Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning-based Path Following Control for a Vehicle with Variable Delay in the Drivetrain

In this contribution we propose a reinforcement learning-based controller able to solve the path following problem for vehicles with significant delay in the drivetrain. To efficiently train the controller, a control-oriented simulation model for a vehicle with combustion engine, automatic gear box and hydraulic brake system has been developed. In addition, to enhance the reinforcement learning-based controller, we have incorporated preview information in the feedback state to better deal with the delays. We present our approach of designing a reward function which enables the reinforcement learning-based controller to solve the problem. The controller is trained using the Soft Actor-Critic algorithm by incorporating the developed simulation model. Finally, the performance and robustness is evaluated in simulation. Our controller is able to follow an unseen path and is robust against variations in the vehicle parameters, in our case an additional payload.
Design and Verification of UAV Maneuver Decision Simulation System Based on Deep Q-learning Network

With the development of artificial intelligence technology and the demand of air combat, autonomous maneuver decision of UAV has become a popular research direction. Scholars at home and abroad have researched on autonomous air combat maneuver decision of UAV in depth based on various technologies and have achieved some results, among which the maneuver decisions based on the reinforcement learning are more efficient. But at present, the simulation targets used to test the effectiveness of the UAV autonomous maneuver decision method are relatively fixed, which cannot reflect the complexity of the enemy's maneuver strategy under real air combat conditions. This paper designs and develops a set of UAV autonomous maneuver decision man-machine air combat system based on deep Q-learning network, which is built from three subsystem: air combat environment simulation subsystem, the manned aircraft operation simulation subsystem and UAV self-learning subsystem. It has been verified that the man-machine air combat system proposed in this paper can effectively simulate real air combat and verify the effectiveness of the UAV autonomous maneuver decision.
Estimation of 2D Bounding Box Orientation with Convex-Hull Points - A Quantitative Evaluation on Accuracy and Efficiency

Estimating the bounding box from an object point cloud is an essential task in autonomous driving with LiDAR/laser sensors. We present an efficient bounding box estimation method that can be applied on 2D bird's-eye view (BEV) LiDAR points to generate the bounding box geometry including length, width and orientation. Given a set of 2D points, the method utilizes their convex-hull points to calculate a small set of candidate directions of the box yaw orientation, and therefore reduces the searching space – usually a fine partition of an angle range (e.g. [0, $\pi/2$)) as in the previous solutions – to find the optinal angle. To further improve the efficiency, we investigate the techniques of controlling the number of convex-hull points, by both applying approximate collinearity condition and downsampling the raw point cloud to a smaller size. We provide comprehensive analysis on both accuracy and efficiency of the proposed method on the KITTI 3D object dataset. The results show that without obviously sacrificing the accuracy, the method, especially when using approximate convex-hull points, can significantly improve the time of estimating the bounding box orientation by almost one order of magnitude.
Water Quality Classification Using Data Mining Techniques: A Case Study on Wang River in Thailand

In order to survive, the creatures need water, food, air, and residency. However, there are many water crises that influence water quality or cause water shortages. Rivers, which are important freshwater sources for human consumption, are often waste caused by their activities. This research utilizes data mining techniques to create classification models for water quality issues. The results are used in the application to alert the public about water quality problems that are aimed at changing their behavior. The data set consists of nine input features to identify dissolved oxygen in the next quarter, divided into two levels: “good” and “bad”. The process of data preparation using various methods applies to the raw data before creating the classification model. This research proposes neural networks with a multilayer perceptron and k-nearest neighbor as a classifier called MLP-kNN. The results show that the proposed model is more effective when compared to various MLP algorithms. The classification rate is more than 0.95 while the F-score of both two classes is more than 0.9. Finally, the proposed model is implemented on the web application to report and prepare for water utilization planning. Contribution-Application of data mining classifies a water quality on the Wang River, Thailand.
LaCNet: Real-time End-to-End Arbitrary-shaped Lane and Curb Detection with Instance Segmentation Network

Accurate and robust detection of lane and curb in urban areas is essential to many real-world intelligent vehicle applications. Existing vision-based researches treat lane detection and curb detection separately due to the nature of curb detection problem relying on 3D features, which is not efficient when driving in a real world. This paper presents a unified network to incorporate these two tasks together by taking advantage of the powerful feature learning ability brought by deep convolutional neural networks. The resulting unified network provides valuable road boundary information by curb detection even when lane markings are not visible during vehicle navigation. Another significant capability coming with the proposed method is able to accurately differentiate various lane and curb instances with tiny gaps or complex spatial relationships which are thought as the biggest challenge in real driving situations. To achieve this, the network is specially designed to guide lane and curb instances to learnable kernel through pixel grouping. This learnable kernel is capable to handle any number of arbitrary-shaped lanes and curbs no matter which angle the vehicle is heading at. In the end, the presented approach is evaluated on two datasets (BDD100K and self-collected dataset) scoring 32 FPS processing speed. Results are very encouraging with more than 98% F1 measure for both lane and curb detection on the self-collected dataset.
Learning to Compensate for the Drift and Error of Gyroscope in Vehicle Localization

