Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Harmonic Characteristics of Inverters and Rectifiers

The grid-side current harmonic characteristics of photovoltaic grid-connected inverters and three-phase voltage-type rectifiers based on different modulation methods are studied. Impact. Considering the influence of dead zone effect on output voltage waveform, under different dead zone time, the influence of dead zone on harmonics is analyzed, and the harmonic generation mechanism of dead zone effect is theoretically explained. Finally, the simulation model of inverter and rectifier is built in MATLAB/Simulink environment, and the theoretical analysis results are verified by Fast Fourier Transform.
Optical detection of angular acceleration rate for MEMS-based gyroscopes

This paper explores a novel way of sensing the angular velocity rate change of MEMS based gyroscopes with optical methods. In the capacitive sensing mechanism, displacement due to angular rate induces differential capacitance, requiring many supporting filtering and amplification sub-systems and circuits in the device to eliminate the noise. One of the most important aspects of using optical sensing elements with MEMS technology is the elimination of capacitive sensing used in conventional MEMS based gyroscopes. Conventional bulky optical gyroscopes based on Sagnac effect, with very small drift-rate parameters, is difficult to be implemented with MEMS technology due to the requirement of large area enclosed by the laser ring and fiber optic paths. Light interference effect of optical properties can permit the phase shift measurement of the light wavelength. In this paper, the combination of optical and mechanical systems is studied for estimating angular acceleration rate by Coriolis Effect.
The effect of queue discipline on waiting time variance

It is usual in the theory of queues to assume that customers are served in the order of their arrival. In some applications (e.g. telephone engineering), however, other forms of queue discipline are more realistic. The precise effect of any such change on the waiting-time distribution of a customer will depend on the procedure envisaged (random service, “last come, first served”, etc.), but it is possible to make certain general statements. Thus it is well known that, under certain conditions, the mean is independent of the queue discipline. The purpose of the present note is to consider the variance of waiting time, and we shall prove that this is a minimum when the customers are served in order of arrival. Thus this is, in a sense, the “fairest” queue discipline. This does not, of course, mean that other procedures may not be justified when different criteria are taken into account.
Numerical Solution of the Controlled Rayleigh Nonlinear Oscillator by the Direct Spectral Method

This paper presents two numerical methods for solving the controlled Rayleigh nonlinear oscillator problem. The first method is based on construction of interpolation polynomials to approximate the states and subsequent control using the roots of Legendre polynomials as collocation nodal points. In the second method, we look for a piecewise continuous approximation polynomial for each state and a piecewise constant function for the control function. We show that this method leads to better results and enables us to accurately compute switching times. These methods are easy to implement, and yield very accurate results.
Air Damping Effects on Different Modes of AlN-on-Si Microelectromechanical Resonators

Piezoelectric thin films have enabled demonstrations of a wide range of piezoelectric-on-silicon (PoS) resonant transducers in microelectromechanical systems (MEMS) industry today. While the actual ambient pressure (p) levels in vacuum packages can greatly affect device performance, a comprehensive study and quantitative understanding of dynamical characteristics of multimode PoS resonators under varying pressure is still lacking and needs to be investigated. In this work, we report the first measurements and analyses of air damping effects on both bulk and flexural resonance modes of AlN-on-Si (AlN/Si) bulk acoustic resonators (BARs). Experimental results are presented for the resonant frequency (f) and quality (Q) factor with p varying from 760Torr down to 100μTorr. For AlN/Si resonator’s bulk mode, f keeps a nearly constant value of 10.291MHz over the pressure range and the Q factor starts to decline when p is above 10Torr. For AlN/Si resonator’s flexural mode, f is stable at 595.3kHz below 20Torr and drops to 595kHz at 760Torr. For the Si-only device’s bulk mode, f ~10.127MHz is independent of p, and a high Q ~257,000 is maintained when p < 3Torr. The present work clearly reveals the different effects that air damping exerts upon the bulk and flexural modes of AlN/Si MEMS resonators, providing new quantitative knowledge and insight for device engineering.
Hybrid HMSIW Cavities Antenna With a Half-Pentagon Ring Slot for Bandwidth Enhancement

