a compact circuit composed of a low-precision ramp generator and a small digital block. The self-test principle is demonstrated on a spiking neuron circuit design in 0. 35μ$m$ CMOS technology.
3D ultra-wideband high selective bandpass FSS

A new 3D ultra-wideband high selective bandpass frequency selective surface is presented in this paper. Interleaved subwavelength metal frames produce multiple transmission poles, leading to a sharp roll-offs at the edge of passband. The simulation results show that the FSS can achieve 11.8GHz band-width, covering the ultra-wide band from 6GHz to 18GHz. A large Rectangular coefficient indicates the FSS has high frequency selectivity and good suppression ability of the passband to the stopband.
A Reference Model for Big Data Analytics

The goal of big data analytics systems is to help business decisions supported by pieces of evidence from voluminous and diverse but uncertain data with high processing speed to create value. However, how the goal can be achieved is unclear and involves further exploration. In this paper, we propose a reference model for big data analytics which can help extract business questions and rationally evolve the questions into optimal analytics operationalizations explicitly considering underlying assumptions to achieve big data analytics goals aligned with business goals. It consists of 4 sub-models, Business Question Extraction Model, Big Data Analytics Evolution Model, Analytics Algorithm Reference Model, and Goal-Oriented Optimal Selection Model. We applied our reference model to a shipment decision in the retail business as an empirical study. We compared it with existing solutions in diverse aspects.
Switched Capacitor Converter-Based PWM Plus Phase-Shift Control Multiport Converter with Differential Power Processing Capability for Photovoltaic Systems

Photovoltaic (PV) systems consist of multiple dc-dc converters, such as a front-end converter for PV panel control and bidirectional converter for battery regulation. In addition, a differential power processing (DPP) converter is desirably installed to preclude partial shading issues of the significant reduction in energy yield and occurrence of multiple maximum power points. To simplify such PV systems by reducing the converter count, this paper proposes a novel multiport converter (MPC) based on a switched capacitor converter (SCC) with DPP capability. The proposed MPC can be derived by integrating a noninverting PWM buck-boost converter, SCC, and phase-shift (PS) SCC with sharing switches, realizing the simplified system and circuit. The proposed MPC can regulate the load and battery by PWM and PS controls, respectively, while precluding the partial-shading issues by its DPP capability. The experimental verification using a 150-W prototype demonstrated the load and battery could be independently regulated with preventing the partial shading issues.
A Device to Device (D2D) Spectrum Sharing Scheme for Wireless Industrial Applications

In this paper, we present a spectrum sharing scheme for Device to Device (D2D) deployments in a wireless Internet of Things (IoT) industrial environment. Interference management for this scheme relies on the relative distance between participating users or devices. The D2D links can access spectrum by sharing resources with cellular users or by using dedicated channels. The presented scheme maximises the throughput of admitted D2D links without compromising the Quality of Service (QoS) requirements of users in the network. An optimisation approach is adopted to solve the spectrum sharing problem resulting in an efficient allocation and throughput maximisation for all participating users and devices.
Physiological Effectivity and User Experience of Immersive Gait Rehabilitation

Gait impairments from neurological injuries require repeated and exhaustive physical exercises for rehabilitation. Prolonged physical training in clinical environments can easily become frustrating and de-motivating for various reasons which in turn risks to decrease efficiency during the healing process. This paper introduces an immersive VR system for gait rehabilitation which targets user experience and increase of motivation while evoking comparable physiological responses needed for successful training effects. The system provides a virtual environment consisting of open fields, forest, mountains, waterfalls, animals, and a beach for inspiring strolls and is able to include a virtual trainer as a companion during the walks. We evaluated the ecological validity of the system with healthy subjects before performing the clinical trial. We assessed the system's target qualities with a longitudinal study with 45 healthy participants in three consecutive days in comparison to a baseline non-VR condition. The system was able to evoke similar physiological responses. The workload was increased for the VR condition but the system also elicited a higher enjoyment and motivation which was the main goal. The latter benefits slightly decreased over time (as did workload) while they were still higher than in the non-VR condition. The virtual trainer did not show to be beneficial, the corresponding implications are discussed. Overall, the approach shows promising results which renders the system a viable alternative for the given use case while it motivates interesting direction for future work.
Emotion-Based Music Player

Nowadays, people tend to increasingly have more stress because of the bad economy, high living expenses, etc. Listening to music is a key activity that assists to reduce stress. However, it may be unhelpful if the music does not suit the current emotion of the listener. Moreover, there is no music player which is able to select songs based on the user emotion. To solve this problem, this paper proposes an emotion-based music player, which is able to suggest songs based on the user's emotions; sad, happy, neutral and angry. The application receives either the user's heart rate or facial image from a smart band or mobile camera. It then uses the classification method to identify the user's emotion. This paper presents 2 kinds of the classification method; the heart rate-based and the facial image-based methods. Then, the application returns songs which have the same mood as the user's emotion. The experimental results show that the proposed approach is able to precisely classify the happy emotion because the heart rate range of this emotion is wide.
Ellipsoidal Set Membership Filtering with Reduced Conservatism Applied to Relative Localization Between Aircraft Based on GNSS and UWB Ranging

