Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Algorithm for Driver Monitoring System using Deep Learning Approach

The driver monitoring system (DMS), also known as driver attention monitor, plays an important role for vehicle safety systems. In DMS, the system detects the driver's activities such as the state of being a driver sleepy or failure to give sufficient attention to avoid accidents. To reduce the driver mistakes while driving, the system warns the driver with an alarm sound or vibrations. For the efficient implementation of DMS, the system should work in real time without any delay. However, the current DMS has many challenges implementation to the complexity of network implementation. To reduce the DMS complexity for real time implementation, we propose an optimization algorithm, which uses a camera images for monitoring the driver activities. From the input camera images, we extract the driver's state information from the region of interest (ROI). In addition, the proposed system also extracts the driver's head pose and gaze information and monitors the driver states during driving. The experiment results from the proposed methods show accurate driver's state information and warns the driver immediately when any mistake occur from driver side.
Secure Transmission Designs for NOMA Systems Against Internal and External Eavesdropping

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same resource block to improve the spectral efficiency. Whereas due to the resource sharing, a security flaw of NOMA emerges in the presence of internal untrusted users, especially untrusted near users who are closer to the base station and can easily access the confidential information for paired far users. To mitigate the flaw, in this paper, we investigate the reliable and secure transmission of NOMA systems with untrusted near users, and propose joint beamforming and power allocation (JBP) scheme for the scenario. Meanwhile, from security point of view, we extend to a worse-case scenario where both untrusted near users and external eavesdroppers exist, and propose joint artificial noise aided beamforming and power allocation (JANBP) scheme to achieve a reliable and secure transmission for the scenario. The exact and asymptotic closed-form expressions of secrecy outage probability (SOP) for the two scenarios are derived to evaluate the secrecy performance achieved by the proposed schemes, respectively. The analysis and simulation results show the superiority of the proposed JBP and JANBP schemes in terms of combating internal and external eavesdropping, and also indicate the two schemes can achieve the same SOP at high SNR.
Trajectory Deformation Based on Energy Optimization and Obstacle Avoidance

In this paper, on the basis of energy optimization and obstacle avoidance, the trajectory generation of the robotic arms of two joints is carried out by human-robot interaction considering the physical limit of joints. Different from the related work, we not only consider the energy consumption of the manipulator in the process of trajectory generation, but also consider the feasibility of the trajectory, and increase the inequality constraint on the original equation constraint to avoid the robotic arm exceeding the joint limit. After transforming the original optimization problem into a quadratic programming problem with equality and inequality constraints, we use neural network to solve the optimal trajectory. Finally, the simulation results show that the increased joint constraint can ensure the actual trajectory of the generated trajectory and improve the safety of the actual operation.
Development of Path Tracking Controller for An Autonomous Tracked Vehicle

This study presents the development of autonomous tracked vehicle and its path tracking control. The path tracking controller was developed based on the previous study and later modified by adding a PID controller to improve the sensitivity of the controller. Then, the tracked vehicle and controller proposed were tested by using a Matlab/Simulink software. The validation process was completed at two different path; straight line path and hook path. The result shows a big improvement for both cases where an improvement of 40% had been archived by using a proposed controller in this study.
Design of a stiffness-adjustable micro-motion stage

The manufacturing errors of flexure hinge are accumulated in their operation, which is not conducive to the control of the motion stage and the high manufacturing accuracy leads to high costs. Therefore, a novel micro-motion stage with adjustable stiffness is proposed to ensure that the motion stage has better performance at low cost. The design formulas are deduced and verified by using finite element analysis (FEA). The practical performance of a manufactured motion stage is measured and the adjustable capability of the stage is tested. Experiment results indicate that the motion stage can effectively compensate the performance error caused by the manufacturing error of flexure hinge.
Plane Loop Closure Based Point Cloud Registration Using Structured Light Sensor

