Miracle-dz/UniFAR-FTUs huggingface dataset:
NAO Robot as Demonstrator of Rehabilitation Exercises after Fractures of Hands

In this paper we demonstrate our original work on robot's assistance to physiotherapists during rehabilitation exercises with patients who just healed hand fractures. We have analyzed 18 exercises and using the Choregraphe programming tool for the NAO humanoid robot, a choreography has been made for them. While each exercise is in progress, NAO first explains to patients how an exercise should be performed, repeats those instructions and counts how many times an exercise was repeated raising in that way their motivation to keep on exercising. The implementation on NAO and testing in the laboratory conditions showed the feasibility and usefulness of the robot as a physiotherapy assistant. The paper concludes with the discussion about the challenges encountered and their possible solutions.
Optimal scheduling of critical peak pricing considering photovoltaic generation and electric vehicle load

This paper proposes Load Serving Entities (LSEs) model that purchases energy in the wholesale market by optimally operating Critical Peak Pricing (CPP) considering Electric Vehicle (EV) load and photovoltaic (PV) generation. In the proposed scheme, LSEs determine critical event schedule and hourly bidding schedule of band capacity in power exchange for frequency control (PXFC) market. In addition, imbalance penalties due to various uncertain parameters are involved in the stochastic optimization problem with Monte Carlo scenarios. This problem is formulated as mixed integer nonlinear program (MINLP). In the simulation section, the optimal critical event schedule described in this paper is numerically verified. Also we show the profit of LSEs is affected by level of uncertain parameters. This result provides intuition that several factors should be considered in the critical peak event operating strategy of LSEs not only market conditions but also characteristics of demands and uncertainty parameters.
Research on Energy Management Strategy of Supercapacitor Energy Storage System in Urban Rail Transit Based on Fuzzy Logic Control

In order to reasonably control the charging/ discharging of the energy storage system and maximize the recovery of regenerative braking energy, this paper proposes a dynamic charging/discharging threshold strategy based on fuzzy logic control. The effects of train operation state, SOC and current of supercapacitors are considered comprehensively. And the effectiveness of the control strategy is validated by taking the Beijing Metro Batong Line as an example. The results show that the output energy of the rectifier unit decreases by 14% and the cumulative output energy of the supercapacitors increases by 23.7% after adding the fuzzy logic controller.
Energy Efficient Binary Adders for Error Resilient Applications

Next Generation portable systems need to be geared up to address the challenges of energy efficient processing. Approximate Computing is one of the promising methodologies that relies on captivating property of inherent error resilience of various multimedia applications. This paper proposes quality - energy optimal approximate adders based on systematic decomposition of full adder and higher dimensional adders are designed using the energy efficient and low error full adders. Novel approximate full adder with 87.5% accuracy is designed. The 8-bit, 16-bit binary adders are analyzed by incorporating the designed full adder. The proposed 8-bit approximate adders have the accuracy of 75.2%, 56.6% for 3 bits, 4 bits approximation respectively. 16-bit approximate adder with 8-bit approximation have an accuracy of 43% for an energy savings of nearly 50%. The designed adders when employed in Image Blending, Average PSNR, PSNR-HVS, PSNR-HVSM are found to be around 78dB, 37dB, 41dB respectively. The Application of Image brightness enhancement is analyzed with different constants (50,100,128) and different image sizes. The Image denoising is implemented and the Average MSE is found to be 0.06 and 0.2 for Gaussian, Salt & Pepper Noised image of size 1024 × 1024. The proposed energy efficient adders can sufficiently be used in multimedia applications without much loss of PSNR in real time.
State-of-the-Art Review of Autonomous Intelligent Vehicles (AIV) Technologies for the Automotive and Manufacturing Industry

