Miracle-dz/UniFAR-FTUs huggingface dataset:
Clutter Filtering Using a 3D Deep Convolutional Neural Network

Echocardiographic imaging is a well-established clinical modality to assess cardiac morphology and function. However, accuracy of visual reading as well as performance of algorithms designed to measure cardiac characteristics, and as a consequence the diagnostic value of the echocardiographic imaging, can significantly be degraded by the presence of image artifacts such as (stationary) reverberations. Although several filtering techniques have been proposed in the literature to tackle this problem, the current study sought to investigate whether deep learning can offer better artifact removal. Given the spatiotemporal nature of the reverberation artifacts, a 3D (i.e. 2D plus time) deep convolutional neural network (3D U-Net) was trained to filter simulated artifacts that were superimposed onto ultra-realistic synthetic 2D echocardiographic sequences. Performance of 3D U-Net was compared with the singular value decomposition (SVD) technique. The obtained results confirmed the utility of deep learning for artifact filtering and showed its advantage over SVD in handling moving artifacts.
Object-Level Segmentation of Indoor Point Clouds by the Convexity of Adjacent Object Regions

The issue of achieving an appropriate segmentation for indoor point cloud scenes remains difficult. Although available methods continue to improve the benchmark performance, more attentions need to be paid to deal with the drawbacks of inaccurate or incomplete segments in division. To push the research to the next level, this work proposes an learning-free algorithm for the segmentation of indoor point clouds which consists of two stages. The first stage extracts edges of RGBD point clouds and applies them in the voxel clustering process to avoid generating supervoxels which are situated across object boundaries. After this pre-segmentation, a two-phase merging procedure is presented in the second part. By conducting region growing on optimized supervoxels, a set of local regions is obtained. Then we propose to define the convexity-concavity of adjacent regions based on the observations of object structures and merge the convexly connected regions to achieve object-level segmentation. This algorithm is straightforward to implement and requires no training data. Experimental results show that it produces supervoxels with plausible boundaries and arrives at better object-level segmentation.
Enhanced Hydrogen Gas Detection Using SAW Sensor Through Oxygen Pre-Treatment

Hydrogen gas sensing is crucial in a variety of applications, including power generation, fuel cells, automotive, metallurgic and manufacturing industries. Some of the requirements for a hydrogen sensor are reliability, robustness, stability, fast response and recovery times. Surface acoustic wave (SAW) technology can offer these characteristics, and others such as small size and weight, wireless operation capability including multiple sensor interrogation, mass production reproducibility and low cost. In this work, an improvement in room temperature H2 gas sensor response time and stability is reported through the use of O2 treatments. The exposure to O2, after interaction with H2, reconditions the Pt-Al2O3 composite thin-film used both as the electrode and H2 sensing layer. As a result, the SAW sensor frequency response recovery is over five times larger during the initial 20 minutes when compared to the exposure to N2 inert gas. In addition, the O2 treatment allows the recovery of the sensor to a state close to the one prior to the reduction by H2 within about an hour, thus leading to a stable device response, which is very difficult to achieve under the inert N2 environment even after tens of hours. The method is thus very promising in recovering the surface of sensing devices which rely on metallic composite electrodes and oxide films exposed to reducing environment.
Twofold-Multimodal Pain Recognition with the X-ITE Pain Database

Automatic pain recognition has great potential to improve pain management. In this work, we investigate multimodality in pain recognition in two regards. First, we compare and combine multiple sensor modalities, which capture both behavioral and physiological pain responses. Second, we compare and distinguish the heat and the electrical pain stimulus modalities in both phasic (short) and tonic (long) variants. Experiments show that (1) pain intensity can be recognized automatically in all stimulus variants, (2) that pain of different qualities (heat and electical stimuli) can be distinguished, (3) that electrodermal activity (EDA) is the best performing single modality, and (4) that fusion with modalities can improve results further.
Deep Neural Networks for Depression Recognition Based on Facial Expressions Caused by Stimulus Tasks

