Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel compact ultra-wideband (UWB) bandpass filter with triple-notched bands

A novel compact microstrip ultra-wideband (UWB) bandpass filter with three sharply notched bands and good selectivity is presented in this letter. Triple-notched bands filtering effects are achieved by adding open-circuited stubs and introducing asymmetric dual-line coupling structure to the feed lines, providing a high degree of adjusting freedom and not increasing the filter size. The frequencies of the notched bands and the upper stopband transmission zero can be independently controlled by adjusting the structural parameters of feed lines. Finally, a microstrip UWB filter with triple-notched bands is simulated and fabricated with good agreement.
Effect of hydrodynamic slippage on electro-osmotic flow in zeta potential patterned nanochannels

The effect of hydrodynamic slippage on the electro-osmotic flow in a nanochannel with thick electrical double layers whose wall surface potential has a periodic axial variation is studied. The equations of Stokes flow are solved exactly with the help of the Navier slip boundary condition and the Debye–Huckel linearization of the equation governing the potential of the electrical double layer. Each periodic cell of the flow field consists of four counter-rotating vortices. The cross-channel profile of the axial velocity at the center of the cell exhibits three extrema and a reversed velocity zone near the channel axis of symmetry. The size of the extrema and that of the reversed velocity zone increases with increase in the degree of slippage. In the limit when the wavelength of axial variation in surface potential is much larger than the channel width, the flow characteristics are interpreted in terms of the lubrication approximation. In the limit when the electrical double layer is much thinner than the channel height, the effect of slip is modeled by a Helmholtz–Smoluchowski apparent slip boundary condition that depends on the pattern wavelength.
Magnetoplasma Effects in Solids

Plasmas in solids show a more complex behavior than in gases since they reflect the symmetry properties of crystals. Since the carrier concentration has a wide range in semiconductors and metals, the plasma phenomena can be studied from microwaves to the ultraviolet. The effect of magnetic fields on the electromagnetic properties of plasmas has been experimentally investigated at microwave and infrared frequencies and has been utilized to measure dielectric constant and band structure of such solids in the limit of low magnetic fields. The magneto-plasma exhibits effects analogous to the galvanomagnetic phenomena. However, near resonance in the classical limit, they show up as depolarizing effects in semiconductors and also give rise to a new type of cyclotron resonance under anomalous skin conditions in metals.
Eigenstructure assignment for linear time-varying systems via differential Sylvester's equation

At+act . _ This work is concerned with the assignment of . the desired eigenstructure for linear time-varying systems such as missiles, rockets, fighters, etc. De. spite its well-known limitations,. gain-scheduling control appeared to ‘be focus of the research efforts. Scheduling of frozen-time; frozen-state controller for fast time-varying dynamics is known to ,be mathematically fallacious, and practically -hazardous. Therefore, recent research,efforts are being directed towards applying time-varying controllers. In this ,paper,, i). we introduce a differential algebraic eigenvalue theory for linear time-varying systems, and ii) we also propose an eigenstructure assignment scheme for linear time-varying systems via differential Sylvester’s equation based upon the newly developed notions. The whole design procedure of the proposed eigenstructure assignment scheme is very systematic. The scheme can be used to determine the stability of linear time-varying systems easily as well as provide a new horizon of designing controllers. for the linear time-varying systems. The presented method is illustrated by numerical example.
Symplectic maps, variational principles, and transport

Symplectic maps are the discrete-time analog of Hamiltonian motion. They arise in many applications including accelerator, chemical, condensed-matter, plasma, and fluid physics. Twist maps correspond to Hamiltonians for which the velocity is a monotonic function of the canonical momentum. Twist maps have a Lagrangian variational formulation. One-parameter families of twist maps typically exhibit the full range of possible dynamics--from simple or integrable motion to complex or chaotic motion. One class of orbits, the minimizing orbits, can be found throughout this transition; the properties of the minimizing orbits are discussed in detail. Among these orbits are the periodic and quasiperiodic orbits, which form a scaffold in the phase space and constrain the motion of the remaining orbits. The theory of transport deals with the motion of ensembles of trajectories. The variational principle provides an efficient technique for computing the flux escaping from regions bounded by partial barriers formed from minimizing orbits. Unsolved problems in the theory of transport include the explanation for algebraic tails in correlation functions, and its extension to maps of more than two dimensions.
Properties of the Benjamin–Ono equation

The question is raised as to whether the Benjamin–Ono equation, a nonlinear partial differential integral equation, is a completely integrable Hamiltonian system. The answer is almost certainly ’’yes. ’’ Particular solutions suggest the form that general polynomial constants must have. The structure of these and an algorithm to compute them is given. Explicit formulas are given for the first six.
Approximation stochastique de vecteurs et valeurs propres

