Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of Signal Analysis Techniques in Investigation of Rotor Broken Bars and Misalignment Defects on low power Induction Machines

Induction motors are the important constituent of the industrial machinery used in present date, mainly in the field of power generation, goods manufacturing, fuel refineries and other energy sectors. A large number of machines makes the system much complex. Moreover, these machines are responsible for the production activities undertaken in an industry. If any fault occurs on machine the overall production process hampers leading to consequent loss in income and dangerous condition that may lead to hazardous accidents. Thus, there arises the need of protective system and a proper maintenance strategy to tackle down these conditions. Monitoring the machine’s condition cannot avoid breakdowns, but it may forecast the likelihood of their occurring in the future by assessing various machine metrics. Also, the protective system may protect the equipment but it could not stop the causes that may lead to failure of equipment. The machine gives the signals in the form of temperature, vibrations, oil condition, noise, quality and quantity of stator currents and voltage, etc while operating through which the abnormality may be known. A huge energy loss can be prevented if a proper diagnostic method could be employed. Motor Current Signature Analysis (MCSA) is one of the techniques used to know the condition of machine by sensing the current drawn through the motor. A proper targeted action plan can be generated after knowing the results of motor current signature analysis while running the motor, as the process is online moreover non-destructive in nature.MCSA is capable of detecting an electrical signal that is comprised of harmonic current components that are the direct by-product of one-of-a-kind rotating flux components. Variations in the normal functioning of the motor may cause modifications to the fundamental by introducing harmonic components of the motor’s supply current. In this work, a short introduction to the approach is presented, covering topics such as the basics, fault detection strategies, and the current signature of different faults conditions. A detailed analysis of amplitude of harmonics and the sidebands associated has been carried out to differentiate the faults.
A CNN-LSTM Network for Classification of Attention Deficit Hyperactivity Disorder from EEG Data

Attention deficit hyperactivity disorder (ADHD) is one of the most common neuropsychiatric disorders in children, with core symptoms of inattention, impulsivity, and hyperactivity. As a result, early detection and treatment are critical. At this point, ADHD can be diagnosed using an electroencephalogram (EEG). This study proposes a model for identifying the EEG of children with ADHD. Our proposed model is made up of three parts: temporal convolutional blocks, spatial convolutional blocks, and Long Short Term Memory (LSTM), and because EEG contains more temporal information than other data, we build a model that can extract more temporal information. This study included 24 subjects, including 12 children with ADHD and 12 normal children. The experimental results show that our proposed model outperforms Support Vector Machine (SVM) and EEGNet, with our model achieving correct classification rates of 92.29%, 92.76%, and 90.91% on the three backs, respectively, and 94.25% on the full data, demonstrating the model's feasibility and applicability to the ADHD recognition problem.
Output Feedback-based Finite-time Containment Control of Mobile Robots

The nonholonomic constraints are challenges for the control of the mobile robots, especially when there are unknown external disturbances and unmeasured velocities. For the case when the velocities cannot be measured directly and there are unknown external disturbances at the dynamic level, some coordinate transformation is utilized skillfully to design the finite-time extended state observers in this paper. Then, two output feedback-based finite-time containment controllers using the backstepping control strategy are proposed to drive the mobile robots converge to the convex hull spanned by the virtual leaders. In addition, rigorous proofs are also presented to prove the conclusion. Finally, the effectiveness of the proposed control protocols are verified by the simulation results.
Characteristics of two-dimensional lattice models from a fermionic realization: Ising and XYZ models

We develop a field theoretical approach to the classical two-dimensional (2D) models, particularly to 2D Ising model (2DIM) and $XYZ$ model, which is simple to apply for calculation of various correlation functions. We calculate the partition function of 2DIM and $XY$ model within the developed framework. Determinant representation of spin-spin correlation functions is derived using fermionic realization for the Boltzmann weights. The approach also allows formulation of the partition function of 2DIM in the presence of an external magnetic field.
Solar system tests and interpretation of gauge field and Newtonian prepotential in general covariant Hořava-Lifshitz gravity