Self-localization is an essential technology for autonomous vehicles. Building robust odometry in a GPS-denied environment is still challenging, especially when LiDAR and camera are uninformative. In this paper, We propose a learning-based approach to cure the drift of gyroscope for vehicle localization. For consumer-level MEMS gyroscope (stability ∼10° /h), our GyroNet can estimate the error of each measurement. For high-precision Fiber optics Gyroscope (stability ∼0.05° /h), we build a FoGNet which can obtain its drift by observing data in a long time window. We perform comparative experiments on publicly available datasets. The results demonstrate that our GyroNet can get higher precision angular velocity than traditional digital filters and static initialization methods. In the vehicle localization, the FoGNet can effectively correct the small drift of the Fiber optics Gyroscope (FoG) and can achieve better results than the state-of-the-art method.
A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods

Classical Visual Simultaneous Localization and Mapping (VSLAM) algorithms can be easily induced to fail when either the robot's motion or the environment is too challenging. The use of Deep Neural Networks to enhance VSLAM algorithms has recently achieved promising results, which we call hybrid methods. In this paper, we compare the performance of hybrid monocular VSLAM methods with different learned feature descriptors. To this end, we propose a set of experiments to evaluate the robustness of the algorithms under different environments, camera motion, and camera sensor noise. Experiments conducted on KITTI and Euroc MAV datasets confirm that learned feature descriptors can create more robust VSLAM systems.
CFVL: A Coarse-to-Fine Vehicle Localizer with Omnidirectional Perception across Severe Appearance Variations

Visual localization in vehicle navigation remains a crucial image retrieval task to determine the best matched image. Developing an efficient algorithm to address the localization issues of vehicle is highly difficult, for severe appearance variations with vehicles moving around can bring about significant challenges and big obstacles. In this paper, we propose the CFVL framework which takes panoramas into use in the localizer and the system processes from coarse to fine, in order to attain more robust and stable descriptors. NetVALD descriptors based on explicit panorama construction, which are regarded robust to appearance changes, are extracted in the coarse stage, while Geodesc keypoint descriptors, which are believed to detect more detailed information, are utilized in the fine stage, so as to perceive the accurate localization. A comprehensive set of experiments is carried on several datasets with different appearances across seasonal cycling, illumination variations, diverse traversals, and so on, to verify the effectiveness of the coarse stage and fine stage in our system. Brute Force (BF) matching and Fundamental Matrix mapping are utilized to match and locate correct locations after coarse stage and after fine stage. The accuracy of the coarse matching and fine matching are verified separately. Our system is demonstrated to be with high location recall, generalization capacity across different environments.
Cosensitization Properties of Glutathione-Protected Au25 Cluster on Ruthenium Dye-Sensitized TiO2 Photoelectrode

Cosensitization by glutathione-protected Au25 clusters on Ru complex, N719-sensitized TiO2 photoelectrodes is demonstrated. Glutathione-protected Au25 clusters showed no significant changes in properties after adsorption onto TiO2 particles, as confirmed by optical absorption spectroscopy, transmission electron microscopy, and laser desorption/ionization mass spectrometry. Adsorption property of the glutathione-protected Au25 clusters depends on the pH, which affects the incident photon-to-current conversion efficiency (IPCE) of the TiO2 photoelectrode containing Au25 clusters. When pH 7. The IPCE of a TiO2 photoelectrode sensitized by both glutathione-protected Au25 clusters and N719 was increased compared with photoelectrodes containing either glutathione-protected Au25 clusters or N719, which suggests that glutathione-protected Au25 clusters act as a coadsorbent for N719 on TiO2 photoelectrodes. This is also supported by the results that the IPCE of N719-sensitized TiO2 photoelectrodes increased upon addition of glutathione. Furthermore, cosensitization by glutathione-protected Au25 clusters on N719-sensitized TiO2 photoelectrodes allows that wavelength of photoelectric conversion was extended to the near infrared (NIR) region. These results suggest that glutathione-protected Au25 clusters act not only as a coadsorbent to increase IPCE but also as an NIR-active sensitizer.
Development of Human-Robot Communication Technologies for Future Interaction Experiments

