Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability by Donor Type, Sex, Race, BMI and Within Practitioner in Viscoelastic Response (VisR) Ultrasound Derived Mechanical Anisotropy Assessments in Healthy Kidney Allografts, In Vivo

Degree of mechanical anisotropy in renal cortex can be assessed by evaluating the ratio of viscoelastic response (VisR) parameters achieved when the long axis of a spatially asymmetric ARF excitation PSF is aligned along versus across nephrons. In this work, intra-observer reproducibility and variability in VisR derived DoA assessment in renal cortex due to donor type, sex, race, and BMI is evaluated in healthy human kidney allografts, in vivo. Transcutaneous, in vivo VisR imaging was performed in 20 kidney allografts 2 months post-transplantation using a Siemens Acuson Antares™ imaging system and a VF7-3 linear array transducer. Ratios of VisR derived relative elasticity (RE), relative viscosity (RV), and peak displacement (PD) obtained with the ARF short-axis aligned along versus across nephrons were calculated. Patients were divided based on their sex (male: N=14 versus female: N=6), race (African American: N = 6 versus Caucasian: N=14), donor type (living: N=14 versus deceased: N =6), and BMI (BMI >30: N= 8 versus BMI< 30: N= 12). Among the 20 patients, 16 patients were also imaged one month later (i.e., 3 months’ post-transplantation) to study intra-operator variability. All patients had stable serum creatinine levels without clinical indication for biopsy. PD, RE, and RV ratios were not statistically different (Wilcoxon, p>0.05) due to sex, race, donor types, or BMI. The median absolute percent difference in (PD, RE, RV) ratios were (23, 13, 23) due to sex, (5, 3, 3) due to race, (0.35, 14, 13) due to donor type, and 8.6, 6, 12) due to BMI. Ratios of PD, RE, and RV were not statistically different between 1st and 2nd imaging events with mean percent difference in (PD, RE, and RV) ratios of (-0.04, -0.11, and - 0.01). These findings suggest that VisR outcome measures are robust to potentially confounding demographic factors and reproducible by a trained practitioner.
HIBoosting: A Recommender System Based on a Gradient Boosting Machine

Based on explicit data, collaborative filtering is one of the most valuable technologies of a recommender system. However, the further development of a recommender system has been restricted to some extent by the problems of cold start and data sparsity. To weaken the effect caused by data loss, some implicit feedback data are typically introduced into the recommendation such as social information and attribute data. In this paper, we propose a heterogeneous information boosting model for recommendations (HIBoosting). The model takes advantage of different network meta-paths to mine the potential information of user social networks, object metadata and interactive data in heterogeneous information networks. For different semantic information, we use a random-walk based on a meta-path to calculate the similarities of users or items, which are used to extract the low-dimensional embedded features of users or items by factorization, construct user profiles and item profiles. Finally, the embedded features are used for integrated learning by a gradient boosting decision tree (GBDT) to provide users with better recommendation services. The model not only integrates heterogeneous information in information networks, but also makes full use of mining latent relations of heterogeneous information networks. Experiments on the CiaoDVD and Lastfm datasets show that HIBoosting has made great progress in the accuracy of a recommender system.
Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances. In this paper, we propose a two-stage nonnegative matrix factorization algorithm. During the first stage, $k$ -means clustering is first employed to obtain the estimated endmember matrix. This matrix serves as the initial matrix for NMF during the second stage, where we design a new cost function for the purpose of refining the solutions of NMF. The two-stage NMF model is solved with multiplicative update rules, and the monotonic convergence of this algorithm is proven with an auxiliary function. Numerical tests demonstrate that our two-stage NMF algorithm can achieve accurate and stable solutions.
Single-Phase Active Split-Source Inverter with High AC Gain

Single-phase split-source inverter is an attractive single-stage topology, as it can realize buck-boost function in a very compact way. However, this topology suffers from higher voltage stresses on dc-link than two-stage structure, especially under low ac gain conditions. This paper therefore analyzes the substantial reason of this problem. Then, based on the analyzing result, a general structure is proposed to solve the problem, and one practical implementation based on reverse-blocking IBGT is presented. Besides, a very simple SPWM modulation scheme is proposed together to improve its performance. Finally, compared with the existing SSI and its extensions, the proposed circuit can achieve higher ac gain with the same dc-link voltage. Its working principle and merits are explained in detail and also verified by experimental results.
Achieve Secure and Efficient Skyline Computation for Worker Selection in Mobile Crowdsensing

