Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction and Application of Inertial Sensors-Based Kinematic Parameters in a Clinical Walk Test among Stroke Survivors

A new framework for adopting inertial sensors in a clinical test was proposed and tested in this study. A self-assembled-and-coded, inertial sensor-based wearable system was validated by comparing it with a commercialized optical tracking apparatus. Twenty-five post-stroke patients were enrolled in a clinical walk test while wearing this validated system to simultaneously assess the affected lower extremities’ functional walk competency through extracted kinematic parameters. Their average walking speeds were correlated with various gait parameters, such as the ranges of motion of the individual joints along the sagittal plane and the low back motion. The validation results proved this developed system is precise and accurate. The average walking speeds showed a modest correlation with the range of motion of the hip (r = 0.33) and a moderate and negative correlation with the motion along the coronal plane of the low back (r = -0.55). Thus, this framework supports a new method to adopt wearable devices for clinical application. It also broadens the application of the clinical walk test as an integral assessment tool for assessing functional walking competency and gait parameters, which is feasible for rehabilitation canters to monitor post-stroke patients.
Design and Implementation of Remote Monitoring System for Rare Trees Based on LoRa Communication

In recent years, rare plants on the earth have gradually disappeared, so it is urgent to inspect and protect their health. A rare tree monitoring system compatible with NB-IOT (Narrow Band Internet of Things) is designed based on the low-power data transmission technology of LoRa (Long Range Radio). The system takes stm32f103c8t6 as the core of the controller, collects information through temperature and humidity sensor, attitude sensor, infrared pyroelectric sensor and sound sensor, builds a server to store the information and data back to the server, uses the terminal to access the server, and reminds the operator when the amount of information exceeds a certain threshold, Finally, the real-time monitoring of rare trees in the field is realized.
Scale-Adaptive Selection Network with Dynamic Focal IoU Loss

Feature pyramid network is wildly applied to predict the targets at different scales in many one-stage detectors. However, most of them treat all feature levels without level-wise attention. As a result, different levels may capture the same object and predict totally different labels, which lead to lower AP on evaluation. Besides, many classic detectors aim at relieving the imbalance between hard and easy samples on classification task, while we find the fact that hard-easy imbalance also exists in localization. To general detection task, bboxes regressed at high IoU are much less than those at low IoU so that detector can easily be biased towards suboptimal bboxes and localization accuracy will be harmed. Based on these two points, in this paper, we propose a Scale-Adaptive Selection Network (SASNet) with a novel Dynamic Focal IoU (DF-IOU) loss. The Scale-Adaptive Selection Network introduces multi-scale attention mechanism into feature pyramid so as to assign attention weight for feature maps on each level, which enables the network to select the dominant levels for prediction and alleviate the prediction conflicts between different levels. Furthermore, we design Dynamic Focal IoU loss to increase loss contribution of easy targets so that the coordinates of these easy targets can regress better and the bounding box will fit tighter. Our experimental results show that our SASNet with DF-IoU loss can increase average precision of objects at small, medium and large scale on the MS COCO and CCPD dataset.
Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection

The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren’t falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.
System design of a low-power three-axis underdamped MEMS accelerometer with simultaneous electrostatic damping control

