Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Affordable Virtual Reality Solution for Cardiopulmonary Resuscitation Training

Cardiopulmonary Resuscitation (CPR) is an essential skill for medical professionals. Currently, CPR training is carried out using mechanical manikins which have drawbacks when it comes to the realism of the simulation, cost and durability. In the meantime, Virtual Reality (VR) has entered a new phase of wider availability and affordability in the consumer market. Therefore, a low-cost VR-based solution can now be proposed to address some of these issues with the mechanical manikin. Hence, this study presents an approach where the mechanical manikin has been augmented with VR. To test the viability and acceptance of this solution, a user based evaluation was carried out with a group of experts and novices in CPR. Despite their contrasting experience and knowledge of CPR, both groups have expressed favourable views towards the basic solution presented in this paper.
Distributed Value-Function Learning with Linear Convergence Rates

In this paper we develop a fully decentralized algorithm for policy evaluation with off-policy learning and linear function approximation. The proposed algorithm is of the variance reduced kind and achieves linear convergence with $O$(1) memory requirements. We consider the case where a collection of agents have distinct and fixed size datasets gathered following different behavior policies (none of which is required to explore the full state space) and they all collaborate to evaluate a common target policy. The network approach allows all agents to converge to the optimal solution even in situations where neither agent can converge on its own without cooperation. We provide simulations to illustrate the effectiveness of the method in a Linear Quadratic Regulator (LQR) problem.
FACPM: Crop Hand Area with Attention

Hand tracking is an important part of Human-Computer Interaction (HCI). Computer vision based hand tracking can free the users and help HCI more natural. Hand area should be cropped from the image before hand tracking. Inspired by the success of deep learning, we use CNN to crop the hand area. We proposed a new method named FACPM to crop hand area with attention mechanism and Feature pyramid networks. We adopt VGG as base network, to improve the speed of the framework, unimportant network is cut off. FPN model is used for different scale features and can perform better on tiny feature. Attention model is used for distributing the weights of different features in FPN. A better cropped image can lead to a more precise 3D hand pose estimation and can be used in hand tracking.
Design of Triband Antenna for WLAN and WiMAX Application

A triband antenna is designed, which is fed by coplanar waveguide (CPW), and operated at 2.45 GHz, 3.5 GHz and 5.2 GHz. To operate the three bands above, the antenna is grooved with three symmetrical L-shaped slots and one slot of rectangular ring. The CPW structure is used to reduce thickness. The overall dimension, 25 mm × 26 mm × 1.5 mm, is obtained. The designed antenna has been fabricated, and the experimental results show that it can work well in the frequency bands of WLAN and WiMAX.
Eco-VTF: Fuel-Efficient Vessel Train Formations for All-Electric Autonomous Ships

In this paper, a distributed control approach is proposed to enable fuel-efficient Vessel Train Formations (VTF) in inland waterways and port areas for addressing the efficiency and environmental issues of transport over water. For path tracking, collision avoidance, and consensus over the VTF speed a distributed Model Predictive Control (MPC) algorithm is adopted which uses the Alternating Direction Method of Multipliers (ADMM) to guarantee path following and consensus between vessels. The all-electric Direct Current (DC) configuration is considered for the Power and Propulsion Systems (PPS) of the autonomous vessels under study. Considering their PPS specification, the vessels negotiate with each other to agree on the most efficient speed for all the vessels in the VTF. Simulation results suggest that a significant amount of fuel saving can be obtained by using the proposed approach.
CNN Compression-Recovery Framework via Rank Allocation Decomposition With Knowledge Transfer

Low-rank decomposition is an effective way to decrease the model size of convolutional neural networks (CNNs). Nevertheless, selecting the layer-specific rank is a difficult task, because the layers are not equally redundant. The previous methods are mainly by manual, require expertise, or do not consider the different sensitivity of each layer. This paper proposes a rank allocation decomposition (RAD) method to decompose network by allocating rank for each layer automatically. The idea is to transform the combinatorial optimization problem of rank into a constrained optimal search problem, which can be solved by a greedy algorithm. To recover accuracy of the decomposed network, a novel knowledge transfer based approach is introduced, named SchoolNet. It aligns outputs and intermediate responses from the original (teacher) network to its compressed (student) network while transferring dark knowledge from a strong (headmaster) network with high accuracy to the student network. The experimental results from several advanced models, including AlexNet, VGG-16, and ResNet-50, demonstrate that our scheme can reduce parameters significantly while maintaining a high accuracy level. Specifically, for the VGG-16 on Birds-200 dataset, we achieve $48\times $ compression rate with even 0.13% top-1 accuracy improvement, which outperforms the state-of-the-art remarkably.
Generalized Coverage Control for Time-Varying Density Functions