We study spherically symmetric, stationary vacuum configurations in general covariant theory (U(1) extension) of Hořava-Lifshitz gravity with the projectability condition and an arbitrary coupling constant $\lambda$, and obtain all the solutions in closed forms. If the gauge field $A$ and the Newtonian prepotential $\varphi$ do not directly couple to matter fields, the theory is inconsistent with solar system tests for $\lambda\not=1$, no matter how small $|\lambda-1|$ is. This is shown to be true also with the most general ansatz of spherical (but not necessarily stationary) configurations. Therefore, to be consistent with observations, one needs either to find a mechanism to restrict $\lambda$ precisely to $\lambda_{GR}=1$, or to consider $A$ and/or $\varphi$ as parts of the 4-dimensional metric on which matter fields propagate. In the latter, requiring that the line element be invariant not only under the foliation-preserving diffeomorphism but also under the local U(1) transformations, we propose the replacements, $N \rightarrow N - \upsilon(A - {\cal{A}})/c^2$ and $N^i \rightarrow N^i+N\nabla^{i}\varphi$, where $\upsilon$ is a dimensionless coupling constant to be constrained by observations, $N$ and $N^i$ are, respectively, the lapse function and shift vector, and ${\cal{A}} \equiv - \dot{\varphi} + N^i\nabla_{i}\varphi + N(\nabla_{i}\varphi)^2/2$. With this prescription, we show explicitly that the aforementioned solutions are consistent with solar system tests for both $\lambda=1$ and $\lambda\not=1$, provided that $|\upsilon-1|<10^{-5}$. From this result, the physical and geometrical interpretations of the fields $A$ and $\varphi$ become clear. However, it still remains to be understood how to obtain such a prescription from the action principle.
Correlated valence bond state and its study of the spin-1/2 J1-J2 Anti-ferromagnetic Heisenberg model on a square lattice

We propose a class of variational wave functions, namely the correlated valence bond states, for the frustrated Hamiltonian in the paramagnetic phase. This class of wave functions admits negative amplitude and the same sub-lattice pairing when a bipartite lattice is considered, thus suffers from the negative sign problem. However if applied to small systems, the sign problem is manageable using the standard variational Monte Carlo method. We optimize the wave functions for the J1-J2 Anti ferromagnetic Heisenberg model on a square lattice in the coupling region J2/J1 \in [0.45 : 0.56] for system sizes L = 4, 6, 8. To calculate the correlation functions and the order parameters for larger systems, we make the extensive Monte Carlo samplings using the variational parameters optimized at system size L = 8. We find that the paramagnetic phase is a gap-less spin liquid in the entire range of J2/J1 \in [0.45 : 0.56] with a gap-less singlet excitation and a gaped triplet excitation.
Column-oriented data model for data-intensive systems

Traditional relational row-oriented databases have some performance issues in the case of data-intensive systems that process large amounts of data. On the other hand, a column-oriented approach to data presentation has shown promising results when applied in analytical processing. This paper shows a design for a webhook (event notifications) software system to facilitate the usage of the columnar approach in data-intensive software systems. It will be an example of using the column store where the analytical requirement is not the primary one. The approach can be applied to any system in the data-intensive context, which has many data points and requirements to store, fetch, and aggregate based on a few specific from many fields.
Noise cancellation for continuous wave mud pulse signal based on Bi-ConvLSTM

Mud pulse telemetry technique is a common method of data transmission used in measurement while drilling (MWD). During the data upload process, the continuous wave mud pulse signal received on the ground is completely covered by noise due to interference of various noises. Therefore, this paper proposes a continuous wave mud pulse signal noise cancellation method based on bidirectional convolutional long short-term memory neural network (Bi-ConvLSTM), which transforms the signal denoising problem into a time series data regression problem of neural network. First, the sample data set is built using the simulated mud pulse signal, which includes a useful signal without noise and the corresponding noise-containing signal. The established neural network is then trained with the sample data set to obtain a neural network model that has a good denoising effect. Finally, the trained model is validated with the validation set samples with different signals to noise ratio (SNR). After the network model denoising, the SNR of the signal increased by about 18 − 30 dB and the correlation coefficient increased by about 0.65 − 0.89. The trained model can effectively suppress the noise from the continuous wave mud pulse signal, which confirms the effectiveness and feasibility of the method proposed in this paper.
A 0.18 pJ/Conversion Step Hybrid Structure based ADC

This paper presents the hybrid design of 8 bit ADC incorporating binary search ADC for area saving and flash ADC for higher speed operation. This ADC architecture is divided into two stages: the first stage is designed using binary search topology while the second stage is designed using flash architecture. The binary search stage is set to generate only 6 bits and flash stage is set to generate remaining 2 bits. With this hybrid configuration, the ADC consumes 12.8mW of power and sampling rate of 533 MSPS when operated at 1.8V.
Decentralized Tube Model Predictive Control for Arbitrarily Mixed Vehicle Platoons with HDV Uncertainties