Research in Autonomous Intelligent Vehicles (AIV) has been done for the past 25 years and is continuously creating advancements and capabilities. AIVs will out-strip Automated Guided Vehicles (AGV) as leaders of material handling equipment. It is the ability of AIVs to operate remotely and safely with repeatability at the request and demand of the Manufacturing Execution System (MES). Integration of AIV technologies will increase productivity through consistent and seamless transportation of product in current manufacturing environments. AIV technology removes the mundane laborious manual operations from the human operator, offering workers the ability to work in a more meaningful role within the production line. Increased manufacturing output will ultimately benefit the economy and job security. This review paper will examine AIV technologies for advanced flexible manufacturing systems to improve manufacturing processes. The removal of conveyors and the inclusion of AIVs will promote flexibility within the factory floor and increase the realisation of Industry 4.0
Multiscale Coupling of Spacecraft Charging Model With Electric Propulsion Plume Simulation

When designing spacecraft with electric propulsion (EP) devices, it is important to assess spacecraft integration to ensure that the important components are not subject to significant sputtering by high-energy ions. In addition to the EP device and its plume, surface charging of spacecraft has to be modeled properly, as surface potential can directly affect the sputtering rate. Three surface charging models are incorporated into the spacecraft module of the numerical simulation framework–Thermophysics Universal Research Framework (TURF), and these include: 1) dielectric; 2) conductive; and 3) charge propagation models. The charge propagation model has been upgraded to solve the surface charge distribution implicitly, allowing a wide range of electrical conductivity values without causing the simulation to become unstable. Each of the charging models is verified against a simple problem where an analytical solution can be determined. Then, the coupling of the surface charging model and a hybrid particle/fluid model is tested in a more complex problem, where the floating potential on a sphere immersed in plasma is to be obtained. Finally, the surface charging model in an EP plume simulation is demonstrated. These problems are multiscale in that the charging model has to resolve an electron timescale (i.e., plasma oscillation) while the particle time step has to be orders of magnitude larger than the electron timescale in order to maintain a long enough sampling window for the ion current to effectively reduce the statistical noise. Therefore, two separate time steps are introduced for a stable convergence of the coupled models.
Guaranteeing safety of animals under risk of fire: conceptual framework and technical issues analysis

The paper introduces the conceptual framework of a Distributed Sensor Network for the monitoring of the wooded areas under risk of fire in the so-called “Vesuvius’ red zone”. Main goal is the determination of the OPtimal Evacuation Route for Animals (OPERA) in case of fire, for all the animal species living in the Mount Vesuvius’ surrounding areas, under the perspective of the Disaster Risk Management principles.
A Modified Droop Control Method for Parallel-Connected Three-Phase Inverter in Railway Auxiliary Power Supply

In this paper, a modified droop control method is proposed to minimize variation of common bus voltage. The proposed control method models line impedance between load and auxiliary power supply to compensate for voltage drop of power line. The expected voltage drop of the modelled line impedance is mathematically analyzed and applied to the traditional droop curve to design the modified drop equation. The controller has a cascaded structure with inner current and voltage control loops and outer power control loop. A droop curve for power control is applied a modified droop equation to reduce the load voltage drop due to the line impedance. The validity of a modified droop control is verified using two 85kVA auxiliary power supply units connected in parallel.
The Obstacle Detection Method of UAV Based on 2D Lidar

With the widespread use of UAVs in daily life, there are many sensors and algorithms used to ensure flight safety. Among these sensors, lidar has been gradually applied to UAVs due to its stability and portability. However, in the actual application, lidar changes its position with the movement of the UAV, resulting in an offset in the detected point cloud. What’s more, when the lidar works, it scatters laser light from the center to the surroundings, which causes the detected point cloud to be externally sparse and dense inside. This point cloud with uneven density is difficult to cluster using common clustering algorithms. In this paper, a velocity estimation method based on the polynomial fit is used to estimate the position of the lidar as it scans each point and then corrects the twisted point cloud. Besides, the clustering algorithm based on relative distance and density (CBRDD) is used to cluster the point cloud with uneven density. To prove the effectiveness of the obstacle detection method, the simulation experiment and actual experiment were carried out. The results show that the method has a good effect on obstacle detection.
Calculation of the Remnant Magnetization and Magnetic Saturation Characteristics for Sintered NdFeB Permanent Magnets Utilizing Finite Element Transient Simulations