In the field of manufacturing such as additive manufacturing and rapid prototyping, the establishment of a 3D model of workpieces has great significance for the quality inspection, modeling, subsequent research, and application of the workpiece. However, there are still some problems in incompleteness and poor reduction for the complete 3D model of the workpiece, which limits the efficiency and accuracy of workpiece 3D quality inspection. In addition, due to the limitation of the field of view in the 3D measurement system, the generated local point cloud cannot completely describe the 3D information of the workpiece. To solve these problems, this paper proposes a point cloud registration method based on the structured light sensor, which combines loop closure optimization through the planar features in the local point clouds. This method considers the planar features to constrain the point clouds, and the point clouds were re-registered to improve the accuracy of point cloud registration. The feasibility and effect of the method are verified in the simulation and experiment, and the method can be applied to the fields of quality inspection.
Robot System and Fastening Force Control for Sealing Blocking Plates of Steam Generator

During the nuclear power plant maintenance period, the primary side manhole of the steam generator will be opened. In order to prevent water in the primary circuit of the reactor from flowing into the water chamber, it's necessary to block the steam generator primary side cold-hot end and primary circuit pipe connection. This maintenance operation has a high dose of radioactivity and is not suitable for human operation. The paper introduces a blocking plate manipulation robot for this environment. The mechanical structure was designed in detail, and a positioning method based on image recognition was proposed. The control strategy based on the position and current was proposed for the bolt fastening mechanism. Finally, based on the prototype, the absolute positioning accuracy experiment was conducted, and the results were processed and analyzed.
Artificial Prognosis of Cardiac Disease using an NN: A Data-scientific Approach in Outlier Handling

Cardiovascular diseases cause approximately one third of all yearly global deaths. Addictives and physical inactivity trigger in humans discernible syndromes such as elevated blood pressure, increased blood sugar, unhealthy levels of cholesterol, portliness, chest-pain etc. which are signs of the existence of a probable heart disease. There being multiple predictive indicators of cardiac diseases, this poses a machine-learning (ML) problem. This paper proposes a three-layer, NN-based binary classifier to prognosticate presence of heart disease utilizing UCI (University of California, Irvine) ML Heart Disease repository. The feature-space has been assembled using filtering methods that employed univariate and bivariate exploratory data analysis (EDA). Extracted features have been corroborated by inferential statistics, applying two-sample t-tests and Chi-squared ($\chi^{2}$) independence tests. The research exploits different data engineering techniques—eventually raising the accuracy to an average 88.33% and to a highest 91.66%—namely, distribution specific division, natural and unnatural handling of anomalous outliers. The consistent, stepwise improvement in performance has been k-fold cross-validated and signified by performing one sample t-tests and ANOVA-tests on $\mathrm{k}=5$ runs of the techniques each. The simplistic model outperformed traditional ML and provided comparable accuracy achieved by hybrid approaches in much lower computational cost by applying statistical preprocessing on data.
A 0.7 μw Explicit Bandgap Less POR Circuit With Brownout Detection

This paper presents a circuit for a power-on reset (POR). It uses a self-control mechanism to have the POR threshold different from the brown out reset (BOR) threshold. The change of threshold voltage happens after the output settles to its high value. It consumes a low current in the steady-state while functioning reliably to detect the BOR condition. It consumes 0.7uW under a 1.8V supply and provides a reliable POR/BOR circuit. Its simple architecture guarantees a good performance that has been verified by measurement. It covers wide supply ramp up and ramp down rates and occupies an area of 8250um2.
An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional Neural Networks

The Philippines gave coffee a unique identity and taste in the form of Barako coffee, a variant that came from the species of Coffea liberica. However, the crop yielded faces a challenge in coping up with several widespread diseases leading it to lose in quantity and quality. Barako became a less prioritized crop compared to Excelsa, Robusta, and Arabica. The troublesome method of identifying diseases like rust, spots, and insect infestation caused many losses for farmers due to improper diagnosis and treatment. This research aims to apply deep learning methods to alleviate the problem. A deep convolutional neural network was optimized to perform the difficult task of classifying diseases to assist farmers in applying an appropriate treatment. With several experiments, this study develops several models to distinguish the best possible model that can yield the most accurate results. This study acquired 3958 high-resolution images to train the desired model. The result achieved a 100 percent accuracy rate while other models misclassified due to overfitting problems. Proper tuning of hyperparameters can address overfitting together with an appropriate choice of optimizers resulting in an efficient classifier. Moreover, this research identified potential future research works to apply the model for real-life scenarios.
Cognitive Load Measurement Using Galvanic Skin Response for Listening Tasks

