Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability Analysis of Aero-Equipment Components Life Based on Normal Distribution Model

In order to describe the failure and life rule of aeronautical equipment components more truly and improve the accuracy and efficiency of reliability analysis, a reliability data analysis method based on general normal distribution is proposed to meet the needs of modern aeronautical equipment fault prediction. The purpose of this paper is to establish a reliability analysis model for normal distribution of aeronautical equipment components, and to present a probability plot method for directly judging the type of distribution. Then, a method for estimating the parameters of normal distribution is presented, which is accurate, simple and easy to implement, in order to improve the accuracy and applicability of reliability analysis. Finally, an application example in the field of aeronautical equipment support is given. The analysis results show that the use of probability plots to determine the type of probability distribution is simple and feasible. The maximum likelihood estimation method is used to estimate the fault distribution function and data characteristics, and the fitting accuracy is relatively high.
Memory-Efficient Architecture for Accelerating Generative Networks on FPGA

Generative adversarial networks (GANs) are a class of artificial intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks: a generative network (generator) and a discriminative network (discriminator). These two networks compete with each other to perform better at their respective tasks. The generator is typically a deconvolutional neural network and the discriminator is a convolutional neural network (CNN). Deconvolution performs a fundamentally new type of mathematical operation which differs from convolution. While the FPGA-based CNN accelerators have been widely studied in prior work, the acceleration of deconvolutional networks on FPGA is rarely explored. This paper proposes a novel parametrized deconvolutional architecture based on an FPGA-friendly method, in contrast to the transposed convolution implementation in CPUs and GPUs. Hardware design templates which map this architecture to FPGAs are provided with configurable deconvolutional layer parameters. Furthermore, a memory-efficient architecture with a new tiling method is proposed to accelerate the generator of GANs, by storing all intermediate data in on-chip memories and significantly reducing off-chip data transfers. The performance of the proposed accelerator is evaluated using a variety of GANs on a Xilinx Zynq 706 board, which shows 2.3x higher speed and 8.2x off-chip memory access reduction than an optimized Vanilla FPGA design. Compared to the respective implementations on CPUs and GPUs, the achieved improvements are in the range of 30x-92x in speed over an Intel 8-core i7-950 CPU, and 8x-108x in terms of Performance-per-Watt over an NVIDIA Titan X GPU.
An Improved Fabrication Process for Micro Hemispherical Resonator Gyroscope

This paper reports an improved fabrication process for micro hemispherical resonator gyroscope $(\mu \mathbf{HRG})$, which enhance consistency of quality factor, frequency and capacitance gaps. Micro hemispherical resonator is formed from fused silica substrate through thermal deformation on fast whirling platform individually. Following procedures including laser ablation, metallization and integrating to electrodes are accomplished on wafer-level and optimized through experimental data. The resonators are electrically characterized in vacuum chamber. Frequency for $\mathrm{n}=2$ wineglass mode ranges from 7kHz to 8kHz and quality factors range from 100,000 to 160,000, which are significant to stabilize and enhance gyro's performance. The accomplished device operates in force-rebalance mode in vacuum packaging, realizing a full-scale range above $\pm 200\deg/\sec$ and bias instability of 0.63 deg/hr.
Efficient Task Allocation Using Intelligent Bacterial Foraging Optimization (IBFO) Algorithm in Cloud

Cloud usage increases with increase in computational demands. The number of tasks for execution in cloud increases which ends up in complexity of scheduling tasks to resources in an energy efficient manner and with reduction of computation time. To resolve this issue Bacterial Foraging Optimization (BFO) algorithm proves to handle energy and time consumption efficiently. Though Bacterial Foraging Optimization (BFO) is one of the widely known and robust algorithm for handling multi-objective optimization problems, the algorithm is basically static and is run for a fixed number of iterations. Due to the inflexibility of the algorithm, there exists a need for the improvement of the existing Bacterial Foraging Optimization (BFO) algorithm. This paper rolls out an improved version of Bacterial Foraging Optimization (BFO) called Intelligent Bacterial Foraging Optimization (IBFO) algorithm that is dynamic based on the problem. Intelligent Bacterial Foraging Optimization (IBFO) algorithm is found to be more efficient than the existing Bacterial Foraging Optimization (BFO) algorithm in task scheduling in cloud environment.
Additive Manufacturing of K/Ka/Q/V-Band Feed-Horns

In this work the design and the Selective Laser Melting (SLM) manufacturing of single- and dual-band high-performance horns operating within the Ku to V bands are considered. A smooth wall architecture has been preferred in order to simplify the manufacturing and enhancing the SLM quality. Three prototypes have been designed, built and successfully tested.
Research on Open-Circuit Fault Protection and Positioning Isolation of Constant Current Remote Supply System in Cabled Underwater Information Network

