Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Yaw Rate Control For Lane Change Maneuver Using Fuzzy PID Controller And Neural Network Controller

Vehicle safety and control is attracting attention increasingly in an attempt to improve the stability and manoeuvrability of vehicles. Three degrees of freedom vehicle dynamic model (called planar vehicle model) is established. Based on theories of fuzzy PID control and neural network based-Controller, controller of vehicle stability is designed by using the method of direct yaw rate control and the two different control strategies. The controllers were compared under one road condition which is a lane change of manoeuvre. By comparing and analyzing the control effect of fuzzy PID control and neural network based-Controller, the result shows as follows: the two controllers improved the yaw rate to follow the reference yaw rate but, using the fuzzy PID controller gave a better and closer path for the desired path of yaw rate compared to using of the neural network controller.
Ferroelastic crystals and their nonlinear elastic properties observed in frequency range from gigahertz to milihertz

The experiments reported in this article have been performed to unify the results of earlier and recent determinations of parameters describing the elastic properties of Rb4LiH3(SO4)4, LiCsSO4 (LCS) and KH2PO4 crystals measured by different experimental methods. The above crystals undergo a second-order phase transition, incommensurate phase transition (PT) and a first-order but close to second-order phase transition, respectively. To investigate the elastic properties of the crystals in the frequency range from 10−1 to 1010 Hz, five experimental methods were applied: dynamic mechanical analysis, piezoelectric resonance, composite oscillator bar, ultrasonic wave propagation and Brillouin light scattering. The results of the experiments allowed identification of the processes contributing to the elastic response of the crystals investigated. Moreover, for the LCS crystal a model of the incommensurate PT was presented.
Nonlinear Control Strategy of Full Bridge Shunt Active Power Filter

In this paper, considering the problem of control the full bridge shunt active power filter operating with nonlinear loads. The main control objectives are: (i) compensation of harmonics current and the reactive power caused by the nonlinear load in order to ensure a perfect power factor correction (PFC) at the point of common coupling (PCC); (ii) regulation the DC voltage of inverter. A nonlinear control strategy is proposed to achieve the main objectives taking into account of grid impedance, and of all non negligible parameters of circuit. The inner-loop control is designed, using the Lyapunov technique, to cope with the compensation issue. The outer-loop control ensures the DC voltage regulation of inverter. The control performances are analyzed using the averaging theory. Simulation results show the achievement of above objectives and also the effectiveness of the proposed control.
Modeling of a Resistive Voltage Divider by Rational Functions: Uncertainty Evaluation

This article describes the modeling of a resistive voltage divider (RVD) by rational functions using the vector fitting (VF) tool. The method is applied to ratio and phase displacement measurements over a wide frequency range. A Monte Carlo simulation-based sensitivity analysis was carried out to properly estimate the model uncertainty. The modeling tool proved to be an advantageous alternative with suitable adherence to the traced points, not exceeding a few parts in 106. Real and imaginary parts can be taken into account in the same fitting, preserving their correlation for the uncertainty estimation. Such a black-box approach can also help to estimate intermediate and above limit points since the transfer function is given by the tool.
Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection

Electroencephalography (EEG) signals have been proven to be one of the most predictive and reliable indicators for estimating driving fatigue state. However, how to make full use of EEG data for driving fatigue detection remains a challenge. Many existing methods include a time-consuming manual process or tedious parameter tunings for feature extraction, which is inconvenient to train and implement. On the other hand, most models ignore or manually determine EEG connectivity features between different channels, thus failing to thoroughly exploit the intrinsic interchannel relations for classification. In this article, we introduce a new attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN) model, aiming to conquer these two issues in a unified end-to-end model. AMCNN-DGCN starts with attention-based multiscale temporal convolutions to automatically learn frequency filters to extract the salient pattern from raw EEG data. Subsequently, AMCNN-DGCN uses dynamical graph convolutional networks (DGCNs) to learn spatial filters, in which the adjacency matrix is adaptively determined in a data-driven way to exploit the intrinsic relationship between channels effectively. With the temporal–spatial structure, AMCNN-DGCN can capture highly discriminative features. To verify the effectiveness of AMCNN-DGCN, we conduct a simulated fatigue driving environment to collect EEG signals from 29 healthy subjects (male/female = 17/12 and age = 23.28±2.70 years) through a remote wireless cap with 24 channels. The results demonstrate that our proposed model outperforms six widely used competitive EEG models with high accuracy of 95.65%. Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix.
OH Formation in the Photoexcitation of NO2 beyond the Dissociation Threshold in the Presence of Water Vapor