With the growth of the global population, the proportion of individuals with depression has rapidly increased; it is currently the most prevalent mental health disorder. Although existing studies on depression have mainly examined the several databases, which comprise facial images and videos of non-Chinese subjects, there are few effective databases for a Chinese population. In this study, we first create a depression database by asking participants to perform five mood-elicitation tasks. After each task, their facial expressions are collected via a Kinect. In the depression database, the facial feature points (FFP) and facial action units (AU) are obtained. We build a range of deep belief network (DBN) models based on FFPs and AUs to extract facial features from facial expressions, named 5DBN, AU-5DBN and 5DBN-AU. We evaluate all proposed models in our built database, and the results demonstrate that (1) the recognition performance of the AU-5DBN model is higher than that of the 5DBN-AU model, and that of the single feature model is the lowest; (2) The performance of depression recognition in the positive and negative emotional stimuluses are higher than that of neutral emotional stimulus; (3) The classification rate for females is generally higher than that for males. Most importantly, the constructed database is from a real environment, i.e., several psychiatric hospitals, and has a certain scale. The experimental results show higher recognition performance in the database; thus, the proposed method is validated as effective in identifying depression.
Classification for Multiple Power Quality Disturbances Based on Deep Forest

In order to solve the problem of power quality disturbance which is increasingly prominent in smart power grid, a method of power quality disturbance classification based on Deep Forest (gcForest) was proposed. Firstly, disturbance feature are automatically extracted from the original dataset of power quality disturbance waveforms by the Multi-Grained Scanning and the feature representation of the original dataset is obtained. Secondly, the depth feature is acquired by the Cascade Forest. Finally, Cascade Forest procedure will be repeated till convergence of validation performance to achieve power quality disturbance classification. In addition, the robustness of the proposed method is verified by using different sampling frequency and adding noise to the data. The results indicate that the method proposed can accurately identify 9 types of power quality disturbances including 2 complex disturbances, and have good anti-noise capability.
Experimental Comparison of DC/DC Converters for Wide Input Voltage Ratio Applications

DC/DC converters should provide a constant output voltage even when operating in a wide input voltage range. For this purpose the duty-cycle is usually adapted to the input/output ratio, which often leads to conflicts during the design process when large changes are made. On the one hand, with high duty-cycles, e.g., the current stress of the components can be high, while the voltage stress is low, and vice versa. On the other hand, efficiency might only be high for one operating point, while it declines for others. These contrasts need sophisticated considerations during the design process. Therefore, this paper presents an experimental comparison of eight DC/DC converter topologies for wide input voltage ratio applications. Based on the efficiencies of four individual converters, two methods for increasing efficiency, especially with regard to the wide input voltage ratio, are examined and compared in detail.
Fixed Frequency Model Predictive Control of Three-level Bi-directional Flying Capacitor DC-DC converter in DC microgrid

This paper presents a fixed frequency model predictive control (FFMPC) of a three-level bi-directional flying capacitor DC-DC converter for energy management application in a DC microgrid. The presence of three voltage levels give the converter advantage of having reduced voltage stress on the power switches and low ripple in its inductor current. Additionally, the capability of having bi-directional power flow enables the converter to integrate energy storage devices such as the battery to a DC microgrid effectively. An FFMPC algorithm is formulated using the developed mathematical model in order to yield the dual objective of bi-directional power flow and flying capacitor voltage balancing. In this paper the performance of the FFMPC algorithm is compared with the conventional finite control set model predictive control (FCS-MPC) algorithm in terms of dynamic response and inductor current ripple. A significant reduction in current ripple was observed using the proposed algorithm. Furthermore, a DC microgrid comprising photo-voltaic (PV) system, load, and battery are considered to assess the effectiveness of the designed FFMPC algorithm under varying load and PV power injections.
Induction Machines Bearing Failures Detection and Diagnosis using Variable Neighborhood Search

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. The failure detection is managed by a clustering graphical representation creating transition classes. Motivated by the computational complexity of the problem, a Variable Neighborhood Search (VNS) metaheuristic is developed including well-designed local search algorithms for data clustering to the system diagnosis. Computational experiments carried out on the PRONOSTIA experimental platform data show that the proposed algorithm seems to be efficient and effective.
Graphical Modeling of Communication Architectures in Network Control Systems with Traceability to Requirements

For modeling Networked Control System (NCS) a standardized notation is still missing. Approaches like the Unified Modeling Language (UML) or the Systems Modeling Language (SysML) are widespread and accepted, yet remain very general and do not offer support for domain specific problems. Additionally, UML/SysML do not offer graphical support to model domain specific solutions e.g. for network architectures in NCS. UML and SysML-based approaches like MARTE, TURTLE, UML-QoS or SysML-Sec provide more guidance to model network architectures, embedded systems and network systems. However, they still lack a well-defined graphical notation, also referred as concrete syntax, to support the modeling process. To strengthen the modelling, an existing graphical notation is extended, which enables traceability to requirements and the modelling of safety-relevant properties. As case study, a train network architecture is provided to show the feasibility of this notation. Together with industrial experts, the communication architecture of a train-part is modeled and linked to its requirements. The graphical representation includes the necessary information to define the communication in the NCS and is understandable for engineers of the domain without UML/SysML knowledge. The approach was evaluated with experts from industry.
Historical Improvement Optimal Motion Planning with Model Predictive Trajectory Optimization for On-road Autonomous Vehicle