Recently, consumer electronics industry has known a spectacular growth that would have not been possible without pushing the integration barrier further and further. Micro Electro Mechanical Systems (MEMS) inertial sensors (e.g. accelerometers, gyroscopes) provide high performance, low power, low die cost solutions and are, nowadays, embedded in most consumer applications. In addition, the sensors fusion has become a new trend and combo sensors are gaining growing popularity since the co-integration of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope provides complete navigation information. The resulting device is an Inertial measurement unit (IMU) able to sense multiple Degrees of Freedom (DoF). Nevertheless, the performances of the accelerometers and the gyroscopes are conditioned by the MEMS cavity pressure: the accelerometer is usually a damped system functioning under an atmospheric pressure while the gyroscope is a highly resonant system. Thus, to conceive a combo sensor, a unique low cavity pressure is required. The integration of both transducers within the same low pressure cavity necessitates a method to control and reduce the ringing phenomena by increasing the damping factor of the MEMS accelerometer. Consequently, the aim of the thesis is the design of an analog front-end interface able to sense and control an underdamped three-axis MEMS accelerometer. This work proposes a novel closed-loop accelerometer interface achieving low power consumption. The design challenge consists in finding a trade-off between the sampling frequency, the settling time and the circuit complexity since the sensor excitation plates are multiplexed between the measurement and the damping phases. In this context, a patented damping sequence (simultaneous damping) has been conceived to improve the damping efficiency over the state of the art approach performances (successive damping). To investigate the feasibility of the novel electrostatic damping control architecture, several mathematical models have been developed and the settling time method is used to assess the damping efficiency. Moreover, a new method that uses the multirate signal processing theory and allows the system stability study has been developed. This very method is used to conclude on the loop stability for a certain sampling frequency and loop gain value. Next, a CMOS implementation of the entire accelerometer signal chain is designed. The functioning has been validated and the block may be further integrated within an ASIC. Finally, a discrete components system is designed to experimentally validate the simultaneous damping approach.
Research and Implementation of Flow Pattern Recognition for Gas-liquid Two-phase Flows Based on GoogLeNet

In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level.
Aerodynamic Optimization of a Wind Turbine Blade Designed for Egypt's Saharan Environment Using a Genetic Algorithm

This work aims to optimize the aerodynamic parameters (airfoil chord lengths and twist angles smoothed using Bezier curves) of the NREL 5MW wind turbine and a wind turbine designed for site-specific wind conditions to increase the wind turbine's annual energy production (AEP) under this site conditions. This optimization process is carried out using a Genetic Algorithm (GA) developed in MATLAB and coupled with NREL's FAST Modularization Framework. The results shows that after optimizing the NREL 5MW wind turbine design, the AEP was improved by 5.9% of the baseline design AEP while a site-specific designed wind turbine using Schmitz equations shows 1.2% improvement in AEP. These results shows that optimization of wind turbine blade aerodynamic parameters for site-specific wind conditions leads to improvement in AEP and hence decreasing cost of energy generated by wind turbines.
Analysis of the phase transition of patch colloidal particles and the molecular dynamics simulation

Anisotropic particle system means that the interaction between particles in the system depends not only on their spatial coordinates, but also on the orientation of each particle due to the influence of many factors such as structure, shape, electronic properties and environment. The introduction of orientation coordinates makes the phase space describing this kind of system have higher degrees of freedom and more complex symmetry. Therefore, the study of its general properties has more basic significance in physics. Patch colloidal particles, which have become one of the research hotspots in the field of soft matter physics in recent years, are a typical mesoscopic physical system with anisotropic interaction. In this paper, the two-dimensional patch colloidal particle system is studied as follows: (1) the phase structure of the system is preliminarily studied and analysed. It is found that the phase behaviour of the system at high temperature is similar to that of isotropic Lennard - Jones system is similar. Under low temperature, the system with low patch coverage has strip structure in the condensed phase and complex self-assembly structure in the gas phase; (2) It is found that the long-term diffusion belongs to isotropic normal diffusion, while the short-term diffusion is anisotropic ballistic diffusion, and the particle motion direction is always perpendicular to the patch orientation; (3) The melting behavior of the system is studied. It is found that the melting is a two-step phase transition, which is the same as that of the two-dimensional isotropic colloidal system;
Modality-Invariant and -Specific Representations with Crossmodal Transformer for Multimodal Sentiment Analysis

Human emotion judgments usually receive information from multiple modalities such as language, audio, as well as facial expressions and gestures. Because different modalities are represented differently, multimodal data exhibit redundancy and complementarity, so a reasonable multimodal fusion approach is essential to improve the accuracy of sentiment analysis. Inspired by the Crossmodal Transformer for multimodal data fusion in the MulT (Multimodal Transformer) model, this paper adds the Crossmodal transformer for modal enhancement of different modal data in the fusion part of the MISA (Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis) model, and proposes three MISA-CT models. Tested on two publicly available multimodal sentiment analysis datasets MOSI and MOSEI, the experimental results of the models outperformed the original MISA model.
Defect detection in the textile industry using image-based machine learning methods: a brief review

