Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergency Stop System of Computer Vision Workstation Based on GMM-HMM and LSTM

Voice recognition and command technology for applications with industrial robots is a relatively new field in the intelligent manufacturing industry. It offers a number of advantages over other methods of communication with robots, as it requires fewer specialized skills to manipulate the robot workstation. Additionally, using voice commands can help reduce the number of industrial injuries caused by contact with machinery, thus potentially save operators' lives in emergency situations where external assistance is not immediately available. This study presents a design of a Cartesian robot workstation which is equipped with a voice recognition system controlled by audio commands, as well as a vision perception system. The vision perception system uses the Real Sense depth camera that captures information about the coordinates of the work pieces, which is processed by SSD algorithm. The voice recognition system has been developed with an algorithm which combines both LSTM and HMM, and it has good performance in term of both efficiency and accuracy in controlling normal operation as well as emergency stop for our robot grasping workstation.
Mitigating Cyber-Security Risks using Cyber-Analytics

Cybercrime is a global issue that has taken over the digital space. Data is sent over networks every second, information is compromised, and systems are exposed to multiple threats. This paper on Cyber Security Analytics aims to deliver real-time analytics and operational intelligence in a simple format. Cyber Security Analytics includes real-time data collection, continuous monitoring of security controls, log processing and analysis, and immediate flagging of suspicious activity. This research study provides a fundamental platform for cybersecurity enthusiasts by detailing how to mitigate cybersecurity risks by utilizing a whole new and different perspective of AI and ML. This paper also focuses on IT issues that affect the real world. It also discusses potential vulnerabilities in an organization's IT infrastructure, networks, hardware, and software and reports on hacking attempts to develop more robust security procedures. This paper begins by understanding the various cybersecurity problems and challenges and how machine learning shapes cybersecurity analysis. This paper also considers multiple data science and predictive modeling applications in cybersecurity. This paper is expected to aid in developing predictive as well as detective modeling products and counter-cyber-attack solutions.
Day Ahead Load Forecasting using Random Forest method with meteorological variables

This paper focuses on short-term load forecasting for the day ahead using an Ensemble learning-based Random Forest method. The study uses real-time hourly load data and meteorological data from Bengaluru city, Karnataka, India, to predict the load. The inputs considered for the load forecasting are load profile data, dry bulb temperature, dew point temperature, and humidity data for 31 days from January 1, 2021, to January 31, 2021. The results obtained from the Random Forest model are compared with those obtained from the Ensemble learning-based Bootstrap Aggregation model to evaluate the effectiveness of the proposed method. The study uses statistical parameters such as Maximum Absolute Percent Error (MAPE), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE) to analyze the predicted load. The findings indicate that the proposed Random Forest model yields better results, with a Mean Absolute Percentage Error (MAPE) of 2.75% compared to the other Ensemble learning-based Bootstrap Aggregation method.
A finite-volume solution to the geophysical electromagnetic forward problem using unstructured grids

ABSTRACTThe application of unstructured grids can improve the solution of total field electromagnetic problems because these grids allow efficient local refinement of the mesh at the locations of high field gradients. Unstructured grids also provide the flexibility required for representing arbitrary topography and subsurface interfaces. We investigated the generalization of the standard Yee’s staggered scheme to unstructured tetrahedral-Voronoi grids using a finite-volume approach. We discretized the Helmholtz equation for the electric field in the frequency domain and solved the problem to find the projection of the total electric field along the edges of the tetrahedral elements. To compute the electric and magnetic fields at the observation points, an interpolation technique was employed, which uses the edge vector interpolation functions of the tetrahedral elements. To verify the presented scheme, two examples were evaluated, which revealed the computation of the total and secondary fields due to ele.
Multi-Input Bridgeless Charger with Reduced Filter Capacitance for PV Panel Powered LEV

The conventional approach of charging the multi-input light electric vehicle (LEV) is to employ a power converter for each source with a common junction point at the DC-link capacitor. The DC-link capacitor is usually a large electrolytic capacitor designed to filter the ripple current at the output. This paper presents a dual input power factor corrected (PFC) charger with active power decoupling (APD) circuit for LEV powered by the grid and solar photovoltaic (PV) panel. It reduces the component count of the power converter and DC-link capacitance at the output. The bridgeless architecture at the grid input reduces the conduction loss in the power converter. Dual loop control with a feed-forward network is employed for grid input and APD control. A proportional-integral (PI) controller is implemented for effective maximum power point tracking (MPPT) of PVinput. The performance of the charger and controller is verified by MATLAB simulation under different dynamic conditions. The IEC 61000-3-2 standard is adhered to as a reference to maintain the total harmonics distortion (THD) of grid current.
An Efficient Speaker Identification Approach for Biometric Access Control System

This work proposes an efficient cepstral-frequency domain based acoustic feature as a speaker identification solution for reliable biometric access control system. The Convolutional Neural Network (CNN) trained for this purpose uses the amalgamation of cepstral-frequency domain based acoustic features such as Power Normalized Cepstral Coefficients (PNCC) and Formant as PNCC-F. The PNCC-F with CNN classifier demonstrates an increase in identification efficacy. The speaker identification accuracy in clean, as well as noisy environment, has been used to evaluate the effectiveness of PNCC alone and in tandem with the formant feature. This work has been executed in a Python 3.8. 8 environment using the standard database with 43 speakers called VidTIMIT. The efficiency of the PNCC-F feature was further evaluated in a real-time noisy environment by mixing babble, factory, and machine gun noises from NOISEX-92 database to speech samples with 0 to 20 dB of distortion. The proposed PNCC-F feature surpassed the conventional PNCC feature in a clean environment by 2.34%, and outperformed at all SNR levels for all different noises.
Comprehensive Mathematical Modelling and Design of DM EMI Filter for Totem-pole PFC Converter

In this paper, the mathematical modeling of the Totem pole Power Factor Correction (PFC) converter is presented and compared with the simulation model and the hardware experiment. In the simulation model and the hardware experiment the control system consists of output voltage loop controller used to regulate the output voltage 400V and inner current loop controller helps in keeping the power factor closer to unity. The work shows the mathematical modeling of the input current FFT analysis. The input current generated through the mathematical model and the simulation model are also compared as a function of Total Harmonic Distortion (THD). This paper also illustrates the design method for EMI filter to suppress the Differential mode (DM) noise and to improve the power quality at the front end of the converter. The 500-W prototype of Totem-Pole PFC has been implemented with 110V ac on the input side to get 400V dc at the output end with 100kHz of switching frequency. The demonstration of the converter was made at the rated power of 500W and achieved the power factor of 0.983. The implementation of the DM EMI filter at the front end provides the required attenuation to meet the FCC class A EMI standard.
Sound Field Control by Using Equivalent Input Disturbance method

Low-frequency noise, such as road noise from automobiles and interior noise from bullet train cars, is detrimental to passenger comfort. Active noise control provides us with a way of suppressing low-frequency noise. It generates a sound of the same amplitude and opposite phase as noise and uses it to cancel the noise. Conventional active noise control is complicated to apply. Thus, the objective of this study is to establish a sound field control system by using the equivalent-input-disturbance method. System identification was performed to obtain an ideal plant of a duct. The control system is constructed based on the equivalent-input-disturbance approach with a low-order controller. The experimental results showed a suppression effect of about 50% of the sound field.
Electricity Theft Detection Employing Machine Learning Algorithms

Electricity theft is one of the biggest problems for smart grids. As a result of the reliance of current procedures on certain equipment, it cannot be easily detected. Additionally, the techniques don’t effectively extract useful information from highly dimensional power usage data, which raises the incidence of false positives and restricts their output. This paper intend to design and develop a machine learning classifier that can distinguish between legitimate customers and those who are committing power theft by tampering with electricity meters or by other means. Prepaid users will be the ones we test the most for this, along with their long-term monthly bill history. In this paper, we tried to classify electricity users based on their regular bills and any notable changes to those bills, and then further categorise them into customers who are genuine and those who are responsible for fraud and theft. We have applied several machine learning techniques: Logistic Regression (LR), Support Vector Machines (SVM), Naïve Bayes Classifier (NB), Decision Tree (DT), Random Forest (RF) and Adaptive Boosting (AdaBoost) in order to discover the power theft and stop additional losses to the grid. The main motivation of this work is to find the best machine learning model that is the most effective, conserve electricity from waste, and prevent economic loss at the same time.
A Model-Based Approach for Robotics Education with Emphasis on Embedded Systems

Typically, universities with a focus on technical sciences provide courses where students have to design control systems, to implement these control systems on embedded hardware and to verify the functionality of their implementations. Hence, the students work in groups and implement practical demonstrators based on given problem statements. In order to design, implement, and test robotic applications, it is mandatory to utilize expertise within the field of robotics and the field of embedded systems. The combination of expertise within both fields (robotics and embedded systems) is a highly demanded skill set, which is required to work for companies with focus on aviation, automotive, and even emerging applications for agricultural technology. The technical complexity of these applications is increasing almost exponentially, which requires abstract model-based approaches to ease the design flow of such implementations. This paper introduces a model-based approach for students within robotics and/or embedded systems degree programs. Moreover, this paper describes the state-of-the-art workflow to implement problem statements within the field of robotics and embedded systems (tools, approach and test), gives and overview of the model-based approach for students within these field of applications, and shows the integration of the results into courses based on a control system model.
A Transpose-SELDNet for Polyphonic Sound Event Localization and Detection

Human beings have the ability to identify a particular event occurring in a surrounding based on sound cues even when no visual scenes are presented. Sound events are the auditory cues that are present in a surrounding. Sound event detection (SED) is the process of determining the beginning and end of sound events as well as a textual label for the event. The term sound source localization (SSL) refers to the process of identifying the spatial location of a sound occurrence in addition to the SED. The integrated task of SED and SSL is known as Sound Event Localization and Detection (SELD). In this proposed work, three different deep learning architectures are explored to perform SELD. The three deep learning architectures are SELDNet, D-SELDNet (Depthwise Convolution), and T-SELDNet (Transpose Convolution). Two sets of features are used to perform SED and Direction-of-Arrival (DOA) estimation tasks in this work. D-SELDNet uses a Depthwise convolution layer which helps reduce the model’s complexity in terms of computation time. T-SELDNet uses Transpose Convolution, which helps in learning better discriminative features by retaining the input size and not losing necessary information from the input. The proposed method is evaluated on the First-order Ambisonic (FOA) array format of the TAU-NIGENS Spatial Sound Events 2020 dataset. An improvement has been observed as compared to the existing SELD systems with the proposed T-SELDNet.
Research on the Pricing Strategy of Stock-linked Structured Financial Products Based on Cholesky Decomposition and Monte Carlo Simulation

Taking multi-stock-linked structured financial products as the research object, this paper uses Cholesky decomposition method and Monte Carlo simulation method to make theoretical analysis and pricing research on stock structured financial products with complex income design terms. Select a 6-month stock-linked structured financial product issued by HSBC to carry out an empirical analysis on the pricing of the product, so as to show the application of Cholesky decomposition combined with Monte Carlo simulation pricing method in multi-asset structured financial products. The research results show that Cholesky decomposition combined with Monte Carlo simulation pricing method is more suitable for the pricing of multi-stock-linked financial products whose asset prices follow a normal distribution; Empirical analysis finds that the pricing of HSBC's financial products is high and the actual rate of return is low.
Factors Associated With Function-Focused Care Among Hospitalized Older Adults Living With Dementia

This article reports a study that was designed to describe the incidence of pain among older hospitalized patients with dementia and to evaluate the factors that influence pain among these individuals. It was hypothesized that function, behavioral and psychological symptoms of dementia, delirium, pain treatment, and patient exposure to care interventions would be associated with pain. Patients who performed more functional activities had less delirium. They also experienced higher quality-of-care interactions and were less likely to have pain. The findings from this study support the relationship between function, delirium, and quality-of-care interactions and pain. It suggests that it may be useful to encourage patients with dementia to engage in functional and physical activity to prevent or manage pain. This study serves as a reminder to avoid neutral or negative care interactions among patients with dementia as a strategy to mediate delirium and pain.
Development and Validation of a 54K Genome-Wide Liquid SNP Chip Panel by Target Sequencing for Dairy Goat

As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52–35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region (chromosome 8: 31,500,048-31,519,064). The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats.
Design Technology Co-Optimization Strategy for Ge Fraction in SiGe Channel of SGOI FinFET

FinFET devices and Silicon-On-Insulator (SOI) devices are two mainstream technical routes after the planar MOSFET reached the limit for scaling. The SOI FinFET devices combine the benefits of FinFET and SOI devices, which can be further boosted by SiGe channels. In this work, we develop an optimizing strategy of the Ge fraction in SiGe Channels of SGOI FinFET devices. The simulation results of ring oscillator (RO) circuits and SRAM cells reveal that altering the Ge fraction can improve the performance and power of different circuits for different applications.
Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

Abstract. In recent years, deep neural networks have exhibited promising performance in the single-image super-resolution (SISR) task by designing complex network structures from the low-resolution (LR) images to the high-resolution (HR) images and learning the mapping function. However, the SISR task is a widely known ill-posed problem. Since it may map one LR image input to multiple HR images, there are always undesired structural distortions in the recovered SR images. We creatively propose a SISR network with dynamic multi-task learning and multi-level mutual feature fusion (DMMFSR), alleviating structural distortion problems while maintaining more edge structure. Specifically, we utilize a DML block (DMLB) to train low-frequency and high-frequency image information extraction networks so the two networks can mutually promote each other. In addition, we also use a multi-level mutual feature fusion block to fuse image and edge features at different levels. Extensive experiments demonstrate the effectiveness of our proposed DMMFSR. Meanwhile, our model achieves comparable peak signal-to-noise ratio and structural similarity scores on multiple SR benchmarks compared with state-of-the-art SR methods.
Delay-dependent observer design and observer-based stabilization of linear neutral delay system

The delay-dependent stability criterion,the state observers design and observer-based stabilization are dealt with for linear neutral delay systems.Firstly,a delay-dependent stability criterion is developed based on a method of linear matrix inequality(LMI). Secondly,an LMI-based observer design for the error dynamics is presented.Thirdly,the observer-based stabilization problem of the neutral delay systems is solved by means of the solvability of two LMIs.Finally,two numerical examples are used to demonstrate the validity and effectiveness of the proposed approaches.
Spectroscopic and photometric studies of main sequence M stars and a search for late-type dwarfs in the solar vicinity.

As any introductory astronomy student knows, M dwarfs are the most common stars in the Galaxy and are the faintest of the core hydrogen burners. A comprehensive study of these faint objects is crucial to our understanding of the stellar composition of the Galaxy and necessary for a more complete knowledge of the transition between main sequence M stars and their slightly less massive counterparts, the brown dwarfs, which never achieve hydrogen burning in their cores. In this thesis, a spectroscopic catalog of 125 K and M dwarfs is first presented. This catalog covers the wavelength range from 6300 to 9000 A, near where these objects emit most of their light. Eight of these spectra, covering classes M2 through M9, are combined with infrared spectra from 0.9 to 1.5 μm to create a second catalog. The two sets of spectra are used to search for temperature-sensitive atomic lines and molecular bands, which are then used in fitting the observed spectra to a sequence of theoretical models. As a result, a new temperature scale for M dwarfs is determined, and this scale is more accurate than previous determinations which have depended on blackbody energy distributions. The sequence of spectra is also used to compare the spectrum of the brown dwarf candidate GD 165 B to known M dwarfs. Furthermore, the spectral catalog is used in an attempt to separate the spectra of faint companions from their M dwarf primaries in systems where the two objects are too close for conventional spectroscopy to resolve the individual components. A survey for faint M dwarfs is also launched using the data acquired through the CCD/Transit Instrument (CTI) on Kitt Peak, Arizona. Follow-up spectroscopy is presented for 133 of these objects, and several more very late M dwarfs are identified. This spectroscopy combined with photometric data from the CTI are used to construct a luminosity function for M dwarfs which is in excellent agreement with determinations from previous surveys. Finally, possible avenues for future work are discussed. These include spectroscopic follow-up of the reddest of Luyten's proper motion objects – the first results from which have uncovered, in just twelve observations, two objects of type M7 and one of type M8, among the coolest objects yet recognized. Future searches, such as an all-sky survey for objects of extremely high proper motion, are also outlined.
Use of Ensemble Learning to Detect Buffer Overflow Exploitation

Software exploitation detection remains unresolved problem. Software exploits that target known and unknown vulnerabilities are constantly used in attacks. Signature-based detection techniques are limited to known exploits and susceptible to circumvention. Current research on the use of Machine Learning (ML) for software exploitation detection is limited in quantity and use cases. Existing research lacks the use of public datasets, discussions of feature importance, and elaboration of parameters that affect data preparation and subsequently model performance. This paper presents ML models based on different ensemble algorithms to detect software exploitation using runtime traces. We focus on buffer overflow vulnerabilities in user-space applications within Windows Operating Systems (OS), given the prevalence of the type of vulnerability and the OS. We utilized a publicly available raw dataset of 11 Windows applications under exploitation. Multiple distinct models (based on Random Forest and XGBoost) are created and tested. Testing was performed several times using various aggregation parameters and different testing applications. Our results demonstrate that we can achieve up to 100% recall with 0% false positive rate. We report on the different parameters that must be addressed to curate runtime traces and demonstrate their impact on the performance of the ML models. We demonstrate that the proper training of models on a subset of exploitation techniques enables the model to detect techniques never seen before, such as return-oriented programming. Finally, we conclude with a discussion of the important features that had the highest impact on each of the models, along with the key takeaways.