Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Invasive Visual Computing Modelling for Obstruction Inspection of Pipes in the Fertilizer Industry

In the context of industrial automation, one of the areas that have seen a lot of increased demand is the fertilizer industry, that has to keep up with agricultural needs around the world. These factories have several ducts where unwanted chemical reactions take place, resulting in congealment of product and clogging of the pipes over time. These obstructions compromise production and the whole machinery, causing a major financial loss, hence the need for a non-invasive method to determine to what extent a pipe or duct is obstructed. This paper presents a thermal imaging approach to model clogging and deposit distributions within pipes, rooted in visual computing, along with the use of laser based 3D scanning for calibration and result validation.
Low-Cost Robot Focused on Teaching Advanced of Mobile Robotics Techniques

This work proposes the development of a low-cost autonomous robot for academic purposes. The robot is developed from parts not initially developed for robotics, such as a vacuum cleaner. As a processor, the robot uses a Raspberry Pi associated with the ROS framework. The developed robot is capable of performing Simultaneous Localization and Mapping (SLAM) actions. The goal is to use the proposed robot in the classroom to teach mobile robotics to students and researchers. To validate the use of the robot in the classroom, a SLAM class was held where one class had contact with the robot and another did not. The class that had contact with the robot presented better results in the questionnaire about the subject.
A Systematic Mapping Study on Multi-Sensor Fusion in Wheeled Mobile Robot Self-Localization

Sensor fusion is a well-explored area with constant research over the last thirty years for application in mobile robot localization. This systematic mapping study (SMS) identifies areas for more primary studies to be conducted. The goal of the study is to identify the main methods of sensor fusion and sensor types, aiming at application with multiple sensors in indoor and/or outdoor environments. As a result of the present study, it was observed a trend of applying vision, ultrasound, laser, and encoder in multi-sensor fusion systems. It's also remarkable that most systems use two kinds of sensors. Researches published in the last five years (2019) point to the Kalman filter as the predominant state-of-the-art sensor fusion method applied to wheeled mobile robot (WMR) localization. In this systematic study, we also identified which conferences have the largest number of multi-sensor fusion publications applied to mobile robots.
Modeling Multilayered Samples of Inorganic and Organic Speckle Structures

We present the numerical models, analysis, and evaluation of a multilayered periodic structure applicable within nanoelectronics. The models can be advantageously employed in the characterization of electromagnetic parameters, thus helping researchers and designers to solve problems associated with nanoelectronics, nanophotonics, resonance-based sensors, and related components. The given models of a large periodic structure are designed to evaluate the parameters of electromagnetic wave propagation within speckle elements. Utilizing the interpreted results, a functional sample structure can be experimentally fabricated after basic nano-design changes. In the described context, the outcomes of a numerical analysis of the investigated simple nanoelectric multilayer structure are also introduced.
The effect of a diet and exercise intervention on body composition and cardiometabolic risk factors in postpartum women

Obesity among women is a public health problem in the United States. Pregnancy may be one of the causes of this, with 56% of women of childbearing age being overweight or obese. Excessive weight gain during pregnancy and postpartum weight retention may increase a woman’s risk of obesity and chronic disease later in life. Moderate calorie restriction and exercise interventions have been shown to reduce body weight and improve body composition during the postpartum period. While weight loss interventions have been successful, high attrition rates limit the widespread effectiveness of these interventions. Furthermore, there is a lack of research examining the effects of a weight loss program on cardiometabolic risk factors in this population. Therefore, the primary aim of the studies in this dissertation were: 1) To determine the effect of a diet and exercise intervention on weight, body composition, and cardiometabolic risk factors, 2) to improve lifestyle behaviors through improved diet quality and cardiovascular fitness, and 3) to describe the relationship between chronic inflammation and bone mineral density (BMD) in overweight and obese postpartum women. The first study concluded that a home-based diet and exercise program resulted in greater reductions in weight, waist circumference, sagittal diameter, and abdominal fat mass compared to a control group. Additionally, the intervention resulted in a significant improvement in cardiovascular fitness. The second study concluded that the intervention improved triglyceride concentrations, decreased insulin resistance, and improved metabolic syndrome risk factors. Finally, the third study concluded that the intervention resulted in less loss of BMD at the total body and hip and less loss of BMC at the hip, lumbar spine, and femoral neck after controlling for lactation status and weeks postpartum at baseline. Change in cardiovascular fitness was a predictor of change in total hip and femoral neck BMD, while change in inflammation was a predictor of change in total body BMD. These results suggest that a home-based, diet and exercise intervention is effective in improving body composition, cardiovascular fitness, and some cardiometabolic risk factors in overweight and obese postpartum women. Additionally, moderate aerobic activity may attenuate bone loss during a weight loss program.
MM-wave Patch Antenna with Embedded Photoconductive Elements for 1-bit Phase Shifting

