Miracle-dz/UniFAR-FTUs huggingface dataset:
The Trajectories of Heavy, Solid Particles in a Two-Dimensional Jet of Ideal Fluid Impinging Normally upon a Plate

An expression is derived for the flow field in a two-dimensional jet of ideal fluid impinging normally upon a plate situated at an arbitrary distance from a parallel sided orifice. Numerical data are given when the orifice is at an infinite distance and in three other cases. In each example the trajectories of solid particles carried by the fluid are calculated by a stepwise method, and the conditions determining whether a particle will strike the plate or not are found.
Cooperative Jamming for Eavesdropping Surveillance in Wireless Networks

This paper investigates the eavesdropping surveillance issue via cooperative jamming in a wireless network consisting of a suspicious transmitter, a suspicious receiver, a legal monitor and multiple potential jamming nodes. In such a network, the legal monitor attempts to intercept the suspicious message and also prevents the suspicious receiver from receiving the message with the help of jamming nodes. To this end, we propose two selection schemes of jamming nodes. One scheme is to randomly select some nodes from the potential jamming nodes, and another one is to select the jamming nodes which have less interference to the legal monitor and more interference to the suspicious receiver. Under each scheme, we maximize the eavesdropping rate by optimizing the transmit power of jamming nodes. Simulation results are presented to illustrate our findings.
Fast Modular Exponentiation Methods for Public-Key Cryptography

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer $x$ to power $k$ and reducing the result modulo some integer $n$. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.
Theoretical Analysis and Designing a Dual-Bridge LLC Resonant Converter for On-Board Charger

This paper proposes a unidirectional DC/DC converter architecture based on dual-bridge LLC (DBLLC) resonant converter for on-board charge (OBC) application. The LLC resonant converters are the preferred choice to select of DC/DC converter topology for OBC due to it provides many advantages such as high efficiency, high power density. The conventional LLC resonant converters work at fix duty cycle and variable frequency control. In this paper, a converter based on the dual-bridge LLC resonant converter operating at fixed frequency with the pulse width-modulated (PWM) control will be applied as an isolated stage in the OBC 7.4 kW to improve the power density and to reduce conduction losses. The advantage of operating at the resonant frequency is the switching losses are reduced since the soft-switching technique is achieved over the full load range. Furthermore, the detailed design procedure of a transformer is presented along with the transformer losses and also the switching losses analysis. The proposed topology is designed for 400 VDC input to 220-380VDC output conversion, operating at 100 kHz and in the range from 4.8 kW to 7.4kW. All theoretical analyses and the simulated results on the proposed converter are provided to verify the system performance.
Combining Vehicle BUS and Vital Sign Sensors for Continuous Health Monitoring During Driving: Concept and First Results

Continuous health monitoring is the key to change the paradigm in medicine from diagnostics & therapy to prediction and prevention. As humans, on average, spend about 1 hour per day in a car, we target vital sign recording in the vehicle. The different BUS systems in the vehicle deliver information that will help to distinguish automatically artifacts due to movement from pathologic irregularities of the signals. In our research car, the Drivetrain controller area network (CAN), the Suspension System and Driver Assistance CAN, and the Comfort CAN provide 33 electronic control units (ECU) with about 3,700 signals. Furthermore, we suggest redundancy for vital sign recording. We integrate electrocardiography (ECG) and photoplethysmography (PPG) in the steering wheel, imaging PPG in the windscreen, and phonocardiography (PCG) in the seatbelt. Our preliminary results obtained from steering wheel-based ECG deliver reliable recordings from about 45.62 % of the driving time. We expect an increase in that portion of our redundant sensing approach. However, as compared to the current selective measures of less than 60 sec, 20 min to 30 min per day may help to prevent significant adverse health events such as stroke and heart failure as well as those arising from chronic illnesses.
A novel assay for rapid HIV-1 protease detection using optical sensors and magnetic carriers

In this work, a very simple electrochemical HIV-1 protease biosensor useful for the development of an inexpensive lab-on-a- chip (LOC) device was constructed. The detection mechanism was designed to minimize the complexity either in the recognition receptor immobilization step or during the detection itself. The magnetic self-assembled monolayer of HIV-1 protease substrate peptide was able to detect as low as 10 pg/ml of the protease within 25 minutes with high specificity.
A Queueing-theoretic Analysis of the Performance of a Cloud Computing Infrastructure: Accounting for Task Reneging or Dropping

Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate $\Delta\lambda$ for a fixed service rate $\mu)$ results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking.
Enhancing the Resolution of Ancient Artworks using Generative Adversarial Networks

Inp ainting is a form of conservation that involves filling in damaged, deformed, or missing areas of an artwork in order to present a whole image. This method may be imp lemented in both traditional and digital forms of visual art, including chemistry-based photo prints, paintings done in oil or acrylic, sculptures, and digital video and still photos. The process of increasing the quality of a picture by filling in missing pixels using a digital image editor is known as “digital image inpainting. ” Through the utilization of the Generative adversarial network, the focus of this investigation is to improve both the originality and the overall quality of the input picture. to provide a process for the long-term digital preservation and repair of historical works of art for future generations.
Dense Region Detection of Streaming Social Media Datasets using Blockchain-based Secure Computation

The popularity of blockchain technology is increasing day by day, and it has a widespread usage as a reliable database structure. Blockchain has been utilized across a wide range of industries, such as IoT, social media, robotics, government, supply chain, and healthcare. As the usage of blockchain is emerged in social media studies, the problem of storing and analyzing streaming social media data in a safe, anonymous, and reliable manner is emerged. Within the scope of this problem, in this study, the adaptation of the streaming social media dataset to the blockchain systems and analysis of this data are carried out. A new algorithm of Dense Region Detection Algorithm (DRDA) is proposed to detect dense social regions of urban areas. Streaming data are collected from New York City, and the proposed DRDA is performed by miners to determine the dense regions of New York. As a result, dense regions with intensive tweet sharing in New York are detected and discussed.
Data-Driven Output Regulation of Uncertain 6 DOF AUV via Lagrange Interpolation

This article proposes a data-driven method for tracking control of the autonomous underwater vehicle (AUV) with matched disturbances and time-varying model’s uncertain parameters. The method is established by combining the conventional model reference control principle with a corresponding procedure for step-wise updating controller parameters so that the model error between the AUV and an appropriately chosen linear stable model converges to zero. The update procedure of controller parameters is created based on Lagrange interpolation technique, hence it acts entirely on the experimentally collected data from the AUV. The expected tracking performance by using this proposed data-driven controller has been theoretically authenticated and through numerical simulation.
Impact of Gradient Descent Optimization Techniques for Classification of Hyperspectral Remotely Sensed Images Using 3D CNN

Rapid technological advances has seen massive developments in the application domains such as satellite, aerial and Unmanned Aerial Vehicles remote sensing. In the era of Fourth industrial revolution, both Artificial Intelligence and RS technologies play important role in society around the globe. The acquisition of RS image data from satellite RS can be in a form of visible, infrared, multispectral, and hyperspectral. Various resolution are derived from this different image acquisition sources. Hyperspectral RS however has emerged as a great topic in the RS research society at large. Deep Learning algorithms such as Convolutional Neural Networks (CNN) have gained a lot of attention for image classification tasks. CNN makes use of gradient descent for optimization such as Adam, SGD, Adamax, Adadeta, Nadam and Adagrad. The gradient-based algorithms affect the output performance of the classification algorithm. In this study, we evaluate the impact of six DL optimization techniques, and the model performance of a 3D CNN in terms of accuracy. The experimental simulation is performed using publicly available hyperspectral dataset, which is called University of Pavia (UP). To avoid overfitting, dropout was utilized on the model. The results show that implementation of a 3D CNN HSI classification, performed better with Adam and Nadam. In future, we plan to run the optimization models to check the computational efficiency and fine-tune the models network structure and improved accuracy.
LCL Filter Design in Three-phase AC/DC Power Factor Correction for Electric Vehicle Fast Charging Application

This paper presents an LCL filter design for a 40 kW three-phase grid-interactive converter, which is used in electric vehicle off-board fast chargers. As compared to the conventional filter, the LCL filter is widely used in the AC/DC power factor correction converter due to its harmonic attenuation performance and system stability. The LCL filter parameters are designed based on the system power, AC input frequency, PWM frequency, and it was performed by simulating and detailed theoretical analysis. The control method of the system is a double current closed-loop control method to achieve a good response for AC converter side current and DC side voltage. The system performance analysis is including the fast dynamic response, the efficiency of the system, the DC ripple voltage, and AC current harmonic. The simulation results are shown to verify the proposed filter design method.
n2MFn2: Non-negative Matrix Factorization in A Single Deconstruction Single Reconstruction Neural Network Framework for Dimensionality Reduction