This paper presents an efficient, robust, comfortable, and real-time motion planning framework for on-road autonomous vehicles. This proposed framework aims to enhance the performance of motion planning in complex environments such as driving in the urban area. It uses a path velocity decomposition method to separate the motion planning problem into path planning and velocity planning. The novelty lies in the use of Historical data in the $SL$ coordinate in the framework of a tree version of Rapidly-exploring Random Graph (RRT*) technique in path planner, called HSL-RRT*, which grows the path tree efficiently by the data from previous planning cycle. The velocity planner uses a Nonlinear Model Predictive Controller (NMPC) to generate optimal velocity along the path generated from the path planner, taking account of vehicle constraints and comfort. Analytic and simulation results are presented to validate the approach, with a special focus on the robustness and efficiency of the algorithm operating in complex scenarios.
Parameter Identification of PMSM based on MRAS with Considering Nonlinearity of Inverter

An accurate real time parameter estimation of permanent magnet synchronous machines (PMSMs) is essential to achieve a high performance of a drive system. However, the accuracy of the estimation is affected by the nonlinearity of the inverter. In this paper, an approach for parameter identification based on model reference adaptive system (MRAS) with considering the nonlinearity of the inverter is proposed. Compared to conventional compensation strategies, different turn-on and turn-off delay time of the high- and low-side IGBTs is considered, which makes the compensation more accurate. With the proposed method, the voltage disturbance of the inverter can be successfully compensated and the identification accuracy can be significantly improved. In addition, the total harmonic distortion (THD) of the phase currents and voltages can be greatly reduced, which improves further the performance of the drive. Experimental results demonstrate the effectiveness of the proposed method.
Audio Visual Multimodal Classification of Bipolar Disorder Episodes

Bipolar disorder is a highly prevalent and complex medical syndrome of multifactorial origin. In this paper, we propose an audio visual multi-modal framework for classifying the different episodes (Remission, Hypomania or Mania) of bipolar disorder. To represent the temporal dynamics of face and body poses, we propose to compute the Motion History Histogram (MHH) of facial landmarks as well as Histogram of Displacement Range (HDR) of body keypoints as the visual features. For audio features, functionals of the low level descriptors (LLDs) of speech are computed as global features. Each feature stream is input into a Convolutional Neural Network (CNN) to get the initial classification result of the patient's episode, which are then concatenated into a vector and fed into a random forest for the final classification. Experimental results on the development set of Audio Visual Emotion Challenge (AVEC2018) Bipolar Disorder Sub-Challenge demonstrate that the proposed visual features and bipolar disorder classification framework achieve promising results with the unweighted average recall (UAR) reaching 0.749, which is better or comparable with the state of the art results.
Kalman Filtering of Discrete LPV Diabetes Mellitus Model for Control Purposes

In this study we investigate the application of state estimation by using linear parameter varying (LPV) based extended Kalman filtering (EKF) concerning type 1 diabetes mellitus. We consider the widely used Cambridge model which has high complexity and several unfavorable properties from control engineering perspective. The applied model is transformed and scheduling variables are selected to keep the applicability of the selected complete LPV discretization method. We investigate two LPV models which are selected based on different benefits. Two EKFs are designed and the operation of them are compared to the original nonlinear model. According to the results both EKF can be applied despite their essential differences. Moreover, there is no significant deviation between their performance. Namely, both designed discrete LPV kind EKF estimated the states of the reference system with applicable error.
Computational Modeling of Psycho-physiological Arousal and Social Initiation of children with Autism in Interventions through Full-Body Interaction