The paper presents a development approach formm-wave patch antenna with 1-bit (0/180°) phase shifting. The proposed design employs a stacked patches structure, where the lower patch is excited symmetrically from an embedded single-pole double-throw switch. The switch is based on controllable photoconductive elements, where each element represents a high-resistivity silicon piece, which conductivity is modulated by a light source. Measured results demonstrate achievable (23–26) GHz −10-dB matching bandwidth and radiated field phase switching of (170-173)°. The approach can find its applications in beam steering antenna arrays and switchable monopulse antennas.
The Implementation of Visual Odometer Based on Raspberry Pi and Robot Operating System

The visual odometer (VO) is an essential part of visual navigation and plays an essential role in the position estimation of mobile robots. Currently, VO is mainly designed and implemented on PC, and it is inconvenient to test on the medium and small-sized mobile robots. In this paper, we designed the VO on Raspberry Pi microcomputer board based on Robot Operating System (ROS). We divided the VO into different parts according to the function, which is realized by the distributed parallel nodes in the ROS. The real-time performance is guaranteed by multi-nodes in the limited calculation of the Raspberry Pi, and the development mode of multi-nodes is also convenient for updating or changing the arithmetic of VO in the future. Experimental results show that the VO proposed in this paper has high accuracy and exceptional real-time performance.
SABER: An Infrastructure-Agnostic Benchmark Tool for Elasticity Evaluation on Cloud Foundry Based Platforms

Elasticity can be considered as one of the main features of cloud computing. It can be described as the ability to dynamically provision (or de-provision) resources to meet an application’s demand. Cloud-native platforms support this feature, allowing applications to be vertically or horizontally scaled. However, evaluating the performance of elasticity in cloud-native platforms is not a trivial task. These platforms tend to focus on providing higher abstractions to their customers, which typically results in the absence of low-level metrics that are required for measuring and evaluating elasticity performance. Therefore, this paper presents SABER, an infrastructure-agnostic benchmark tool for elasticity evaluation of Cloud Foundry based platforms. SABER collects low-level metrics regarding elasticity typical procedures (e.g., allocation and de-allocation of resources) and can be used to evaluate elasticity on different cloud infrastructures. Furthermore, we present an evaluation of SABER running on Pivotal Application Service, a Cloud Foundry based platform provided by Pivotal Web Services.
Optimal Indirect Power Predictive Controller in the Alpha-Beta Reference Frame for Grid-Connected Converters

This article presents an indirect power predictive optimal controller applicable to grid-connected converters. The VSI converter is used as an interface between distributed generators, energy storage systems and the ac-grid. The objective of the research project is to develop a controller with high static and dynamic features, that performs with low computational burden and low switching stress. The first step to develop the controller is to select the appropiate reference frame. The $\alpha\beta$ frame is used for its simple structure and easy implementation. Then, the proposed methodology consider the controller performance to define the sampling period, the prediction horizon and the switching pattern. Results show that an increment in the sampling period results in a major harmonic content, and if the prediction horizon increases, the number of calculations is higher without improving the controller performance. The main features of the proposed controller are: low sampling times and a two-steps prediction horizon. With these features the VSI efficiency rises, while the systems's losses and harmonic distortion are reduced.
Simulation and Evaluation of Different Mobility Models in Disaster Scenarios

Peoples trapped in the disastrous areas may have chances to survive if they are rescued in seventy two hours. Ad hoc networks are considered more suitable for such scenarios due to infrastructure-less feature. Two different mobility models - Reference group Mobility Model and Disaster Area Model are generally used in such scenario. In this paper, various ad hoc routing protocols such as destination distance vector routing protocol, dynamic source routing protocol, ad hoc on demand routing protocol and ad hoc on demand multipath routing protocol are discussed and analyzed using reference group mobility models and disaster area model. Also these protocols are compared using various performance metrics such as packet delivery ratio, delay, throughput, control overhead, average energy consumed etc.
New Local Difference Feature for Off-Line Handwritten Signature Verification

In this work, we propose a new textural feature for solving offline handwritten signature verification. The proposed feature that is called Local Difference Feature (LDF) is a LBP-like texture descriptor. LDF calculates differences between a central pixel and eight neighbors taken on a specific neighborhood radius. Differences are coded into binary thresholded values before evaluating the histogram of codes. The verification step is achieved by SVM classifier trained on genuine signatures. Furthermore, the test stage is performed on both genuine signatures and skilled forgeries. Exepriments are conducted on GPDS and CEDAR datasets. Results obtained showed that LDF outperforms classical features such as Histogram of oriented gradients and local binary patterns. Also, the comparison with the state of the art highlights the LDF robustness for handwritten signature characterization
Vector Control Based Speed and Flux estimation in Switched Reluctance Motor Using ANN Controller