This article presents the numerical calculation of the remnant magnetization and magnetic saturation characteristics for sintered neodymium-iron-boron (NdFeB) permanent magnets (PMs) utilizing 3-D finite element (FE) transient simulations. The nonlinear anisotropic properties of the sintered NdFeB PMs are employed to model a sintered NdFeB cylinder magnet. In addition, a magnetizing solenoid is coupled with an external equivalent current pulse circuit to analyze the magnetization process of the sintered NdFeB magnet. The remnant magnetization flux of the sintered NdFeB PM is compared and validated with numerical results and special laboratory magnet measurements published in the literature. On the other hand, a magnet saturation study is carried out; applying different saturation flux densities to the PM and the remnant magnetization flux is computed for each case. The saturation curve is computed, and the minimum saturation flux density is obtained to fully saturate the sintered NdFeB PMs. Finally, some virgin and demagnetized sintered NdFeB PMs are magnetized and saturated in the laboratory, and the minimum saturation flux density and the saturation curve are measured and compared with the FE simulation results. The results presented in this article show that the sintered NdFeB PMs are fully magnetized if a minimum saturation flux density of 3 T is applied during the magnetization process. The FE methodology presented in this article can be utilized by magnetizing fixture manufacturers and PM manufacturers to design efficient magnetizing fixtures and to predict the correct magnetization and saturation of different commercial grades of the sintered NdFeB PMs or other PM materials utilized in the magnetic industry and in special applications as PM motors.
Evaluation of Agreement Between Static Posturography Methods Employing Tensometers and Inertial Sensors

This study investigates the agreement between two methods used for the assessment of human balance system. Static posturography based on tensometers, using a commercially available platform, is used as the reference method. An alternative approach is a portable prototype MediPost system that utilises inertial sensors developed by the authors. Both approaches determine the movement of the subject’s centre of mass, quantifying this movement in terms of angular speed. Data for the evaluation of agreement were obtained from 205 subjects, with each subject simultaneously tested with both methods. During the tests, the subject performed a set of standard procedures involving quiet standing in an upright position. In order to verify the agreement between the evaluated methods, the Bland-Altman, concordance correlation and intraclass correlation coefficients were used. In addition, the trajectories of the centre of gravity were compared. The obtained results show good agreement between the verified methods, even though they are based on different physical phenomena.
Rank-one Tensor Approximation with Beta-divergence Cost Functions

$\beta $-divergence cost functions generalize three popular cost functions for low-rank tensor approximation by interpolating between them: the least-squares (LS) distance, the Kullback-Leibler (KL) divergence and the Itakura-Saito (IS) divergence. For certain types of data and specific noise distributions, beta-divergence cost functions can lead to more meaningful low-rank approximations than those obtained with the LS cost function. Unfortunately, much of the low-rank structure that is heavily exploited in existing second-order LS methods, is no longer exploitable when moving to general $\beta $-divergences. In this paper, we show that, unlike in the general rank-$R$ case, rank-1 structure can still be exploited. We therefore propose an efficient method that uses second-order information to compute nonnegative rank-l approximations of tensors for general $\beta -$ divergence cost functions.
Multi-layer Reactive Power Control of Solar Photovoltaic Systems in MV Distribution Network