This paper describes our development of human-robot communication technologies, which will be used in future experiments to investigate the effects of different types of communication in human-robot interactions. Using the human kinematic chain and facial points, obtained with methods from the literature, we propose geometric analyses for recognition and interpretation of human pointing gestures and gaze direction. We developed a virtual agent with voice, facial expressions, and gaze direction to interact with humans. The pointing gestures system presented mean success rate of 77.8% (s.d. 16.4%) in experiments and the implementation of the facial points detection algorithm resulted in mean errors of 0.52 cm (s.d. 0.29 cm) in the horizontal iris centers distance and 0.67 cm (s.d. 0.31 cm) in the vertical distance between eyes and mouth, both important attributes to estimate human gaze direction. Those results are satisfactory for the intended application.
Practical Stability of Positive Switched Nonlinear Time-Varying Systems

In this paper, practical stability is concerned for positive switched nonlinear time-varying (PSNTV) systems and positive switched linear time-varying (PSLTV) systems with disturbances. Based on a new definition on practical stability which can fully reflect the positivity of every component of the solution, sufficient conditions on practical stability of PSNTV systems are given by using the multiple max-separable Lyapunov function method. As an application of our criterion, sufficient criteria on practical stability are also provided for PSLTV systems with disturbances. Through our criterion, the systems' practical stability can be judged directly from the algebraic characteristics of the systems. At the end, examples are presented to illustrate the effectiveness of our results.
Gauging attention of students in an e-learning environment

The effectiveness of pedagogy lies in whether the student is able to grasp the concepts explained in the lecture. In classrooms lectures, professors can understand from the body language of the students whether they are understanding the subject. However, this becomes difficult in virtual classrooms. Virtual classes have recently taken over, across the globe owing to the outbreak of the COVID-19 pandemic. The sudden shift to virtual teaching makes it tough for professors to keep a watchful eye on the students, resulting in a poor knowledge gain at the students’ end. Taking into account various metrics, this paper proposes a three fold solution to ease this cumbersome task of keeping a check on the attentiveness of the students. The posture-based attentivity detection model resulting in an accuracy of 99.82%, while the other two methods to measure drowsiness and emotions are also promising.
Indoor Mobile Localization Based on A Tightly Coupled UWB/INS Integration

A growing research effort has been dedicated to indoor localization specifically the integration of multiple local positioning techniques, allowing to profit from their advantages. However, integration methods that rely mainly on practicable measurement models or algorithm efficiency. This paper proposes a tightly coupled UWB/INS method for indoor pedestrian applications. In indoor environments, wireless signal outage and severe multipath propagation very often lead to large ranging errors and make Ultra-Wide Band (UWB) based localization barely possible. On the other hand, Micro-Electro-Mechanical System (MEMS) Inertial Navigation System (INS) is necessary to conquer its constant bias, drift, and accumulated errors with time. Based on the characteristics of UWB and inertial measurements, a loosely coupled model using an Extended Kalman Filter (EKF) and a tightly coupled model using another EKF are put forward, which aims to reduce the accumulated error and multipath effects for the indoor mobile tracking. The experiment results demonstrate that, when compared with the accuracy of UWB, the combination of UWB/INS increases by 60.61% with the tight integration model and 31.47% with the loose integration.
Fast Nested Key Equation Solvers for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes nest Reed-Solomon (RS) or BCH sub-codewords to generate codewords belonging to stronger RS or BCH codes. Their hyper-speed decoding and good error-correction capability make them one of the best candidates for next-generation terabit/s digital storage and communications. The key equation solver (KES) in the nested decoding stage causes clock frequency bottleneck and takes a large portion of the GII decoder area. Recent architectures reduce the critical path to two multipliers and rely on the application of the slow-down technique to further reduce it to one. The slow-down technique requires two sub-codewords to be interleaved in the nested KES. However, most of the time, the nested decoding only needs to be carried out on one sub-codeword and half of the clock cycles are wasted. This paper proposes two fast nested KES algorithms, both of which have one multiplier in the critical path without applying slow-down and accordingly reduce the latency of the nested KES to almost a half. The short critical path is achieved by algorithmic reformulations that enable the pre-computation of the scalars in parallel with polynomial updating. Our second design adopts scaled versions of the polynomials to enable product term sharing so that the number of multipliers in each pair of processing elements is reduced from 8 as in the first design to 4. Novel scaling and combined scalar computations are developed to keep the critical path one multiplier. For an example GII code over $GF(2^{8})$ that has 3 nested codewords, our designs achieve 49.9% reduction on the number of clock cycles needed in the nested KES compared to prior designs. Besides, our second design requires 22% less area than the first one under the same timing constraint.
Use of GNU Radio as a Validation and Visualization Tool in Communications Electronic Support Project

