Miracle-dz/UniFAR-FTUs huggingface dataset:
Movie Recommendation Based on Mood Detection using Deep Learning Approach

With each passing day, new technologies are introduced to humans, bringing them closer to computers and forming a strong bond between them. Image processing is a boon to the world in today's technological age. In the realm of image processing, many research fields have emerged, such as mood detection, object detection, signature detection, and so on, with mood detection emerging as the most popular research area today. The most delicate way to interpret a human's mind, as well as a human's demand, is through facial expression. A human's desire, such as watching a movie, may be predicted using this facial expression, which saves consumers time and effort in looking through a movie list. This paper represents an approach of movie recommendation based on mood detection that employs a couple of neural networks such as CNN, VGGNet, Inception, MobileNet, and DenseNet. These neural networks can recognize facial expressions and can also propose movies based on this information. At last, we compare the results of our datasets to the results of the collected datasets.
Dielectric Characterization of Curved Structures Using Flangeless Open-Ended Waveguide Measurement

Open-ended waveguide measurement of layered structures is a well-developed method for determining dielectric and thickness properties of the layers. A full-wave electromagnetic model has been previously developed, which includes the effects of the high-order modes, as a forward model describing the complex reflection properties of layered structures. The open-ended waveguide flange design has also been optimized to closely approximate the electromagnetic assumptions made in the electromagnetic model. However, in certain measurement situations the size of the flange creates gaps between the aperture of the waveguide and the structure being tested. This is often the case for uneven or curved surfaces, which affects the accuracy of dielectric property measurements. This paper presents an empirical method to account for the effect of a flangeless open-ended waveguide probe to enable measurement of curved surfaces and significantly reduce the undesired effect caused by such gaps.
Online Calibration of Installation Errors of SINS/OD Integrated Navigation System Based on Improved NHC

The integrated navigation system of strapdown inertial navigation system (SINS) and odometer (OD) is a commonly used vehicle positioning system. The installation errors caused by the difference between the installation positions of SINS and OD mainly include the installation deflection angle and lever arm, which will reduce the positioning accuracy. The traditional calibration method is to expand the installation deflection angle error and lever arm error into state variables, the velocity difference under nonholonomic constraint (NHC) is taken as the measurement to establish the Kalman filter model for online calibration, but this method seriously depends on the sensor accuracy. In order to calibrate the installation errors of low-precision SINS, this paper improves NHC, adds the difference between the centripetal acceleration measured by odometer and calculated by SINS as measurement on the basis of the original measurement, and introduces a low-pass filter to reduce the interference of vehicle vibration on the accelerometer.The experimental results show that the proposed algorithm has better convergence of the installation deflection angle, and the error between the estimation results of the forward lever arm and the actual measurement results is less than 0.1m; After compensating the installation errors of the improved NHC, the maximum positioning errors in the east and north directions are reduced by 63.19% and 43.51% respectively, the height error is also greatly reduced, and the positioning accuracy of the integrated navigation system is significantly improved.
Secondary-Side Resonating LLC Converter for Reducing Transformer Voltage in High Power Applications

The data centers of the telecommunication infrastructure are recently consuming more electric power than before. This increasing electrical burden is requiring a small but high-power isolated power converter with a high step-down ratio. Conventionally, the LLC converter is regarded to be a promising candidate, although this converter needs vacant space for galvanic isolation due to extremely high voltage caused by the resonance in the primary side, i.e. high voltage side, of the isolation transformer, which hinders the compact implementation inside the server computers in high power design. The purpose of this paper is to propose a novel LLC converter topology to reduce this extremely high voltage by locating the LC resonator on the secondary side. The performance analysis revealed that the proposed converter exhibits a similar frequency-voltage characteristic enabling the boost and buck operation modes as well as the small rectifier conduction loss similar to the full-wave rectifier with a center-tapped transformer. The simulation confirmed the operating principles and reduction of the resonance voltage amplitude of the proposed LLC converter in comparison with the conventional LLC converter. Furthermore, the experiment also supported the operating principles, which suggests the usefulness of the proposed converter for a high voltage dc power supply system.
Common Mode Noise Reduction of Two-Phase Interleaved Boost Converters with Integrated Magnetics Utilizing Balanced Technique

