Miracle-dz/UniFAR-FTUs huggingface dataset:
LQR-based Predictive Energy-saving Control for Commercial Vehicles Based On Slope Information

To effectively improve the driving economy of commercial vehicles by integrating intelligent control technology, this paper develops a predictive energy saving control system based on Linear Quadratic Regulator (LQR) using road slope information. The system realizes the co-optimization of the longitudinal speed and gear position of vehicles. We build a simulation model in a commercial software of MATLAB/Simulink to verify the performance of the proposed LQR algorithm, and the simulations for the proposed controller and ProportionalIntegralDerivative (PID) controller are carried out under the virtual and real road conditions. The experimental results show that the proposed LQR control strategy can achieve a fuel saving rate of 2% to 5% compared to the baseline method.
A Review on Online and Offline Handwritten Gurmukhi Character Recognition

Various deep learning and machine learning algorithms are working to address existing problems in applications of pattern recognition and natural language processing. Handwritten text recognition is one of those applications which have major concerns. In handwritten text recognition, a large body of research work has already been reported for non-Indian scripts, but it's not so for Indian scripts. The same is applicable to Gurmukhi. Gurmukhi is a northern Indian language and it is the fourteenth most spoken language in the world. Due to excessive cursiveness and structural similarity among the characters in Gurmukhi handwriting, text recognition is a complex research problem. Sometimes the quality recognizers don't give satisfactory results on it. In the present article, a review of online and offline handwritten character recognition techniques and their performance comparison is given in Gurmukhi script. Researchers working in the field of optical character recognition will find the in-depth analysis presented in this article useful in understanding when to employ which classifier for the best outcomes. A survey on the number of research publications in a particular time span (2008 to 2019) in Gurmukhi handwritten character recognition is also presented here.
Temperature Compensated D-Band SiGe LNA with Supply Current Monitoring for Self-Healing Radiometers

In this work we present a SiGe D-Band Low Noise Amplifier (LNA) for radiometric applications near the water absorption line at 183 GHz. The LNA covers the frequency range near 188 GHz with a peak gain of 23 dB and a minimum noise figure of 10.5 dB. Space hardening techniques are also presented in order to mitigate the LNA performance variation due to orbital changes and therefore, to achieve a more stable and constant sensitivity, NETD, of the radiometer. First, a current sensor is designed for bias changes monitoring caused by radiation. The reference voltages of the bias circuits are left as tuning-knobs to compensate the effect of radiation. Furthermore, this bias circuit also compensates the temperature dependence of the LNA. The gain variation is reduced from 11 dB to 2 dB and the NETD is keeped below 0.4. Thanks to these techniques a complete self-healing system could be implemented if they were extended with a processing block.
Real Time Sensor Monitoring Using Local Database Cache Method

Internet technology is growing rapidly along with the increasing use of smartphones, but this development is not balanced with an equal distribution of internet access. Many related studies on monitoring utilizing smartphones use a single data source from an online database. so that when there is a connection interruption, it will interfere with the monitoring process. In this study, a monitoring system was created using the local database cache method, with current sensor and voltage sensor data sources. This system utilizes the Rest API for online databases and SQLite for offline databases. Based on the test results, there was a significant increase in the speed of data requests. For applications that are connected to the Rest API that is adapted to the data source, it can improve the quality of parsing data with a response time of 531 ms with a data size of 245 bytes, for online database access to get an average speed of 1463.3 ms, for SQLite databases to get an average speed of 121.5 ms.
Research on Handling Stability Control Strategy of Distributed Drive Electric Vehicle

In this paper, a vehicle dynamic model and the corresponding hierarchical control structure are proposed for the handing stability control of a distributed drive electric vehicle. The upper layer of the proposed control scheme adopts a sliding mode controller for the motion tracking. Then, an optimal controller is used for the torque distribution in the middle layer. Besides, the stability control of single-wheel and muti-wheel failure is discussed. Considering the difference between the distributed in-wheel motor will directly affect the control effect of vehicle dynamics, an adaptive control method is proposed to compensate motor characteristics in the lower layer. Finally, the simulation results are executed to validate the effectiveness of the proposed control scheme under various working conditions.
Processor Optimization of an Energy-Efficient NDIR CO2 Wireless Sensor Node