For automatic formation flight and applications such as automatic aerial refueling, relative positions between aircraft need to be accurately estimated. However, to ensure flight safety, guaranteed consistent uncertainty estimates are of equally crucial importance. This paper presents a novel Iterated Extended Set Membership Filter (IESMF) providing ellipsoidal regions guaranteed to contain another aircraft's position in a formation. Two easily implementable modifications to the Extended Set Membership Filter are shown to greatly reduce its conservatism. The filter runs in fixed time and handles asynchronous observations. As another novelty, we take into account parametric uncertainty of the observation equation. The proposed filter is applied to a realistic UAS localization benchmark simulation. We compare the new filter to a computationally very strong but much more expensive nonlinear set inversion algorithm and find that similar performances can be achieved. Also, simulation results illustrate the effective reduction of filter conservatism by a few iterative updates.
Adaptive NFTSM-Based Fault Tolerant Control for a Class of Nonlinear System With Actuator Fault and Saturation

In this paper, an adaptive fault-tolerant control (FTC) strategy is proposed for a class of nonlinear systems in the presence of actuator fault, actuator saturation, and unknown external disturbance. The mathematical model of a second-order nonlinear system with actuator fault is first given, and a fault estimation observer is designed to estimate the occurred time-varying actuator fault. On this basis, the adaptive FTC strategy is proposed using both nonsingular fast terminal sliding mode (NFTSM) and radial basis function neural networks (RBFNNs) techniques to compensate for the negative effects caused by time-varying actuator fault and external disturbance. Another adaptive FTC scheme is further presented under actuator saturation case, and the Lyapunov stability analysis demonstrates that the designed FTC approach could guarantee that the trajectory of sliding mode dynamics could converge to a small neighborhood of the origin within a finite time. Compared with some existing results, the FTC approach proposed in this paper has better fault acceptability. Finally, the proposed adaptive FTC approach is applied to the attitude control of rigid spacecraft, and the simulation results illustrate the advantages of the designed control scheme.
Characterization of Two-Dimensional Surfaces Based on Generalized Boundary Conditions and Surface Susceptibilities

This paper presents a review on generalized boundary conditions (GBCs) in surface electromagnetics (SEM) for characterization of two-dimensional surfaces. Five representative features are chosen to describe electromagnetic characteristics of surfaces, according to which GBCs for different kinds of surface problems can be derived. Based on GBCs, scattering properties of given surfaces can be computed analytically, once their characteristic parameters, the effective surface susceptibilities, are determined from limited sets of full-wave simulations.
Unifying Structural Proximity and Equivalence for Network Embedding

The fundamental purpose of network embedding is to automatically encode each node in a network as a low-dimensional vector, while at the same time preserving certain characteristics of the network. Based on the nodes’ embeddings, downstream network analytic tasks such as community mining, node classification, and link prediction, can then be easily implemented using traditional machine learning methods. In recent years, extensive network embedding methods have been proposed based on factorization, random walks, deep learning, and so on. However, most of them focus mainly on preserving the structural proximity of network nodes, where highly interconnected nodes in a network will be represented closely together in the embedded vector space. While in many real-world networks, existing studies have revealed that high-order organizations (e.g., network motifs and graphlets) may be related to specific network functions. In this case, nodes far apart but with a similar organization in a network (i.e., structural equivalence) may have similar network functions. Accordingly, in this paper, we present a hybrid embedding method that unifies both structural proximity and equivalence (SPaE) of a network. Specifically, we adopt the concept of graphlet degree vector (GDV) to measure structural equivalence between network nodes. Through carrying out experiments on both synthetic and real-world datasets, we evaluate the performance of the hybrid embedding method in tasks of node clustering, node classification, and visualization. The results demonstrate that the proposed SPaE method outperforms several state-of-the-art methods when the network analytic tasks are not merely related to structural proximity. Finally, we also conduct experiments to evaluate the flexibility, robustness, and parameter sensitivity of the hybrid embedding method.
A Survey of Artificial Neural Networks with Model-based Control Techniques for Flight Control of Unmanned Aerial Vehicles