Cognitive load is a kind of load which continuously imposes on our cognitive system at the time of performing any cognitive work. Now a day's measurement of cognitive load with the help of galvanic skin response is becoming popular because of its low cost and easily capturing process. In this paper, we have measured and shown the cognitive load variation for listening tasks with some variation in difficulty level of tasks. In our research we have collected galvanic skin response data which are a combination of seven listening tasks including three difficulty level. Then we calculated some features of this data. Analysis of variance test was performed for statistical evaluation of this data. Finally, we got that with the increase of task difficulty level cognitive load is increasing. The main application of this work is that it is favorable for better learning. We know listening is a very important process of learning. That's why our experiment can be used to find a better way of giving lectures, which will produce less cognitive load. As listening is one of the important way of learning, so this will not only enhance ability to process information but also help to create long-term memories.
A Two-Stage Battery Charger with Active Power Decoupling Cell for Small Electric Vehicles

This paper describes the design and control of a two-stage on-board battery charger for small electric vehicles, with slow and fast charging capacity. Although little discussed in the literature, this type of application requires greater attention due to the low nominal battery voltage, which results in high output current during the fast charging operation. To ensure a good quality of the grid current for both power conditions, a modified bridgeless boost rectifier is used as front-end converter. At the second stage a phase-shifted full-bridge with a current doubler rectifier is selected to ensure zero voltage switching and reduce the transformer secondary winding current. Moreover, an active power decoupling cell is employed in order to reduce dc-link capacitance and, consequently, increase the converter reliability and power density. Finally, the operation of the proposed battery charger under steady-state and dynamic conditions is verified by means of simulations.
A Review on Three-phase AC/AC Power Converters Derived from the Conventional Indirect Matrix Converter

The growing permeation of green renewable energies into electrical systems has been considerably remarked in industry. Thriving integration of these external sources into the current grid massively depends on allotted power converters and enormously counts on their specifications and control methods. In this scenario, and in relation to what was previously mentioned, this paper reflects a review of three-phase AC/AC power converters derived from the conventional indirect matrix converter
Forest Mapping from Hyperspectral Image Using Deep Belief Network

Forest type is very important in the management of the forest ecosystem. Previous literatures demonstrate that deep belief network (DBN) gain the excellent classification performance. This research attempts to classify the forest type from hyperspectral image using DBN. To begin with, the Restricted Boltzmann Machines and deep belief networks are briefly reviewed. Then, the effect of network depth and the number of hidden units on the accuracy and Kappa efficient is discussed. Furthermore, forest type is classified using DBN by Python language, comparing with typical classification methods, such as support vector machine (SVM). The experimental results show that the number of layers is 3 and the number of nodes is 256 are the optimal network structure for forest type identification. The overall accuracy is 85.8% and the coefficient is 0.785, which is better than the classification performance of support vector machine. In general, deep belief network has the potential to be the dominant method of forest mapping by hyperspectral image.
Rumor Spreading Model Considering Individual Activity and Refutation Mechanism Simultaneously

With the rapid development of social networks, an increasing number of factors will affect the spread of rumors. In this paper, we simultaneously consider individual activity differences and rumor-refuting nodes, and we propose a new multifactor model that can be used to better analyze the process of rumor spreading in practice. The equilibrium point and the basic regeneration number R0 of the model are calculated via the differential dynamics method and the next generation matrix method. Moreover, the local asymptotic stability of the equilibrium of the model is determined based on the Routh-Hurwitz criteria. The global stability is proved via the geometric method which is based on the use of a higher-order generalization of Bendixson’s criterion. Finally, the theoretical results are verified via numerical simulation in a homogeneous network, and the effects of individual activity and the refutation mechanism on the rumor spreading process are analyzed. We find that the proposed mechanism of rumor refutation can effectively reduce the density of spreaders and the rumor duration.
Cycle-Consistent InfoGAN for Speech Enhancement in Various Background Noises

