Miracle-dz/UniFAR-FTUs huggingface dataset:
Descriptive Study and Analysis Of Crowd Sourcing Techniques in Mobile Social Media Networks

Nowadays wearable devices and smartphones have been embedded with sensors, like microphones, global positioning systems (GPS), thermometers, cameras, and accelerometers, which use a sensing paradigm, called mobile crowd sensing. Several individuals employ their mobile devices for extracting and sharing data corresponding to their wish. Mobile crowd sensing is advantaged over traditional sensor networks because of its extensive coverage, high sensing accuracy, and low cost,. Accordingly, this survey presents a distinct mobile crowd sensing techniques. Thus, this review article provides a detailed review of 25 research papers showing the mobile crowd sensing techniques, like task assignment-based methods, group-based recruitment system, green mobile crowd sensing-based techniques and so on. Moreover, an elaborative analysis and discussion are made concerning the evaluation metrics, employed methods, utilized datasets, a tool for implementation, publication year, and energy consumption. Eventually, the research gaps and issues of various mobile crowd sensing techniques are presented for extending the researchers towards a better future scope.
Efficient Dynamic Fixed-Point Quantization of CNN Inference Accelerators for Edge Devices

This paper presents a dynamic fixed-point quantization flow. Different bit-width configurations of features, weights, and partial sums can be evaluated comprehensively over the state-of-the-art convolutional neural networks. The proposed quantization scheme simplifies the hardware design for on-device inference with the help of our analysis framework. The experiment results show that the energy consumption can be improved by up to 29.55% with little accuracy degradation by representing features/weights of 8 bits, and partial sums of 16 bits.
An Intelligent Calibration Method for Camera with Occlusion

In order to overcome the need for the user to give additional information on the calibration template during the camera calibration process, or the fully automatic calibration point recognition algorithm can not accurately calibrate the intrinsic parameters of the camera within the occlusion situation, the paper proposes an intelligent calibration method based on GSI coded point in the presence of significant occlusions. The method uses the GSI target attached with the GSI code to calibrate the intrinsic parameters of the camera, and proposes an automatic detection and matching algorithm for the image feature points under the new calibration plate. In addition, it can maintains the intrinsic parameter calibration accuracy of the calibration plate under partial occlusion. The detection algorithm is efficient and free of parameters. The final experimental results show that the error is very small when the calibration plate is partially occluded, which can meet the calibration accuracy requirements of the camera. The recognition and matching algorithm is robust.
Trilinos 4.0 tutorial.

The Trilinos Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. The goal of the Trilinos Project is to develop parallel solver algorithms and libraries within an object-oriented software framework for the solution of large-scale, complex multiphysics engineering and scientific applications. The emphasis is on developing robust, scalable algorithms in a software framework, using abstract interfaces for flexible interoperability of components while providing a full-featured set of concrete classes that implement all the abstract interfaces. This document introduces the use of Trilinos, version 4.0. The presented material includes, among others, the definition of distributed matrices and vectors with Epetra, the iterative solution of linear systems with AztecOO, incomplete factorizations with IF-PACK, multilevel and domain decomposition preconditioners with ML, direct solution of linear system with Amesos, and iterative solution of nonlinear systems with NOX. The tutorial is a self-contained introduction, intended to help computational scientists effectively apply the appropriate Trilinos package to their applications. Basic examples are presented that are fit to be imitated. This document is a companion to the Trilinos User's Guide [20] and Trilinos Development Guides [21,22]. Please note that the documentation included in each of the Trilinos' packages ismore » of fundamental importance. « less
Annealing Experiments on the Quality Factor of Fused Silica Cylindrical Shell Resonator

The quality factor (Q factor) of resonators in Cylindrical Vibration Gyroscope (CVG) directly determines the drift characteristics and noise performance of the gyroscope. Annealing can reduce the residual stress and internal friction loss of materials effectively, thus increasing the Q factor significantly. In this paper, a kind of fused silica cylindrical shell resonator was presented. The design of the resonator, fabricating steps, and the annealing experiment were introduced. A laser Doppler vibrometer was utilized to measure the resonant frequency and the Q factor of resonators before and after annealing. We performed the annealing experiment on three resonators, the best results showed that after annealing, the Q factor increased by 498.3% (from 28955 to 173232, measured in 120Pa).
SDR-Based DoA Estimation Using ESPAR Antennas with Simplified Beam Steering