Worker selection is always one of the fundamental issues in Mobile Crowdsensing (MCS) applications. However, the selection of reliable workers still pose big challenges to the MCS platform, due to either the large number of candidates or the dynamic nature of participating workers. In this paper, aiming at addressing the above challenges, we propose a privacy-preserving worker selection scheme based on (probabilistic) skyline computation technique. Our proposed scheme is characterized by selecting a subset of reliable and suitable workers for a certain task without revealing the workers' relevant personal information. Security analysis shows the proposed scheme can achieve the workers' privacy-preservation. In addition, the performance evaluation also validates its efficiency and effectiveness.
Fault-Tolerant Symmetrical Six-phase Induction Motor Drive Based on Feed-forward Voltage Compensation

Fault-tolerant multiphase drives have received plenty of attention from the research community over the past two decades due to the increased emphasis on reliability of power electronics and drives systems. Among different types of fault, open circuit or open phase faults have been well studied and different fault-tolerant control approaches have been proposed in the past. While fault-tolerant control based on closed-loop approach have been shown to give good performance, it is possible to tolerate open-phase fault using just simple feed-forward voltage compensation. In this paper, the operation of a multiphase induction machine during open phase fault is examined, and the fundamental factors affecting its performance are discussed. Based on the analytical discussion, it is demonstrated that by calculating x-y voltages and adding to the control voltage in feed-forward manner, torque oscillations due to open phase fault can be easily compensated. Compared to the closed-loop approach, the feed-forward method is superior is terms of simplicity and transient response.
Altering body perception and emotion in physically inactive people through movement sonification

Physical inactivity is an increasing problem. It has been linked to psychological and emotional barriers related to the perception of one's body, such as physical capabilities. It remains a challenge to design technologies to increase physical activity in inactive people. We propose the use of a sound interactive system where inputs from movement sensors integrated in shoes are transformed into sounds that evoke body sensations at a metaphorical level. Our user study investigates the effects of various gesture-sound mappings on the perception of one's body and its movement qualities (e.g. being flexible or agile), the related emotional state and movement patterns, when people performed two exercises, walking and thigh stretch. The results confirm the effect of the “metaphor” conditions vs. the control conditions in feelings of body weight; feeling less tired and more in control; or being more comfortable, motivated, and happier. These changes linked to changes in affective state and body movement. We discuss the results in terms of how acting upon body perception and affective states through sensory feedback may in turn enhance physical activity, and the opportunities opened by our findings for the design of wearable technologies and interventions in inactive populations.
A Centrifugal Chemiluminescence Detection Platform and Its Application for Nucleic Acid Virus (H7N9)

Chemiluminescence detection has been used to determine the concentration of nucleic acid virus. At present, chemiluminescence detection is mostly a large, immovable chemiluminescence detection instrument, and some steps to extract supernatant still need to be completed manually. To solve this problem, a centrifugal chemiluminescence detection system was established. The CMOS detector with high sensitivity and small volume was combined with centrifugal force micro-fluidic platform to realize the integration process of sample reaction and detection. The system has been successfully applied to the detection of nucleic acid virus (H7N9). The detection process of nucleic acid virus (H7N9) completed in the original laboratory was designed on a microfluidic chip. The flow direction of liquid is driven by centrifugal force platform, and the whole reaction process is controlled by logic components on the chip. Finally, the luminous intensity is detected by CMOS detector.
Practical Estimation Method of Thermal Sensation Using an Infrared Array Sensor

In heating, ventilation, and air conditioning control, it is crucial to maintain the comfort of residents. Thermal comfort is typically assessed using the predicted mean vote (PMV) index. PMV depends on six factors: air temperature, mean radiant temperature, air velocity, air humidity, metabolic rate, and clothing insulation. Although PMV can be estimated by measuring these factors directly, this process is costly because multiple sensors are required. Furthermore, measuring metabolic rate and clothing insulation is especially costly because expensive and complex sensors are required. To solve these problems, this paper proposes a practical method for estimating PMV by estimating metabolic rate and clothing insulation using a low-cost infrared array (IrA) sensor. In this study, an IrA sensor called “Grid-EYE” is adopted. PMV parameters other than air velocity and humidity can be measured when the proposed method and an IrA sensor are implemented in an air conditioner. Human detection is done using the temperature map captured by the sensor and their PMV values are estimated individually. Heat sources around people are also detected and their influence on PMV estimation is evaluated. Practical experiments demonstrate the validity of the proposed method by providing estimated PMV values close to theoretical values and real sensations. Therefore, the proposed method can contribute to providing comfortable living spaces and improving energy consumption and amenities efficiently.
Deep Domain Adaptation for Facial Expression Analysis