A substrate-integrated waveguide (SIW) antenna is an implementation of a waveguide antenna in a microstrip antenna. The microstrip antenna, as a low-profile antenna, suffers from narrow impedance bandwidth. This paper proposes a novel bandwidth enhancement method for solving a narrow impedance bandwidth. Bandwidth enhancement was achieved by using quad-resonant frequencies. The quad-resonant frequencies are resulted from the hybrid half-mode substrate-integrated waveguide (HMSIW) cavities between the inner and outer cavities. The inner HMSIW cavity has a half-pentagon ring slot that disturbs the TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {101-inner}}$ </tex-math></inline-formula>, TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {102-inner}}$ </tex-math></inline-formula>, TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {202-inner}}$ </tex-math></inline-formula>, and TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {103-inner}}$ </tex-math></inline-formula> modes, whereas the outer HMSIW cavity consists of two outer quarter-mode substrate-integrated waveguide (QMSIW) cavities. The outer HMSIW cavity generated the TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {102-outer}}$ </tex-math></inline-formula> and TE<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {202-outer}}$ </tex-math></inline-formula> modes. The hybrid cavities were fed simultaneously using a port with a quarter-wavelength feed transmission line. The hybrid HMSIW cavities generated resonant frequencies that were close to each other. Additionally, this method was applied to the X-band frequency with a low-profile substrate. The simulated impedance bandwidth and peak realized gain was 33.86% (<inline-formula> <tex-math notation="LaTeX">$8.98-12.64$ </tex-math></inline-formula> GHz) and 7.97 dBi, while the measured impedance bandwidth and peak gain were 31.83% (9.14 – 12.6 GHz) and 7.62 dBi, respectively. Strong agreement was observed between the measurement and simulation results.
Economic Valuation of Power Grid Frequency Security and the Participants Share Specification

The increasing penetration of non-synchronous renewable resources has made lots of concerns about frequency security. Therefore, power system studies have tried to address this issue in static and dynamic manners. Although considering the reserve in a static way has the advantage of simplicity, it does not ensure the security. This is indicated in this paper by presenting an extended Economic Dispatch (ED) considering all types of reserves and their practical limitations. Also, integrating numerous dynamic simulations to the problems makes them impractical for large-scale grids. In addition, using estimation methods is not accurate enough due to the simplifications. This paper introduces the Frequency Security Value (FSV) for the first time and proposes its calculation algorithm for a specified schedule based on a Frequency Security Index (FSI). Then, an algorithm is proposed for allocating this FSV to the participants based on their technical values. Also, a sensitivity analysis platform is presented for calculating the effect of various modifications. These concepts may have lots of applications, especially in power grid scheduling considering frequency security and payment of ancillary services. The effectiveness and correctness of the proposed methods and algorithms are demonstrated based on the simulation studies of a real large-scale power grid.
High Performance and Hardware Efficient Stochastic Computing Elements for Deep Neural Network

Three high performance and hardware efficient stochastic computing elements are proposed for the implementation of deep neural network. A hybrid stochastic multiplier which combines unipolar coding and bipolar coding is proposed to achieve efficient trade-off between high accuracy and low hardware consumption. Then, a stochastic accumulative parallel counter is present to achieve high accuracy stochastic-to-binary conversion with much less hardware consumption. Finally, a stochastic ReLU function is designed to precisely realize ReLU function in stochastic computing without approximate error.
High-Q and Low-Motional Impedance Piezoelectric mems Resonator through Mechanical Mode Coupling