The coverage control problem for robotic networks focuses on distributively coordinating the positioning of multiple dynamic agents to provide sensor coverage across a bounded region in two dimensional space. The associated optimal coverage problem seeks to position these agents so as to minimize an associated coverage cost. This coverage cost is typically defined with respect to a density function that is used to bias the network towards desired configurations. Previous approaches to this optimal coverage problem have addressed both static and dynamic environments through the choice of density function; however, stability guarantees for time-varying densities are restricted by significant technical assumptions that simplify the underlying proofs at the expense of limited applicability. In this paper, a generalized algorithm is presented that guarantees practical stability under relaxed technical assumptions. The algorithm, and its convergence, is illustrated via simulation examples.
Three-dimensional (3D) Dynamic Obstacle Perception in a Detect-and-Avoid Framework for Unmanned Aerial Vehicles

In this paper, a 3D dynamic obstacle perception is developed in a detect-and-avoid (DAA) framework for unmanned aerial vehicles (UAVs) or drones. The framework requires only an end point coordinate for collision-free path-planning and execution in an environment with dynamic obstacles. The sense portion of the DAA framework takes data from an mmWave sensor and a depth camera while the detect portion of the framework updates a probabilistic octree when static and dynamic obstacles are sensed. Perception of dynamic obstacle was achieved by implementing an algorithm that clears the sensor’s field of vision before computing the occupied voxels and populating the probabilistic octree. The avoidance portion of the framework is based on rapidly-exploring random tree (RRT) but the framework is flexible to allow other types of planners. This work develops the DAA framework for a UAV in a dynamic 3D environment by modifying the MoveIt framework. The framework is implemented on a UAV platform equipped with an on-board computational unit. The simulation and indoor experiments were conducted, which show that the modified DAA framework with dynamic 3D obstacle perception can successfully sense, detect and avoid obstacle. Additionally, the proposed perception method reduced the path re-plan time.
Model Predictive Torque Control of a Wind Turbine System Test Bench in Hardware-in-the-Loop Operation

This paper presents a Model Predictive Control of the flange torque of a Wind Turbine System Test Bench drive train as the core of a Hardware-in-the-Loop simulation. A System Test Bench enables the efficient and reproducible investigation of the complex behavior of an entire Wind Turbine drive train only without the rotor and tower, as these complicate the test significantly. Instead, a motor applies the torque, yet the altered drive train setup changes its dynamic considerably. This results in unrealistic loads and thus makes the operation with the original Wind Turbine control impossible. The proposed control restores the original behavior by controlling the flange torque between the Wind Turbine and Test Bench drive train according to a reference from a Hardware-in-the-Loop simulation of the rotor. It considers the motor constraints and is robust both against deviation of its internal model and time delay. In simulation, the Model Predictive Control emulates the realistic Wind Turbine drive train dynamic perfectly in part load, though with less performance, yet still robustly, in full load due to the motor constraints. It is already implemented on a dSPACE system integrated in the System Test Bench with a research Wind Turbine. The Hardware-in-the-Loop concept itself is validated experimentally with a simpler control with less performance.
A Shared-bridge Converter with Reversed Current Direction for Active Magnetic Bearing Drive

Active magnetic bearing(AMB) is widely used in high speed rotating machinary, and the power electronic converter is a key technology of AMB [1]. A shared-bridge converter has been put forward for multi-axis AMB drive, which can make fully use of devices and reduce the cost [8]. However, the high current rating of shared-phase-leg brings challenge to the devices and the power losses of converter cannot be reduced by this topology. In this paper, a novel topology for AMB drive with reversed current direction is put forward. Comparing with the shared-bridge converter, the novel topology can significantly reduce the current stress of devices on the shared-phase leg by using one more power electronics switch and diode. Meanwhile, the power losses of the converter can be greatly reduced. The principle and the implementation method are introduced and compensation is applied to eliminate the effect of dead-time. Simulation results have successfully validated the feasibility of methods of modulation and dead-time compensation. Suspension and rotating tests have carried out on a 4-axis AMB test rig with the converter of novel topology. The experimental results have verified that the novel converter meets the requirements of AMB drive.
Cooperative Circumnavigation of a Host Spacecraft by Multiple Microsatellites

This paper addresses the trajectory analysis, mission design, and control law for multiple microsatellites to cooperatively circumnavigate a host spacecraft. The cooperative circumnavigation (CCN) problem is first defined to drive a group of microsatellites to a predefined planar ellipse concerning a host spacecraft while maintaining a geometric formation configuration. Nest, a CCN control law is proposed, where the artificial potential functions and the affine Laplacian matrix are applied to fulfill the requirements of a CCN mission. Finally, simulation results are given to show the feasibility of this approach.
Exploiting Digital Identity for Mobility in Fog Computing