Deep learning has attracted a lot of attention in various fields over the past few years, including facial expression recognition. However, applying deep learning techniques to facial expression recognition is not straightforward. There are several drawbacks for successful deep expression recognition systems. Besides the lack of sufficient training data, facial expressions convey various inter-personal morphological and character differences. Therefore, an expression recognition network often suffers from overfitting and missing generalizability. However, multiple learning techniques, generally known as domain adaptation, have been proposed to address the lack of sufficient data and missing variance. Consequently, facial expression recognition may profit from domain adaptation. In this paper, we evaluate the applicability of deep domain adaptation for facial expression recognition. We describe two domain adaptation frameworks, one for single frame facial expression analysis and one for sequence-based facial expression analysis based on the Self-Ensembling method defined in [1]. The former is evaluated on the CK+ dataset [2], [3], the latter on the SenseEmotion database [4] of the University of Ulm. Our results indicate that domain adaptation is mostly applicable for person-specific facial expression recognition.
Nondestructive Incipient Crack Detection based on Wavelet and Jensen-Shannon Divergence in the NICA framework

The nondestructive crack detection is an important issue in industrial engineering. However, the detection of incipient cracks that can cause non obvious changes in the conductive material impedance map is difficult. In our paper, we propose a new method based on wavelet and Jensen-Shannon divergence in the framework of Noisy Independent Component Analysis (NICA) to address this problem. The source signals with fault features are obtained by the application of the Independent Component Analysis regarding the noise. Then, the wavelet decomposition is considered as the denoising method to partially reduce the noise influence. The Jensen-Shannon divergence(JSD) which has been proved as an efficient incipient fault detection algorithm in previous works is used here for incipient crack detection. The detection performances of the proposed method is compared with the ones obtained with the Kullback-Leibler divergence often proposed in the literature.
Optimum Design of Planar Transformer for LLC Resonant Converter using metaheuristic method

Isolated, high-density DC-DC converters play a crucial role in the power supply unit of information technology industries. By virtue of high efficiency and high power density, LLC resonant converters-among the rest of DC-DC converters-are suitable candidates for this application. The magnetic structure of this converter, however, is a bulky, dissipative part, which has a considerable portion of power loss. Improvement in the size and efficiency of the LLC transformer will lead to an overall enhancement of power density and efficiency of the converter. Nevertheless, the optimization of planar transformers for the LLC converter has not been studied enough. This paper proposes a metaheuristic-based framework for achieving an optimal transformer design in terms of enhancing power density and efficiency. This method is applied to a low profile PCB-based planar transformer designed for a240W, 380V/48V, LLC resonant converter, and the results are compared with those of the conventional design methods.
Design and Optimization of Concentric Tube Robots Based on Surgical Tasks, Anatomical Constraints and Follow-the-Leader Deployment

Many neurological disorders are characterized by the focal and anatomically definable lesions within the brain parenchyma. Traditional treatment may introduce major trauma in neurosurgery and conventional medical devices can only trace straight trajectories. To overcome these problems, a design and optimization method for a patient-specific concentric tube robot (CTR) satisfying the constraints of anatomy, surgical tasks and follow-the-leader (FTL) deployment is proposed in this paper. CTR is a tentacle like continuum robot that can work inside confined and complex biological chambers with the ability of tracking complex 3D trajectories. It consists of pre-curved superplastic tubes with hollow cavities to accommodate the surgical tools. These merits make the CTR well suitable for minimally invasive surgeries. This paper introduces a design framework that utilizes preoperative MRI data to configure patient-specific CTR for single and multiple tasks with the minimum number of tubes. A constant curvature circular arc model is built to solve the problem of inverse kinematics. Two iterative optimization methods for single and multiple tasks are proposed to optimize the parameters of the CTR. Initial waypoints of the CTR are produced based on the FTL deployment. The waypoints are then refined using a Follow Shape Rapidly-exploring Random Tree algorithm (FSRRT) for cases that the initial configurations of the CTR cannot completely satisfy the FTL deployment. Simulations and experiments are carried out on a human brain model to validate the proposed methods. The parameters of CTR including the entire length, curvature, radius angle, number, diameter, arc length and the waypoints are obtained. The errors of the FTL deployment are found to be within 2.1mm.
Investigating the Performance of selected Data Storage Concepts for AutomationML Models