The growing penetration of photovoltaic (PV) systems in distribution networks causes various power quality issues e.g. voltage rise, network losses. To deal with the overvoltage, inverter-based reactive power contribution is one of the most commonly proposed approaches. However, using standard strategies with fixed droop parameters limits the control effectiveness. Moreover, much reactive power absorbed by PVs around noon might increase the network loss, overload the substation consequently, especially in the network with long distribution feeders. In this paper, a combined centralized and local control method is proposed with the suggestion of multilayer structure. Droop parameters of local control are updated every 15 min by centralized control to minimize reactive power flow through substation while keeping voltages in permitted levels. By avoiding overloading substation, more PV generation can be added to the feeder.
Time Series Features Extraction Versus LSTM for Manufacturing Processes Performance Prediction

In this article is addressed the complexity of predicting the performance of manufacturing processes in cyber-physical systems in cases when the products go through hundreds of operations and when the data that is recorded when the manufacturing processes are performed is not enough in order to make accurate predictions and thus to determine those operations that might lead to low performance results. This research challenge is approached by comparing two methods namely, one method based on highly scalable hypothesis tests and machine learning predictors and one method based on a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). In addition to the critical comparison of the two approaches in terms of performance, this research work addresses challenges such as the determination of the best threshold for distinguishing between performant and unperformant processes, the identification of the most frequent patterns in unperformant processes and the consideration of several techniques for replacing the missing data given the complexity of manufacturing processes.
Faulty Data Detection in mMTC Based E-health Data Collection Networks

In the fifth generation mobile communication (5G) and beyond, massive machine type communication (mMTC) is regarded as a key technology to support the universal Internet of Things (IoT) devices. As a promising vertical application area of IoT, e-health received many attentions from academic, medical and industry. To provide reliable suggestions for doctor, guaranteeing the authenticity of the sensory data during the data collection procedure is the first and fundamental step. Focusing on the mMTC based e-health data collection scenario, we propose the threshold-based decision faulty sensory data detection (FSDD) scheme by analyzing the correlation among several physiological parameters to guarantee the authenticity of the collected data. In the proposed FSDD scheme, machine learning algorithm is exploited to predict the ground truth value of a certain physiological parameter. By searching the dynamic optimized threshold to optimize the performance of the system, the FSDD scheme provides great performance improvement in high detection accuracy and low false alarm. Furthermore, the proposed FSDD scheme can recognize the deteriorated health condition. The effectiveness of proposed FSDD scheme is verified by simulating on a real medical database.
On the Performance and Fairness of LTE-U and WiFi Networks Sharing Multiple Unlicensed Channels

The Long Term Evolution-Unlicensed (LTE-U) scheme has been proposed to exploit the unlicensed spectrum, especially the 5 GHz band, for cellular networks to further increase capacity. Since the 5 GHz band has already been used by WiFi networks, the carrier sense adaptive transmission (CSAT) scheme has been proposed LTE-U access points (LAPs) to harmoniously coexist with WiFi access points (WAPs), where duty cycles are used by LAPs to leave certain time slots that only allow WAPs to access the unlicensed band. However, the performance of the CSAT scheme has not been sufficiently analyzed for multiple unlicensed channels (UCs) in a large-scale network. In this work, we derive the explicit expressions of downlink successful transmission probabilities (STPs) of LAP users and WAP users for a large-scale multi-UC network using stochastic geometry tools. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum throughput of LTE-U and WiFi users, are analysed versus the duty cycle and the LAP density. Furthermore, the optimal duty cycle is obtained based on the derived STPs in the duty-cycle and non-duty-cycle durations. Our results show that for a given number of UCs and WAP density, and with the optimal duty cycle, the optimal LAP density increases with the increasing number of UCs for maximizing the fairness, but leads to a poorer minimum throughput performance.
Subspace-Based Method for Direction Estimation of Coherent Signals with Arbitrary Linear Array