The pulsed-laser-excitation/resonance fluorescence technique was used to assess the efficiency of OH formation following photoexcitation of NO2 at discrete wavelengths beyond the photodissociation threshold in the presence of water vapor: NO2* + H2O → HONO + OH. Excitation at wavelengths between 432 and 449 nm was found to lead to OH production via a facile sequential two-photon absorption by NO2, leading to O(1D) and thus to OH in the presence of H2O, i.e., NO2 + hν → NO2*, NO2* + hν → NO2**, NO2** → NO + O(1D), O(1D) + H2O → 2OH. The cross section for the transition NO2** ← NO2* was found to be similar to that for the NO2* ← NO2 transition at 435 nm. At 532 nm, the two-photon process is not sufficiently energetic to form O(1D), and OH is not observed. An upper limit of approximately 7 × 10-5 is found for the reactive quenching of NO2* by water vapor relative to collisional quenching. The atmospheric relevance of OH formation via NO2 excitation is discussed.
Zero-Determinant Strategy to Enhance Cooperation in Intrusion Detection Networks

With the enlargement of cyberspace scale and development of the network attack technologies, collaborative intrusion detection technology which can effectively improve the security of network systems has been a research focus in recent years. This kind of intrusion detection technology based on distributed computing and cooperation theory is composed of several cooperative intrusion detection systems (IDS). However, individual IDS should be selfish and aim to maximize its own benefits, and might not be effective in cooperative manner in intrusion detection task. This may damage system performance. Therefore, it is of great significance to study the cooperative mechanism among these IDS nodes. In this paper, we simulate the interactions between individual IDS node as a repeated game model by using zero-determinant strategy to deal with the “free riding” behavior, because no matter what the behavior of the adversary is, the game participants can keep the payoffs in the ideal value and achieve the maximum of system payoffs. The numerical results demonstrate that Zero-determinant strategy can enhance the cooperation in intrusion detection networks effectively.
Statistical Fitting of Wind Speed Data for Determination of Wind Power Potentials in Saudi Arabia

This paper present a statistical analysis of wind speed data for the Kingdom of Saudi Arabia. The study aim to determine the best probability distribution function for the wind speed data of some selected sites in the Kingdom. This is achieved by analysing a few parameter distribution functions. The probability distribution functions used in the analyses were Weibull, gamma, and Rayleigh distributions. In order to determine the best fits, a mean annual error was used. Results shows that the Weibull distribution has a 0.000601 annual average error, followed by the gamma distribution with an annual error of 0.362221, and finally, the Rayleigh distribution with an annual distribution error 0.63323. This indicates that the sites under study are suitable for electrical power generation in both grid-connected and off-grid modes. Comparing the annual error of each site shows that the gamma distribution performs better compared to other distributions. The results determined the best site for wind speed and very important for various microgrids designs in the Kingdom of Saudi Arabia.
Towards Computer Aided Diagnosis of Autism Spectrum Disorder Using Virtual Environments

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that are associated with characteristic difficulties to express and interpret nonverbal behavior, such as social gaze behavior. The state of the art in diagnosis is the clinical interview that is time intensive for the clinicians and does not take into account any objective measures of behavior. We herewith propose an empirical approach that can potentially support diagnosis based on the assessment of nonverbal behavior in avatar-mediated interactions in virtual environments. In a first study, ASD individuals and a typically developed control group were interacting in dyads. Head motion, and eye gaze of both interlocutors were recorded, replicated to the avatars and displayed to the partner through a distributed virtual environment. The nonverbal behavior of both interaction partners was recorded, and resulting preprocessed data was classified with up to 92.9parcent classification accuracy, with the amount of eye area focus and the average horizontal gaze change being the most relevant features. We expect that such systems could improve the diagnostic assessment on the basis of objective measures of nonverbal behavior.
Fabric Defect Segmentation Method Based on Deep Learning

Fabric defect detection plays an essential role in the textile production process, which was widely applied in the textile industry. For fabric defect detection, many algorithms have been proposed. However, lots of important problems, such as the accuracy of detection, the computational complexity of the algorithm, and data imbalance, still needed to be addressed for application in industrial production. In this article, we propose an efficient convolutional neural network for defect segmentation and detection. The design of this framework significantly alleviates the manual annotation cost of the data set; it only needs few defect samples combined with standard samples to learn the potential feature of defects and obtain the location of defects with high accuracy. The network is divided into two parts: segmentation and decision. First, the fabric data set without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as the raw materials for training the decision network. Finally, a well-trained network is used to obtain the location of defects with high accuracy. The proposed method only demands almost 50 defect samples to get accurate segmentation results and can achieve the requirement of real-time detection with a speed of 25 frames per second (FPS). The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness.
On the Lack of Anonymity of Anonymized Smart Meter Data: An Empiric Study