On etudie l'estimation recursive par approximation stochastique des vecteurs et valeurs propres d'une matrice M-symetrique B, dont on fait des observations bruitees. On demontre, par une methode de martingales utilisee pour les processus du type Robbins-Monro, la convergence presque sure en direction d'un processus general, ayant plusieurs cas particuliers classiques, vers un vecteur propre, puis la convergence de la norme. On etudie ensuite la convergence presque sure de deux processus d'approximation stochastique de la valeur propre correspondante.
Bénard-Marangoni Instability In A Layer Of Micropolar Fluid

Thermal stability in respect of surface tension and buoyancy effects are investigated for a layer of micropolar fluid for which the lower boundary is a rigid conductor and the upper boundary is free and subject to a general thermal condition. It is found that the fluid has many stability characteristics of the Newtonian fluid, but that the microstructure leads to greater stability in respect of both agencies.
Classification of EEG Signals on Standing, Walking and Running Dataset using LSTM-RNN

Undoubtedly one of the most important strands of the brain-computer interface (BCI) method is an alternate communication method via brain signals. BCI converts electroencephalogram (EEG) signals from a perception of activity in the brain into user action utilising software and hardware. BCI has piqued the interest of researchers in a wide range of disciplines, such as cognitive science, deep learning, pattern matching, drug treatment medicine, etc. Patients suffering from neuro and cognitive disorders can be assisted through BCI, potentially enabling communication via gestures or just mental imagination. In this paper, a novel combination of Discrete Wavelet Transform (DWT) for extracting the best features and Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted for classifying the EEG signals acquired during standing, walking and running on a treadmill. The dataset used is freely downloaded from Open Science Framework repository. The proposed DWT-LSTMRNN method delivers 96.7% accuracy while classifying four different signals, and thus has the potential to be investigated further on BCI competition datasets that will pave way for a real-time application.
Time-frequency Complex Mask Network for Echo Cancellation and Noise Suppression

This work investigates the integration of traditional methods and deep learning technique in acoustic echo cancellation (AEC) application. To that end, the generalized cross correlation (GCC) algorithm is explored to align the far-end signal and the echo in the near-end microphone signal, and the echo is estimated in the near-end microphone signal by a use of adaptive filtering. After that, both the error signal and estimated echo are sent to a time-frequency complex mask neural network (TFCN) to suppress residual echo and environmental noise. In TFCN, the dual channel signal of error and estimated echo are processed by LSTMs in frequency domain, and by concatenating the resulting signal, it is converted back to time-domain. Finally, in time domain, a convolutional network is utilized to produce the final target speech. Experimental results show that the proposed framework is robust to blind test set, and effectively removes echo and noise, and achieves an excellent performance in AECMOS scores. The subjective average score of the proposed method is 4.41, which is 0.54 higher than the INTERSPEECH2021 AEC-Challen2e baseline.
High-Performance Matrix Multiplication on the New Generation Shenwei Processor

As a critical operation in numerical computing, matrix multiplication is widely used in many high-performance applications. The new generation Shenwei processor is the newest many-core processor with powerful computing capacity, thus it's necessary to enable highly efficient matrix multiplication on it. In this paper, we present a detailed implementation of double-precision matrix multiplication on Shenwei processor and further perform deep optimizations in terms of both memory access and computing. We employ a three-level task partitioning scheme mapping blocked matrix multiplication to each mem-ory hierarchy to improve computational parallelism and data reusability. And the dual broadcasting algorithm enables efficient parallel computing on chip. For further optimization, we present a three-level latency hiding strategy to bridge the performance gap between computing and memory access, where the hybrid data prefetch effectively hides DMA memory access and RMA communication costs. And the adaptive blocking algorithm is proposed for sake of the generality of algorithms towards various matrix sizes and shapes. High-performance matrix multiplication is highly correlated with the compute kernel, and we propose the register-buffering instruction scheduling with latency hiding strategy to maximize pipeline efficiency. The experiments show that the performance of our algorithm can reach 2 TFLOPS at large scales, near the theoretical peak performance. And the hybrid prefetch can improve performance by 82 %, while the adaptive blocking not only improves performance but also smoothes the performance jitter of irregular matrix multi-plications. The compute kernel with instruction scheduling is highly optimized and can achieve more than 380% performance improvement.
Hybrid Android Malware Detection Model using Machine learning Algorithms