A switch reluctance motor (SRM) is individually excited, doubly-salient electric machine having characteristics of torque production due to variable reluctance. A increased activity in the intelligent control methods consisting artificial neural networks (ANN) and fuzzy have made them suitable for SRM applications. This paper presents a study of different controllers for switch reluctance motor. The stator current and flux are estimated using ANN Technique. The ANN controller uses a switching table and vector control method to generate gating signals. MATLAB/Simulink is used for fixed parameters of SRM. The advantages of the ANN model is that no prior knowledge is required (model or equation)
ConForm: In-band Control Plane Formation Protocol to SDN-Based Networks

Although OpenFlow-based SDN networks make it easier to design and test new protocols, when you think of clean slate architectures, their use is quite limited because the parameterization of its flows resides primarily in TCP/IP protocols. Besides, despite the many benefits that SDN offers, some aspects have not yet been adequately addressed, such as management plane activities, network startup, and options for connecting the data plane to the control plane. Based on these issues and limitations, this work presents a bootstrap protocol for SDN-based networks, which allows, beyond the network topology discovery, automatic configuration of an inband control plane. The protocol is designed to act only on layer two, in an autonomous, distributed and deterministic way, with low overhead and has the intent to be the basement for the implementation of other management plane related activities. A formal specification of the protocol is provided. In addition, an analytical model was created to preview the number of required messages to establish the control plane. According to this model, the proposed protocol presents less overhead than similar de-facto protocols used to topology discovery in SDN networks.
Time-varying group formation tracking for second-order multi-agent systems with switching directed topologies

Time-varying group formation tracking problems for second-order multi-agent systems with switching directed topologies are studied, where the followers are divided into subgroups to form pre-defined time-varying sub-formations while tracking the leader of the subgroups that they belong to. Firstly, a time-varying group formation tracking protocol is constructed using local relative state information of each agent and its neighbors. Then an algorithm to design the protocol is proposed. Based on the piecewise Lyapunov function theory, the stability of the proposed algorithm is proven and feasibility constraints as well as the threshold for the dwell time are also presented. Finally, a numerical simulation is provided to demonstrate the effectiveness of the theoretical results.
Comparison of Sigma-point Update Framework in Cubature Kalman Filter for Tightly Coupled GNSS/INS

New sigma-point update frameworks are investigated and compared in this paper, where the nonlinear approximation residuals represented by the posterior sigma points error are transformed directly to construct new sigma points. However, whether it is favorable to include likelihood function error in the update of sigma points for tightly coupled GNSS/INS is still need to be answered. The 3rd-degree and 5th-degree cubature rule for cubature Kalman filter (CKF) are adopted to verify the effectiveness of different sigma-point update algorithms. Numerical simulation indicates that the usage of sigma points error from prior probability density function (pdf) and likelihood function can speed up the convergence rate of CKF, and in terms of complexity and ease of use it is better to select the transformation based on prior pdf only.
Grazing-incidence X-ray reflectometry and fluorescence analysis of the metallic-coated sinusoidal diffraction grating

Grazing incidence X-ray reflectometry (GIXRR) and fluorescence (GIXRF) analysis of the metallic-coated diffraction grating has been conducted. The grating has the sinusoidal-type groove profile derived from the atomic-force microscopy measurements. Using the developed methods of GIXRR and GIXRF, we theoretically and experimentally investigate angular dependences of the specular reflected and emitted radiation intensity of the Al-coated 400 mm grating with a Cr adhesion layer of the thickness of ~2 nm on the SiO2 substrate working in extreme conical and classical diffraction mounts for the incident wavelength of ~0.1541 nm and the grazing-incidence angle range of ~0.05–0.5 deg. The specular reflectance (the 0-th order efficiency) as well as the specular fluorescence intensity have been found from solutions of the respective vector Helmholtz equations. In order to determine the fluorescence intensity, we have used the approach based on a method of fundamental parameters using the reciprocity theorem.
Performance Improvement of QPSK MODEM in AWGN Channel Implemented in FPGA

The paper describes a method of implementing a QPSK system in FPGA, the system being contrived in a manner where it provides an improved performance in an AWGN environment. To emulate the AWGN environment, we decided to use a method that largely resembles the Box-Muller method that generates two independent random variables with a normal distribution. The generated number sequences representing noise are then separately added to the I-channel and the Q-channel. Raised Cosine Filtering is used for the smoothening of signals. To illustrate the improvement in performance that FEC codes could provide, we use (2, 1, 7) convolutional encoding after pulse shaping, then introduce the noise, and Viterbi decoding is used to correct the errors. The entire system has been simulated using ModelSim PE Student Edition 10.4a and implemented using Xilinx XC6SLX45 Spartan 6 FPGA with the aid of ChipScope Pro software.
Design of Z-Axis Position Servo Control System for Laser Processing Based on Fuzzy PID Control

