Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved-ORB-based SLAM Using RGB-D Data

RGB-D cameras have become a popular sensor for Simultaneous Localization and Mapping (SLAM) because they can provide RGB and depth information at the same time. The spatial location of feature points of SLAM is obtained by its corresponding depth information from RGB-D sensors. However, the depth information of the feature at the edge of the object is very noisy, and the measurement is often inaccurate. At the same time, there is poor real-time performance, low accuracy, and much difficulties of dense construction in ORB-based Visual SLAM (visual SLAM, VSLAM). Therefore, an improved ORB features based SLAM framework with corresponding method using RGB-D data is proposed in this paper. We extract and match features with improved ORB. Then the local map is used to perform local optimization for pose estimation, and an efficient key frame screening method is designed. The proposed method effectively reduces errors, enhances robustness, and builds a dense map of the environment.
Preoperative Prediction of Infection Stones Using Radiomics Features From Computed Tomography

Preoperative prediction of infection stones from CT images could provide additional information for treatment planning. We developed a radiomics algorithm that could apply data from non-contrast-enhanced CT images to distinguish infection stones from non-infection stones. This retrospective study included 98 patients with clinically confirmed infection kidney stones and 59 patients with non-infection kidney stones. Fifty-four radiomics features extracted from CT images were reduced to 27 key features by the LASSO algorithm, for which a radiomics signature was built with ensemble learning based on bagged trees. Multivariable logistic regression analysis was then used to develop a radiomics nomogram incorporating the radiomics signature and independent clinical factors. The radiomics signature, which consisted of morphological features and textural features, was significantly associated with infection kidney stones. Ensemble learning based on bagged trees could differentiate infection kidney stones from non-infection kidney stones with 90.7% accuracy, 85.81% sensitivity, 93.96% specificity, a 91% positive predictive value and a 91% negative predictive value. Predictors incorporated into the individualized prediction nomogram included the radiomics signature, white blood cell count and urine culture. Decision curve analysis demonstrated that the radiomics nomogram had potential clinical application for infection stone prediction.
Automated Heartbeat Classification Exploiting Convolutional Neural Network With Channel-Wise Attention

Long-term Electrocardiogram (ECG) analysis has become a common means of diagnosing cardiovascular diseases. In order to reduce the workload of cardiologists and accelerate diagnosis, an automated patient-specific heartbeat classification method based on a customized convolutional neural network (CNN) is proposed in this paper. The parallel convolutional layers with kernels of different receptive fields in the network are responsible for extracting multi-spatial deep features of the heartbeats, and the channel-wise attention module is adopted to selectively emphasize the informative features, which are beneficial to distinguish different classes of beats. To facilitate the extraction and emphasis of the important features, each heartbeat is segmented and stacked to form the multi-channel network input according to the basic temporal characteristics of the main components (P wave, QRS complex, and T wave) in the ECG. Besides, to further improve the network generalization and achieve better performance on various ECG of new patients, a method of intra-record sample clustering is proposed to select the representative heartbeats to construct the training set. The proposed method classifies heartbeats into five classes (normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unclassifiable beat (Q)). Validated on the MIT-BIH arrhythmia database, our approach demonstrates performance superior to several state-of-the-art methods. In addition to an average accuracy and specificity of over 99%, this method achieves a sensitivity of 95.4% and a positive predictivity of 97.1% for VEB class, and a sensitivity of 81.1% and a positive predictivity of 90.0% for SVEB class. With high classification performance and pathological heartbeat detection accuracy, the proposed method is promising for clinical device applications.
Forecasting Model for Urban Traffic Flow with BP Neural Network based on Genetic Algorithm

In this paper, urban traffic flow forecasting model is built with back propagation (BP) neural network based on genetic algorithm (GA). The model is established based on the real-time traffic data includes the phase start time, phase length, signal cycle length and traffic flow. To make sure that the collected traffic data is accurate, the traffic data is preprocessed by correcting the unusual data with data filtering method. BP neural network is then used to establish the traffic flow prediction model. The weights and thresholds are optimized by using GA. Finally, the measured data is used to test the prediction model and the results show that the proposed model can forecast the traffic flow effectively.
A Algorithm of Counting Fire Branches Numbers for Early Fire