Malware in general, is a serious threat that affects a wide range of users on an everyday basis. Since Android is the most popular mobile operating system, it became a target for fraudulent and malicious activities caused by malware developers that can result in devastating effects. With that, many Android malware applications continue to come into existence and throughout the years, several solutions have emerged, while Android malware has also smartly improved its capabilities to avoid being detected. Hence, a stronger detection model should be developed by improving the current existing Android malware detection techniques. The study created a multi-class hybrid Android malware detection model which investigates the effectiveness of combining system permissions as the static feature and network traffic features as the dynamic feature whereas K-Nearest Neighbor, Random Forest, and XGBoost as the machine learning classifying algorithms. The said classifiers are compared based on various evaluation metrics such as recall, precision, accuracy, F1-score, and Receiver Operating Characteristics Curve (ROC). All classifiers produced a result higher than 90% in accuracy, precision, recall, specificity, and F1-Score. Meanwhile, it is also revealed that our created model can compete with other models that are existing from the previous works wherein the best model is the Random Forest setup C which has a total of 18 features. The said model got a 98.3% recall, 98.4% precision, 98.3% accuracy, 98.3% F1-Score, and 99.9% ROC-AUC.
End-to-end efficient cascade license plate recognition system in unconstrained scenarios

Automatic License Plate Recognition (ALPR) has become a necessity for today’s urban information and transportation systems. However, the variable and unconstrained environment in real-world applications makes the license plate (LP) detection and recognition task still challenging. In this work, we design a robust end-to-end trainable LP recognition system. It employs a simple cascade framework to achieve efficient and accurate LP detection and recognition tasks. The system corrects LPs in the LP detection phase, which greatly improves LP recognition accuracy in nonideal views. Adding the attention module (containing both channel attention and spatial attention) to the LP recognition network enhances the adaptability of the network and helps the accurate delineation of characters. Experimental results show an average recognition accuracy of 98.7% on the CCPD dataset and a very good 94.4% on the Challenge sub-dataset. In addition, simultaneous recognition of multiple LPs can be achieved on the invisible CRPD dataset, demonstrating the excellent generalization capability of our system.
Excess Tunnel Current in Silicon Esaki Junctions

At low forward biases, a high current flows in Esaki junctions due to band-to-band tunnelling. At sufficiently high biases the current flows by normal forward injection. Between these two bias ranges, the current is unexpectedly high and has been called the excess current. A comprehensive experimental study has been made of this excess current in silicon junctions. It is shown that the properties of the excess current observed so far can be accounted for by a mechanism originally suggested by Yajima and Esaki, in which carriers tunnel by way of energy states within the forbidden gap. Based on this model, the following expression for the excess current, ${I}_{x}$, is proposed: ${I}_{x}\ensuremath{\sim}{D}_{x}\mathrm{exp}{\ensuremath{-}(\frac{{\ensuremath{\alpha}}_{x}{W}_{1}{e}^{\frac{1}{2}}}{2})[\ensuremath{\epsilon}\ensuremath{-}e{V}_{x}+0.6e({V}_{n}+{V}_{p})]},$ where ${D}_{x}$ is the density of states in the forbidden gap at an energy related to the forward bias, ${V}_{x}$, and the Fermi energies on the $n$ and $p$ sides are ${V}_{n}$ and ${V}_{p}$, respectively, $e$ is the electron charge, $\ensuremath{\epsilon}$ is the energy gap, ${W}_{1}$ is the junction width constant, and ${\ensuremath{\alpha}}_{x}$ is a constant containing a reduced effective mass, ${m}_{x}$. This formula describes the observed dependence of ${I}_{x}$ (i) on ${D}_{x}$, observed by introducing states associated with electron bombardment, (ii) on $\ensuremath{\epsilon}$, studied by the temperature variation of the diode characteristics, (iii) on ${V}_{x}$, verified from semilogarithmic plots of the forward characteristics, and (iv) on ${W}_{1}$, tested by using junctions of different widths. From these experiments, ${m}_{x}=0.3{m}_{0}$ to within a factor of 2.
Adaptive Model Selection for Video Super Resolution

The recent breakthrough of video super-resolution (VSR) techniques has shown impressive results in many computer vision tasks, with deep neural networks as the dominant approach for building VSR. While a wide range of VSR models is available with different processing capabilities and overhead, there is no consensus on the best model to use. This is because the optimal choice changes depending on the input video and the quality requirement. We present AssyVSR, an adaptive framework to dynamically choose the right VSR model to use from a model pool based on the input video and quality requirement. AssyVSR achieves this by using machine learning to build a model selector based on classification. Our classifier takes as input a set of numerical feature values of the input video and then predicts the best model to use for a given video resolution. The model is trained offline from training data, and the trained model can be applied to any unseen videos. AssyVSR further employs transfer learning to quickly update its model selector when the pool of candidate models changes without incurring significant training overhead. We evaluate AssyVSR by applying it to a representative mobile computation offloading environment using real hardware and over 3000 videos. Experimental results show that AssyVSR outperforms the state-of-the-art (SOTA) model selection scheme and the SOTA super-resolution model by producing higher-quality videos and reducing the response time by over 60% when compared with the SOTA super-resolution model.
Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks, including denial-of-services (DoS) attacks, false-data injection attacks, camouflage attacks, and actuation attacks (AAs). Inspired by the concept of digital twin, a new two-layered protocol equipped with a safe and private twin layer (TL) is proposed, which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer. First, a topology-repairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme, which saves communication resources considerably. The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated. Second, a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed. Moreover, this novel adaptive controller can achieve uniformly ultimately bounded convergence, whose error bound can be given explicitly. The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.
Multi-User Dynamic Spectrum Access Based on LRQ Deep Reinforcement Learning Network