One of the most commonly used approaches for handling complex datasets with high dimensions is dimension-ality reduction. In this scenario, a single deconstruction single reconstruction neural network model for non-negative matrix factorization technique under neural network framework has been developed aiming towards low rank approximation. With the help of hierarchical learning, the pervasiveness of the non-negative input data has been processed to produce a part-based, sparse, and meaningful representation. A modification of the He initialization technique to initialize weights maintaining the non-negativity criteria of the model, has also been proposed. Necessary modification of the ReLU activation function has been made for inhibiting a layer's entire population of neurons from simultaneously adjusting their weights. Regularization has been used in the design of the model's objective function to minimize the risk of overfitting. To prove the competency of the proposed model, the results have been analyzed and compared with that of six other leading dimension reduction techniques on three popular datasets for classification. The analysis of the same has justified the effectiveness and superiority of the model over some others. Additionally the computational complexity of the model has been discussed.
Modeling a monocrystalline Cu(In,Ga)Se2 single junction solar cell grown on a GaAs substrate

A polycrystalline Cu(In,Ga)Se2 (CIGS) single junction solar cell model is developed with dependencies on the molar fraction of In and Ga with a 0.8 eV Shockley-Read-Hall (SRH) trap level above the valence band. The simulated performance of this solar cell over molar fraction compares well to data published in the literature using the SRH minority carrier lifetime to fit the trend in open circuit voltage. The material parameters are then used as a foundation for a numerical model of a monocrystalline CIGS solar cell grown on a GaAs substrate with an emphasis on modeling the CIGS/GaAs interface where a molar fraction gradient in CIGS forms due to lattice mismatch induced inter-diffusion of Ga and In from the substrate and CIGS layers. Without strain effects due to the lattice mismatch, the CIGS monocrystalline solar cell has an efficiency of 18.6% under the AM1.5G spectrum (1000 W/m2) with a short circuit current density of 36.5 mA/cm2, an open circuit voltage of 0.66 V and a fill factor of 77.4%. However, when reasonable strain effects are considered, such as the formation of strained induced interface defects and threading dislocation densities (TDD), the efficiency degrades to 6% for TDD < 1x107 cm-2. The models are able to reproduce a similar structure’s measured performance using a TDD of 1.5×107 cm-2 and a surface recombination velocity of 108 cm/s at the CdS/CIGS interface.
Customized First Order Convex Optimization Algorithm for Powered-Descent Guidance

This paper introduces the extrapolated proportional integral projected gradient (ePIPG) method, a newly developed first-order method, for second-order cone programming problems (SOCP), and its customized implementation to solve the 3-DoF powered-descent guidence problem. The ePIPG solvers parse the problem parametets automatically to fully utilize the problem sparse structure. In addition, different from the classic interior-point method, being a first-order method, ePIPG only relies on simple algebra operations, for example, projection onto convex sets, multiplication of matrix and vector and vectors addition. The numerical experiment shows that the customized ePIPG solver is significantly faster than ECOS and GUROBI, two advanced convex optimization solvers, thus is a potential method for future rocket landing missions.
Movement Disorders Detection in Parkinson’s Patients using Hybrid Classifier

Parkinson’s disease (PD) is a slow-progressing condition with symptoms such as tremors in the hands, arms, legs, and face that get worse gradually. PD patients might have difficulties in movement and completing daily activities as the condition progresses. Despite the fact that there really is no fixed remedy for Parkinson’s disease, the symptoms of the disease can be alleviated with the use of some medications. The patient can live a normal life by reducing the symptoms that the condition causes. At this point, it’s critical to avoid early disease identification and development. In this study, time-frequency based feature extraction strategy such as signal energy, signal variance, signal frequency, empirical mode decomposition (EMD) along with population-based incremental learning (PBIL) Optimization algorithm and convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) has been used as a classifier to detect walking disorder in Parkinson’s disease patients. The suggested model has given 89.4% and 93.5% precision over freezing of gait (FOG) dataset and Juutinen’s dataset respectively. It is concluded that the suggested model’s classification results has been outperformed the classification results obtained using the data’s original features. The proposed system can be used to further refining the analytic system for longer measures in free-living environments that can be helpful in daily life activities especially in medical field.
Anomalous Diffusion in a Linear Plasma Generator