This study is part of a larger project that wants to foster social initiation behaviors in children with Autism Spectrum Disorder (ASD). We approach this through a full-body interactive Mixed Reality (MR) experience that mediates a face-to-face play session between an ASD child and a non-ASD child. The goal of this study is to obtain a data model that allows us to evaluate the goodness of the MR system compared to a typical social intervention strategy based on construction tools (in this case LEGO bricks) which acts as the control condition. In this paper we present our first analysis of the arousal generated by the MR experience compared to that generated in the control condition. We address this by analyzing psychophysiological data recorded during the social interaction behaviors in the ASD child while playing with the non-ASD child. We followed a repeated-measures design with two conditions: our full-body interaction MR environment and the typical social intervention strategy based on LEGO bricks. To measure physiology, Electrocardiogram (ECG), Electrodermal Activity (EDA) and Accelerometer (ACC) data were acquired through a wearable designed by our lab. We used machine learning techniques to analyze the huge amount of multimodal data from the ASD children obtained during 18 trials (3 female and 15 male). As a result, we were capable of classifying social initiation behaviors of ASD children during the MR environment sessions and those occurring during the LEGO construction sessions based on the psycho-physiological data sources. This is a first sign showing that our MR system has specific properties, compared to a traditional construction-based intervention, which potentially provide a new interesting context to intervention in ASD.
Using Gaussian Processes with LSTM Neural Networks to Predict Continuous-Time, Dimensional Emotion in Ambiguous Speech

In continuous emotion recognition (CER) applications, commonly used models of the emotional content of speech cannot represent some aspects of the behavior of the dimensional emotion values that are the targets of prediction, such as ambiguity, as these models include only a single value for each point in time for each emotional dimension. In this paper, we first propose a model for the emotional content of speech that uses a Gaussian process (GP) to define a distribution that incorporates the inherent ambiguity of emotional speech. Then, we propose a predictive CER system which combines this model alongside LSTM neural network techniques that have that have previously been shown to perform well on this task. When tested on a practical CER task using the RECOLA dataset, this combined LSTM-GP approach is shown to achieve similar or higher performance to a LSTM neural network on its own when measured on mean-only terms. When the variance of the distribution is also incorporated into the performance measure, the LSTM-GP system, which predicts a distribution over the continuous-time outputs rather than just a time series of predicted values, is shown to be able to more realistically model the underlying ambiguity of the emotional content of the speech recordings.
Stability and Performance Analysis of Grid-Connected Inverter Based on Online Measurements of Current Controller Loop

The amount of grid-connected three-phased inverters is increasing rapidly. In a weak grid, the non-ideal grid impedance decreases the control performance and can even compromise the system stability through load effect. The stability assessment of the inverter-grid interface has been assessed extensively through state-space and impedance-based methods. The current work presents stability analysis method based on the load-affected loop gain of the innermost control loop, which includes the effect of phase-locked loop and grid impedance. The stability analysis is carried out by assessing modeled and measured loop gains using the Nyquist criterion, step responses, and system closed-loop poles. The stability issues originating from grid impedance or too high phase-locked loop bandwidth are accurately predicted by examining the innermost control loop.
Parameter Communication Consistency Model for Large-Scale Security Monitoring Based on Mobile Computing

With the application of mobile computing in the security field, security monitoring big data has also begun to emerge, providing favorable support for smart city construction and city-scale and investment expansion. Mobile computing takes full advantage of the computing power and communication capabilities of various sensing devices and uses these devices to form a computing cluster. When using such clusters for training of distributed machine learning models, the load imbalance and network transmission delay result in low efficiency of model training. Therefore, this paper proposes a distributed machine learning parameter communication consistency model based on the parameter server idea, which is called the limited synchronous parallel model. The model is based on the fault-tolerant characteristics of the machine learning algorithm, and it dynamically limits the size of the synchronization barrier of the parameter server, reduces the synchronization communication overhead, and ensures the accuracy of the model training; thus, the model realizes finite asynchronous calculation between the worker nodes and gives full play to the overall performance of the cluster. The implementation of cluster dynamic load balancing experiments shows that the model can fully utilize the cluster performance during the training of distributed machine learning models to ensure the accuracy of the model and improve the training speed.
Joint Dynamic User Pairing, Computation Offloading and Power Control for NOMA-based MEC System

To tackle the massive access and different computation offloading requirements of IoT devices in 5G MEC system, Dynamic User Pairing NOMA-based offloading is considered in this paper. An overall energy consumption minimization framework is established by joint optimizing user pairing, of-floading decision and uplink power control under the time and maximum multiplexing number of user constraints. Due to the combinatorial nature of the formulated mixed integer non-linear programming problem, we adapt two-phase approach to solve the challenging problem. First, a low-complexity heuristic algorithm is designed to solve the user pairing problem. After we get the sub-optimal user pairing scheme, the original problem is proved to be a convex problem, we then get the optimal offloading decision and transmission power of devices. Numerical results show that the proposed DUP NOMA-based offloading scheme can significantly improve system performance in terms of energy consumption comparing to OMA and Fixed User Pairing NOMA based offloading.