In this paper, we propose an interpolated computationally efficient subspace-based method without eigendecomposition (ISUMWE) for the direction-of-arrivals (DOA) estimation of narrowband coherent signals in arbitrary linear arrays. ISUMWE estimates DOA based on the outputs of the virtual array by using an interpolation transform technique. Therefore, it overcomes the common restriction of uniform linear array (ULA) geometry when estimating coherent signals and becomes suitable for more general array geometry than ordinary methods. Meanwhile, the coherency of incident signals is decorrelated through a linear operation of a matrix formed from the cross-correlations between some sensor data in a designed virtual array which can be computed from the linear transformation of sensor data in the real array, where the effect of additive noise is eliminated. Consequently, the DOA can be estimated without performing eigendecomposition, and the noise pre-whitening which is required in traditional interpolation procedure can be avoided. As a result, the ISUMWE extends the application of the original subspace-based method without eigendecomposition (SUMWE) into arbitrary linear array with high accuracy and low computational complexity. The numerical results demonstrate the validity of the proposed method.
A Hybrid Semantic Relatedness Algorithm by Entity Co-Occurrence and Specialized Word Embeddings

As the amount of biomedical literature increases, measuring semantic relatedness between two entities is one of the important tasks from the perspective of finding meaningful biological relationships. In this paper, we propose a hybrid semantic relatedness algorithm for biomedical knowledge discovery. We incorporate a co-occurrence approach to capture hidden relationship and adopt specialized word embeddings by considering both direct and indirect entity relationships. We analyze and evaluate our proposed method with other well-accepted methods such as co-occurrence, Word2Vec, COALS, and random indexing by comparing top entities related with Alzheimer’s disease. In addition, we conduct a series of analyses including gene, pathway, and gene-phenotype relationship analysis.
Generic substitution 1

We examine the importance of prices, doctor and patient characteristics, and market institutions for the likelihood of choosing generic drugs instead of the more expensive original brand-name version. Using an extensive dataset extracted from The Norwegian Prescription Database (NorPD) containing all prescriptions written in March 2004 and 2006 on 23 different drugs (chemical substances) in Norway, we find strong evidence for the importance of both doctor and patient characteristics for the choice probabilities. The price difference between brand and generic versions and insurance coverage both affect generic substitution. Moreover, controlling for the retail chain affiliation of the dispensing pharmacy, we find that pharmacies play an important role for patients’ willingness to substitute. In markets with more recent entry of generic drugs, the brand-name loyalty proves to be much stronger, giving less explanatory power to our demand model.
Strain Engineering in AlGaN/GaN HEMTs for Performance Enhancement

The heterostructure device designs are extending from Silicon to compound semiconductors e.g. III-V. Unlike use of the strain technology in Si devices, stressing methods have not yet been intentionally used in III-V semiconductor devices. In this work, we examine the potential of using strain engineering technology during device fabrication to alter GaN HEMT performance. We examine the process-induced stress effect on the electrical performance of AlGaN/GaN HEMTs via TCAD simulation.
Exploring optimal back bias voltages for ultra low voltage CMOS digital Circuits in 22 nm FDSOI Technology

This study presents a strategy to determine optimal body bias voltages for ultra low voltage digital circuits in the 22 nm Fully Depleted Silicon On Insulator Technology (FDSOI). The efficiency of body biasing for achieving high functional yield has been investigated by using reverse back bias voltages for HVT devices. The strategy has been evaluated through the design of an ultra low voltage Xor based adder at supply voltages varying from 140–160 mV and temperature range 27–50 °C at 1 kHz frequency. The adder under optimal body bias consumes 4.67 percent less energy than zero body bias at Vdd=150mV and frequency of 1 kHz. The adder is fully functional for one thousand Monte Carlo simulations at optimal back bias voltage. The yield has improved by 0.4 percent in optimal back bias voltage compared to zero body bias. The results show the lowest Energy per cycle, variability and high functional yield for the obtained optimal body bias voltage. Also, additional analysis confirms the dependency of optimal body bias voltage on the switching activity and operating conditions for a given technology. We also show that the relative energy variability is larger than the delay variability over the back bias voltage range.