In the control problem of a mixed vehicle platoon including connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) in an arbitrary sequence, the behaviors of HDVs can not be described and predicted by accurate models because of disturbances and modeling errors. Considering the uncertainties of HDVs, a decentralized tube model predictive control (MPC) algorithm based on the feedback control and feedforward control is proposed for CAVs in the mixed vehicle platoon. Firstly, we construct a generic nominal system model and an actual system model for each sub-platoon based on the linearized kinematics of HDVs and CAVs. Secondly, the tube invariant set sequence centered on the nominal system trajectories is constructed with the feedforward control, and then the actual trajectories of all vehicles in each sub-platoon are restricted in the tube with the feedback control. Finally, numerical simulations are given to verify that the proposed tube MPC algorithm can overcome the uncertainties of HDVs to achieve formation control of the mixed vehicle platoon.
Nondestructive verification of continuous-variable entanglement

An optical procedure in the context of continuous variables to verify bipartite entanglement without destroying both systems and their entanglement is proposed. To perform the nondestructive verification of entanglement, the method relies on beam-splitter and quantum nondemolition (QND) interactions of the signal modes with two ancillary probe modes. The probe modes are measured by homodyne detections, and the obtained information is used to feed forward modulation of signal modes, concluding the procedure. Characterizing the method by figures of merit used in QND processes, we can establish the conditions for an effectively quantum scheme. Based on such conditions, it is shown that the classical information acquired from the homodyne detections of probe modes is sufficient to verify the entanglement of the output signal modes. The processing impact due to added noise on the output entanglement is assessed in the case of Gaussian modes.
Cloud-based Distributed Predictive Consensus Control of Heterogeneous Multi-agent Systems with Random Communication Constraints

The distributed consensus control problem of multi-agent systems is investigated in this paper. A cloud-based distributed predictive consensus control scheme is proposed to actively compensate the random network delays and packet dropouts between the cloud and each agent, where each controller is designed in the cloud. Then, necessary and sufficient conditions simultaneously reaching stability and consensus of multi-agent systems are derived, which is independent of the random network delays and packet dropouts. Finally, simulation results are shown to verify the effectiveness of proposed scheme.
DETC-Based Networked Predictive Control for the NCSs with Deception Attacks and Time Delay

In this paper, the dynamic event-triggered networked predictive control problem is investigated for networked control systems (NCSs) under deception attacks. First, the system states are estimated by the Luenberger observer. Then, the networked predictive control (NPC) strategy is developed to actively compensate for the time delay. In order to save the limited communication resource, the dynamic event-triggered control (DETC) method is proposed by using additional dynamic variables. By applying the Lyapunov method, sufficient conditions for the stability of the considered NCS with deception attacks and time delay are formulated. Furthermore, the controller gain and observer gain matrices are obtained by solving the corresponding convex optimization problem. Finally, the effectiveness of the proposed control method is demonstrated via a simulation example.
Distributed Thrust Allocation Method for Heading Control of Unmanned Surface Vehicle

Thrust allocation is an important part of motion control of marine crafts. Given the problem that the traditional centralized thrust allocation method has low fault tolerance, or thrust allocation fault-tolerant control requires complicated control strategy adjustment or reconstruction, a distributed thrust allocation method is proposed. Aiming at the heading control of marine crafts, this method regards the thrust allocation problem as a distributed optimization problem that each rudder cooperates to achieve the goal of global optimization. The simulation results show that this method can not only ensure effective heading control but also has good fault tolerance in the case of several typical rudder faults by comparing with the traditional centralized thrust distribution algorithm.
Approximation of free-discontinuity problems by elliptic functionals via Gamma-convergence

defined on the space SBV (Ω) of special functions of bounded variation are studied, where ∇u denotes the approximate gradient of u, and Su is the set of the discontinuity points of u. In a two-dimensional setting, Su represents the contours of the object in a picture and u is a smoothing of an imput image. Energies of the same form arise in fracture mechanics for brittle solids, where Su is interpreted as the crack surface and u as the displacement outside the fractured region ([4]). Problems involving functionals of this form are usually called free-discontinuity problems, after a terminology introduced by De Giorgi (see [11], [5], [7]). The Ambrosio and Tortorelli approach [6] provides a variational approximation of the Mumford and Shah functional (1) via elliptic functionals to obtain approximate smooth solutions and overcome the numerical problems due to surface detection. The unknown surface Su is substituted by an additional function variable v which approaches the characteristic of the complement of Su. The approximating functionals have the form ∫
Comparison of Different Edge Detection Techniques Used in License Plate Localization

Features like edge, color, global, etc. are used for License plate Localization (LPL). Edge/global feature detection is one of the most common features used in LPL. Edge Detection techniques (Like Sobel, Canny, Prewitt, etc.) are used in LPL. In this Paper, a comparison of different edge detection techniques used for LPL in various environmental and physical conditions (Like poor light condition, Dusty, shadow, etc.). The plot of the sum of edge intensities across a line is used for analysis and to find the region of the License Plate (LP).
CSI based Performance Investigation of Single Cell - Multi user MaMIMO System

In the revolution of wireless communication system multi-user MIMO is one of the proliferation antenna technology in 5G currently being developed to meet the need for high data speed and better service quality for high band applications. This one of theextensive techniques that can support the 5G communication system for advanced multi-antenna system is the Massive MIMO (MaMIMO) technique is that base station with a larger number of antennas A cell with a lesser number of subscribers.This study focuses on fading channels in the system using the OFDM approach.The system overall performance is simulated with distinctive situations of channel state data by the use of pilot training. Linear precoding methods are used to resolve the Multi-User Interference (MUI) at receiver. That knowledge of CSI has an impact on system performance due to channel estimation errors. In the unknown CSI conditions, the Bit error rate (BER) of the system is a bit higher than that of perfect. using a massive range of antennaarray considerably growth the spectral performance without bound, each in best and substandard CSI condition and it's visible that spectral performance also suffers from the channel, in which the LOS channel performance outperforms Rayleigh channel performance.
On Efficient Delay-Aware Multisource Multicasting in NFV-Enabled Softwarized Networks

To comply with security and performance policies, multicast communication requires inline service that chains an ordered sequence of virtualized network functions (VNFs) with an emerging paradigm of network function virtualization (NFV). As many-to-many multicast pattern is widely used especially in current MEC, multimedia and big data industries, in this paper we focus on multi-source NFV-enabled multicasting in software-defined networks (SDN). By jointly investigating VNF placement and multicast tree routing strategy, it is envisioned to construct service function tree with minimum network resource expenditure while yielding lower delay. We explore this problem in static and online scenarios individually. In static situation with a bunch of multicast requests to be served, a column generation based computing model is first formulated, based on which an approximation algorithm MDNM that leverages logic of Viterbi and puts emphasis on resource optimization and segmental delay satisfaction is developed. In online situation with a single incoming request, another ILP model to enhance VNF instance reuse is established. Then, SVM algorithm giving importance to source determination and VNF instance consolidation is designed to efficiently construct a service function tree with minimum resource usage and delay. Simulation results show that the gap between each of the two proposed algorithms and their respective ILP models is marginal, and both MDNM and SVM outperform the state-of-art work.
Diagnosis of Pneumonia utilizing X-rays and other Image Processing techniques based on Containers and Deep Learning

The medical field is the most delicate of all known professions, and achievements in this field are a source of pride for the entire human population. One such attempt is the system described in this research report. Despite the knowledge that pneumonia can be prevented with a vaccine, it is the greatest cause of death worldwide. It is possible to diagnose it using chest X-rays. It is a time-consuming procedure that requires accuracy. We aim to create a system that can predict if a user has Pneumonia by analyzing the patient’s chest X-ray images and using CNN to improve the system’s accuracy.
The optimal strategy against Fictitious Play in infinitely repeated games

With the rapid development of Artificial Intelligence(AI), the game between human and machine/AI becomes more and more common and important. In this paper we will investigate such infinitely repeated games. We will study the optimal play of the human given the machine/AI adopting the Fictitious Play(FP) for all different kinds of $2 \times 2$ games under the assumption of a payoff parameter being rational. We give the optimal strategy of human which is proven to lead the system into a cycle after finite times. The results show that FP algorithm can be exploited easily and heavily. Analysis in this paper might shed some light on the result for more general and complicated situations.
Slow state based consensus for two time-scale agent networks under directed graphs

This paper studies the consensus problem of multi-agent systems under directed graphs, where each agent has two time-scale features. Unlike some existing consensus protocols which use both the fast and slow states or only appropriate for undirected graphs, two novel consensus protocols based on slow states are proposed to achieve consensus of multi-agent systems with strongly connected directed graphs. The proposed protocols have the advantages of simple structure and implementation simplicity, and their efficiency is demonstrated by simulation results.
Constellation: An Open-Source SoC-Capable NoC Generator

In response to growing application diversity, System-on-Chip (SoC) architectures have become increasingly heterogeneous, with diverse cores and accelerators, as well as non-uniform memory systems. However, existing open-source design frameworks for SoCs and NoCs (Network-on-Chips) have been unable to facilitate design exploration of heterogeneous SoC architectures with irregular NoCs. We present Constellation, a new NoC RTL generator framework designed from the ground up to support integration in a heterogeneous SoC and evaluation of highly irregular NoC architectures. Constellation implements a highly decoupled specification system that allows an architect to specify an exponentially large design space of irregular virtual-channel wormhole-routed NoC architectures. Additionally, Constellation provides a diverse set of systems, regression tests, and evaluation tools to provide confidence in the correctness and performance of the generated hardware. Constellation is open-sourced and integrated into the Chipyard SoC design framework, allowing full-system exploration of heterogeneous SoC architectures with irregular memory fabrics.