Cyber-Physical Production Systems (CPPSs) engineering relies on the effective and efficient coordination and collaboration of participating engineers from various disciplines implying a proficient knowledge exchange between them. Au-tomationML (AML) provides a standardized, XML-based data format allowing to exchange engineering data, which receives more and more attention as an engineering data model. When using shared data exchange platforms, efficient data storage for AML models is success-critical in CPPS engineering. The purpose of this paper is to draft a novel, flexible evaluation framework in the context of AML model storage, modification, and retrieval and to evaluate two particular data storage paradigms, i.e., XML-(BaseX) and graph-based (Neo4J) databases. Based on a common best practice API, we developed a prototype solution enabling a flexible exchange of underlying data storage paradigms for AML models. We used an academic AML data set for the performance evaluation of two selected database engines for common storage tasks. First results showed that BaseX performs better for creating, updating, and deleting operations while Neo4J performs better for reading operations. While BaseX efficiently supports storing and retrieving AML models, we also observed querying limitations in the AML API. Nevertheless, in the context of AML data storage evaluations, the selected data sets, and the solution approach can be seen as an initial benchmark.
Femtosecond Laser Fault Injection into External SRAM Implementations

The sensitive area of the integrated circuit is affected by the femtosecond laser pulse, which causes single-event effect. The more common types of errors are single-event upsets (SEU) and multiple-bit upset (MBU). Unlike other pulsed lasers, when the femtosecond laser interacts with the semiconductor, the main physical processes are not only linear absorption, but also two-photon absorption. It is difficult to describe such phenomena with multiple mechanisms. This paper presents a statistical model to estimate the probability of a single-event effect caused by femtosecond laser fault injection. The model combines the effective space and bit filp probability of a single duty cycle of an integrated circuit, and uses an error feedback system based on FPGA to control and monitor the error-prone Static Random-Access Memory (SRAM). The result predicted by the model are more consistent with the actual experiment result. It can be used as reference values.
Review on Wind Diesel Systems Dynamic Simulation

Wind diesel power systems (WDPS) are isolated microgrids which combine diesel generators (DGs) with wind turbine generators (WTGs) to supply a community of consumers. A WDPS can work in three mode of operation: Diesel Only (DO) where only the DGs supply power, Wind Only (WO), where only the WTGs supply power and Wind Diesel (WD), where both type of generators supply power. WDPS dynamic modeling allows short-term simulations in order to test the WDPS stability and power quality. This paper does a review of recent papers on the subject of WDPS dynamic simulation taking into account factors as the WDPS operation mode simulated, the WTG type used in the WDPS or the use of Energy Storage Systems to increase system power quality and stability. Finally using the modeling capabilities of Matlab-Simulink a WDPS consisting of one DG, one WTG, load and a dump load (DL) is modeled. The WDPS is simulated against load and WTG power variations and simulation results show the use of the DL to increase the WDPS stability.
Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter

Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under arbitrary load and speed variations in a laboratory setup of a pitch drive.
Comprehensive Steady-State and Small Signal Analysis of a Full Order Off board Bi-directional Plug in Electric Vehicle (PEV) Charger for Vehicle to Grid (V2G) Operation

The off board bi-directional battery charger offers the optimal solution for the multi-objective problem of cost and weight reduction and a reliable electricity grid. This paper studies an off board bi-directional battery charger for electric vehicle application. A full order dynamic model of the system is developed capturing the dynamics and nonlinearity of the battery. The model employs the natural variables of the system (real and reactive power) which makes for a straightforward control. The steady state model is derived and a comprehensive steady state studies performed in the two region of battery operation: constant current and constant voltage. Moreover, the system dynamic model is presented in the state space form from where it is linearized for small signal analysis. A simplified small signal model predicts the dominant dynamics of the charging system.
Performance Analysis and Mode Selection in Fog Computing Based Cooperative Vehicular Networks

In order to enhance road safety, improve traffic efficiency and provide data entertainment services to drivers and passengers, vehicular networks (VNETs) have been received abroad attention, while fog computing is deemed as a promising paradigm for processing massive data and improving network performance. As two fundamental wireless communication modes in fog computing based VNETs (FVNETs), vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications can meet the diverse communication requirements to some extent. However, due to the constraint of V2V communication distance and the limitation of the single access point performance, it is very challenging to provide high quality services to meet the requirements of different traffic priority levels. To tackle this problem, in this paper we investigate the adaptive access mode selection algorithm in downlink cooperative FVNETs (FCVNETs), in which three communication modes are considered, including V2V mode, V2N mode and the vehicle-to-cooperative-network (V2CN) communication mode. By applying stochastic geometry method, the data transmission rate of different modes is analyzed, where the randomly vehicular network topologies are modeled as Cox processes. The analytical results take into account the comprehensive impact of channel attenuation, node intensity and the interference. Moreover, based on the corresponding mathematical expressions, an adaptive access mode selection algorithm is proposed to provide different priority services. Simulation validates the accuracy of the analytical results and proves that the proposed algorithm can achieve significant performance gains.