High-power density and high efficiency in boost converters are required in several applications such as motor drive systems in eco-friendly vehicles and power conditioning systems used in renewable energy systems. To realize these requirements, applying compound semiconductor devices such as GaN and SiC are attractive options because they can drive at high-switching frequency with low power losses, and it is known to contribute to the miniaturization of passive devices. However, when driving power devices with high switching speed at high-switching frequency, EMI (Electro-Magnetic Interference) noise with a wide spectrum is produced. To attenuate EMI noise, large EMI filters have to be equipped. When using large EMI filters, the downsizing effect of power converters is reduced. To countermeasure the above problem, this paper proposes interleaved boost converter with integrated magnetics capable of Common-Mode (CM) noise reduction without adding EMI filters. The noise reduction is realized by the balanced technique, which is known as an effective noise reduction technique from a circuit topology perspective. In addition, interleaved circuit topology and integrated magnetics are contributed to realizing high-power density converters. The effectiveness of the proposed converters is discussed from simulation and experimental viewpoints.
A Lightweight, Plug-and-Play and Autonomous JTAG Authentication IP for Secure Device Testing

As any other circuits, secure devices need to be tested to ensure their reliability. Nevertheless, test infrastructures, such as JTAG or scan chains, can maliciously be used to steal secret data stored or processed in secure devices. In this paper, we explore a lightweight solution to protect JTAG access based on a challenge-response authentication protocol. A JTAG-authentication dedicated IP is presented. Design alternatives for quick IP plug-and-play, security, area and test time optimization are presented and evaluated.
Computationally Efficient Hybrid Algorithm for Peak to Average Power Ratio Reduction in Multiuser MIMO OFDM System

Thereare some of the constraints encountered in Multiuser Multiple Input and Multiple Output Orthogonal Frequency Division Multiplexing (MU-MIMO-OFDM) system are high Peak to average power ratio in the sent signal and intervention happened among various users. This paper proposes a new hybrid model algorithm to beat the issue of high Peak to average power ratio (PAPR). Unlike past calculations which are clipping & FITRA handled issues like computational complexity and PAPR. To get assessments of the hyper-parameters associated with previous model, as well as the signal, a variational Expectation-Maximization (EM) algorithm is developed. In addition, the combined Generalised Approximate Message Passing (GAMP) is embedded into a finite difference EM architecture, resulting in a significant reduction in the computational cost of the desired approach. With our intended algorithm, carry out a significant presentation improvement over existing strategies as far as the PAPR decrease and Computational complexity.
Transmission lines Fault Detection and Classification Using Deep Learning Neural Network

With the advancements in artificial intelligence techniques, deep learning has attracted colossal international attention. The deep learning approach has been used in a variety of power system applications and has shown to be effective. Using various deep learning techniques, several endeavours have been produced to classify faults on transmission lines. This paper presents a fault detection and classification method based on Long Short-Term Memory networks are a special type of recurrent neural network proficient at acquiring long-term dependencies. This unique method can classify raw process data directly without any feature extraction or classifier design. The three phase currents of one end are taken as input in the proposed scheme. In this paper a deep learning tool accessible in the MATLAB was used for training and testing purpose. This proposed technique is capable of detecting and classifying No Fault, as well as all sorts of line and ground faults with satisfactory results. These different faults are simulated with various parameters to check the adaptability of the method. In study this, an IEEE 9-bus system designed in MATLAB/Simulink environment for analysis.
Classification of Linear Processes Type Using Convolutional Neural Networks

There is an increasing demand to develop fast and reliable models to identify the class of control systems in developing online and plug-and-play controllers. In this paper, to perform automatic, reliable, and fast classification of linear processes, it is proposed to use Convolutional Neural Networks (CNNs). A process can be: unstable or stable, integrally or self-regulated, non-minimum phase or minimum phase, first-order or second-order, oscillatory damping, or over-damping. We consider six different classes of linear processes accordingly. The CNN is designed and trained to predict the class of linear processes by taking only their step responses. The results show clearly that the CNN has high generalization and accuracy in determining the behavior class of the process even in the presence of noise, delay, and high order dynamics.
Remaining Useful Life Predictions for Turbofan Engine Using Semi-supervised DBN-LSTM Joint Training Model