After over a decade of research into the privacy of smart meters, the sensitivity of an individual household’s fine-grained energy readings is undisputed. A plethora of research contributions aim at protecting the privacy of end users while at the same time providing the energy supplier (and others) with sufficient data for safe operation, billing, and also forecasting purposes. The transmission of fine-grained readings is generally considered acceptable as long as they cannot be linked to the households they originate from (i.e., anonymized readings). Martinez et al. just recently pointed out that the typical provision of aggregated readings at the end of a billing period could compromise this anonymity, as the individual readings must sum up to the respective aggregate. In this short paper, we complement their research by examining the privacy implications of published aggregates of previously anonymized energy readings:We simulate attacks on a real world data set (Smart*), particularly investigating the implications of different parameter combinations such as aggregation group sizes, considered time spans, and reading precision to gain insights into theoretic risks, e.g., from an incautious choice of parameters.
ReAL: A New ResNet-ALSTM Based Intrusion Detection System for the Internet of Energy

The Internet of energy (IoE), envisioned to be a promising paradigm of the Internet of things (IoT), is characterized by the deep integration of various distributed energy systems. However, the fusion of heterogeneous IoE communication networks creates a new threat landscape. To thwart and mitigate various types of cyber threats to IoE networks, this paper proposes a novel intrusion detection system (IDS) based on a designed residual network with attention long short term memory (ReAL). Specifically, we design a light gradient boosting machine (LightGBM)-based feature selection method to identify the most useful features. Then, a residual network (ResNet) and a long short term memory neural network with an attention mechanism (ALSTM) are employed, to extract temporal patterns of network traffic events. After that, these patterns are orchestrated to identify the anomalies in IoE networks. The high effectiveness of the proposed IDS is validated on a real IoE dataset.
A New Design of Fractal Aperture Coupled Koch Snowflake Reconfigurable Multiband Antenna

A new frequency reconfigurable multiband fractal antenna is proposed in this paper. The radiated part has a Koch snowflake structure that is etched with two slots; one is an inverted U-shaped slot, and the other one is rectilinear. The reconfigurability is achieved by 2 PIN diodes, and the feeding is ensured by exciting the microstrip trace, which leads to electromagnetic coupling with the patch via the aperture. The antenna size is 30x30x3.7mm3 and the simulation results show that the proposed antenna operates in the 9.86−11.76 GHz and 19.55 - 23.8 GHz bands. Also, controllable resonance frequencies in the WiMAX band of 3.33–3.68 GHz and6.12−6.75 GHz bands are achieved.
LoRaDRL: Deep Reinforcement Learning Based Adaptive PHY Layer Transmission Parameters Selection for LoRaWAN

The performance of densely-deployed low-power wide-area networks (LPWANs) can significantly deteriorate due to packets collisions, and one of the main reasons for that is the rule-based PHY layer transmission parameters assignment algorithms. LoRaWAN is a leading LPWAN technology where LoRa serves as the physical layer. Here, we propose and evaluate a deep reinforcement learning (DRL)-based PHY layer transmission parameter assignment algorithm for LoRaWAN. Our algorithm ensures fewer collisions and better network performance compared to the existing state-of-the-art PHY layer transmission parameter assignment algorithms for LoRaWAN. Our algorithm outperforms the state of the art learning-based technique achieving up to 500% improvement of PDR in some cases.
Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.
Automated Home-Based Physiotherapy

