Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Feature Extraction Techniques for BCI Processing

Brain computer interface (BCI) is used to identify electrical activity in human brain using the electroencephalog-raphy (EEG). EEG records the electrical activity by placing the electrodes on the scalp. By using the recorded information it will able to classify the different types of abnormalities happening in brain. For extracting the information from signal without losing any information, some feature extraction methods have been used using deep learning concepts. The methods are Time Frequency distribution, Fast Fourier Transform, Eigen Vector, Wavelet Transform, Auto Regressive, Independent Component Analysis, Principal Component Analysis, Empirical Method Decomposition, Hilbert Huang Transform and Local Discriminant bases. Comparison have been done between the methods to show how effectively methods without losing an information. The classification accuracies of the feature methods are compared with each other. The study concluded the hybrid methods such as domain specific and automated features togeather shown better performance.
A ZPE Based Frequency Control Approach to Realize Soft Switching in Contactless Power Transfer of Energy Storage System Applications

This paper proposes a zero phase angle error (ZPE) based frequency control approach to realize soft switching in inductive power transfer (IPT) charging of energy storage system applications. IPT is influenced by many parameters, such as the coupling coefficient between the transmitting and receiving coils and the load power requirement. This introduces new challenges, such as hard switching of the power converters in the system, which increases the power losses and degrades the power conversion efficiency. Furthermore, the switching devices need to be selected with higher ratings which increases the cost of the converters. To address these issues, an optimal frequency operation of the converter is proposed to achieve soft switching and transmit the maximum power to the load. The optimal frequency control is developed by minimizing the ZPE between the excitation voltage and current from the transmitting side. This makes the controller not require information on the IPT system's receiver side voltages and currents to achieve the aforementioned goals. Furthermore, the performance of the proposed control algorithm is validated using digital simulations. Finally, the IPT system is developed and tested for a maximum power of $1.7\text{kW}$ under open loop conditions, with experimental results presented in this paper.
MIMO controller design for two-input DC-DC converter without de-couplers

An analysis and controller design of the multi-input multi-output DC-DC conversion system is reported here. The controller designed by solving standard H∞ based Riccati equation is of very high order, which are practically not feasible to design, therefore LMI based techniques can be utilized in which, structure and nature of the controller can be fixed by introducing some new sets of constraints on the structure of controller state-space matrices. Here, de-centralized type of controller is designed without de-coupling the original system and considering the interactions in the actual system. The performance of designed controller is investigated in terms of performance parameters in the presence of input and load side disturbances and found satisfactory.
Architectural Improvement and Performance Evaluation of 1D-to-2D Array Conversion Priority Encoder

Priority Encoders (PE) are essential component in time critical applications like interrupt controller and CAM and are useful in fields like image processing, on-chip testing applications and computer architecture optical communication. In this paper, we have used PE design using the 1D array to 2D array conversion method to implement 4-bit, 16-bit, and 64-bit PEs. This method is used because of its scalability and huge advantage in saving time. We have implemented structural improvements in the 1-D to 2-D array architecture using the 180nm CMOS process. For PE at 64-bit operating at a voltage of 1V, our design provides a 63% improvement in transistor count and an 88.09% improvement in operating frequency as compared with the reported work.
Intraday variability in compact extragalactic radio sources

We present the data of three observing cam- paigns with the VLA performed to study intraday vari- ability in compact extragalactic radio sources. The rst campaign (May 1989) lasted 5 days; total intensity as well as linear polarization data were obtained at 2, 3.6, 6, and 20 cm wavelength for 9 program sources. In the second observing run (February 1990), a ve-antenna subarray of the VLA was used for 25 days to monitor the sources 0716+714, 0917+624, and 0954+658, with dense sampling at the same four wavelengths. In the third run (October 1992), which lasted 22 days, 8 program sources were mon- itored at 3.6, 6, and 20 cm wavelength, and 2 additional sources at 6 cm only, again with a ve-antenna subarray. We present summary tables, light curves, and structure functions from these data sets.
Common-Ground Single-Stage Transformerless Inverter using Switched-Capacitor for Single Phase PV Application