Fog computing is the answer to the emerging need of mobility support, mostly due to the deployment of the Internet of Things. In this paper, motivated by the many security and privacy issues found in handling device’s handover in Fog computing, we propose a new approach based on the possibilities offered by the recent EU regulation eIDAS, for improving the security in the identification of users, when they require data mobility.
Three-Phase DC-Type Isolated Electric Vehicle Charger Featuring Zero Voltage Switching

This work presents a flexible isolated soft-switched AC-DC converter well-suited for implementing a Fast DC-type electric vehicle battery charger. The proposed topology is based on a modified direct-conversion galvanic isolated AC-DC forward-type converter featuring zero-voltage-switching or ZVS. The proposed circuit modifications allows the system to perform well in terms of losses when the converter is designed to cope with a wide range of electric vehicle battery voltages, e.g. 200 to 1000 V. In fact, the new functionality added limits the typical large free-wheeling times which would occurs in the state-of-art solutions for low battery voltage levels. This yield to great reduction on conduction losses in the semiconductors. The paper gives a detailed description of the proposed circuit, and suited modulation and feedback control schemes. Simulation results are shown attesting the feasibility and also verifies the advantages of the presented power converter for a 50 kW application.
Detecting and Predicting Anomalies for Edge Cluster Environments using Hidden Markov Models

Edge cloud environments are often build as virtualized coordinated clusters of possibly heterogeneous devices. Their problem is that infrastructure metrics are only partially available and observable performance needs to be linked to underlying infrastructure problems in case of observed anomalies in order to remedy problems effectively. This paper presents an anomaly detection and prediction model based on Hidden Markov Model (HMM) that addresses the problem of mapping observations to underlying infrastructure problems. The model aims at detecting anomalies but also predicting them at runtime in order to optimize system availability and performance. The model detects changes in response time based on their resource utilization. We target a cluster architecture for edge computing where applications are deployed in the form of lightweight containers. To evaluate the proposed model, experiments were conducted considering CPU utilization, response time, and throughput as metrics. The results show that our HMM detection and prediction performs well and achieves accurate fault prediction.
Integral Sliding Mode Control for Fractional Order Descriptor Systems

Here, we propose the construction of a sliding mode controller (SMC) for the fractional order descriptor (FOD) systems. This controller design mainly aims to stabilize the FOD system even in presence of matched disturbances by rejecting the disturbance. The controller is designed using an integral sliding mode (ISM) technique. Here, the control law is framed in such a way that it is at least continuous, by which it helps in eliminating the harmful chattering effects of the actuator and also meets the admissibility requirement related to control for the FOD systems.
MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015

In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. In this paper we present a dataset of 3275 Tweets from the months September and October 2015. These Tweets are annotated regarding their relevance of containing quantitative movement information of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for creating an automated extraction / prediction system.
Which metrics to use for RF indirect test strategy?

This paper aims at opening a discussion on the quality assessment of indirect test strategies in the context of analog and RF integrated circuit testing. Many parameters may influence the prediction efficiency of the indirect test model (choice and number of indirect parameters taken into account, learning algorithm used to build the model…). In order to evaluate the quality of a given model, several metrics can be evaluated, that reflect either the average prediction error, a global reliability or a misclassification rate. But what are the most pertinent metrics to reflect the level of confidence that can be expected from the indirect test to efficiently replace a traditional test based on RF measurements? Which metrics can lead to an informed choice of an indirect test strategy for its stability and predictive power? These considerations are investigated in this paper and illustrated in a practical case study.
Design and Implementation of a Constant Current and Constant Voltage Wireless Charger Operating at 6.78 MHz

Recently wireless Power Transfer (WPT) has received a great interest due to its advantages such as better user convenience and better safety as compared to the conventional charger with cord. In a WPT charging system operating at MHz range, a DC/DC converter to provide the accurate voltage and current to the battery for its safety. This paper presents about the design and implementation of a high frequency class D Wireless Power Transfer system which makes it possible to charge the Lithium Batteries by the Constant Current (CC) and Constant Voltage (CV) Charge with a pair of coil. Since the charger can provide automatic transfer from CC to CV charge, the battery can be fully charged without the help of an additional DC/DC converter. The simulation and 50W-6.78-MHz hardware experimental results are presented to verify the feasibility of the proposed method and to evaluate the performance of the proposed wireless battery charger.
Sherlock Holmes of Cache Side-Channel Attacks in Intel's x86 Architecture