Traditionally, computer vision solutions for detecting elements of interest (e.g., defects) are based on strict context-sensitive implementations to address contained problems with a set of well-defined conditions. On the other hand, several machine learning approaches have proven their generalization capacity, not only to improve classification continuously, but also to learn from new examples, based on a fundamental aspect: the separation of data from the algorithmic setup. The findings regarding backward-propagation and the progresses built upon graphical cards technologies boost the advances in machine learning towards a subfield known as deep learning that is becoming very popular among many industrial areas, due to its even greater robustness and flexibility to map and deal knowledge that is typically handled by humans, with, also, incredible scalability proneness. Fabric defect detection is one of the manual processes that has been progressively automatized resorting to the aforementioned approaches, as it is an essential process for quality control. The goal is manifold: reduce human error, fatigue, ergonomic issues and associated costs, while simultaneously improving the expeditiousness and preciseness of the involved tasks, with a direct impact on profit. Following such research line with a specific focus in the textile industry, this work aims to constitute a brief review of both defect types and Automated Optical Inspection (AOI) mostly based on machine learning techniques, which have been proving their effectiveness in identifying anomalies within the context of textile material analysis. The inclusion of Convolutional Neural Network (CNN) based on known architectures such as AlexNet or Visual Geometry Group (VGG16) on computerized defect analysis allowed to reach accuracies over 98%. A short discussion is also provided along with an analysis of the current state characterizing this field of intervention, as well as some future challenges.
A Comparative Study on Part-of-Speech Taggers’ Performance on Examination Questions Classification According to Bloom’s Taxonomy

Examination questions classification according to Bloom’s Taxonomy uses Natural Language Processing (NLP) approach, a series of text processing approach that generally can divided into the keywords identification stage and then the identified keywords classification to Bloom’s Taxonomy levels stage. Since this NLP approach is a pipeline processes, the keywords identification stage’s performance in term of accuracy is affecting the subsequent stage - the identified keywords classification and subsequently limits the final accuracy performance of the questions classification. The keywords identification stage’s performance is mainly depending on the effectiveness of Part-Of-Speech (POS) tagging. Thus, this paper aims to identify the best performing POS tagger in keywords identification stage and enhance the tagger’s performance with rule-based approach to achieve high accuracy performance and benefit the subsequent keyword classification and then the questions classification accuracy. The Perceptron tagger and the Stanford POS tagger are selected to be evaluated their performance in identifying the keywords of the randomly selected 200 examination questions from STEM subjects. This paper has observed the Stanford POS tagger is the best performing tagger in POS tagging with accuracy of 80.5%. Some rules are applied to the POS tagging to improve the accuracy further to 91.5%.
Influence of Normalization Techniques on Multi-criteria Decision-making Methods

One of the most significant steps in solving multi criteria decision-making (MCDM) problems is the normalization of the decision matrix. The consideration for the normalization of the data in a judgment matrix is an essential step as it can influence the ranking list. This study investigates the effects of normalization on an AHP-VIKOR hybrid method in the selection of Web services. The Web services considered in this research offer similar functionalities but with different Quality of Services (QoS). For the purpose of this study, ten web services were selected. Each of these services were evaluated against five difference QoS requirements namely, response time, throughput rate, latency, availability, and reliability to determine the most appropriate Web services. The five normalization techniques employed are linear sum, linear max, linear max-min, enhanced accuracy and vector normalization. It was observed that different ranking lists are produced when applying different normalization techniques to the VIKOR method and normalization has an influence on the final ranking list.
Anomaly Detection and Analysis Based on Deep Autoencoder for the Storage Tank Liquid Level of Oil Depot

In the industrial control system of oil depot, the change of storage tank liquid level is closely related to the transportation process and site management of oil depot. It is of great significance to detect the abnormality of storage tank liquid level data for the safe and stable production of oil depot. A data-driven anomaly detection strategy is proposed by analyzing the non-periodic time series data of the oil storage tank liquid level. Based on the convolutional autoencoder algorithm to learn the features and patterns of a large number of samples, the strategy is carried out by reconstructing the samples and calculating the reconstruction error, which not only does not rely on the labeled samples, but also improves the detection precision. This paper chooses three algorithms of convolutional autoencoder, RNN (Recurrent Neural Network) autoencoder and LSTM (Long Short-Term memory) autoencoder for experimental analysis. Experiments were carried out on the oil tank historical data set and NAB (Numenta Anomaly Benchmark) simulation data set respectively. The results show that the accuracy of convolutional autoencoder is 98% and the F1 score is 82%, which is more practical for the scenes with real-time requirements.
UCB-DQN based Joint Beamforming and Power Control for Software Defined UAV Swarm Network