In this paper, the morphological characteristics of fire – branches number are studied. An image algorithm for calculating the number of flame branches is proposed and a method is design to evaluate the algorithm. Through this algorithm, fuel fire, paper fire and car lights are analyzed in both time domain and frequency domain. According to the results, the number of the fire branches changes slowly, and the frequency is almost all under 1Hz. The branches numbers of the car lights image are constant, so the method can be used to distinguish the fire from the car lights through image.
Design and Analysis of Multiple Independent Outputs Time-sharing Driving System for a Flexible Robot Arm

A novel time-sharing driving system for robots was proposed. By using the time-sharing driving system, one actuator can drive 2 independent outputs. The working principle of the driving system was introduced and a flexible robot arm was designed based on the driving system. The flexible arm has 6 DOF and only 4 actuators. With small number of actuators, the control and sensing system of the robot arm can be simplified at a large scale. The control and mathematical model of the robot arm are analyzed, which can provide more theoretical principle for design and optimization. The proposed robot arm and the time-sharing driving system are rather flexible, highly compliant and safe for human interaction. They can be widely used in many kinds of automatic system that needs manipulators and simple control and sensing system.
High-Performance Automotive Radar: A review of signal processing algorithms and modulation schemes

The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly automated driving, which will in turn progress into fully autonomous, self-driving cars. To work properly, these functions first must be able to perceive the car's surroundings by such means as radar, lidar, camera, and ultrasound sensors. As the complexity of such systems increases along with the level of automation, the demands on environment sensors, including radar, grow as well. For radar performance to meet the requirements of self-driving cars, straightforward scaling of the radar parameters is not sufficient. To refine radar capabilities to meet more stringent requirements, fundamentally different approaches may be required, including the use of more sophisticated signal processing algorithms as well as alternative radar waveforms and modulation schemes. In addition, since radar is an active sensor (i.e., it operates by transmitting signals and evaluating their reflections) interference becomes a crucial issue as the number of automotive radar sensors increases. This article gives an overview of the challenges that arise for automotive radar from its development as a sensor for ADAS to a core component of self-driving cars. It summarizes the relevant research and discusses the following topics related to highperformance automotive radar systems: 1) shortcomings of the classical signal processing algorithms due to underlying fundamental assumptions and a signal processing framework that overcomes these limitations, 2) use of digital modulations for automotive radar, and 3) interference-mitigation methods that enable multiple radar sensors to coexist in conditions of increasing market penetration. The overview presented in this article shows that new paradigms arise as automotive radar transitions into a more powerful vehicular sensor, which provides a fertile research ground for further investigation.
Deep Convolutional Neural Networks with Curriculum Learning for Facial Expression Recognition

Facial expression recognition, which plays a vital role in human-machine interaction (HMI), has attracted increasing attention from researchers over the past few years. However, it is still a challenging task for identifying significant discriminative facial features that represent the characteristics of each expression. In this paper, we apply a strategy of curriculum learning (CL) to facial expression recognition to produce effective and discriminative facial representations. Firstly, a learning curriculum is designed by measuring the complexity according to the distance between each sample and the clustering center determined by unsupervised density-distance clustering method in the feature space. Secondly, we develop a multistage training process where a main model is trained by continuously increasing the complexity of training set. The experiment results indicate that our proposed model can achieve a high recognition accuracy of 72.11% on the FER-2013 dataset for seven facial expressions, which outperform other state-of-the-art methods.
A Novel Scheme for Performance Evaluation of an IEC 61850-Based Active Distribution System Substation

With the integration of Distributed Energy Resources (DERs) into the distribution networks, the Active Distribution Network Management is becoming quite challenging. The DERs are integrated into the distribution networks through distribution substations. For achieving greater control and ease of operation substations are being automated by employing substation communication network based on standardized communication. IEC 61850 standard has emerged as the most popular standard due to its object-oriented modeling and interoperability features. To ensure smooth and efficient operation of substation automation system, the performance requirements for intra substation communication needs to be evaluated. This paper presents modelling of data traffic in the substation according to IEC 61850 and provides validation through simulation as well as through experimental setup. In the experimental setup, a real bay is replicated in the laboratory for performance validation. The scheme has been validated for VLAN and priority tagging implementation with the obtained simulation results.
Molecular Genetics of Kidney Stone Disease