CO2 gas sensors are rapidly growing in importance since they can be easily deployed to assess air quality inside buildings, improving people's life. According to medical studies, we know that exposure to high levels of carbon dioxide on a daily basis poses a risk to people's health with adverse effects at different levels, such as on the neurological, cardiovascular, and respiratory systems. Moreover, the COVID-19 pandemic has shown how important it is to monitor air quality and ensure good ventilation to prevent infection through airborne transmission paths. Some studies estimate the likelihood of indoor airborne infection transmission based on continuous CO2 measurements. This work presents the design of a 32-bit microprocessor based on the RISC-V ISA architecture intended for energy-efficient signal processing in wireless sensor nodes. In particular, the processor is optimized for its application in non-dispersive infrared (NDIR) CO2 sensors. We use asynchronous demodulation to extract the information from the sensor. Therefore, we generate digital quadrature signals for demodulation, requiring the computation of trigonometric functions by the RISC-V processor. We try different strategies to optimize the processor design and the demodulation process to minimize energy consumption while measuring CO2 levels.
Comparative Analysis of PV Parameter Extraction Algorithms

This study looks at the challenge of parametric estimation in photovoltaic (PV) module with manufacturer information from the perspective of evolutionary computation. A non-linear non-convex optimization problem is created using the data sheet provided by the PV manufacturer, which includes information on maximum power, open-circuit, and short-circuit values. Comparative analysis is made between particle swarm optimization, artificial electric field algorithm, whale optimization algorithm, crow search algorithm, harmony search algorithm and a hybrid algorithm of PSO and pattern search for parameter extraction of a mono-crystalline Sanyo-HIT215 solar panel. The results are tabulated and I-V characteristics plotted.
Design Flow for The Implementation of Obfuscated Finite State Machines

Hardware obfuscation is a useful technique for IP (intellectual property) protection. Several previous works have paid attention to the design of obfuscated FSM (finite state machine). Their common way is to insert an extra obfuscation mode to prevent the attackers from entering the normal mode. Based on the concept of obfuscation mode, in this paper, we study the design flow for FPGA implementation. The proposed design flow includes two main steps: RTL coding (for the obfuscation mode) followed by FPGA synthesis. Experiments with a real circuit show that the FPGA implementation can work without any degradation on circuit speed.
Empirical Methods, Anomaly Detection and Preventive Measures of Web Attacks

The digital world has captured great market response in recent years. Users are almost completely dependent on the web for availing of products and various services. Web applications are known to solve one-stop solutions for users. Considering the importance of client data, attackers take advantage of vulnerabilities associated with its process. According to a Gartner security threat report; around 90% of vulnerabilities are present in the application layer. Web content is generally updated very frequently. This opens up the door for invaders to perform their intended tasks. In this paper, we tend to explore the various quotidian web attacks and reveal some serious vulnerabilities found in modern web applications. The literature summary includes articles from reputed journals and conferences. Our analysis presents a comprehensive and detailed study of the categorization, prevention, detection, and mitigation of these web attacks. The strength of this article lies in the basis of the detailed statistics that help for designing an innovative solution for preventing these types of attacks in the future.
Motion Planning Framework for Autonomous Vehicle with Surrounding Vehicle Motion Prediction in Highway Scenarios

The predictive motion prediction of surrounding vehicles is an essential precondition for precise collision risk assessment and path planning. In this paper, we propose a motion planning framework for autonomous driving vehicle considering surrounding vehicle motion prediction in highway scenarios. Firstly, Interactive multi-model is put forward to predict lateral positions of the surrounding vehicles. Then, with the predicted trajectory, optimization-based motion planning methods is utilized to process risk assessment model and reference path tracking error. In terms of control module, apply the MPC method for local trajectory tracking with vehicle's dynamic constraints. Finally, the simulation results in highway scenarios verify the capability of proposed framework.
Deep Learning with Fractional Order Operaters Lagrangian Method for Space Robot based on Sliding Mode-based Fixed-time Control

Many approaches have been influential in the robotics field because of deep learning (DL). As space robots need more reliability and stability, model-free algorithms with deep learning have particular advantages over the traditional methods in space environment. In this paper, we present an original robot current/torque prediction based on robot dynamic system with deep learning. Also, we add sliding mode-based fixed-time controller to improve the control performance. It has analysed manipulator current information through robot dynamic property’s matrix nature from fewer samples. This method has significant benefits in terms of robot current/torque identification and tracking. It also performs well in robustness and learning rates. This generic method has developed to solve a variety of problems using deep learning and data filtering with manipulator dynamics process, which includes deep learning with fractional order differential operators, robot dynamics and Kalman smoothing. We verified our algorithm into a real two-joint space robot on air-floating platform in zero gravity environment. The final results show it can learn to predict current/torque based on robot dynamics and complete the finitetime convergence. This paper made several key contributions to the fields of current/torque identification and prediction with manipulator dynamics and deep learning in space robot models. It performs very well in robot current/torque tracking and predicting new situations.
Model of a 9-level transformerless RV Topology Grid-Tied-Inverter for PV applications