Speech enhancement is one of the crucial research topics applied to various fields. In addition, due to the progress of wireless communication technology, the need for speech enhancement research to remove various background noise occurring in the real world is increasing. Recently, a speech enhancement model based on generative adversarial learning, which can build a significant loss function by itself, has been proposed and outperformed the conventional methods. However, these models assume parallel datasets for learning, and there is a problem that the performance decreases for the signal containing various kinds of noise. This paper proposes a novel speech enhancement model based on generative adversarial network (GAN). The proposed method additionally uses cycle-consistency loss for learning on non-parallel datasets, where the InfoGAN mechanism is used to cluster noise information in an unsupervised learning manner. The proposed model can form cluster-specific mapping by using the obtained clustering information. We quantitatively verify the speech enhancement performance of the proposed method through several metrics such as MOS, SI-SNR, and PESQ, and achieve about 55% better MOS performance than the previous GAN-based models.
National Economic Status Inference from the World Trade Network

Quantifying the economic status of nations is significant for economic development. Apart from the traditional economic indicators, many new, granular and real-time economic indicators based on various socioeconomic and economic datasets are reported. In this paper, by employing the world trade data, we characterize the economic status from three dimensions, including the monetary dimension, the network structure dimension and the trade diversification dimension. The results show that network centrality indicators can explain up to 85.4% of the variance of the GDP, which perform best compared with indicators of the other two dimensions. The results show the importance of the network structure in inferencing the economic status of nations, and provide a new perspective for characterizing and understanding the national economic status.
A Radiation-Hardened Dual-Direction SCR Based on LDMOS for ESD Protection in the Extreme Radiation Environment

To mitigate the total ionizing dose (TID) effects of the dual-direction silicon-controlled rectifier, based on laterally diffused metal–oxide–semiconductor (LDMOS-DDSCR), a common-centroid layout arrangement with multiple octagonal units is proposed and fabricated in an 18-V Bipolar-CMOS-DMOS process. The symmetrical characteristics of the bidirectional electrostatic discharge (ESD) protection are verified by transmission line pulse tests. Gamma-ray irradiation experiments demonstrate that the proposed device is tolerant of TID effects up to 100 krad(Si), whereas the traditional finger-type LDMOS-DDSCR loses its function at 50 krad(Si). Finally, the ESD performance is also characterized at high temperatures which confirms that the proposed device is an excellent candidate for space applications.
Comparative Study of Control Systems for a Photovoltaic Inverter with LCL Filter

The LCL filter for on-grid inverters are a cheaper and smaller substitute for the L filter. Many researches have proposed solutions to the control system of LCL plus inverter plant, which is challenging due to the resonance effects. This paper presents a review of some linear and nonlinear control methods frequently cited in literature through simulation comparisons under three scenarios: ideal case with no grid inductance, with a small value of inductance and finally with an increase in the line frequency.
Hardware-Assisted Virtualization for Heterogeneous Automotive Applications

Today's car manufacturers are racing in deploying new innovative functionalities in modern cars like human-machine interface (HMI) technologies, cloud-based services, vehicle ad-hoc networks (V ANET) and autonomous driving. Such new technologies increase the complexity of vehicle's E/E architecture and adds new requirements on automotive software systems. This can specially be seen in cockpit domain units like Advanced Driver Assistance Systems (ADAS), Infotainment Head Units (IHU) and Telematics (TEM). The software applications of such units now exhibit large variations in requirements in terms of safety, security and connectivity as they are involved in both in-Vehicle network communication and cellular vehicle communication (V2X). In addition to that, Original Equipment Manufacturers (OEMs) are heading towards consolidating multiple units into single high computing platform. Although this simplifies the networking model of the vehicle, it adds more challenges on the architecture of automotive software systems. This paper focuses on utilizing hardware-assisted virtualization techniques to allow consolidating these heterogeneous applications on the same hardware. The performance of the proposed approach is evaluated to proof meeting the requirements of such applications.
Initialization of Doubly-fed Induction Generator Wind Turbines Using Noniterative Method

This paper uses a noniterative method to obtain steady-state operating conditions of grid-connected doubly fed Induction generator (DFIG) - based wind energy systems. An accurate steady-state model of DFIG is employed in supersynchronous mode operation of DFIG along with existing steady-state model in subsynchronous mode. Computational time required for the calculation of steady-state variables by both iterative and noniterative methods are compared. It is shown that noniterative methods are superior in terms of computational performance and can provide the closed-form solutions. The approach can be applied to define steady-state variables for both eigenvalue analysis and time-domain simulations.
Power Control Based on Multi-Agent Deep Q Network for D2D Communication

In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution).