Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Time–Frequency Information With Prior for SAR Radio Frequency Interference Suppression

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys useful data in the same frequency band, causing an adverse impact on the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal and cannot effectively protect and reconstruct the useful information. Besides, the current semiparametric algorithms have large computations and limited generalization ability. To address these issues, this article proposes a prior-induced interference suppression network (PISNet) to achieve RFI suppression and useful signal recovery in time–frequency domain (TFD). Both narrowband and wideband interferences are uniformly modeled as a sparse distribution in the TFD, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conforms to the prior. The proposed approach innovatively integrates deep learning with semiparametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).
Transformerless HERIC Inverter with Modified Unipolar PWM to Decrease Grid-Injected Current's THD

Compared to bipolar, unipolar PWM (UPWM) is advantageous for switching the single-phase H-bridge inverter as it generates an output differential voltage with lower THD. However, the oscillatory nature of the common-mode voltage in UPWM contributes to the ground current in single-phase grid-connected PV inverters, which is the motivation of the various transformerless inverters with UPWM. Although, the benefits of UPWM (mainly the good characteristics of output differential voltage) in existing transformerless inverters is somehow missing. In this paper, after analyzing different switching schemes of HERIC (highly efficient and reliable inverter concept) converters, a modified UPWM is proposed in which the full sawtooth carrier and sinusoidal reference waveforms (changing between negative and positive maximums) are compared in order to increase the number of voltage pulses and reduce the THD of grid current. The superiority of the proposed UPWM has been confirmed by comparative simulation studies over a 1-kW single-phase grid-connected PV inverter.
Power Control in massive MIMO Networks Using Transfer Learning with Deep Neural Networks

Power control (PC) plays a crucial role in massive multiple-input-multiple-output (mMIMO) networks. There are several heuristic algorithms, like the weighted mean square error (WMMSE) algorithm, used to optimise the PC. In order these algorithms to perform the power allocation they require high computational power. In this paper, we address this problem through the application of machine learning (ML)-based algorithms as they can produce close to optimal solutions with a very low computational complexity. We propose the use of transfer learning with deep neural networks (TLDNN) under the objective of maximising the sum spectral efficiency (SE). The evaluation results demonstrate that the TLDNN approach outperforms the deep neural network (DNN) based PC and is twice faster than the WMMSE based PC.
Evanescent-wave coupled right angled buried waveguide: Applications in carbon nanotube mode-locking

We present an evanescent-field device based on a right-angled waveguide. This consists of orthogonal waveguides, with their points of intersection lying along an angled facet of the chip. Light guided along one waveguide is incident at the angled dielectric-air facet at an angle exceeding the critical angle, so that the totally internally reflected light is coupled into the second waveguide. By depositing a nanotube film on the angled surface, the chip is then used to mode-lock an Erbium doped fiber ring laser with a repetition rate of 26 MHz, and pulse duration of 800 fs.
A Distributed Policy Gradient Algorithm for Optimal Coordination of Mobile Sensor Networks

In this paper, we study the optimal deployment problem in mobile sensor networks. By recasting it as an optimal coordination problem of multiagents, a new distributed policy gradient algorithm is proposed based on the minimization of the overall cost for all agents. The proposed algorithm relies on local information exchanges among neighboring agents without the requirement of known system dynamics. The policy gradient is computed based on sampling the trajectory under the perturbed control policy. The control policy is parameterized and the adaptive parameter update is carried out following the negative gradient of the overall cost. The rigorous analysis of the proposed algorithm is provided.
Comparison of Aerosol at Four Baseline Atmospheric Monitoring Stations

Abstract Particles were captured by electrostatic precipitators or impactors at atmospheric monitoring bases in Alaska, Hawaii, Tasmania and Antarctica, and examined by electron microscopy. Particle number and mass distributions for radii ≲0.06 μm were similar at all sites. The particles appeared to be composed mainly of sulfuric acid (Alaska and Hawaii) or ammonium sulfate (Tasmania and Antarctica). Those of larger size showed marked differences in concentration size distribution and composition at the four sites. Those in Alaska differed from the remainder in that more than half contained insoluble inclusions and almost all particles were coated with, or wholly composed of, sulfuric acid. The Tasmanian aerosol was dominated by sea salt for particles with r > 0.1 μm, and many but not all of the micron-sized particles had benzene-soluble or ether-soluble components. At all except the Tasmanian site a maximum occurred in the mass distribution curve at a radius of ∼0.3 μm, and it is suggested that this is d.
A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System

Over the last years, the engine calibration task has mostly been conducted based on the engineers’ knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required.
An Effect of Limb Position in Motor Imagery Training Paradigm in Immersive Virtual Environment

Motor imagery (MI) training-based Brain-Computer Interfaces (BCI) improved individuals' motor function by inducing direct alterations in the sensorimotor area. Virtual Reality (VR)-based MI training has been identified as a promising technique for achieving high performance. However, the physical limb position should be considered when designing a better training task. This paper investigated the effect of induced MI activities when the virtual arms were at normal and shifted down position. The paradigm used of Virtual Reality (VR) to simulate the situation of having realistic and unrealistic arms position in an immersive virtual environment. Analyses of electroencephalograms (EEGs) revealed significant differences in MI activity levels between two positions on both the left and right sides. During shifted arms MI, the negative power regions were found in beta and gamma bands on the contralateral hemisphere in time-frequency analysis. Resting vs. normal position arms MI and resting vs. shifted position arms MI classification accuracy reached 80 % and 63 %, respectively. Overall, these findings suggested that taking into account the realistic physical position of virtual limbs is critical for optimizing MI training performance.
Type of Training Recommendation Based on Body Fat Prediction Using LASSO Regression

Bodybuilding is a unique sport that has measurement of aesthetic, instead of performance. Body fat is one of significant metric in body building. Every bodybuilder, from beginner up to athlete level, is expected to have an ideal body fat according to their body preference, either bulky or slim body. Bodybuilder can create a more targeted exercise program by knowing their body fat percentage. However, measuring body fat percentage accurately tends to be expensive and cumbersome. Hence, the purpose of this research is to help every bodybuilder in calculating body fat percentage and giving tips about their current and future type of training easily and for free. The type of training recommendation is based on the predicted body fat and several answer for specific questions about their body preference. The model for predicting body fat is calculated using least absolute shrinkage and selection operator (LASSO) regression which achieved 73.43% in accuracy.
A Semi-Algebraic Description of Naive Bayes Models with Two Hidden Classes

Discrete Bayesian network models with hidden variables de ne an important class of statistical models. These models are usually de ned parametrically, but can also be described semi-algebraically as the solutions in the probability simplex of a nite set of polynomial equations and inequations. In this paper we present a semi-algebraic description of discrete Naive Bayes models with two hidden classes and a nite number of observable variables. The identi ability of the parameters is also studied. Our derivations are based on an alternative parametrization of the Naive Bayes models with an arbitrary number of hidden classes.
Optimized Implementation of Segmentation CNNs in GPU SoC Devices

Nowadays segmentation Convolutional Neural Networks (CNNs) are used in industrial quality control systems to accurately measure the shape and size of production defects. However, these models are quite big and require quite powerful computing systems to be deployed in real applications. In order to obtain fast, power-efficient, and affordable implementations of segmentation CNNs for industrial quality control, in this paper execution time, memory usage, and power consumption measurements are carried out in different modern embedded GPU SoC devices, using different optimization strategies. Results show that segmentation CNNs can be deployed in embedded devices using FP16 or INT8 quantization with negligible accuracy loss and high throughput, up to 240fps.
Small-size sonic crystals with strong attenuation bands in the audible frequency range

Recent experiments have proved that sonic crystals containing locally resonant structures exhibit strong sound attenuation bands at frequencies about two orders of magnitude smaller than predicted by Bragg’s theory. The effect is well reproduced here by means of two-dimensional numerical simulations of the elastic wave propagation in a 13 cm slab of locally resonant sonic crystals. Three strong attenuation bands are found in the frequency range from 0.3 to 6.0 kHz. A heuristic model is proposed, which allows one to predict the resonance frequencies in good agreement with the numerical simulations.
Modelling and optimal control system design for quadrotor platform – an extended approach

This article presents the development of a mathematical model of a quadrotor platform and the design of a dedicated control system based on an optimal approach. It describes consecutive steps in development of equations forming the model and including all its physical aspects without commonly used simpliﬁcations. Aerodynamic phenomena, such as Vortex Ring State or blade ﬂapping are accounted for during the modelling process. The inﬂuence of rotors’ gyroscopic eﬀect is exposed. The structure of a control system is described with an application of the optimal LQ regulator and an intuitive way of creating various ﬂight trajectories. Simulation tests of the control system performance are conducted. Comparisons with models available in the literature are made. Based on above, conclusions are drawn about the level of insight necessary in creation of control-oriented and useable model of a quadrotor platform. New possibilities of designing and verifying models of quadrotor platforms are also discussed.
Synthesis of Decentralized Variable Gain Robust Controllers with Guaranteed L2 Gain Performance via Piecewise Lyapunov Functions for a Class of Uncertain Large-Scale Interconnected Systems

For a class of uncertain large-scale interconnected systems, a design method of decentralized variable gain robust controllers with guaranteed ${\mathcal{L}_{{2}}}$ gain performance based on piecewise Lyapunov functions is shown in this paper. Furthermore, LMI-based sufficient conditions for the existence of the proposed decentralized variable gain robust controller are shown. Finally, the effectiveness of the proposed robust controller is presented through a simple numerical example.
Optical interference effect on pattern formation in thin liquid films on solid substrates induced by irradiative heating

The pattern formation in thin liquid films on solid substrates induced by irradiative heating is investigated. A model to describe the evolution of both the film surface profile and temperature field in the system is developed, in which the energy absorption into the film and substrate, and the energy reflection to which optical absorption and interference contribute are taken into account. The model consists of a thin film equation that describes the time evolution of the film surface profile and a heat equation for the substrate. The former is obtained within the framework of the long-wave approximation, in which the fluid layer is assumed to be sufficiently thin compared to the lateral length scale, while the latter is unconstrained by the substrate thickness. In order to examine the interference effects on the pattern formation, focus is placed on a transparent film/absorbable substrate system irradiated by a monochromatic wave with laterally uniform intensity distribution. In such a case, the energy .
Dominant hydrogen-oxygen complex in hydrothermally grown ZnO

A hydrogen-related defect labeled as $\mathrm{H}\text{\ensuremath{-}}\mathrm{I}*$, observed in as-grown hydrothermal ZnO, is studied by means of infrared absorption spectroscopy. The defect possesses a stretch local vibrational mode at $3577.3\phantom{\rule{0.3em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$ that is associated with a single hydrogen atom bound to oxygen with the $\mathrm{O}\ensuremath{-}\mathrm{H}$ bond oriented along the $c$ axis of the crystal. $\mathrm{H}\text{\ensuremath{-}}\mathrm{I}*$ can be destroyed only after anneal for several hours at $1200\phantom{\rule{0.2em}{0ex}}\ifmmode^\circ\else\textdegree\fi{}\mathrm{C}$. Possible microscopic models of $\mathrm{H}\text{\ensuremath{-}}\mathrm{I}*$ are discussed.
Analysis and Design of Data Warehousing and Business Intelligence Guidelines Using DAMA-DMBOKv2

The amount of data entered and stored separately can lead to loss of information, mixed processing from memory, and data redundancy that affects data quality. Data warehousing and business intelligence management can help improve data quality and consistency. This study aims to create guidelines for data warehousing and business intelligence management processes that can be implemented safely within a company or organization. The DAMA-DMBOK framework only defines data warehousing and business intelligence processes in general. It is required to make data warehousing and business intelligence management guidelines, including detailed steps and activities. The guidelines that have been made need to be assessed in order to measure how well the process can be implemented and relied on by the company. This research is important to make it easier for companies to improve storage quality, manage data, improve data quality, and produce reliable decisions.
Degeneration through coalescence of the q-Painlevé VI equation

Starting from the q-discrete form of the Painleve VI equation we obtain its degenerate forms by applying the procedure of coalescence of singularities. The whole cascade of degenerate forms is thus obtained leading to new forms for the discrete Painleve IV and V equations. The Lax pairs of these discrete Painleve equations are explicitly constructed, thus confirming their integrability.
A Supervised Ensemble Machine Learning Model To Predict Diabetes At Early Stage

Diabetes is becoming one of the world's most common and chronic diseases, due to the complications generated in different organs of the body. It is required to be detected early so that prompt treatment and be provided and also prevention can be done from progressing. The bulk of us nowadays lead lives that are marked by a lack of physical activity and high consumption of junk food. In such a situation, it is vital to detect the sickness as soon as possible. The goal of this research is to anticipate disease based on symptoms. The work is done in such a way that the system takes the user's symptoms as input and creates output, which is disease prediction. In the proposed work total of five models are implemented and tested for prediction. After individual model implementation, the ensemble model using the Voting method is implemented and evaluated for accuracy. As an individual model, the K-Nearest Neighbor and Decision tree performed well. The proposed ensemble model's overall accuracy is estimated to be around 83 percent.
An exploration of Fog procedures in comparison with IoT, design, and assessment issues