Software Defined Radio (SDR) aims to implement components once implemented on hardware like mixer, filter, amplifier, modulator/demodulator, detector on general purpose processor or embedded platforms. We can examine SDR components under two parts, hardware and software. RF front end is the hardware part where analog signal processing occurs, that is analog signal is converted to digital signal and vice versa via ADC/DAC. Software, the other part runs on general purpose processor, digital signal processor, FPGA or heteregenous computational unit and it is responsible for digital signal processing. In this work, we use Aselsan receivers as the hardware component and GNU Radio as the software component that together make up SDR. Thanks to built-in signal processing blocks in GNU Radio, we can test and validate both receiver and firmware components easily and rapidly. GNU Radio can also be utilized as a graphical user interface by means of builtin visualization blocks.
Semantic Segmentation of Retinal Vessels Using SegNet

Automated retinal vessel segmentation is useful for diagnosis of many pathological diseases such as diabetic retinopathy, hypertensive retinopathy, and glaucoma. In this paper, an algorithm for semantic segmentation of retinal vessels based on semantic pixel-wise segmentation (SegNet) is presented where the network is initialized using the VGG-16 network. The High-Resolution Fundus (HRF) image database has been used for training and testing the network, where in this dataset 15 images from healthy, 15 images from diabetic, and 15 images from eyes with glaucoma are hand labeled for retinal vessels. Because of the limited number of hand-labeled images, first, a data augmentation has been applied where reflection, scaling, translation, and rotation are used for this purpose. Then class weighting is applied to minimize the issues due to the imbalanced dataset. When tested with HRF dataset, sensitivity has been estimated as 73.72%, specificity has been estimated as 94.85%, and accuracy has been estimated as 93.23%.
A comprheensive review of Visual Programming Tools for Arduino

This document presents a systematic review of Visual Programming Tools for the Arduino Board. The Arduino board is an embedded platform that focus on enabling an easy way to teach embedded systems, but, due to its success, it ended up being used in different teaching levels, ranging from primary to university. The board was also applied in the teaching of robotics and other contexts since it is relatively cheap, easy to program and its community has produced a considerable amount of free educational material. Even though Arduino is a success it is still maturing in terms of educational purposes, aside the electronic knowledge that needs to be explored, the board is programmed, by default, with c++, which is considered not friendly to Computer Thinking beginners. Trying to solve this problem, many solutions apply Visual Programming, which is proven to make it easy for unexperienced and even children to learn the basis of Computational Thinking, in the educational context with Arduino. Even though there are many solutions applying visual programming for Arduino, there are no systematic review or survey on this subject, meaning that it is hard to identify where are the gaps and which are the problems that are already solved. Therefore, on this paper, it was performed a systematic review on the subject, the method found 372 papers, which 31 were selected for full review. The reviewed solutions where discussed and categorized, we expect that the presented results could be used as guideline for new research in the area and by educators and companies that are looking for visual programming tools for the Arduino board.
Polysaccharides derived from tragacanth as biocompatible polymers and Gels

Tragacanth gum (TG) is a natural gum whose biomedical applications are limited because of the low water solubility and the possibility to form only weak water-insoluble gels. An innovative method to produce water-soluble tragacanth (WST) is assessed in this work. WST structural characterization indicates a high-molecular weight polyuronic acid, which can undergo gelling by ionotropic complexation. Biological characterization shows no cytotoxicity on Hela, HepG2, and L929 cell lines. Furthermore, TG-based and WST-based gel beads prepared by ionic crosslinking with ferric and zinc ions are studied. Ferric WST gels exerted better cell adhesion with L929 cells than ferric alginate gels. These characteristics make WST a promising candidate for tissue engineering and drug delivery applications. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
Visual Drone Terrain Classification: A Manual Classification Approach

This research investigates a method for performing manual classification of the terrain in imagery from the on-board camera of an unmanned aerial vehicle, to develop classifiers for systematic terrain classification. Drone images were captured across rural County Donegal in Ireland, and software was developed to manually label the terrain in these images, labelled in a lattice of 30 x30-pixel tiles. This dataset was used to train both a classic computer vision model and a Convolutional Neural Net model to classify the type of terrain under the UAV. The accuracy of the computer vision approach to the classification was compared to that of a Convolutional Neural Network trained using the Semantic Segmentation approach. The Convolutional Neural Network classifier was found to be the most accurate approach, achieving an fl score of 0.95.
Overview and comparison of some outdoor radio propagation models in Wireless Sensor Networks

Outdoor radio propagation models are very important because they determine the coverage area of the transmitter and the propagation losses in the wireless sensor network (WSN). We can use these models as well for defining the communication radius between the nodes at a particular power and frequency of transmission. This not only determines the required power of the transmitter but also specifies the battery life of the sensor nodes (SN).