Kidney stone disease (KSD) is a multifactorial disease caused by a complex interaction of genetic and environmental factors. It causes significant morbidity and medical care expenses worldwide, which can be found commonly in tropical territory including Thailand. A majority of kidney stones are composed of calcium and it is usually associated with a metabolic abnormality such as hypercalciurea, hyperoxaurea, hyperphosphaturia, hyperuricosuria, hypocitraturea, and a defect of urinary acidification. However, few defects associated with monogenic forms of KSD have been identified. To date, molecular genetic studies to investigate genetic contribution of multifactorial KSD have been extensively performed and a number of causative genes have been recognized, but they vary among populations. In Thailand, the highest prevalence of KSD was observed in the Northeastern (NE) area. Its etiology and pathogenesis are still obscure and unique characteristics with no conditions of increased urinary solutes such as hypercalciurea, hyperoxaurea, and hyperuricosuria that are different from what has been reported in other ethnic groups are shown. The study to identify causative genes will provide more understanding of pathogenesis underlying KSD and also the unique pathogenesis of KSD in NE Thai patients. These will lead to improved quality and efficiency of patient care, diagnosis, prevention and control of diseases and complications. The knowledge of reported monogenic and multifactorial KSD, and the update on molecular genetic studies to investigate genes responsible for the disease in NE Thai patients are reviewed in this article.
Research on Detection and Recognition Algorithm of Road Traffic Signs

Traffic Sign Recognition (TSR) is a key technology in safe driving assistance systems. This paper works on a traffic sign detection and recognition system. First, traffic sign images under natural conditions are collected to establish a traffic sign image database. And then the areas containing traffic signs in the images can be detected using the color and shape based method. Finally, the traffic signs can be recognized by using the template matching method. This system can realize real-time online detection and recognition of traffic signs. And the method used in the system has been proved effective by the experiments.
Role Adaptation and Force, Impedance Learning For Physical Human-Robot Interaction

In this paper, the upper limb exoskeleton robot and human collectively accomplish coordination manipulation in unknown environment. A robot’s role adaptation law is proposed based on game theory. According to the human’s intention which is characterized by the interaction force, the role adaptation law can adjust robot’s role to lead or follow the task in the real time. On the other hand, when the robot interact with unstable environment to accomplish contact task, a new controller which concurrently adjust the force, impedance is proposed to compensate environmental disturbance and guarantee the interaction stability. Experiments have been proved the effectiveness and performance of the proposed coordination controller.
Grasping Objects From the Floor in Assistive Robotics: Real World Implications and Lessons Learned

This paper presents a system enabling a mobile robot to autonomously pick-up objects a human is pointing at from the floor. The system does not require object models and is designed to grasp unknown objects. The robot decides by itself if an object is suitable for grasping by considering measures of size, position and the environment suitability. The implementation is built on the second prototype of the home care robot Hobbit, thereby verifying that complex robotic manipulation tasks can be performed with economical hardware. The presented system was already tested in real apartments with elderly people. We highlight this by discussing the additional complexity for complete autonomous behavior in apartments compared with tests in labs.
Neural Network Sliding Mode Control of Airborne Photoelectric Stabilized Sighting Platform

Aiming at the problem of closed-loop performance degradation caused by actuator saturation of airborne photoelectric stabilized aiming platform actuator in the application of traditional sliding mode variable structure control, radial basis function (RBF) neural network (NN) was used as compensator to improve the sliding mode variable structure control law, and a neural network sliding mode control (SMC) method considering the input limitation of actuator was proposed. Simulation results show that the application of the controller can compensate for the limited control input and improve the visual axis stability of airborne photoelectric stabilized sighting platform.
Smoothing Method For Nonlinear Optimal Control Problems With Inequality Path Constraints

A novel smoothing method based on control vector parameterization technique for solving nonlinear optimal control problems with inequality constraints is developed in this paper. The proposed algorithm discretizes the original nonlinear optimal control problem by using parametization quadratic penalty function and smoothing function. The nonlinear optimal control problems with inequality path constraints can be converted into vector parameterization by adjusting penalty factors. The equivalence of the transformation for optimal control problem with inequality path constraints is proved. The theorems show the proposed smoothing method for nonlinear optimal control problems is effective.
Biomechtronic Design of a Supernumerary Robotic Limbs for Industrial Assembly

We present a new wearable robot that can help users by hand-held objects, weight lifting, and simplified tasks. The composition of the supernumerary robotic limbs includes two additional robot arms and an installation backplane that is easy for the wearer to wear. If SRL works closely with the user and acts more human-like, they can be seen as a part of human’s body. Finally, humans will be able to expand existing operations and upgrade their skills, carry out tasks more effectively. This paper summarizes a basic design concept of SRL, establishes the kinematics model and dynamic model of the SRL. The adaptive neural network control method for SRL are described, aimed to track the trajectory.
Effects of Wheel Grouser Angle of Attack on Simulated Robot Wheel Performance on Soft Sand with High Slip Condition