Plasma production and particle transport of the magnetized plasma in the linear device PSI‐2 are investigated by analyzing the radial density and electron temperature pro.les obtained from Langmuir probe measurements. Additional information on the atomic (H) and proton temperatures is obtained from high resolution Doppler spectroscopy. The density profiles are found to be hollow, but do extend radially far beyond the visible rim with an exponential decay length of 3 cm. They can be explained by parallel and perpendicular diffusion in combination with a cylindrical source extending along the magnetic field all over the device. A large perpendicular diffusion coefficient in the range of 5 m2/s is inferred from the experimental results. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Health Misinformation in the Covid-19 Era - Detecting Misinformation on Bi-lingual Corpora using Lexical Features

Social media use spiked amid the COVID-19 pandemic, resulting in an increase in fake news proliferation, especially health misinformation. Many misinformation detection studies have primarily focused on English texts, and of these, very few have examined linguistic features (syntactic, lexical, and semantic). Lexical features such as number of upper-case letters have been shown to improve misinformation detection in English and non-English texts, however, use of lexical features is still in its infancy, and thus warrants further investigation. Therefore, a novel lexical-based health misinformation detection model is proposed using machine learning techniques, specifically focusing on two languages, namely, English, and standard Malay. A new dataset containing fake and real news were developed from a fact- checking portal and local media, targeting news related to COVID-19. Common natural language processing tasks including filtering, tokenization, stemming etc. and lexical feature extraction were administered prior to data modelling. Evaluation on a dataset containing 1060 fake and real news each show Random Forest to yield the best performance with 99.6% for F-measure and accuracy of 96%, followed closely by Support Vector Machine. A similar observation was noted for the Malay corpus. Improved health misinformation detection was observed when linguistic features were included as part of the model, hence implying that the features can be successfully used in detecting fake news.
600-GHz-band Terahertz Imaging Scanner System with Enhanced Focal Depth

Long focal depth property of lens/mirrors is of practical importance in terahertz (THz) imaging systems where the sample position is usually uncertain. A 600-GHz-band THz imaging scanner system using an integrated off-axis parabolic (OAP) mirror is presented for this requirement. Both simulation and experiment results show that a spatial resolution of ~2 mm can be achieved at a focal distance of 100 mm. Moreover, owing to the astigmatism of the proposed OAP mirror, a long focal depth of ~170 mm was validated with the imaging experiment. Finally, a real imaging experiment was provided for recognizing the hidden metal object, which can be potentially used for imaging targets at different distances.
Synthetic Training Data Generation and Domain Randomization for Object Detection in the Formula Student Driverless Framework

Today industries strive toward using data-driven machine learning wherever applicable. Consequently, they re-quire manually or automatically labeled training data sets. Currently, synthetically generating labeled training data sets belongs to the open challenges in machine learning across multiple application fields. In this paper, we propose employing a procedural pipeline combining BlenderProc with domain randomization to create prelabeled training data sets synthetically. Randomizing the domain using uncorrelated random background images, we ensure that the neural network applied for object detection purely learns the object features and is background-independent. Our proposed pipeline yields a solution to create sizeable prelabeled training data sets. We assess the pipeline performance for the application of cone object detection for the formula student driverless competition using no real training and a small real-world training data set for fine-tuning: We show that using the synthetically generated training data fine-tuned with a limited real training data set performs best for object detection. This transfer learning-based, fine-tuned solution also outperforms the benchmark training data set in detecting knocked-ver cones that are neither present in the real nor the synthetic training data set. Consequently, by combining BlenderProc and domain randomization, we provide a solution for formula student teams to generate extensive training data for cone detection and other detection problems relevant to driverless.
A Limit-Achievable Estimator for Range and Doppler Estimation in Pulse-Doppler Radar

This paper proposes a limit-achievable joint range and direction of arrival estimation method in pulse-Doppler radar, named sampling a posteriori (SAP). To evaluate the performance of the SAP, entropy error is introduced and it is proved that the empirical entropy error of the SAP achieves the theoretical entropy error when the number of snapshots tends to infinity. In order to reduce the computation burden of the SAP, a relaxation technique is adopted, which is based on a random number generator and the cumulative distribution function. Numerical simulation is carried out and results indicate that the relaxation technique can effectively reduce the computation burden of SAP at the cost of a insignificant estimation performance loss.