A 9-level Transformerless Grid-tied inverter is an electronic device especially designed to convert DC power into AC power, synchronized with the reference grid and capable to feed the surplus power of solar panels toward the grid. This paper presents a simulation model of a single-phase 9-level Grid-Tied-Inverter (GTI), implemented in the Matlab© software environment. The aim of this simulation model is to analyze the behavior of the 9-level reverse voltage topology and to design a filter to effectively reduce the ripple component injected into the grid to an acceptable value in order to reduce the total harmonics distortions (THD). The synchronization of the inverter to the reference grid is performed via a Phase Lock Loop (PLL). The focus is on developing a pure sine wave inverter that can synchronize with the grid in the shortest time possible and feed surplus power back into the grid.
An Automatic Detection of Military Weapons Using multi-Level CNN Architecture

This study elucidates an automatic detection of weapons used in military based on deep learning. It utilizes multi-level CNN architecture with three layers of CNN to classify three kinds of visual images for detection of weapons in an image. This proposed system achieved an accuracy of 97.7% for the image classification. This experiment was performed on datasets of 2700 images using Tensorflow and Keras.
Setup of a high-precision profilometer and comparison of tactile and optical measurements of standards

Both optical and tactile probes are often used in dimensional metrology applications, especially for roughness, form, thickness and surface profile measurements. To perform such kinds of measurements with a nanometre-level of accuracy (∼30 nm), Laboratoire National de Métrologie et d’Essais (LNE) has developed a new high-precision machine. The architecture of the machine contains a short and stable metrology frame dissociated from the supporting frame. It perfectly respects the Abbe principle. The metrology loop supports reference laser interferometers and is equipped either with an optical probe or a tactile probe of nanometric resolution and linear residuals. The machine allows in situ calibration of the measuring optical and tactile probes by comparison to the laser interferometer measurements, considered as a reference. In this paper, both architecture and operation of the LNE's high-precision profilometer are detailed. A brief comparison of the behaviour (linear residuals) of the confocal chromatic and tactile probes is presented. Optical and tactile scanning of V-grooves artefacts with 75, 24, 7.5, 2.4, 0.75 and 0.24 µm depths are illustrated and discussed. In addition, a comparison between optical, tactile and atomic force microscopy measurements on a VLSI SHS 880-QC is also performed. Finally, a comparison of an optical and tactile scanning of optical aspherical lens with a polymer coating is presented and discussed.
3D Printed Soft Robotic Actuator With Embedded Strain Sensing For Position Estimation

This work shows the development and characterization of a fully 3D printed pneumatic soft robotic actuator with embedded strain gauges to estimate the bending angle of the actuator. The actuator was printed in one go using a multi material Fused Filament Fabrication (FFF) printer. By taking the difference of the reading of two integrated strain gauges, printed using carbon doped TPU, a strong linear relation $(R^{2}=0.97)$ between the bending angle and sensor output is achieved.
Power Relay Module Based Multiple-load Charging Capability Extension

Due to the limited size, the existing Qi standard based wireless chargers are not attractive and even fail to charge multiple devices. This paper explores the passive power relay module to extend the multiple-load capability cost-effectively without modifying the original transmitter. A power relay module constructed with three layers is proposed to rebuild an efficient power transfer path. When it is inserted between the transmitter and receivers, the power will be extracted from the transmitter by the bottom-layer auxiliary relay coil, then delivered to the top-layer relay coil through wired connection, and finally picked up by the receivers. A middle magnetic shield layer is also adopted to simplify the mutual coupling relations. The equivalent circuit model of the proposed system configuration is given based on fundamental harmonic approximation, and further to analyze the voltage gain and power transfer efficiency. Finally, a prototype system is built and measured under various load conditions. The almost load-independent output voltage is achieved, and the peak efficiency reaches 87%.
Photogeneration losses from interface trap density in Passivated Ultrathin CIGS Solar Cell