As the utilization of the IoT is enhancing, incorporated Cloud framework aspects multiple dangers like execution, security, idleness, along with network failure. The foremost part of the fog layer is to give information created using the IoT gadgets close to the edge. Rather than transferring information to the cloud server, information processing and storage are done locally at the fog node. Fog computing, in contrast to the cloud, provides high-quality services with short reaction times. Thus, Fog Computing may be the ideal alternative to permit the IoT to convey a productive and providing highly secure service to a great number of IoT clients. In this paper, we discuss fog and its architecture, features, fog algorithms are classified into three categories: job planning, resource allocation, and problems, fog node and device loading as well as unloading to increase the service performance for IoT devices, research challenges are also discussed.
Three-dimensional Monte Carlo calculation of the VUV electroluminescence and other electron transport parameters in xenon

The paper presents a set of electroluminescence and other transport parameters calculated using a detailed three-dimensional Monte Carlo method, which simulates the drift of electrons in gaseous xenon (p=760 Torr, T=293 K) under reduced electric fields E/N in the 3 to 16 Td range (E/P approximately 1 to 5 V cm-1 Torr-1), which is the region for secondary scintillation production in xenon filled gas proportional scintillation counters. Results are compared with available experimental or theoretical data as well as an earlier one-dimensional Monte Carlo simulation. The calculated parameters are the excitation and scintillation efficiencies and the reduced light yield, together with mean time intervals, mean drift distances and mean number of elastic collisions between xenon excitation collisions. The authors also present electron drift velocities, mobilities and characteristic energies, as well as mean electron energy and electron energy distribution functions.
A Mixed-Lubrication Model of an Electrical Contact During Vibration

In many devices or applications, electrical contacts are exposed to vibrations or sliding conditions and are prone to the fretting-based degradation. Thus, lubricants are often employed in such contacts to reduce sliding wear and fretting corrosion. However, due to the non-conductive behavior of the lubricants with fluorocarbons and hydrocarbons, lubricants lead to a few adverse problems. Also, the fluid dynamics upon excitation, vibration or sliding causes extended breaks or gaps in between the conducting surfaces. In reality, this can be noticed during vibrations occurring as a result of earthquakes or technical personnel maintenance. Factors such as surface roughness and fluid viscosity will determine the time taken (in terms of seconds or even longer) for the two surfaces of the connectors to achieve a purely solid conductive contact. In this work, a coupled structural-fluid theoretical model is developed for evaluating such intermittent contact breaks/gaps when two metallic rough surfaces come in contact. The model is capable of predicting the increase in the fluid film as well as the contact resistance change with time due to the possible connector vibration. The experimentally observed rocking vibration mode seen in connectors and the time-dependent squeeze film lubrication effect are also considered.
High-Order Deep Recurrent Neural Network With Hybrid Layers for Modeling Dynamic Behavior of Nonlinear High-Frequency Circuits

In this article, a new technique for macromodeling of high-frequency circuits and components called high-order deep recurrent neural network (HODRNN) is proposed. This technique explores an alternative approach to learn RNN for time dependencies in a more efficient way resulting in more accurate model. HODRNN uses more memory units to track previous hidden states, all of which are returned to the hidden layers as feedback through various weight paths. Moreover, a new improved structure called Hybrid-HODRNN is proposed for further increasing the modeling accuracy of HODRNN. The proposed Hybrid-HODRNN uses hybrid layers with both single and high orders for taking advantage of HODRNN and also reducing the overfitting problem, which finally leads to a more accurate model. In addition, the proposed method requires less training signals compared to the conventional shallow and deep RNNs in order to create a model with similar accuracy. Also, the obtained models from the proposed method are considerably faster than the transistor-level models while having similar accuracy. By modeling three high-frequency circuits in this article, we conclude that the HODRNN and its hybrid structure offer the ability to create a better macromodel of high-frequency nonlinear circuits than the conventional RNNs, which verifies the superiority of the new macromodeling techniques.