Fault protection measures and interval positioning isolation methods are proposed for open-circuit faults of the constant current remote supply system, in order to realize the efficient and reliable operation of cabled underwater information network. Achieving the protection of the power conversion module in the trunk node by researching the protection circuit, and based on the controllable electrical switching type switch in the main node of the trunk, the system fault isolation circuit was designed and a new method of open-circuit fault location isolation was proposed. The research content has reference significance for the design and construction of cabled network underwater information network in the future.
Design of Multilayer Airborne Radar Data Processor

The paper proposes design of multiple-layer robust airborne radar target tracking system. The design also discusses the algorithms that aid in airborne tracking process, different techniques of clutter mitigation at data processor, false tracks suppression and techniques for improvising track maintenance functionality. The objective of the design is to develop a practical, ready to deploy, modular airborne multiple target tracking system. The design is based on layered approach wherein, each layer of tracking system is designed to meet specific function. The proposed approach has three layers with each layer performing a specific function. The first layer is called pre-processing layer with main functionality of clutter mitigation and suppression of unwanted plots. The second layer is targeted to track non-maneuvering targets and detection of target maneuvers. The main functionality of third layer is to track maneuvering targets. The paper also discusses about the interaction between these tracking layers. Some of the salient issues addressed in the paper are suppression of false tracks arising from clutter leaks, ground moving targets, windmills, ghosts and multipath detections. The false tracks could clutter air situation picture and penalize radar resources. The innovation of the paper is in evolving and partitioning of airborne target tracking algorithms into different layers and seamless integration of these layers. The algorithms focus on reduction of false and unwanted tracks in airborne radar, improvement of detection performance through feedback from non maneuver tracking layer and maneuver target tracking layer. The layered design provides advantages in terms of reusability, configurability, maintainability and is scalable and adaptive. The concept of layering facilitates efficient usage of all algorithms. The layered software concept alleviates the burden placed on combat mission commanders since some of the tasks intelligently collate all information along with historical data and hence resolving ambiguous tracks whenever necessary, rejecting the improbable data and adapt automatically to environmental changes.
Capacitor Voltage Ripple Minimization in Voltage Source Converter for HVDC Applications

In this paper an exhaustive study on the effects of the circulating currents in the modular multilevel converter in HVDC applications is presented. In the first part, an analytical study of the converter is reported, with emphasis on the capacitors voltage ripple in presence of different circulating currents. In particular, the effects of the second and fourth harmonic components in the circulating current are evaluated in a general working point and, in particular, in the more significant working points corresponding to the inverter, rectifier, inductive and capacitive operation modes. The second part is a simulated HVDC plant verification of the voltage capacitor ripple expression.
Dynamic and Privacy-Preserving Reputation Management for Blockchain-Based Mobile Crowdsensing

Mobile crowdsensing (MCS) is an emerging data collection paradigm that exploits the potential of individual mobile devices to acquire mass data in a cost-effective manner. One of the important challenges in MCS application is to resist malicious users who provide false data to disturb the system. In the existing work, the reputation management scheme is an effective way to overcome the challenge. However, most reputation management schemes rely on a semi-honest server and process data in the plaintext domain without considering server security and user privacy. In this paper, we integrate the blockchain and edge computing in the MCS scenario to construct a credible and efficient blockchain-based MCS system, called BC-MCS. To resist malicious users, we present a privacy-preserving reputation management scheme based on the proposed system. Furthermore, we design a delegation protocol to solve the inherent problem of user dynamics in the MCS. The prototype system implemented on the Hyperledger Sawtooth and Android client demonstrates that our scheme can achieve higher utility and security levels in handling malicious users compared with the previous centralized reputation management schemes.
Fast Synthesis of Scattered Waveform Generators in the Near Field

We deal with an approach, based on the Singular Value Optimization (SVO) technique, for the synthesis of array-based generators of near-field complex waveforms. The approach selects the grid wherein enforcing the design specifications and the radiator locations to control the ill-conditioning associated to the determination of the array excitation coefficients. Following the SVO optimization, the array coefficients are determined by a Singular Value Decomposition (SVD). Numerical results are provided for the synthesis of the near-field scattered by a perfectly conducting sphere illuminated by an elementary dipole.
State-Wise LSTM-GRU Method for Ball Screw Prediction

Ball screws play important roles in wide applications related to aircraft equipment and its manufacturing process. For these highly complex systems, their demands for reliability is high and strict. Thus, the precise evaluation and prediction of the health status of the ball screws is necessary. As the degradation trend often varies due to multiple independent or coupled factors, the degradation of the ball screw performance may go through a “decrease-increase-decrease” trend. In such cases, a single degradation model cannot accurately describe how the degradation develops. In this paper, we predict the health of the ball screw by dividing the degradation process into several different trends with the assumption that the trend changes with the status of the ball screw. To achieve this, a new prediction model is proposed based on a state-wise deep learning method of the long short-term memory network and gated recurrent unit (LSTM-GRU). First, the degradation trend of the ball screw is obtained by the combination of Kernel Principal Component Analysis (KPCA) and Mahalanobis distance (MD). Second, the method of the density-based spatial clustering of applications with noise (DBSCAN) is used to evaluate the health status of the ball screw. Then, the proposed method of the state-wise LSTM-GRU is validated on a life-cycle experiment of the ball screw. Compared with the standard long short-term memory network (LSTM) and the method of gated recurrent unit GRU), the results show that the proposed model of state-wise LSTM-GRU can predict the performance state of the ball screw more accurately and more robust, which indicates it is a promising method for the performance prediction of the ball screw.
Energy Efficient CNN Inference Accelerator Using Fast Fourier Transform

We use FFT-based convolution in frequency domain to reduce computational complexity in CNNs. The properties of conjugate symmetry and down-sampling is adopted to further reduce complexity. By eliminating filter weights in CNNs that can save computational requirement but lead to accuracy loss. The simulation result reveals that eliminating filter weights in frequency domain is more accurate than that in time domain. With the proposed design synthesized by TSMC 90 nm CMOS technology, the total latency, power and energy are considerably competitive. As a result, our FFT-based CNN accelerator is energy-efficient.
Experience with PSAT (Power System Analysis Toolbox) as Free and Open-Source Software for Power System Education and Research

This paper describes the assessment of a free and open-source software tool for power system analysis and modeling, namely Power System Analysis Toolbox (PSAT). PSAT is currently used in several universities for undergraduate and graduate courses and research. Several applications of PSAT as an educational and research tool are discussed here. The paper also describes the PSAT Web forum, which provides support to students and researchers all around the world. Finally, the paper presents and discusses the results of a survey on PSAT filled out by the members of the PSAT forum and PSAT users.
ESPAR Antenna for V2X Applications in 802.11p Frequency Band

In this paper an Electronically Steerable Parasitic Array Radiator (ESPAR) antenna prototype for vehicle-to-everything (V2X) applications is proposed. The motivation of this work was to adapt ESPAR antenna concept widely used in Wireless Sensor Networks to improve connectivity and security in V2X communication system. Designed model was optimized for 802.11p frequency band and includes steering circuits based on fast and low-power UltraCMOS SPDT switches. Proposed antenna has compact size and provides unique directional radiation patterns for 360° area scanning. Theoretical and simulated results have been discussed in order to evaluate the antenna parameters with respect to V2X communication system requirements.
Understanding Metastability in SAR ADCs: Part I: Synchronous

By strongly leveraging technology scaling in its building blocks, the successive approximation architecture resides comfortably at both the high-speed and high-resolution frontiers of the analogto-digital conversion landscape [1]-[3]. This particularly digital-friendly set of building blocks includes MOS switches, metal fringe capacitors, and regenerative latches, each of which has analog nonidealities that can limit the attainable speed and resolution of a successive approximation register (SAR) analog-to-digital converter (ADC). Whereas circuit techniques such as bottom-plate sampling and mismatch calibration have a proven track record for mitigating sources of nonlinearity, techniques that address the problem of metastability in the regenerative latch are still under development [4]-[6]. This two-part tutorial aims to help readers follow the most recent advancements in this area, by providing an all-in-one introduction to the subject of metastability in SAR ADCs not yet available in the literature or textbooks. A clear picture of how metastability, noise, and the SAR algorithm interact is a useful prerequisite for deciding where and how potential metastability errors should be dealt with in the design of SAR ADCs.
An Optimized Wideband Ridge Turnstile Junction with Compact Size

This paper presents a broadband turnstile-based orthomode transducer junction achieving by ridge waveguides and a pyramid-shaped scattering element that is adopted as matching stub in the turnstile junction to provide good impedance matching with quad-ridge circular waveguide and single-ridge rectangular waveguides. The proposed structure covers the band from 1.7 to 5.83 GHz with an input reflection coefficient less than -20 dB. The simulated insertion loss is below 0.1 dB and the isolation is better than 55 dB. The dimensions of the junction are only 0. 49λ × 0. 49λ × 0. 72λ at 1.7 GHz.
Deep Learning Based Forecasting in Stock Market with Big Data Analytics

In recent years, due to the technological improvements in computers' hardware and enhancements in the machine learning techniques, there are two increasing approaches for problem-solving as the use of “Big Data” and “Parallel Processing”. Especially with the emergence of Deep Learning algorithms which can be executed parallelly on multi-core computing devices such as GPUs and CPUs, lots of real-world problems are resolved with these approaches. One of the most critical application areas in the Financial Market especially sits on Stock Markets. In this area, the aim is trying to predict the future value of a specific stock by looking at its previous financial data on the exchange process in the market. In this paper, we proposed a system that uses a Deep Learning based approach for training and constructing a knowledge base on a specific stock such as “IBM”. We get time series values of the stock from the New York Stock Exchange which starts from 1968 up to 2018. Experimental results showed that this approach produces very good forecasting for specific stocks.
Mathematical Optimization for Engineering Design Problems

Applications in engineering design and the material sciences motivate the development of optimization theory in a manner that additionally draws from other branches of mathematics including the functional, complex, and numerical analyses. The first contribution, motivated by an automotive design application, extends multiobjective optimization theory under the assumption that the problem information is not available in its entirety to a single decision maker as traditionally assumed in the multiobjective optimization literature. Rather, the problem information and the design control are distributed among different decision makers. This requirement appears in the design of an automotive system whose subsystem components themselves correspond to highly involved design subproblems each of whose performance is measured by multiple criteria. This leads to a system/subsystem interaction requiring a coordination whose algorithmic foundation is developed and rigorously examined mathematically. The second contribution develops and analyzes a parameter estimation approach motivated from a time domain modeling problem in the material sciences. In addition to drawing from the theory of least-squares optimization and numerical analysis, the development of a mathematical foundation for comparing a baseline parameter estimation approach with an alternative parameter estimation approach relies on theory from both the functional and complex analyses. The application of the developed theory and algorithms associated with both contributions is also discussed.
Experimental study on the phase analysis of FMCW radar for vital signs detection

In this work, we address the challenge of the FMCW radar phase analysis known as the phase wrapping. A new solution is proposed based on a recursive phase correction, which does not resort to differentiation or integrations. In addition, the method is implemented and tested for a subject in order to detect heart and breathing rates in a bedroom environment Furthermore, we derive the cardio-respiration waveforms for the subject to demonstrate the feasibility of using the radar for clinical applications.
Slotted Microstrip Leaky-wave Antennas for Backfire to Endfire Beam Steering

Two periodic microstrip leaky-wave antennas providing beam scanning ability from backfire to endfire are presented in this paper. The proposed antennas consist of periodic patterned slots on the ground plane of the microstrip line. Both rectangular and sinusoidal slotted configurations are demonstrated, enabling the backward-to-forward radiation leakage as the operation frequency increases. Experimental results show that the peak gain of antennas exceed 12 dBi and the beam scanning range is 145° and 151°, respectively over a frequency range of 4.0-11.5 GHz and 4.7-7.7 GHz.
Monostatic RCS Prediction from Irregularly Distributed Near-Field Samples Using Plane-Wave Field Synthesis

We present an approach to accurately determine the monostatic RCS of an object under test, by measuring a set of bistatic near-field scattering data, where it is sufficient to sample the scattered field in a small angular range around the transmitting antenna. This is achieved by solving a field synthesis problem for the incident plane wave with respect to the transmitter locations and using the result to weight the outcome of a series of inverse source problems. By expanding the fields in propagating plane waves on the unit sphere, almost arbitrary scan surfaces can be processed efficiently, including highly irregular ones. Thus, the measurement samples of the scattered field may potentially be collected by the use of unmanned aerial vehicles and it may even become possible to determine the RCS of large stationary outdoor targets. The paper outlines the theory of the algorithm and demonstrates its capabilities by means of simulated and measured near-field data.
Assessing E-Learning Acceptance by University Students in Thailand.

The aim of this study is to assess e-learning acceptance by students, using data collected from 377 students at three public universities in Thailand. Using the E-learning Acceptance Measure (Teo, 2010b), participants gave their responses to 21 statements on three factors hypothesised to measure e-learning: tutor quality, perceived usefulness, and facilitating conditions. Results showed that university students in Thailand have an above average level of e-learning acceptance (mean=111.36 out of 147). Among the user domains, age and perceived competence correlated significantly with the factors in ElAM. Using MIMIC modelling, students' e-learning acceptance was found to be significantly different by age and perceived competence.
Tumour Electrotherapy Modelling Using Algebraic Topological Method

Electromagnetic therapy is extensively used in various medical treatments of tumour cells. Different studies reveal that electrotherapy can effectively destroy or shrink tumour cells. Accurate modelling of the electromagnetic interactions with the tumour cells will enable biomedical engineers to develop effective treatment techniques. In this paper, we present a radically different non-mainstream approach based on algebraic topology to model this type of problems. We discuss the basic modelling framework for simulating electric field interactions with tumour cells and suggest a test problem to study such interactions at different skin-depths.