Wheeled rover which is a lightweight wheeled robot is used for navigating over challenging terrain such as a steep soft sand incline. To improve the performance of the wheels on soft surfaces, the wheels are usually equipped with fixed grousers (lugs) on its wheels. However, even with the grousers the wheels tend to experience high slippage and sinks into the soft sand surface, which is caused by the rotating motion of the grousers which excavates the sand underneath the wheel towards the back of the wheel. A solution has been proposed by previous research which reduces sand movement and subsequent sinkage by using a prototype angle adjustable grouser mechanism and proven to improve the rover performance on steep inclines. The purpose of this study is to investigate the effect of grouser angle of attack when moving under the sand surface, towards the interaction between the grouser and sand particle. For the simulation process, Discrete Element Method (DEM) is used to simulate the particle-particle and particle-grouser interaction. The result shows that maintaining a constant angle using the adjustable grouser is able to reduce the amount of sand being excavated from under the wheel towards the back of the wheel as tests in high slipping conditions shows less displacement of sand particles from under the wheel towards the surface of the wheel. The result will be used to assist in analyzing the optimal parameters for wheel grouser design for use on soft sand.
A Health Status-Based Performance Evaluation Method of Photovoltaic System

The large scale application of the photovoltaic (PV) systems is significantly beneficial to the mitigation of energy crisis. The quality and performance of PV systems directly influence the energy yield. It is necessary to establish more scientific and effective methods to maximize the energy yield. In this paper, the concept of health status is proposed to describe the performance of PV systems within a certain period of time. A health status based performance evaluation model is built by the Gaussian mixture models (GMM) and the empirical mode decomposition (EMD). Then, the health index (HI) of PV array is defined. Under the outdoor ambient conditions, the proposed model can sensitively detect the slight performance reduction caused by faults or partial shadings of the PV array in real-time. At last, the proposed method is verified by the simulations and experiments. Experimental results show that on the sunny day the average daily HI is 1.2 and the average performance ratio (PR) is 0.85, which both show the PV array is healthy. When one of the PV modules in the PV array is partially shaded, the PR is still approximate 0.9. However, the calculated HI is greater than the threshold and the fault is reported. The results indicate that the proposed method can sensitively identify pseudo health status that cannot be identified by the PR. The proposed evaluation method based on health status provides an alternative option to assess the performance of the PV systems. Combining with the PR, the comprehensive performance of the PV systems can be reflected more accurately.
Cancer genetics in women

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Observer-Based Event-Triggered Optimal Control for Linear Systems

In this paper, we propose an event-triggered optimal control method for linear systems based on state observer. Firstly, combining with the optimal control theory, the modeling and analysis of the control systems with state feedback observer are studied. Secondly, by setting the minimum performance index in which we considering the system state and the observer state to make the system reach the equilibrium point from the initial state, the optimal feedback control law of the system is obtained on basis of the minimum principle. Thirdly, a state-feedback control system based on an event-triggered observer is studied, where the state of the plant is only transmitted to the observer when the event condition is violated. Then, the asymptotic stability of the control plant and the observer error system is guaranteed by conditions in terms of LMIs(Linear Matrix Inequalities). Finally, a numerical example is worked out to illustrate the validity and effectiveness of the method.
The Conundrum of Success in Music: Playing it or Talking About it?

Nowadays social media are the main means for conducting discussions and sharing opinions. The huge amount of information generated by social media users is helpful for predicting outcomes of real-world events in different fields, including business, politics and the entertainment industry. In this paper, we studied the possibility of forecasting the success of music albums by analyzing heterogeneous data sources spanning from social media (Twitter, Instagram and Facebook) to mainstream American newspapers (e.g., New York Times, Rolling Stones). The idea is to exploit music albums’ pre-release hype and post-release approval to predict the album’s rank with reference to the well-known Billboard 200 album chart, which tabulates the weekly popularity of music albums in the USA. To predict the success of a music album, that is its rank in the chart, we identified metrics based on the messages’ posting trend, the variation of the sentiment associated to such messages, the number of followers of the album’s author, and the importance of the people who talk about it. To evaluate the effectiveness of the proposed metrics we have compared the prediction performances of several models based on supervised learning approaches among those most used in literature. As a result, we obtained that the Random Forest approach is able to predict the music album rank in the Billboard 200 Chart with an expected accuracy of 97%. As a further validation, using this specific model, we also conducted an additional real usage test obtaining an almost matching result (accuracy of 94%).