A high-Q and low-motional impedance (Rm) resonator is achieved by mechanically coupling a low-impedance piezoelectric length extensional-mode resonator with two high-Q single-crystal silicon (SCS) Lame-mode resonators. The mass-weighted quality factor relationship between the mode-coupled resonator and the individual resonating elements has been demonstrated. By properly adjusting the mass ratio of the individual resonating elements, the mode-coupled resonator achieves a record high-quality factor of 172105 for piezoelectric resonators whereas the Rm is maintained as low as 350 Ω at 19.7 MHz. Our results offer opportunity to use MEMS resonators in high-performance temperature-compensated MEMS oscillators (TCMOs) and oven-controlled MEMS oscillators (OCMOs), making these devices a potential candidate for communication and intelligent vehicle applications.
Machine Learning Enabled Hind Foot Deformity Detection Using Individually Addressable Hybrid Pressure Sensor Matrix

We demonstrate a scalable, cost-effective hybrid sensor based on piezoelectric-triboelectric mechanisms for pressure mapping of 36 regions of the hindfoot and precise detection of any deformity. The matrix sensor comprises of individually addressable 6×6 sensors, which prevent pressure cell crosstalk and enhance the detection limit of the sensor up to 170kPa. The sensor can be easily integrated as an in-sole device because it is constructed on a flexible substrate using photo-patternable BTO (Barium Titanate)/SU-8 nanocomposite. The developed sensor has a pressure sensitivity of 34mV/kPa for 0-5kPa and 2.7mV/kPa for 5-170kPa, respectively. Additionally, we apply a convolution neural network (CNN-2D) to extract features from the 36 hybrid signals for precise deformity detection. The developed CNN model has a model loss of 0.1%, 98% accuracy, and a cross-feature differentiation value of 2%.
Sequential Modeling by Leveraging Non-Uniform Distribution of Speech Emotion

The expression and perception of human emotions are not uniformly distributed over time. Therefore, tracking local changes of emotion within a segment can lead to better models for speech emotion recognition (SER), even when the task is to provide a sentence-level prediction of the emotional content. A challenge to exploring local emotional changes within a sentence is that most existing emotional corpora only provide sentence-level annotations (i.e., one label per sentence). This labeling approach is not appropriate for leveraging the dynamic emotional trends within a sentence. We propose a framework that splits a sentence into a fixed number of chunks, generating chunk-level emotional patterns. The approach relies on emotion rankers to unveil the emotional pattern within a sentence, creating continuous emotional curves. Our approach trains the sentence-level SER model with a sequence-to-sequence formulation by leveraging the retrieved emotional curves. The proposed method achieves the best concordance correlation coefficient (CCC) prediction performance for arousal (0.7120), valence (0.3125), and dominance (0.6324) on the MSP-Podcast corpus. In addition, we validate the approach with experiments on the IEMOCAP and MSP-IMPROV databases. We further compare the retrieved curves with time-continuous emotional traces. The evaluation demonstrates that these retrieved chunk-label curves can effectively capture emotional trends within a sentence, displaying a time-consistency property that is similar to time-continuous traces annotated by human listeners. The proposed SER model learns meaningful, complementary, local information that contributes to the improvement of sentence-level predictions of emotional attributes.
Multiple Flipping Strategy LISBF for LDPC Codes

The performance and complexity of low-density parity-check (LDPC) decoders have strict requirements since they are widely demanded in both communication and storage systems. Information storage bit flipping (ISBF) decoder and layered ISBF (LISBF) decoder demonstrate excellent decoding performance and throughput in which the global maximumfinding operation can be calculated in parallel to other decoding operations. However, their error-correction performance is not satisfactory. In this paper, we consider a variety of optimizations based on LISBF and propose a new algorithm named multiple flipping strategy LISBF (MLISBF). First, a high probability is adopted to flip those bits with high energy values. Second, strict flipping with a lower threshold is triggered when the syndrome weight is negligible. Then, we introduce perturbation flipping at specific iterations to help the decoder overcome trapping sets. We designed an efficient hardware implementation which realizes the layered calculation for BF decoders and reduces hardware resources for the MLISBF algorithm.
A Review of Authentication Techniques used for Security in Cloud Computing