Model-based control (MBC) techniques have been successfully developed for flight control applications of unmanned aerial vehicles (UAVs) in recent years. However, their heavy reliance on the fidelity of the plant model coupled with high computational complexity make the design and implementation process challenging. To overcome such challenges, attention has been focused on the use of artificial neural networks (ANNs) to study complex systems since they show promise in system identification and controller design, to say the least. This survey aims to provide a literature review on combining MBC techniques with ANNs for UAV flight control, with the goal of laying the foundation for efficient controller designs with performance guarantees. A brief discussion on frequently-used ANNs is presented along with an analysis of their time complexity. Classification/comparison of existing dynamic modeling approaches and control techniques is provided. Challenging research questions and an envisaged control architecture are also posed for future development.
A Wideband Tightly Coupled Wide-angle Impedance Matching Layer

This paper proposed a wide-angle impedance matching (WAIM) layer for wideband phased array antennas. The proposed WAIM is based on the tightly coupled concept which introduces an additional capacitance between unit cells and provides a wide operating bandwidth. To study the scanning ability of the array, a 32 by infinite and an infinite by 32 linear arrays are analyzed. Viewing from the simulated results, by adding the proposed WAIM over a phased array antenna, the common bandwidth is improved to around 64.6% (from 5.37 to 10.5 GHz) with the scanning ability of ±60° in E-, H- and D-plane. Besides, the WAIM has the advantage of low profile and light weight properties.
A New on-Line Parameters Identification Method for IPMSMs Using Current Derivative Measurement

The d-and q-axis inductances of an IPMSM, especially Lq, vary with magnitude of the current of each axis due to the magnetic saturation. The existing on-line methods using recursive algorithms fail to estimate the change of the machine inductances during zero speed and fast transient operations. This paper proposes a new on-line method to estimate machine inductances using measurements of current derivatives and the DC-bus voltage of the inverter during each PWM cycle. In addition, the stator resistance and permanent magnet flux linkage, which vary with the operating temperature, are identified by using the recursive least square (RLS) technique. Extensive simulation and experimental studies were conducted to verify the robustness and effectiveness of the proposed on-line parameter identification method which estimates all four electrical parameters of the IPMSM.
A New ZVS Switched-Capacitor DC-DC Converter with Low Boost Inductance

This paper proposes a new ZVS switched-capacitor DC-DC converter with low boost inductance. Proposed converter can reduce the boost inductance because applied voltage of the boost inductor was reduced by using flying capacitor $C_{f}$, In addition, since the operating frequency of the boost inductor $L_{f}$ is doubled by shifting the phase between the switches $S_{3}$ and $S_{4}$, boost inductance can be further reduced. It can also achieve zero voltage switching (ZVS) turn-on of all switches using auxiliary inductor $L_{a}$ and capacitor $C_{a}$. The operation principle, interleaving effects and design of passive components to achieve ZVS are presented. Also, comparison of different topologies is presented to shows the superior performance of the proposed converter. Experimental results on a 600W prototype are provided to validate the proposed concept. In addition, experimental waveforms of 2-phase interleaved converter suitable for high power applications are provided.
Blind Mesh Quality Assessment Method Based on Concave, Convex and Structural Features Analyses

Various visual distortions can inevitably affect 3D mesh model during their transmission and geometrical processing. Therefore, how to evaluate visual quality of 3D mesh becomes an important problem and it is necessary to design effective tools for 3D mesh quality assessment. In this paper, we propose a new blind 3D mesh quality assessment method based on the analysis of concave, convex and structural information. Specifically, the shape index and the curvedness are defined as features to describe the concave and convex information, while the vertex scatter and the distribution of the triangular topology area are defined as features that describe structural information. All the extracted features constitute a final feature vector and the random forest regression is adopted to learn a quality predictor from feature space to quality space. Validation experiments on public database show that the proposed method can accurately predict the quality of 3D mesh.
Oscillometric blood pressure waveform analysis: challenges and developments

One of the most important public health problem is represented by hypertension. Early detection, suitable treatment and hypertension control depend on accurate blood pressure measurement. The most used devices to measure blood pressure are based on the oscillometric method and in the years several research works have been carried out aimed at improving their accuracy. In the paper, the last promising contributes provided by the research dealing with the oscillometric blood pressure waveform analysis are presented to highlight the research trends in the field.
Life Estimation of DC-Link Capacitor in Multi-operating Traction Drive System

The reliability of DC-link capacitor in the traction drive system is regarded as a major problem that should be resolved well. Based on this, this paper proposes a life estimation method of DC-link capacitor to realize the reliability evaluation of the traction drive system. Starting from the capacitor life model, the actual multi-operating data is converted into the calculation of traction load through simulation tests, and the capacitor voltage and current ripple signals are obtained. Fourier method by combining the sliding window grouping with the neural network are used to analyze the dynamics of the hot spot temperature, and is optimized by use the Newton's law of cooling. Finally, the capacitor loss is analyzed through the experimental test results, and the safe operation of the system is considered, and the lifetime of the capacitor under multi-operating conditions is shown.