In this paper, we address the problem of deep Q-Network (DQN) based joint power control and beamforming for enabling interference suppression in software defined UAV swarm (SD-UAS) network. We first present the optimization model of joint beamforming and power control (JBPC). Then, to solve this joint optimization problem, we make use of UCB exploration in the learning process of DQN. Simulation results validate that the convergence obtained with the proposed UCB-DQN strategy outperform the DQN learning algorithm, which can to raise the exploration efficiency and sequentially speed up the convergence for the JBPC problem. It is benefit to supress interference and enhance the SD -UAS communication performance.
Distributed Incentive Mechanism Based on Hyperledger Fabric

Crowd sensing is a new type of data acquisition method that can efficiently and diversify the realization of sensing tasks. However, this method currently has some problems, such as data storage being overly dependent on third-party platforms, and there is a lack of reliable data credible evaluation methods. To solve this problem, our paper proposes a distributed incentive mechanism based on Hyperledger Fabric (HF-DIM) in the Crowd sensing scenario. In particular, the following questions are studied: How to achieve distributed incentive to solve the traditional incentive that relies on a centralized platform? How to evaluate the credibility of the sensing data provided by the users? To the former question, we implement a multi-attribute auction algorithm based on smart contracts, and distributed incentives are implemented using blockchain deployed contracts. To the latter question, We propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to evaluate the credibility of the data and establish a reputation index based on the credibility of the data. Through simulation experiments using real data set, the feasibility and effectiveness of the proposed framework and algorithm are verified.
Application of Algorithm of Fuzzy Rule Conclusions in Determination of Animal’s Diseases

Today, the detection of non-communicable animal diseases, the analysis of experimental data and the construction of a mathematical model is one of the main problems. In the article a method for determining the type of disease in cattle using a fuzzy inference rule algorithm is discussed. In our country, diseases with ketosis, microelementosis, ostradistraphia and secondary ostradithraphy in livestock, especially cattle, are determined by errors in determining the type of disease due to their similar symptoms. Since the symptoms of these diseases are interrelated and one of them can cause the other, solving this problem using fuzzy rule algorithms helps to reduce the error in determining the type of disease.
DeepFake Video Detection through Facial Sparse Optical Flow based Light CNN

DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.
Active Obstacle Avoidance Control Strategy Based on Four-Wheel Steering

Active obstacle avoidance control strategy is the main question of intelligent vehicles, and active four-wheel steering is gradually used in the intelligent control system. Considering both tracking performance and driving characters, an active controller with four-wheel steering (4WS) and active rear steering (ARS) based on adaptive model predictive theory (AMPC) is designed. The control architecture is composed a supervisor and an AMPC controller. The supervisor is used to select the appropriate control mode and the AMPC is used to calculate the expected steering angle when the stability indexes over the safety threshold. Finally, the proposed control strategy is simulated via Carsim-Simulink co-simulation. The results show that the integrated controller can track the obstacle avoidance path and has good driving performance.
Comparison of zero-sequence injection methods in cascaded H-bridge multilevel converters for large-scale photovoltaic integration

Photovoltaic (PV) power generation levels in the three phases of a multilevel cascaded H-bridge (CHB) converter can be significantly unbalanced, owing to different irradiance levels and ambient temperatures over a large-scale solar PV power plant. Injection of a zero-sequence voltage is required to maintain three-phase balanced grid currents with unbalanced power generation. This study theoretically compares power balance capabilities of various zero-sequence injection methods based on two metrics which can be easily generalised for all CHB applications to PV systems. Experimental results based on a 430 V, 10 kW, three-phase, seven-level cascaded H-bridge converter prototype confirm superior performance of the optimal zero-sequence injection technique.