The present study investigates the effect of the interface defects for ultrathin Cu (In<inf>1-x</inf>Ga<inf>x</inf>) Se<inf>2</inf> solar cells using Silvaco TCAD. The initial simulation study is carried out with the reference cell configuration in which the PV parameters fit and match the fabricated cell characteristics. Our goal is to investigate the temperature dependence of the performance of u-CIGS solar cells in the temperature range of 10–70 °C and the loss mechanism caused by interface trap density (D<inf>it</inf>) in different cell pitch sizes on cell performance. D<inf>it</inf> defines the amount of carrier’s traps at CIGS/Al<inf>2</inf>O<inf>3</inf> interface to recombine with photogenerated carriers. The recombination mechanisms are found to play a crucial role in cell performance degradation. Further simulations quantify significant improvements in J<inf>sc</inf> and V<inf>oc</inf> for different cell pitch sizes at a fixed opening width in the Al<inf>2</inf>O<inf>3</inf> layer. Consequently, the optimum efficiency has been pointed out at a cell pitch size of 1.5 µm. MgF<inf>2</inf> has been used as an ARC layer to boost the efficiency up to 14 %. The results from these simulations provide insights for passivated u-CIGS solar cell optimization.
Covariance Structures Under Polynomial Constraints: Applications to Correlation and Alpha-Type Structural Models

This paper provides methods for the estimation of covariance structure models under polynomial constraints. Estimation is based on maximum likelihood principles under constraints, and the test statistics, parameter estimates, and standard errors are based on a statistical theory that takes into account the constraints. The approach is illustrated by obtaining statistics for the squared multiple correlation, for predictors in a standardized metric, and in the analysis of longitudinal data via old and new models having constraints that cannot be obtained by standard methods.
Data Analytics for the Cyber Security of an Information System Based on a Markov Decision Process Model

: Intrusion detection is an important research topic in information systems and cyber security. Both a defender and an attacker detect and learn about each other during an intrusion process. The defender can expel the attacker as soon as the attacker is detected or wait and observe to know more about the attacker for the detection and prevention of other attacks in the future. An optimal decision is often required in this situation. Data analytics is conducted to achieve an optimal decision for the cyber security of an information system based on a Markov Decision Process (MDP) model in this study. The state of the information system is completely observable in the model. The model is validated using various algorithms that include policy iteration, value iteration, and Q-learning. Data analytics over a finite planning horizon and an infinite planning horizon is conducted, respectively. The expected total cost for each state is analyzed at various parameters of the transition probability and various parameters of the transition cost.
UNVEILING THE LINKAGES BETWEEN EMERGING STOCK MARKET INDICES AND CRYPTOCURRENCIES

This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
Analysis of Variants Associated with Cystic Fibrosis (CFTR) in Relation to the Known Pathogenic Variant DeltaF508

Cystic Fibrosis (CF) is a genetic disease that affects the thickness of digestive fluids, mucus, and sweat, which often leads to obstructions in body organs ducts. The most common CF mutation is the removal of amino acid phenylalanine at position 508 (deltaF508) on the CFTR gene. On CFTR, there are variants of uncertain significance (VUS) as well as classified variations that may cause similarly negative effects. The missense point variations Q493P, W496R, G500D, and Y515C were VUS when this research project began. The aim of this study was to classify them as harmful, neutral, or beneficial. These variations are non-conservative, meaning that there is a change in the biochemical properties between substituted and original amino acids. A drastic change in the biochemical properties might be detrimental to protein folding and functionality. The variants were assessed through the “sequence-to-structure-to-function” workflow developed by the Prokop lab. Data gathered from several databases were used to determine the variants’ impact on CFTR function by comparing them to all known variants as of October 2019. These variations are non-conservative, meaning that there is a change in the biochemical properties between substituted and original amino acids. A drastic change in the biochemical properties might be detrimental to protein folding and functionality. The variants were assessed through the “sequence-to-structure-to-function” workflow developed by the Prokop lab. Data gathered from several databases were used to determine the variants’ impact on CFTR function by comparing them to all known variants as of October 2019. Prediction program data was gathered for comparison purposes. Variation analyses included molecular dynamics simulations, CFTR structure analysis, and comparison of the four variations to deltaF508. The amino acids 493, 496, 500, and 515 were shown to fall in highly conserved protein regions under high selection. Therefore, protein functional changes are likely if a substitution was to occur. The insight gained from the analyses may be useful in the identification of other variations and could provide information for classified variants. AUTHOR SUMMARY We conducted research to determine effects of variants of uncertain significance (VUS) Q493P, W496R, G500D, and Y515C in the CFTR gene. The CFTR gene contains genetic material to create a membrane protein. Variations of this gene can cause cystic fibrosis. The impacts of Q493P, W496R, G500D, and Y515C were compared to the variant deltaF508 that is known to be associated with cystic fibrosis.