In the industrial field, the health of the machine may decline in the process of working, so it is necessary to regularly maintain the health of the machine. However, traditional health maintenance methods have problems of insufficient or redundant health maintenance. Predictions of remaining useful life (RUL) play a vital role in realizing more accurate system monitoring and health management. In recent years, with the rapid development of industrial big data and deep learning, the use of multi-sensor equipment information and deep learning neural network to predict RUL has made significant progress. However, the current RUL prediction faces the following challenges: (1) Separate Data fusion and RUL prediction steps usually result in a lack of internal connection between the two models; (2) The end-to-end prediction method using a single deep learning neural network does not provide health indicator information about the degradation. (3) The industrial data available for model training is still insufficient. To overcome these shortcomings, a new RUL joint prediction model is proposed in this work. The framework combines data fusion and RUL prediction models in series for simultaneous training, which not only provides continuous visualization of the health degradation of the system, but also ensures the efficiency of RUL prediction. Based on this framework, a semi-supervised joint training model composed of deep belief network (DBN) and long short-term memory (LSTM) is designed. The effectiveness of the proposed method is verified through the C-MAPSS dataset. The application results demonstrated that the proposed method is superior to other state-of-the-art methods.
An Experimental Characterization of Chain of PLLs for Wired Clock Synchronization of UWB Anchors for Indoor Location

The Internet of Things (IoT) enables services in a variety of domains. However, some of them, such as autonomous vehicles, may require information about the location where the sensed data is obtained. Well-established location technologies (e.g. the Global Navigation Satellite System - GNSS) can be used for outdoor devices, but they may fail indoors. On the contrary, Ultra-Wide Band (UWB) technology is one of the most promising solutions for Indoor Real-Time Location System (I-RTLS). In general, these solutions allow for the location of devices (called tags), in relation to nodes placed in known positions in space (called anchors). Several approaches for device localization using UWB technology are known in the literature. Some of them, like the Time Differential of Arrival (TDoA) method, necessitate a time synchronization solution in order to distribute the time accurately among the anchors. The use of a wired method for anchor time synchronization is studied in this research, since the architecture of the clock distribution network may affect the signal accuracy provided to each anchor. The reference method would be to connect each anchor directly to a common clock source, however this solution is the most demanding in terms of wiring. The chain topology, in which the clock is used and then re-distributed by each of the clock network's nodes, is an intriguing option for reducing overall cabling requirements. According to the experimental characterization, the phase error of the clock generated by a chain topology is 9.3% larger than the phase error of the reference topology, but it is still within the usage requirements.
Probability of Unrecognized LiDAR Interference for TCSPC LiDAR

A standard method for distance determination is light detection and ranging (LiDAR), which relies on the emission and detection of reflected laser pulses. When LiDAR systems become common for every vehicle, many simultaneous laser signals will produce mutual LiDAR interference between LiDAR systems. In this paper, we analyze the possibility to recognize mutual interference in time-correlated single photon counting (TCSPC) LiDAR with particular focus on flash systems. We evaluate the LiDAR interference appearance by deriving the expected event distribution for ego and aggressor signal. From that, we calculate the probability of photon detection within each measured signal. This paper shows the high potential of different pulse repetition frequencies to reduce LiDAR interference. Using signal-to-noise ratio (SNR), we define the extinction distance, beyond which the aggressor signal completely extinguishes the ego signal. Applied on different background and laser event rates, we find the connection between ideal LiDAR system designs and lowest probability for unrecognized LiDAR interference. Furthermore, we show the relationship to a specific LiDAR design, which must fulfill eye safety condition and receives lower intensities with increasing target distances. Finally, we present different solutions for the recognition and reduction of LiDAR interference based on our previous results.
On the New Quasi-Newton Equation for Unconstrained Optimization

Quasi-Newton methods (QN) are very useful in finding the optimal solution to the unconstrained optimization problems. The updated Hessian approximations fulfill the equation on each iteration for solving unconstrained optimization problems, hence the quasi-Newton equation is commonly used as the foundation of quasi-Newton algorithms. We propose and examine a novel quasi-Newton technique and its implementation in this research, which uses cubic interpolation to generate a new quasi-Newton equation. Our updates have been proved to produce positive definite matrices when appropriate assumptions are made. The improved BFGS approach has been shown to have global convergence. Comparing the suggested formula to the BFGS method, numerical results reveal that it is better and more efficient.
Parameter Matching Method for the Measurement of Two-dimensional Pulse Streams Signal

Accurate measurement of multi-dimensional pulse streams signal is of great significance in the field of instrumentation and measurement, radar, communication and signal processing. Finite Rate of Innovation (FRI) sampling theory provides a powerful weapon to overcome the signal bandwidth limitation during the measurement which is far beyond the power of the Nyquist-Shannon sampling theory. However, parameter matching in high dimensional space in another dilemma in signal measurement. Based on the Alternating Direction Method of Multipliers (ADMM) framework, this paper proposes a parameter matching method for the FRI sampling of two-dimensional pulse streams signal. We apply the sparse model to the parameter measurement process which can be regarded as finding the optimal projection globally. Different from the classical heuristic algorithm, the proposed method is mathematically guaramteed and it does not damage the advantage of limiting sampling rate of the FRI theoretical framework. The numerical simulation experiments not only verifies the accuracy of our algorithm, but also shows its excellent performance compared with the classical algorithm in extreme conditions.
Direction of Arrival Estimation of Moving Sound Sources using Deep Learning