The changes in technologies has also changed the way we compute. Computing applications provide various types of functionalities. However, a common thing is to secure the same computing system. It requires a high level of developer skills to secure a system. Generally, verifying users before access of services, encryption of data, and techniques of parallel access of information by multiple users is done to ensure only valid users can access the services. One need to verify person, device, process, or service before it access the related service(s). In this paper, we present a review of authentication techniques used in computing computing. It elaborates methods used for traditional authentication using articles, letters, people, passwords, one-time passwords, digital certificates, two-way authentication to latest behavioural, doodles, image sequence, gestures based recognition of users using biometrics, gait-based and their behavioural analytics. It also discusses key features of various methods including gaps and scope of improvement.
Comparison of the torque of flat airgap synchronous couplings versus the magnetization direction of the magnets

A study of flat airgap synchronous couplings uses the analytical formulas of the tangential and radial components of the force between two permanent magnets. These formulas are applied to compare the torque transmitted by three different structures of flat airgap couplings. In the first, the magnets have a radial magnetization direction with respect to the rotation axis. This direction is tangential to the rotation axis in the second structure and parallel to the rotation axis in the third where the force Is computed with a semi-analytical method. The torque of the different couplings is studied and compared. The dependence of the torque on various parameters such as the number of pole pairs and the width, length, and depth of the magnets is also investigated. The analytical and semi-analytical methods used afford efficient optimization of the coupling structure with full compliance with practical and economic constraints.
Machine learning-based malware detection using stacking of opcodes and bytecode sequences

Malware detection is a complex problem. The commonly used signature-based technique cannot detect unknown or zero-day malware. Traditional machine learning-based methods can identify unknown malicious programs but require high domain expertise for feature engineering. This research study presents a new method of malware detection using the stacking of static opcode and bytecode features. At first, bytecode and opcode sequences of executables files are extracted using a disassembler. After that, an NLP technique, TF-IDF, is employed to vectorize the extracted feature map. The extracted features as opcodes and bytecodes are concatenated to combine a single feature map, which serves as input to train machine learning classifiers: SVM, k-NN, and Random Forest (RF). The RF classifier obtained the best accuracy, precision, recall, and F-score results as 98.47%, 98.60%,98.46%, and 98.47% even using small data counts of bench-marked Microsoft BIG data set. The accuracy of the best model is comparatively better than the accuracy of using bytecodes and opcodes as individual feature vectors. The empirical results signify that the proposed method is helpful to the security industries with the benefit of being less dependent on manual feature engineering and can reduce the burden on virus databases by handling large-scale malware.
Application of Deep Learning Network for Humidity Compensation of Semiconductor Metal Oxide Gas Sensors

In this research, we propose a humidity compensation method using a deep learning network for minimizing the effect of humidity on the semiconductor metal oxide (SMO) gas sensors. The SMO gas sensors were fabricated by depositing In2O3 on a suspended microheater platform, and the gas tests were conducted under various humidity conditions using NO2 gas. The gas sensing data and humidity data were simultaneously used as input data of the deep learning network for real-time humidity compensation. Through the proposed method, we successfully improved the accuracy of predicting the concentration of NO2 gas in different humidity conditions.
3D Printed Miniaturized Soft Microswimmer for Multimodal 3D Air-Liquid Navigation and Manipulation

Major challenges of microscale swimmers for biomedical applications could be listed as the 3D motion for air-liquid navigation, selective motion control of multiple swimmers, visual servo automation control and micromanipulation functionality. Here we report a miniaturized soft microswimmer for serving as a wireless microrobotic manipulator in air-liquid environments. We fabricated these micrometric soft swimmers by two-photon 3D nanoprinting of Polydimethylsiloxane (PDMS) and metallization of ferromagnetic nickel layer for magnetic propulsion. We demonstrated for the first time 3D air-liquid navigation and selective motion control of multiple soft swimmers thanks to the multimodal propulsion with magnetic and buoyancy force. We further reduced stick-slip phenomenon by applying high frequency vibration by piezo disc actuator. This allowed real-time visual tracking of the soft microswimmers and also the micromanipulation of microfibers. We believe that this 3D printed and miniaturized mobile agents should enlarge the application areas from in-vitro microfluidics to bio/chemical assay.
Traffic Monitoring and Management System for Congestion Comtrol using IoT and AI