This paper presents a new topology of transformer-less inverters (TLI) for photovoltaic (PV) applications. It is based on the common ground configuration (CGC). CGC TLI has an inherent ability to reduce the leakage current (LC), one of the principal issues. This uses eight switches, two switched capacitors (SC) and a dc linked capacitor to generate the 5-level double-boosted output voltage. This inverter is suitable for grid integration of PV voltage that is lower than the grid voltage because of the boosting capability. The SCs are self-balanced by charging and discharging at a fundamental frequency. The Inverter control is accomplished by level shift pulse width modulation (LS-PWM). Further, component selection procedures and comparative studies are carried out. The performance of the topology is further verified on MATLAB/Simulink.
Optimal Task-Offloading Control for Edge Computing System With Tasks Offloaded and Computed in Sequence

This paper considers the edge computing system (ECS) in which the tasks with dependencies are offloaded and computed in sequence. Different task-offloading orderings come with different ECS memory-cache execution latency (EMCEL) which is caused by writing and reading (WR) the computed results of earlier offloaded tasks between the ECS memory and cache. Therefore, the optimal ordering to offload all the tasks while leading to the minimum EMCEL arises as an interesting issue in practice. This requires to solve a hard exponential explosion optimization problem. To address this issue, this paper first formulates the tasks and their dependencies as a direct acyclic graph (DAG), then converts the exponential explosion problem into a discrete problem that can be solved in polynomial time, and finally develops some theoretical conditions to guide to determine the optimal task-offloading orderings. A novel algorithm called OTOOA to find the optimal task-offloading orderings in polynomial time is proposed. Field experiments show that OTOOA outperforms the existing algorithms. To our best knowledge, this is the initial work towards this issue. Note to Practitioners—For the edge computing system that operates in the application scenarios in which the ECS cache is small while the size of the tasks is relatively large such that it is not allowed for multiple tasks to be processed in the ECS cache parallelly or at the same time, e.g., the execution latency-sensitive and fast big data-processing scenarios in which the multiple tasks depending on the other are offloaded and computed in sequence, this paper helps such edge computing system to improve the operation efficiency with the minimum EMCEL by finding an optimal task-offloading ordering to guide the wireless devices to offload their tasks to the ECS server. Experimental investigations show that the solution proposed here outperforms existing ones.
Assessment of chaotic chimp sine cosine hybrid algorithm for AGC study of complex power system

This paper deals with the application of the designed algorithm in automatic generation control (AGC) problem of different structures, such as interconnected and isolated. The important contribution made by this paper is the application of the validated algorithm in the conventional four-area interconnected model and isolated power system model with different operating conditions to study AGC performance. In the studied systems, a proportional-integral-derivative PID controller is employed, and its parameters are tuned by the chimp optimization algorithm (CHOA), chaotic chimp optimization algorithm (C-CHOA) and chaotic chimp sine cosine optimization (C-CHOA-SC) algorithm methods. Finally, the results are validated by real-time simulation results using d-SPACE.
Performance Analysis of Single-Stage and Two-Stage VSI-fed IM Drive for Solar Pump Irrigation Systems

Performance comparison analysis of two-stage and single-stage induction motor (IM) drive fed by voltage source inverter (VSI) for solar pump irrigation system has been done for the economic consideration of the system in the agriculture field. Both stages have common components of the photovoltaic (PV) system and VSI-fed IM drives with pump load. In both configurations, the incremental conductance (INC) maximum power point tracking technique (MPPTT) has been accustomed to extract for PV array's maximum power. The DC/DC boost converter is featured in a two-stage system and is used to enhance the level of voltage for the VSI's input and the system's load requirement. In single-stage, the PV array is tied directly to the VSI and the whole drive of the IM with the pump. The system uses direct torque control (DTC) strategy for the speed regulation of the IM drive, preserving both the contours of PV array fed IM drive with pump load. To model and simulate the system, MATLAB/Simulink software is employed.
Large‐scale volatility models: theoretical properties of professionals’ practice

Abstract. This article examines the way in which GARCH models are estimated and used for forecasting by practitioners in particular using the highly popular RiskmetricsTM approach. Although it permits sizable computational gains and provide a simple way to impose positive semi‐definitiveness of multivariate version of the model, we show that this approach delivers non‐consistent parameter’ estimates. The novel theoretical result is corroborated by a set of Monte Carlo exercises. A set of empirical applications suggest that this could cause, in general, unreliable forecasts of conditional volatilities and correlations.
Detecting Data Exfiltration Using Seeds Based Graph Clustering