In order to solve the problems of low robustness and poor immunity of traditional Z-axis position servo control system for laser processing, a design scheme of Z-axis position servo control system for laser processing based on fuzzy PID control is proposed. The hardware configuration of the system is improved and servo controller is added to improve the system performance. Combined with fuzzy PID control principle, the system software operation algorithm and flow are optimized to realize the design of Z-axis position follow-up control system for laser processing. The experimental prototype was built by high-precision laser sensor, and the follow-up control of the laser processing system was realized, so that the laser spot kept a constant distance from the surface to be processed. This laid a foundation for the next intelligent control technology of laser processing.
A 2.4 GHz Sub-mW Single-Balanced Self-Oscillating Mixer

This paper provides an overview of self-oscillating mixer (SOM) configurations for low-power applications. A 2.4 GHz sub-mW SOM, implemented in $0.13~\mu \text{m}$ CMOS technology, is also presented. It consists of a voltage controlled oscillator (VCO) stacked on a single-balanced active mixer. The simulations result in only $500~\mu \text{W}$ of power consumption from 1.2V supply voltage, with a conversion gain of 18.2 dB @ 5 MHz and an IIP3 of -4 dBm, the SOM exhibits a single sideband noise figure of 14 dB and a phase noise of -113.5 dBc/Hz @ 1MHz.
Formation of Space Charge Wave with Wide Frequency Spectrum in Helical Electron Beam of Two-Stream FEL Transit Section with Longitudinal Electric Field

The longitudinal electric field effect on the formation of a space charge wave (SCW) with wide frequency spectrum in the helical two-stream relativistic electron beam (REB), which moves in transit section of multiharmonic two-stream superheterodyne free-electron laser, is analyzed. In the framework of cubic nonlinear approximation, it is shown that the use of the accelerating electric field makes it possible to increase the multiharmonic SCW width of the frequency spectrum. It is shown that the retarding electric field causes the increase of the amplitude level SCW harmonics, as well as the decrease of its saturation length.
Dense Mapping from Feature-Based Monocular SLAM Based on Depth Prediction

In recent years some direct monocular SLAM methods have appeared achieving impressive semi-dense or dense 3D scene reconstruction. At the same time, feature-based monocular SLAM methods can obtain more accurate trajectory than direct methods, but only obtain sparse feature point map rather than semi-dense or even dense map like direct methods. With the development of deep learning, it becomes possible to predict the depth map of a scene given a single RGB image. In this paper we demonstrate how depth prediction module via deep learning can be used as a plug-in module in highly accurate feature-based monocular SLAM (e.g. ORB-SLAM). Both accurate trajectory from ORB-SLAM and dense 3D reconstruction from depth prediction can be achieved. Evaluation results show that dense scene reconstruction can be obtained from highly accuarate feature-based monocular SLAM.
Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks

In this paper, a model reference control method based on RBF neural networks is applied to attitude control of quadrotor. The model of a quadrotor is constructed and simplified to obtain the reference model in the same order as the plant. The RBF is trained by using the gradient descent method. Through simulation experiments, MRAC based on RBF has presented good tracking performance on the nonlinear quadrotor system with unknown and changing parameters.
Advanced Chemical Concentration Control for Fabrication of Devices Using SiC

In conventional MEMS fabrication, relatively inert compounds such as Si3N4 are used as an etch stop or mask for creating patterns on wafers. However, materials such as this require insight as to their etch selectivity corresponding to that of the substrate material and are not suitable for high temperature devices. When developing these high temperature compatible devices, components composed of SiC are desired, and may be used as an etch stop due to it being chemically inert. For these applications, it is common for the substrate or sacrificial layer to be either Si or SiO2. A technique for advanced chemical concentration control during processing is critical to be able to maintain a consistent etch rate, a controlled etch depth, and maintain the desired shape of the pattern. Using NIR technology it is possible to monitor both the concentration of chemicals in the bath as well as that of byproducts created from the etching of Si and SiO2. The system can then increase bath life and the ability to etch consistently within and across batches. In the present paper, we present the mechanism of the advanced concentration control, the results of using either TMAH or KOH to etch Si, as well as its applications for the future of SiC integrated devices.
Efficient Online Algorithm for Assigning Resources of Fog Computers and Communication Networks

There is interest in increasing the efficiency of assigning resources to fog demands in the IoT infrastructure while satisfying delay requirements. An online algorithm for assigning the resources of both communication networks and fog computers from the viewpoint of communication carriers is proposed. The proposed algorithm uses the method of the multi-concurrent flow problem studied in network theory. The parameters in the algorithm are also adjusted based on a machine learning approach for various conditions in actual IoT services. Numerical experiments were conducted to show that assignment efficiency in terms of blocking rate can be greatly improved by about 50% with the proposed algorithm, which can be further improved by adjusting the algorithm’s parameters.