A traffic blockage in the middle of a commute affects unimpeded commuting. Automobile gridlock is a significant problem in metropolitan cities, and a lack of technology to handle the same makes globetrotting difficult. Under inadequate traffic surveillance, there is a high volume of traffic that causes high traffic congestion. The Intelligent Transportation System, robust vehicle tracking, and on-road traffic information systems can help break logjams, thus improving travel. Also, this reduces loopholes in traffic management and creates a positive approach to road mobility. This paper addresses the problem by making use of concepts such as Semantic Web Techniques (SWT), the Internet of Things (IoT), and Artificial Intelligence (AI) to resolve traffic congestion. The proposed method involves a simple concept of intelligent road-traffic planning and routing. Moreover, this paper shows the real-time flexibility of the model. The efficiency of more than 90% comes from analyzing a sample of 9,792 vehicles collected from the Kaggle repository.
Drone-Mounted Low-Frequency pMUTS for > 6-Meter Rangefinder in Air

This paper reports a low-frequency piezoelectric micromachined ultrasonic transducer (pMUT) with a small attenuation coefficient to realize the long-distance range finding applications in air. Pulse-detection measurements of one pair of pMUT devices show a >6-meter traveling distance in air under a 37.32 kHz driving frequency. As an example, two pMUT chips are mounted on two drones as the transceiver and receiver, respectively. Measurements of the separation distance are conducted based on the time-of-flight (ToF) principle with up to 32 fps (frames per second) for real-time detections. This demo of drone-mounted pMUT system illustrates the advantages of pMUT in terms of compactness and low power consumption for applications in drones including obstacle avoidance, inter collision prevention, aerial coordination, and acoustic-based vision.
A Survey on VANET attacks and its Security Mechanisms

There has been a recent surge in interest in wireless related VANETs, which are swiftly rising to the top of the priority research list for the smart-transportation system. Such system offers protection and safety measures for both driver and traveler. VANETs differ greatly from mobile ad-hoc-networks in terms of uniqueness, issues, system-design, and application. We address the architecture, security, and problems of VANETs in this study, which summarizes recent state-of-the-art solutions. Second, we go over a detailed study of security systems as well as potential solutions for ensuring secure communication in VANETs. Then, we go over all of the authentication systems that can safeguard the vehicular network from malicious nodes and other threats. This paper discusses about various attacks in VANETS and the security methods used to detect the attacks in real time. The attacks that are considered are collision attacks, sybil attacks, black-hole attacks, and jamming attacks. The security of the network is assured by detecting the threats in a timely manner.
The Double Nucleus of ARP 220 Unveiled

IR imaging at the f/415 focus of the Palomar 200 inch telescope with 0. "39 x 0. "49 resolution is used to determine that the ultraluminous IRAS galaxy Arp 220 has a double nucleus with separation 0. "95. The presence of two closely separated nuclei (330 pc) confirms the circumstantial evidence that Arp 220 is an evolved merger remnant; and the degree of correlation between the IR and cm wavelength radio emission shows that it is an ongoing merger containing two active nuclei accompanied by circumnuclear starbursts of moderate intensity. The extent of the 2.2 µm radiation is evidence that the flux is most probably dominated by starlight. It is pointed out that if the nuclear activity is powered by accretion onto black holes, then a black hole binary will be formed. Such a binary may be an essential ingredient of many quasars, and therefore Arp 220 may suggest another connection between mergers, ultraluminous IRAS galaxies, and quasars.