In this paper, we have presented results of the two most popular and accurate direction-of-arrival (DoA) estimation algorithms that may be implemented in a software-defined radio (SDR) unit equipped with electronically steerable parasitic array radiator (ESPAR) antenna, in which beam steering is performed in a simple and energy-efficient way. Both algorithms rely on IQ waveform samples recorded, for different main beam directions, at the antenna output port, from which information about both amplitude and phase of an unknown signal impinging the antenna has been extracted. Measurement results indicate, that, by using SDR-based calibration setup, it is possible to perform DoA estimation with less than a single degree precision, which, according to author’s knowledge, is the best result available in the literature.
FLiMS: Fast Lightweight Merge Sorter

We have developed a highly-efficient and simple parallel hardware design for merging two sorted lists residing in banked (or multi-ported) memory. The FPGA implementation uses half the hardware resources required for implementing the current state-of-the-art architecture. This is achieved with better performance and half the latency, for the same amount of parallelism. The challenges for the merge operations in FPGAs have been the low clock frequency due to the feedback datapath of the merger being the critical path for timing, and also the high resource utilisation in recent attempts to eliminate/remove the feedback datapath. Our solution uses a modified version of the bitonic merge block, as found in a bitonic sorter, repurposed for performing parallel merge for streaming data. As with the state-of-the-art, it can be considered feedback-less since it only nests one parallel comparison for any desired level of parallelism. This leads to high operating frequency designs, 1.3 times higher than the previous best on our test platform. Since the new design uses 2 times fewer hardware resources, it allows more parallelism and leaves room for additional logic.
Research on Artificial Intelligence Modeling Methods for Internet Public Opinion Information

This paper analyzes the method of using mathematical graph theory and Artificial Intelligence idea to establish the Internet public opinion information model. It uses and compares the different mathematical algorithms – classical mathematic model, naïve Bayesian classifier, advanced time series K-means clustering model, self-organizing map (SOM) neural network algorithm and support vector machine (SVM), which are used in constructing the advanced Artificial Intelligence model. Then, it gives out the process of constructing the complete Artificial Intelligence model. Through theoretical analysis and experiment, the coverage rate and time efficiency with different sample size for the above various algorithms are verified. The results show that the advanced time series K-means clustering model can effectively improve both criteria. Besides, it also analyzes the advantages, applicable conditions and limitations of different models.
Superdirective Dipole Arrays

In this paper we develop and utilize theory for finding excitation currents that maximize the directivity of arbitrary dipole arrays. Such excitation is known as superdirective. It is shown that for arrays above perfectly electric ground, the corresponding optimal currents are purely real. It is also observed that for horizontally oriented dipoles, there exist optimum for spacing and height producing maximum obtainable directivity.
Millimeter-Wave Quad-Port Multiple-Input Multiple-Output Dielectric Resonator Antenna Excited Differentially by TE20 Mode Substrate Integrated Waveguide

In this paper, a differentially excited quad-element multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) is proposed for millimeter-wave applications. In the proposed design, the rectangular DRA is differentially excited in the $TE_{1\delta 1}^{y}$ mode using the dual-slot configuration located on the top wall of $TE_{20}$ mode substrate integrated waveguide (SIW). The proposed MIMO configuration offers both spatial and polarization diversities. For all the four ports, the impedance bandwidth of the proposed antenna is 3.2% (27.5-28.4 GHz), covering the 5G frequency spectrum. The isolation between the input ports is better than 40 dB throughout the operating bandwidth. The antenna radiates along the broadside direction having the cross-polarization level below -30 along the zenith. The peak gain and radiation efficiency of the proposed antenna is 4.2 dBi and 86%, respectively at 28 GHz. Moreover, the diversity performance is found to be good having low value of envelope correlation coefficient across the band-width. The proposed antenna could be the potential candidate for next generation 5G communication system.
Integral ISS-Based Cascade Stabilization for Vectored-Thrust UAVs

We address stabilization of vectored-thrust unmanned aerial vehicles (UAVs): a challenging task due to the peculiar nonlinear underactuated dynamics. According to a well-established approach based on the selection of suitable error variables, the error dynamics is described as a pseudo cascade connection where the attitude subsystem indirectly stabilizes the position dynamics. Unlike existing works, we address stability of this cascade using integral input to state stability (iISS). With this extension it is possible to employ a quasi time-optimal control law to achieve global asymptotic stabilization of any desired position, thus improving the transient performance with respect to existing control designs. A simulation example shows the performance improvement in comparison with a nested saturations stabilizer.
Fuzzy Neural Network based Sliding Mode Control for Hypersonic Vehicles

According to the air-breathing hypersonic vehicle(AHV) model with perturbation of aerodynamic coefficients and uncertain parameters in the system, taking into account the problems of aerodynamic co-efficients approximation and uncertain parameter identification, fuzzy function(FF) and Radial Basis Neural Network(RBNN) based sliding mode control is proposed. AHV control model has the characteristics of multivariable, strong coupling and nonlinear. Applying the powerful function approximation function of fuzzy function to approach the aerodynamic coefficient, using RBNN self-learning identification ability to identify the system uncertain parameters, combined with sliding mode variable structure control, to eliminate the aircraft's buffeting problem to some extent and improve the robustness of the system. Simulation results show that the system can maintain stability after adding velocity step instructions and altitude step instructions, and has strong robustness to uncertain parameters, and overcome the chattering problem.
Broadbeam microstrip patch antenna using higher order modes

The aim of this contribution is to assess the potential of using various microstrip radiating elements excited with different modes to achieve wide pattern beamwidths. The proposed antenna is made in classic microstrip technology and consists of a circular disk and a ring. The two radiating elements are excited for the TM10 and TM31 modes, respectively. Only the disk is fed, and the ring is connected to the disk through a microstrip line.The proposed antenna is matched at the 2.4GHz industrial, scientific and medical band (ISM) and exhibits a bandwidth of 120MHz. The combination of the two radiating elements achieves a 3dB beamwidth of 160 degrees. The maximum gain is 5.7dBi.
Hierarchical implementation of the analytical singularity evaluation technique

Using the Method of Moments, the computation of the near-field interactions requires to evaluate 4D singular integrals. Recently, a method has been proposed to compute these integrals fully analytically, rapidly providing accurate results. In this paper, we reformulate the analytical technique to highlight the different intermediate results that are involved in the full procedure. We show that, by storing and reusing intermediate results, the evaluation of the singular integrals is accelerated by several orders of magnitudes.
ECG Analysis and Heartbeat Classification Based on Shallow Neural Networks

Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. For this purpose, several adaptive filter structures were proposed during the past decades for noise cancellation and arrhythmia detection. Currently there are a lot of devices on the market that analyze ECGs, such as patient monitors, stress test systems, and Holter analysis systems, that are able to detect beats and classify arrhythmia. This paper proposes a system for ECG analysis and heartbeat classification. The proposed solution relies on a combination of machine learning algorithm and a wavelet transformation in order to maximize its performance with the minimum possible training phase. Experimental results with public available data for arrhythmia indicate the efficiency in classifying heartbeats, whereas its low-computational and memory requirements makes it suitable for being implemented as part of an embedded (IoT) system.
An Automatic Ionogram Scaling Technique based on the Nelder-Mead method

The work describes an approach to automatic ionogram scaling, suitable for both bottomside and topside sounding. The algorithm is designed to be used on high-perfomance parallel computing servers. It employs ionogram modeling by simulating radio wave propagation in plasma layers and adjusting the parameters of the layers by Nelder-Mead algorithm until the optimal coincidence with the results of sounding is achieved. The validation of automatic scaling was carried out for F2 layer critical frequency (foF2) by comparison with the data sequence for all previous observation years for a given station. The foF2 deviation quantiles were calculated for different geomagnetic activity levels. An ionogram’s automatic scaling was considered a failure if the result fell in the lowest 0.01 or the highest 0.99 quantile.
Using Commonly Available Software for Conducting Bootstrap Analyses

Bootstrap analysis, both for non-parametric statistical inference, and for describing sample results stability and replicability, has been gaining prominence among quantitative researchers in educational and psychological research. Procedurally, however, it is often quite a challenge for quantitative researchers to implement bootstrap analysis in their research, because bootstrap analysis is typically not an automated program option in statistical software programs. This paper uses a few heuristic analytical examples to show how bootstrap analysis can be accomplished through the use of some commonly available statistical software programs. Until bootstrap analysis becomes an automated program option in standard statistical software programs (e.g., SPSS, SAS), quantitative researchers may have to make do with these or other creative approaches to accomplish bootstrap analysis in their research.
Freezing of Gait (FoG) Detection Using Logistic Regression in Parkinson's Disease from Acceleration Signals

The detection and diagnosis of Parkinson disease (PD) are very important concerning the treatment of this disease. In this work, the freezing of gait (FoG) from subjects with Parkinson disease has been detected by the logistic regression modeling. To complete this work, first, the acceleration sensor has been placed on the ankle of the patients to get the signals. Second, the features from these acceleration signals have been extracted by the Fast Fourier Transform (FFT) algorithm. With the FFT algorithm, the frequency coefficients have been gotten. To diminish the number of features, the statistical measures including variance, maximum amplitude, minimum amplitude, maximum energy, and minimum energy, have been applied to frequency coefficients of these signals. So, for each class (FoG and no- FoG), five parameters have been extracted. Eight patients are having Parkinson disease in the dataset. After feature extraction, the logistic regression modeling has been used to detect the freezing of gait cases from the dataset. The classification of the accuracy of 81.3% has been achieved in the classification of FoG cases having PD from the acceleration signals. In addition to logistic regression, four different classifiers (Linear SVM, Quadratic SVM, Cubic SVM, and kNN) have been used to classify the FoG cases. The obtained results have shown that the proposed method could be used in the detection and identification of Parkinson disease using only a sensor of acceleration.
A Current-Mode Differential Sensing CMOS Imager for Optical Linear Encoder

This paper presents a high speed low noise CMOS imager for optical linear encoder. Dual sensor arrays and the corresponding readout circuit are implemented for incremental and absolute encoded light signal, respectively. For incremental signal, current-mode differential sensing (CMDS) frontend with buffered direct injection (BDI) is proposed for DC cancellation and high response bandwidth. Transimpedance amplifier (TIA) is implemented for current-to-voltage conversion with low-pass filtering function for noise reduction. Programmable amplifier (PGA) is implemented for single-to-differential conversion and full-range fitting for the following 12-bit SAR analog-to-digital converter (ADC). For absolute signal, 42 pixels with the corresponding dual-threshold quantizer are implemented. This work achieves a SNR of 63.4dB, a phase error of 33.8 degree at 3.3MHz incremental input.
A Nearest Neighbor Search Engine Using Distance-Based Hashing

This paper proposes an FPGA-based nearest neighbor search engine for high-dimensional data, in which nearest neighbor search is performed based on distance-based hashing. The proposed hardware search engine implements a nearest neighbor search algorithm based on an extension of flexible distance-based hashing (FDH, for short), which finds an exact solution with high probability. The proposed engine is a parallel processing and pipelined circuit so that search results can be obtained in a short execution time. Experimental results show the effectiveness and efficiency of the proposed engine.
Motor Imagery Based EEG Classification by Using Common Spatial Patterns and Convolutional Neural Networks

EEG signal processing has been an important and engaging issue over the last three decades. It has been used in the applications ranging from controlling mobile robots to analyzing sleep stages. Previously it was used in the applications of clinical neurology such as detecting epileptic seizure, finding epileptiform discharges, diagnosis of epilepsy, etc. Convolutional Neural Network (CNN) on the other hand is one of the most popular and successful method that has been broadly utilized in machine learning problems such as pattern recognition, image classification and object detection. The proposed study focuses on maximizing the classification performance by combining two of the most successful methods: CSP (Common Spatial Patterns) and CNN. Three different setups have been established in order to observe the changes in the validation accuracy of the classifier. At first, a CNN (four convolution layers and a fully connected layer) structure is trained by feeding the raw data. Secondly, five different filters are applied to the original signal and their outputs are utilized in the training of a CNN having the same structure. Thirdly, the original signal has been transformed via CSP into another space where its spatial features are observed more clearly and then classified by the CNN. It is observed that the combination of CSP and CNN gives the best performance with 93.75% validation accuracy.
Design and Performance of Wheel-mounted MEMS IMU for Vehicular Navigation

In modern cars MEMS gyroscopes and accelerometers provide essential measurements for enhancing the stability and control. Both types of sensors have significant noise at low frequencies, limiting the measurement accuracy especially in low dynamic conditions. In addition, uncompensated accelerometer tilt causes large bias to acceleration estimates. For gyroscopes, physical rotation of the sensor can be used to remove the constant part of the gyro errors and reduce low-frequency noise. In ground vehicles such rotation exists conveniently in wheels. When inertial sensors are attached to wheel, both types of sensors provide information on the rotation, gyroscopes naturally and accelerometers via specific force measurement. In addition, as a result of carouseling, accurate wheel heading, roll and pitch estimation can be estimated with high resolution, and the result is nearly bias-free. Combining the wheel orientation to distance traveled via known radius enables classic dead reckoning mechanization (assuming zero slip) and other vehicle dynamics monitoring systems (considering wheel slip as unknown to be solved). In the paper, we provide details of wheel-mounted inertial system hardware and algorithms and show test results for several system configurations and applications. We discuss future system improvements, in particular, system miniaturization and an energy-harvesting development progress for next-generation inertial systems.
Estimating Ambiguity and Using Model Weight To Improve the Positioning Accuracy of a Stand-alone Receiver

Basic positioning methods in GNSS receivers are based on code and carrier phase measurements. In this paper, the carrier phase measurements are considered. They are highly accurate, but limited with integer ambiguity, which is very important. This paper proposes an algorithm to resolve this problem. In addition, it shows the main factors that affect the accuracy of user position. To reduce the user’s position errors, it eliminates the satellites with small evaluation angles and in the calculation, two weight models are proposed and applied. As compared to the positioning solution without weight, the positioning accuracy is improved by about 42.89 % with the SIGMA model and about 53.19 % with the exponential model.