Sound source localization is an important task for several applications and the use of deep learning for this task has recently become a popular research topic. While nearly all previous work has focused on static sound sources, in this paper we evaluate the performance of a deep learning classification system for localization of moving sound sources and we evaluate the effect of different hyperparameters and acoustic conditions. A feedforward neural network is used to estimate the direction of arrival of moving sound sources, with Short Time Fourier Transform input features. Diverse synthetic datasets are generated to represent different acoustic conditions, and hyperparameters are tested to determine which combination results in better direction-of-arrival detection for moving sources. We evaluate the performance of the different combinations in terms of precision and recall, in a multi-class multi-label classification framework, and we find that (1) the number of frequency bins and the reverberation time have a significant effect for localizing high-speed sources, and (2) precision and recall decay slowly at low speeds while dropping sharply at high speeds.
Stability of the Optimal Control Problem Solution

The optimal control problem is considered in the context of the influence of small perturbations on its solution. The definition of stability for a solution of the optimal control problem is introduced. It is shown that stable solutions are feasible. Assertions are given that in order to obtain feasible solutions to the optimal control problem, it is necessary to ensure the stability property when searching for the optimal solution. A method of obtaining a stable solution of the optimal control problem is proposed. An example of comparing stable and unstable solutions is presented.
Elimination of Common-Mode Choke Saturation Caused by Self-Resonance of the EMI Filter in a Variable-Frequency Drive System

The saturation of a common-mode (CM) choke is a practical problem in the electromagnetic interference filter application. As an important factor of the CM choke saturation, the self-resonance of the CM path is analyzed based on the low-frequency CM model of the variable-frequency drive system. Combining the analysis and the conducted emission test results of the system, the main cause of the CM choke saturation in our experiment is studied. Meanwhile, a novel online impedance measurement method is proposed to observe the saturation effect of the CM choke. To eliminate the saturation effect, a new method is proposed in this paper. A damping resistor is connected in parallel with Y capacitor of an electromagnetic interference filter to suppress the self-resonance. Through comparing the experimental results of the original filter and the improved filter, the effectiveness of the resonance suppression method is validated and the saturation of the CM choke is prevented.
Partial Differential Equations and Digital Image Processing : A Review

The wide distribution of digital images in recent years in social media and electronic devices invokes the research activities of developing different algorithms to enhance this type of multimedia. One of the recent research approaches was involving partial differential equations (PDE) on digital image processing operations to enhance the appearance of these images, removing noise, zooming and different other applications. In this paper, a description of the relation between image processing and derivatives is explained followed by a review of recent applications of PDE on digital images in addition to suggesting more possible aspects of research in PDE-Digital image studies.
X-Masking for In-System Deterministic Test

In-system deterministic tests are used in safety-sensitive designs to assure high test coverage, short test time, and low data volume, typically through an input-streaming-only approach that allows a quick test delivery. The output side of the same scheme is, however, inherently vulnerable to unknown (X) states whose sources vary from uninitialized memory elements to the last-minute timing violations. Typically, X values degrade test results and thus test response compaction requires some form of protection. This paper presents two X-masking schemes that complement the primary (or level-A) blocking of unknown values by filtering out those X states that escape the first stage of masking and shall not reach a test response compactor or test result sticky-bits deployed by the on-chip compare framework. Experimental results obtained for eleven industrial designs show feasibility and efficiency of the proposed schemes altogether with actual impact of X-masking on various test-related statistics.
Data-Driven Positive Stabilization of Linear Systems

This paper considers the data-driven control problem for the important class of positive systems. Due to the fact that such systems appear in diverse application areas whereby data are collected for identification and control, they are qualified candidates for data-driven control. Using fundamental concept of persistently exciting data and formulas for data-driven control, we provide an initial attempt to solve the positive stabilization of linear systems by input-output data. The result is useful in the sense that no subspace identification is required to obtain system matrices. With the aid of available results of positive systems and recent development of data-driven analysis of dynamic system, we formulate and solve data-driven positive stabilization using data-dependent linear matrix inequalities. The structural constraint of positivity makes the task challenging. Nevertheless it is possible to use this framework for positive output feedback and robust optimal control problems as well.