Miracle-dz/UniFAR-FTUs huggingface dataset:
A Yaw Rate Based Stability Control for Under-Actuated Vehicles

Full electric vehicles (FEVs) are becoming more and more common. For these vehicles, a widely adopted driveline configuration consists in all-wheel drive (AWD) technology realized by independently drive front and rear axles. In this paper, we investigate the possibility of implementing an electronic stability control (ESC) exploiting only front and rear motor torques. The proposed control is robust with respect to friction variation and easily tunable thanks to a control allocation that permits single-input single-output formulation. The allocator is based on a multi-dimensional linearized tire model that accounts for longitudinal and lateral slip. Despite being an under-actuated configuration, incapable of generating arbitrary yaw moments, experimental tests show that this configuration still passes the EU homologation tests for ESC.
Review of Techniques in Faceted Search Applications

In this article, we present a faceted search with new strategies that reward users with multiple dimensions or filters to scan. This is to allow users to identify the categories that they want and access the required data quicker, but they also contribute to the overall data fee. The cycle will continue but that does not mean that we should (or might) stop trying to improve our knowledge collection, distribution, and management methods. That is why we believe that filtering information overload is an extremely important issue for better decisions. Although there are numerous search engines, most of them cannot always make the search engines accessible details. To obtain information about a certain subject, searchers must perform inquiries. This information overload problem can be solved by exploratory search technologies. It is transparent and has many apps that can enable users to learn important information and increase their knowledge of the mission. The main contribution of this paper is to examine previous exploratory-based research, the technology and existing implementations used, such as faceted search as a method in which filters are built to restrict the search results.
Map-Based Localization with Factor Graphs for Automated Driving using Non-Semantic LiDAR Features

The knowledge of the map-relative pose of a vehicle is crucial for automated driving. This paper presents a localization approach using a graph-based sliding window optimization with non-semantic LiDAR features. In short, this paper introduces and evaluates our overall concept of non-semantic localization, whose idea and steps were depicted in previous publications. In doing so, we do not rely on static, semantic landmarks, like building corners, thus we achieve independence of these infrastructure elements. Our approach consists of three main steps. At first, we collect the input data, i. e. extracting on-board features from LiDAR point clouds. Afterward, the on-board features are associated over time and matched with the features stored in the map. Then, we use the on-board detected features, map matched features, odometry and GNSS measurements to build a factor graph. The factor graph is optimized to estimate the vehicle pose. Our approach is tested on real-world data. The experiment suggests that our method of applying non-semantic elements provides a working localization solution with practical relevance. It also displays that our approach is persistent examining test drives over one and half years along the same route.
THz time domain spectroscopy of liquids

The communication among molecules in a liquid takes place through intermolecular interactions. Molecules talk to each other through dipole moments, quadrupole moments, etc. or sometimes more directly through weak intermolecular bonds, for example, hydrogen bonds. Understanding the structure and dynamics of these intermolecular interactions have proven to be crucial in the quest to obtain a better molecular description of chemical reactivity. If for example, a molecule is vibrationally excited, either as a consequence of a chemical transformation or excitation by a laser, then the molecule will initially relax by intramolecular vibrational relaxation where predominantly the low frequency vibrations are excited. Subsequently, these low frequency modes relax by coupling to the low frequency intermolecular solvent modes. The time scale for these intermolecular interactions is in the range from 0.1 to 10 ps, corresponding to a maximum spectral density at a few THz, thus matching the spectral coverage of THz-time domain spectrometers. This paper will describe recent THz-TDS results on both polar and non-dipolar liquids, in particular emphasizing the relation between result obtained with THz- TDS, depolarized Raman scattering, and OHD-RIKES, and the study of hydrogen bonds in polar liquids and crystals.
Accelerated Elastic Network Regression via Random Projection

In the large-scale data prediction, in order to better describe the overall characteristics of the data matrix and ensure the model accuracy, an elastic network ENLF model using L1 and L2 limiting objective functions is usually constructed. ENLF model improves the performance of the model, but it has low time effect and high computational complexity for large-scale data matrix applications. The k-low rank approximation dimension reduction of large-scale data is carried out by introducing stochastic projection algorithm, and the model is solved by using accelerated proximal gradient descent algorithm and coordinate descent algorithm. Theoretical analysis and experimental results show that the correlation algorithm based on random projection has better convergence speed, time efficiency and lower computational complexity than the traditional algorithm in solving the ENLF model of large scale data, and the accuracy of the model can be guaranteed.
Riding Pattern Recognition for Powered Two-Wheelers Using a Long Short-Term Memory Network

The automatic recognition of different riding patterns in the context of naturalistic riding studies (NRSs) facilitates the behavioral analysis of powered two-wheelers (PTW), which is a challenging problem. In the NRS context, various multivariate time series data are provided using an inertial measurement unit (IMU). Modeling the temporal dependency between riding patterns using state-of-the-art machine learning methods is not a straightforward task and requires the extraction of relevant features. In this article, we suggest the use of recurrent neural networks (RNNs) for modeling the temporal dependence between successive patterns without requiring manual feature engineering. Experiments are carried out using a real-world dataset of instrumented motorbikes. The analysis of the network activations and estimated weights allows us to describe the complex riding patterns. Furthermore, comparisons with state-of-the-art machine learning methods show the effectiveness of RNNs in the identification of riding patterns.
Two-Level Hierarchical Planning in a Known Semi-Structured Environment

The application of motion planning for autonomous vehicles has been primarily focused either in highly structured or unstructured environments. However, many environments in the real-world share the characteristics of both and can be classified as semi-structured. The adaptation of the strategies from other environments to that of semi-structured, although possible, do not produce trajectories with the required characteristics, especially when the environment is dynamic. To that end, this paper introduced a two-level hierarchical planning strategy consisting of a discrete lane-network-based global planner and a Hybrid A* local planner that: (i) generates a smooth, safe and kinematically feasible path in real-time; (ii) considers structural constraints of the environment from an a priori map. Furthermore, a valid lane-network-based sub-goaling strategy is proposed for providing a reference goal during the local planning process. Simulation and live tests have been conducted to evaluate the functionality of the strategy in several case studies. The implementation choices of: (i) using an open-source highly automated driving (HAD) map framework, Lanelet2, (ii) developing as plugins to an open-source navigation stack, Move Base Flex (MBF), allow the proposed strategy to be easily adopted on other autonomous platforms.
A Max Pressure Approach to Urban Network Signal Control with Queue Estimation using Connected Vehicle Data

Max pressure (MP) is a distributed strategy for adaptive urban traffic signal control. Real-time queue estimation for road links is indispensable for MP-based traffic control. All works conducted so far on MP traffic signal control assumed that accurate information of vehicle queues was directly available in real time. This paper studies joint queue estimation and MP control for signalized urban networks with connected vehicles. For the sake of practical significance, the cases of link queue estimation and lane-wise queue estimation were both considered as input to the MP traffic signal control. A congested 3*3 network was emulated using AIMSUN to evaluate the performance of the developed queue estimation and MP traffic signal control algorithms, with study results reported.
Vision-based Drone Navigation for Orbital Inspection of Pole-like Objects

We present a research work on autonomous visual navigation for aerial inspection on man-made construction. In particular, we focus on developing orbital inspection of pole-like objects. Our technical contribution is in two folds. First, we address the problem from a system perspective and design a complete technical solution. Second, we implement the technical design, integrate all functional components, and employ the developed system on a quadrotor for field test.
Mutual Absolute Calibration of Lidar Mounting Positions on Vehicles using CAD Models

Extrinsic calibration of a sensor mounting position on a vehicle is essential for a truthful mapping of the sensor data with respect to the vehicle. However, extrinsic calibration is usually cumbersome due to complex measurement setups or requirements on the environment, e.g., locally planar surfaces. We propose to use mutual detections of sensors mounted on different vehicles for calibrating their mounting positions. To maximize the accuracy of the detected vehicle poses, our developed method uses CAD models of the involved vehicles. Based on the accuracy of each detected vehicle pose, we further derive a formulation to estimate the calibration accuracy. The benefits of our methods are demonstrated using Monte Carlo simulations and real-world experiments with two BMW test vehicles.
Traffic Congestion Detection Using Fixed-Wing Unmanned Aerial Vehicle (UAV) Video Streaming Based on Deep Learning

Population growth in the region has led to increased use of roads that causes traffic congestion. Traffic congestion also occurs during long weekend in outsides city. The roads in the area are usually smooth traffic flow, it becomes very congested. One method of the smart road monitoring system is a fixed camera sensor as input and artificial intelligent as the analysis. However, these methods require a great infrastructure on highways such as: power supply, protective CPU, good power supply and stable computer network connections. This will be difficult to fulfill if applied on roads outside the city. To overcome the problem, we propose a system of vehicles detection and road density classification using Fixed-Wing Unmanned Aerial Vehicle (UAV) video streaming. We chose Fixed-Wing UAV for its advantages: wide range and fast flight speed. The proposed system detects and classifies vehicles. Vehicles are detected and calcified using CNN’s Deep Learning which uses the YOLO architecture. The level of traffic density is determined by the area of the road covered by vehicles to road area. We tested the proposed system using YouTube video and UAV video streaming. Both experimental scenarios have almost the same results. Precisions of recording video UAV and streaming video are: 90.75% and 90%, respectively.
Overcurrent and Short-Circuit Protection Method using Desaturation Detection of SiC MOSFET

This paper present overcurrent and short-circuit protection methods for medium voltage silicon carbide(SiC) MOSFETs. Contrary to other protection methods such as a Rogowski coil, the desaturation detection can be simply designed and applied to the general gate drivers. Thus, this paper proposes the design of the desaturation detection circuit. In order to achieve fast protection performance, additional issues of desaturation detection were described. In the end, the protection circuits are evaluated in short-circuit and overcurrent with the 1200 V SiC module and 1700 V SiC discrete.
Low-Profile, Multiband & Wideband ‘C-Shaped’ Monopole Antenna for 5G and WLAN Applications

A novel, very low profile, multi band and wide band monopole antenna for fifth generation wireless (5G) and Wireless LAN (WLAN) applications is proposed. The footprint of antenna is only 20mm x 15mm and is designed using economical FR-4 substrate of thickness 0.8mm. The compact structure of the proposed antenna composed of two "C-shaped" radiators pointing towards each other along with loading of circular radiator at each corner of C-shaped radiator and a rectangular tuning stub. The complete structure is fed by 50Ω microstrip line resulting in two resonances at 3.9GHz and 6.6GHz. The two resonances achieve 6 dB impedance bandwidth of 76% (3.30-7.35) GHz. The proposed antenna also exhibits a stable gain of around 2.25dBi, efficiency of around 85% and approximately omnidirectional radiation patterns in the functioning bands of interest. Therefore, because of good radiation characteristics, compact in size, and economical and simple in design, the proposed antenna is a good candidate for 5G and WLAN applications.
EMG based Hand Gesture Recognition using Deep Learning

The Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications.
Coriander Classification System Using Machine Learning Techniques

In agricultural science, the use of imagery is of great utility. By means of images, diseases can be detected and identified at an early stage. Another way to use images is for sorting and grading. The need of this work is to create a classification system able to classify the quality of coriander as close as possible to the human eye. The problem in classifying the quality of coriander is that it requires experience and good vision. With an ageing population, this task may become quite tedious. To counterpart this problem, machine learning technique is used to create a coriander classification system. Convolutional neural network (CNN) is used as the machine learning technique as it supports the use of images and is able to treat images of three channel depths. To train the model, the need of a database is of upmost importance. Therefore, a database containing images of fresh coriander and rotten coriander was created. A classification model was created and after evaluation, the optimal accuracy reached 98%. Considering the relatively small database used, the model is considered to be very efficient.
Low-Power Wireless Communication for Wireless Power Transfer Device

The Implantable Medical Devices (IMDs) are playing an increasing role for its benefit in monitoring and detecting a patient’s disease symptoms. One of the challenges faced by the IMD developer is the need for a long lifetime of the devices. Along with the implementation of wireless power transfer (WPT) technology, a low-power communication between the implants and the receiver outside the patient’s body is needed to ensure a low medical cost and risk from the need of battery replacements through surgery. Adopting optical wireless communication, in this article, a novel approach to solve the problem is introduced. Using an unsynchronized pulse-interval modulation (UPIM) technique, a system working principle is addressed, and a prototype is also built within a 10mm x 10mm x 2.3mm form factor. Experiments are conducted to test the system design, ensuring its ability to carry out low-power wireless communication with µW-level power consumption. The result shows that the proposed approach can achieve as low as 392uW power consumption. Finally, the technical challenges along with the possibility for further development is discussed.
FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions

Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding and time-consuming. Deep learning (DL) is emerging as a viable means to accelerate CFD simulations by directly predicting the outcomes of multiple simulation iterations. While promising, existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FlowGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training. As FlowGAN does not rely on knowledge of the underlying governing equations, it can quickly adapt to various flow conditions and avoid the need for expensive re-training. We evaluate FlowGAN by applying it to scenarios of simulating both the whole flow field and selected regions of interest (RoI). Compared to the state-of-the-art DL based methods, FlowGAN significantly reduces the prediction errors by 2.27% while exhibiting a better generalization ability.
Survey of Data Mining Techniques for Intrusion Detection Systems

Nowadays, the number of cyber-attacks is increasing; therefore, it is important for companies or organizations to secure their networks. Intrusion Detection System (IDS) is one of the core components used to secure networks. IDS's role is to detect an attack or attempted attack. The security tools are growing rapidly, along with the increasing number and variety of attacks carried out. The same with the development of IDS, a lot of research has been done to improve the ability of IDS to detect an attack. Many studies have used data mining techniques on IDS. This paper aims to conduct a comprehensive survey on the use of data mining techniques on IDS in the last five years. In this survey, we category the techniques used into several categories, and discuss each category in detail.
Cochlear Implant Wireless Power Transfer System Design for High Efficiency and Link Gain Stability using A Proposed Stagger Tuning Method

In recent years, wireless power transfer (WPT) technology is becoming an increasingly important part of the various applications because of its safety. Especially, medical implant devices such as a cochlear implant are one of the typical application of the WPT technology. Because of the individual body differences, however, the coupling coefficient of the WPT system can be changed. Therefore, the coupling coefficient-insensitivity is very important design issue.In this paper, we propose an stagger tuning method of a cochlear implant WPT system. The proposed method makes the cochlear implant WPT system stable at the link gain regardless of the coupling coefficient. In addition, the design can be optimized Λvith consideration of the power transfer efficiency (PTE) of the WPT system. We verified that the proposed method increased the PTE of the cochlear implant WPT system by 9 % by mathematical calculation.
Interaction aware cooperative trajectory planning for lane change maneuvers in dense traffic

In order to generate favorable trajectories, road users need to cope with interaction among them, especially in dense traffic. Thus, for autonomous cars, the intention of involved vehicles needs to be considered in their motion planning. This paper proposes a general framework for cooperative interaction aware trajectory generation based on multiagent trajectory planning. Possible intentions are distinguished by different cost functions, resulting in different behaviors such as cooperative or non-cooperative. Given observations, Bayesian estimation is used to obtain a probability distribution of the intention models. Considering these probabilities during prediction and planning results in trajectories taking uncertain interaction with surrounding vehicles into account. The performance of the approach is demonstrated via numerical experiments for a lane change scenario in dense traffic.
Predicting the BI-RADS Lexicon for Mammographie Masses Using Hybrid Neural Models

The mammographie image analysis carried out by radiologists describes findings to infer the type of breast pathology to lead patients to adequate treatments. For such an analysis, the experts use a lexicon in the Breast Imaging-Reporting and Data System (BI-RADS), through which the shape and margin of the tumor are described, as well as the breast parenchymal density to determine the risk of developing breast cancer. In this sense, the development of technological tools to assist radiologists in the mammographic description is justified. In this article, two hybrid classification techniques based on neural models are proposed to predict the BI-RADS lexicon attributes for masses. These hybrid models comprise a pre-trained convolutional neural network (AlexNet) for feature extraction and neural models for pattern classification, namely, multilayer perceptron (MLP) and dendrite morphological neuron (DMN). The obtained classification results suggest that the hybrid neural model schemes obtain an accuracy of 91% for predicting the shape and density attributes and an 85% accuracy for predicting margin attributes. The Wilcoxon test indicates no significant differences between both hybrid models. Therefore, the proposed models can be useful for predicting attributes included in the BI-RADS lexicon for masses.
Deep Learning Networks for Vowel Speech Imagery

Speech Imagery (SI) is a successful alternative for communication systems based on Electroencephalographic (EEG) signals that do not need external stimuli like evoked potentials. A recent strategy for SI is to analyze Speech Related Potentials (SRP) features on EEG signals to recognize vowels. However, there is research to be done in the development of machine learning methods for vowel classification in SI signals based on SRP. Therefore, this paper proposes two neural networks to classify vowels in speech imagery signals using SRP: a Convolutional Neural Network called sCNN and a Capsule Neural Network called sCapsNet. The experiments were developed with the DaSalla dataset. According to the results, sCapsNet reports better performance than sCNN and Support Vector Machine (SVM). The average accuracy was 71.9%, 67.63%, and 71.33% respectively. Besides, the capsules of sCapsNet could model by vectors the SRP features regardless of time and differences of SI vowels by subjects.