Dynamic spectrum access (DSA) based spectrum sharing is an effective technique to alleviate the shortage of radio spectrum supply. Many DSA schemes based on deep reinforcement learning (DRL) have been proposed in the literature, but most of them merely dealt with simple heterogeneous cognitive radio networks (CRN). Combining two typical types of DRL networks, namely Long-short term memory (LSTM) and Residual network (ResNet), we proposes a distributed DSA algorithm for complex heterogeneous CRN. The algorithm aims to achieve high channel utilization and control the interference of SU to PU at a low level. The LSTM is used to extract the temporal characteristics of the historical spectrum data sequence to improve the spectrum state prediction capability. In contrast, the ResNet solves the performance degradation problem of deep neural networks caused by network depth and improves the training accuracy. Simulation results show that the algorithm can significantly improve spectrum utilization while controlling the interference of SU to PU.
Energy- and Temperature-aware Scheduling: From Theory to an Implementation on Intel Processor

Temperature, energy, and performance are the key considerations of real-time multicore systems. Thermal hotspots and high temperatures not only degrade reliability and per-formance but also increase cooling costs and leakage current. To address these issues, we propose an energy, performance, and temperature-aware semi-partitioned scheduling technique for sporadic real-time tasks. The proposed approach incorporates DVFS, Dynamic Power Management, and chip floor-plan in clas-sical partitioned scheduling technique. To further reduce temper-ature, energy, hotspots, and gradients, the proposed partitioned scheduling also considers individual task behaviors in the form of hot and cold tasks and applies reactive thermal management techniques such as task migration (hence called semi-partitioned scheduling) for system safety. To our knowledge, this is the first paper that considers energy, performance, reliability, and temperature together in real-time multicore scheduling. Finally, we present the results through experiments on the Intel Xeon 2680 v3 multicore platform. The results show that the proposed approach on average saves 15% power, reduces 3°$c$ average temperature, improves task schedulability, and avoids 100% thermal emergencies compared to the state-of-the-art technique.
Chandra LETGS observation of the variable NLS1 galaxy Ark 564

We present an analysis of the 100 ks X-ray spectrum of the narrow-line Seyfert 1 Galaxy Ark 564, taken with the Low Energy Transmission Grating Spectrometer (LETGS) on board Chandra . U singχ 2 statistics, several continuum models of the time-averaged spectrum of this object are compared, obtaining a semi-empirical solution for the description of the intrinsic emission continuum and a physical solution for the intrinsic absorption of the system. We find that the 0.1−10 keV spectrum can be well described by a power law plus two thermal components that account for the soft step. We are also able to detect and measure several narrow, unresolved absorption lines arising from highly ionized species of C, N, O, and Fe. The material seems to have a velocity consistent with the systemic velocity of the galaxy. This piece of evidence, in addition to the very low observed column density of NH ∼ 10 20 cm −2 ,i s in good agreement with the scenario of a transverse biconical outflow with a gas density of n 3 × 10 12 cm −3 at distances beyond the
Novel Model for the Efficient Vehicle Movement in Vehicular Ad hoc Network Based IOT

Networks that have multiple vehicle nodes that have complete freedom to enter or exit the network are called vehicle ad-hoc networks (VANETs). Establishing a protecting and competent pathway between end-to-end terminals is a complicated job by virtue of to the excessive movement of automobiles in the vehicular network. The selection of a pathway between source and destination relies on two evaluation criteria, namely sequence number and lowest hop count. In this project, this pathway has been formed by adopting a multicasting strategy. This strategy routes the data to select the source nodes from the network. A source node plays an important role in choosing a path between the end-to-end terminals. The methodology designed is implemented in NS2 tool and analytical results are achieved on the basis of some universal performance indices.
Artificial Neural Network for High-Impedance-Fault Detection in DC Microgrids

In this paper, we present a novel method for detect-ing high-impedance faults (HIFs) in DC microgrids. HIFs are more difficult to detect than other types of faults because their voltage and current values are not significantly different from those under normal operating conditions. We propose a recurrent neural network (RNN)-based method that can detect events from the temporal behaviour of a current signal, including HIFs and load changes. The method proves to be accurate, distinguishing between HIFs and other waveforms with a high score above 95% on accuracy and Fl-score metrics.