Intel's x86 architecture has been exposed to high resolution and stealthy cache side channel attacks (CSCAs) over past few years. In this paper, we present a novel technique to detect CSCAs on Intel's x86 architecture. The proposed technique comprises of multiple machine learning models that use real-time behavioral data of concurrent processes collected through Hardware Performance Counters (HPCs). In this work, we demonstrate that machine learning models, when coupled with intelligent performance monitoring of concurrent processes at hardware-level, can be used in security for early-stage detection of high precision and stealthier CSCAs. We provide extensive experiments with four variants of the state-of-the-art CSCAs. We demonstrate that our proposed technique is resilient to noise generated by the system under various loads. To do so, we provide results under realistic system load conditions with an evaluation metric comprising of detection accuracy, speed, system-wide performance overhead and confusion matrix for machine learning models. In experiments, our technique achieves detection accuracy of up to 99.51% for Flush-Reload attack on RSA, incurring a performance overhead of 1.63% and 99.99% accuracy on AES while incurring a maximum performance overhead of 8.28%. Our experimental results show consistency for Flush-Flush attack on different implementations of AES as well.
A Thyristor-based Seamless Winding Changeover Circuit for high efficiency of Electric Vehicle Drive System

This paper propose a novel winding changeover circuit that can maximize the average efficiency in the wide speed range to extend the driving distance of the Electric Vehicle and improve the fuel efficiency. Proposed circuit is Series-Parallel changeover circuit based thyristor. Due to the characteristics of the thyristor, it is possible to achieve a seamless transient state without interruption. So no additional snubber circuit is required and high efficiency and light weight of AC motor system can be expected because of low switch loss. The proposed winding changeover circuit is validated through simulation and experimental results with 80kW AC motor.
A LTCC Ridge SIW Bandpass Filter for Q-band applications

In this paper, a Q-band four-order bandpass filter based on ridge substrate integrated waveguide (RSIW) is presented by using low temperature co-fired ceramic (LTCC) technology. A CPW-SL-RSIW transition is designed for this filter. Prototype of both transition and filter have been fabricated. Good agreements between simulated and measured results are obtained.
Energy Management System Applied in DC Electric Springs

Recently, a novel topology of DC electric spring (DCES) was proposed for household application to solve the voltage stability and power quality issues caused by renewable energy source (RES) in DC micorgrids. The novel DCES consists of a DC/DC three port converter (TPC) and energy storage system. The input port of TPC is connected to RES, and a non-critical load (NCL) and a critical load (CL) locate at other two separate ports. The energy storage system paralleled with DC bus where the CL locates, and composed of bi-directional buck-boost converter (BBC) and battery system, is mainly used to ensure the voltage stability and power quality of the CL. This paper proposes an energy management system (EMS) for the novel DCES. The EMS controls the operating mode transition of the BBC, and changes the operating status of the battery system, and realizes multiple charging and/or discharging mechanism. Therefore, the EMS can ensure the voltage stability of CL, maintain the state of charge (SOC) of battery within a reasonable range, extend the battery life and ensure the safe and reliable operation of energy storage system.
Single Wearable Accelerometer-Based Human Activity Recognition via Kernel Discriminant Analysis and QPSO-KELM Classifier

In recent years, sensor-based human activity recognition (HAR) has gained tremendous attention around the world with a range of applications. Instead of using body sensor network-based recognition systems which are intrusive and increase equipment cost, we focus on the development of efficient HAR approach based on a single triaxial accelerometer. In order to improve the recognition accuracy of the system, a novel recognition approach based on kernel discriminant analysis (KDA) and quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) is proposed. KDA is utilized to extract more meaningful features and enhance the discrimination between different activities. To verify the effectiveness of KDA, three kinds of features including original features, linear discriminant analysis (LDA) features and KDA features are extracted and compared for activity recognition. In addition, QPSO-KELM is compared with two existing classification methods: support vector machine (SVM) and extreme learning machine (ELM), which are commonly utilized in activity recognition. Meanwhile, two comparative optimization methods for KELM are also discussed in the experiment. The experimental results demonstrate the superiority of the proposed approach.
Joint route guidance and demand management for multi-region traffic networks

Traffic congestion occurs as demand surpasses the available capacity of a road network, resulting to lower speeds and longer journey times; with route guidance constituting the primary control strategy to alleviate the problem. However, the effectiveness of route guidance is limited in high-demand conditions. In this work, we proposed a Model Predictive Control (MPC) framework that combines multi-regional route guidance with a novel demand management method. Route guidance is used to minimize the network's density imbalance while demand management is utilized to reduce the conditions that cause congestion. This can be achieved by manipulating vehicle routes (i.e., using route guidance) and/or by instructing a portion of the vehicles to wait at their origin before commencing their journey (demand management). Simulations are conducted to evaluate the performance of the proposed MPC optimization indicating the substantial improvements that can be achieved in traffic flow performance.