Last few decades there is rapid technology development in the medical field. Automation plays an important role in our day to day life nowadays. Because of time constraints, no one wants to waste their time in unnecessary waiting at the hospitals and the development of home-based physiotherapy will be very much useful for such patients to deal with the minor problems. Automated home-based physiotherapy will make the patient more comfortable as he/she can do it at home without any extra medical fee. The role of physiotherapy is very much appreciable in the medical field as there is no need for medications and shows fewer side effects. Physiotherapy includes several methods one of the most famous methods is performing manual therapy (massaging), stretches and exercises, magnetic therapy, and Joint mobilization. Physiotherapy help one who is met with an accident and having fractures helps to reduce the pain, make the body stronger and flexible. This work examines the feasibility of the implementation of home-based physiotherapy by using Inertial Measurement Unit (IMU) regularization and wireless sensor networks
Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Aiming at exploiting speech correlation across consecutive time-frames in the short-time Fourier transform domain, the multi-frame minimum variance distortionless response (MFMVDR) filter for single-channel speech enhancement has been proposed. The MFMVDR filter requires an accurate estimate of the normalized speech correlation vector in order to avoid speech distortion and artifacts. In this paper we investigate the potential of using robust MVDR filtering techniques to estimate the normalized speech correlation vector as the vector maximizing the total signal output power within a spherical uncertainty set, which corresponds to imposing a quadratic inequality constraint. Whereas the singly-constrained (SC) MFMVDR filter only considers the quadratic inequality constraint to estimate the (non-normalized) speech correlation vector, the doubly-constrained (DC) MFMVDR filter integrates a linear normalization constraint into the optimization problem to directly estimate the normalized speech correlation vector. To set the upper bound of the quadratic inequality constraint for each time-frequency point, we propose to use a trained non-linear mapping function that depends on the a-priori signal-to-noise ratio (SNR). Experimental results for different speech signals, noise types and SNRs show that the proposed constrained approaches yield a more accurate estimate of the normalized speech correlation vector than a state-of-the-art maximum-likelihood (ML) estimator. An instrumental and a perceptual evaluation show that both constrained MFMVDR filters lead to less speech and noise distortion but a lower noise reduction than the ML-MFMVDR filter, where the DC-MFMVDR filter is preferred in terms of overall quality compared to the SC-MFMVDR and ML-MFMVDR filters.
Using Semantic Segmentation to Assist the Creation of Interactive VR Applications

The creation of interactive VR applications from 3D scanned content usually includes a lot of manual and repetitive work. Our research aim is to develop a real-world, cross-domain, automatic, semantic segmentation system that enhances the creation of interactive VR applications. We trained segmentation agents in a superpoint growing environment that we extended with an expert function. This expert function solves the sparse reward signal problem of the previous approaches and enables to use a variant of imitation learning and deep reinforcement learning with dense feedback. Additionally, the function allows to calculate a performance metric for the degree of imitation for different segmentations. We trained our agents with 1182 scenes of the ScanNet data set. More specifically, we trained different neural network architectures with 1170 scenes and tested their performance with 12 scenes. Our intermediate results are promising such that our segmentation system might be able to assist the VR application development from 3D scanned content in near future.
Prioritisation of areas for early detection of southward movement of arctic fox rabies based on historical surveillance data in Quebec, Canada

Arctic rabies virus variant (ARVV) is enzootic in Quebec (Canada) north of the 55th parallel. With climate change, increased risk of re-incursion of ARVV in more densely populated southern regions raises public and animal health concerns. The objective of this study was to prioritise geographical areas to target for an early detection of ARVV incursion south of the 55th parallel based on the historical spatio-temporal trends of reported rabies in foxes in Quebec. Descriptive analyses of fox rabies cases from 1953 to 2017 were conducted. Three periods show increases in the number of fox rabies cases in southern regions and indicate incursion from northern areas or neighbouring provinces. The available data, particularly in central and northern regions of the province, were scarce and of low spatial resolution, making it impossible to identify the path of spread with precision. Hence, we investigated the use of multiple criteria, such as historical rabies cases, human population density and red fox ( Vulpes vulpes ) relative abundance, to prioritise areas for enhanced surveillance. This study underscores the need to define and maintain new criteria for selecting samples to be analysed in order to detect rapidly ARVV cases outside the current enzootic area and any potential re-incursion of the virus into central and southern regions of the province.
Exploring EFL Student Teachers’ Perceptions of Student-led Seminars

Student-led seminars have not been widely researched from the student perspective despite their prevalence in different streams of education literature. Based on four focus group interviews involving 24 students from a university in Saudi Arabia, this study evaluated student-led seminar sessions from the perspective of EFL student teachers, to generate relevant themes and categories. The themes identified comprised: preparing for seminar sessions, implementing seminar sessions, and receiving feedback after seminar sessions. Results found that students acknowledged the advantages of student-led seminars, but were worried about engaging in student-led seminars. Others preferred to receive guidance from the instructor, and some believed collaborative work was more important than individual work. It is essential to investigate students’ perceptions to obtain a more thorough understanding and allow the formulation of an effective framework implementing student-led seminars.