Identifying devices of interest within Internet Protocol (IP) networks is essential for Cyber security and network management. However, the large variety and number of devices within networks, and the ubiquitous encryption of network traffic poses significant barriers. We present a scalable and encryption resilient technique to identify devices of interest based solely on their affiliation to public internet services. In this work, devices of interest are those that are suspected of engaging with suspicious or unexpected servers; e.g., facilitating the malicious exfiltration of sensitive intellectual property data. Graph-based clustering was used to reveal devices of interest based on the similarity of their network behaviour to a set of pre-known malicious devices acting as seeds. The motivation for our technique was driven by a case study for which a subset of malicious devices were known; however, the vast majority remained undiscovered. We conducted experiments to demonstrate viability of our technique as applied to this use-case. Detection accuracies of 94% were achieved, and malicious devices from the case study were successfully identified to aid data driven decisions by Cyber analysts.
Degradation of the VISSR Visible Sensor on GMS-3 during June 1987–December 1988

Abstract Degradation of the VISSR (Visible-Infrared Spin Scan Radiometer) visible sensor on the GMS-3 is estimated for a period from June 1987 to December 1988 by monitoring reflectances obtained from four targets on the earth. Samples of the highest brightness of midday cloud images and albedos observed over three desert target areas (the Great Sandy Desert, the Taklamakan Desert, and the Gobi Desert) are employed as targets. The results show that the brightness measured by the satellite sensor had appreciably deteriorated during the study period. The rate of degradation is estimated to be 5.3% per year from the analysis of the highest brightness, and 3.7%, 3.7%, and 5.0% for the case of the Great Sandy Desert, the Taklamakan Desert, and the Gobi Desert, respectively. For the average of four cases, the estimated rate of degradation is 4.4% per year.
Multi-Agent Reinforcement Learning Based Optimal PV-ESS Control In Grid

The increasing utilization of renewable energy sources, such as photovoltaic (PV) power, has led to a growing interest in managing surplus PV power in order to generate additional profits. In particular, the use of energy storage systems (ESS) for handling surplus PV power has gained significant attention due to their ability to control the unstable and erratic nature of solar power systems. This paper presents an optimal ESS control scheme based on multi-agent reinforcement learning (MARL) that maximizes grid benefits. The proposed method is evaluated in a grid environment that includes a central ESS, multiple PV power prosumers, and consumers. The results of our empirical study demonstrate that the proposed method generates an additional profit of 18% to 36% compared to the current method used by Korean power providers for calculating prosumer profits. Furthermore, we discovered was found that as the proportion of prosumers in the total population increases, energy efficiency also increases proportionally.
Asset pricing models with machine-learning method

Traditional asset pricing theories and models are facing more and more challenges in empirical study. Machine learning provides a new tool for asset pricing research. Due to the low signal-to-noise ratio and concept drift of financial data, the theoretical constraints of economics are very important for the applicability of machine learning in asset pricing. Firstly, this paper introduces seven multi-factor asset pricing models based on ad hoc sparsity constraints, summarizes the characteristics and shortcomings of traditional asset pricing models. Then, we display the challenges of machine learning facing in empirical application of asset pricing, formulate the targeted economic constraints. Finally, we further discuss the possible future trends of machine learning algorithms in asset pricing.
H-Bridge Inverter with Optimum Phase-Angle Shift for Electric Vehicle Propulsion Motor Drive

Induction motor (IM) drives for electric vehicle (EV) application require large number of batteries connected in parallel which gives rise to circulating currents within the battery circuit. In this paper, a new H-bridge inverter-based IM drive system topology is proposed to reduce the zero-sequence voltage (ZSV) problem in parallel connected batteries. Moreover, the proposed topology inherently suffers from ZSV problem. To find the optimum phase shift angle to minimize the ZSV, an optimization approach has been adopted. The simulation results indicate that the phase shift angles obtained through optimization eliminate the problem.
A posteriori analysis of finite element discretizations of stochastic partial differential delay equations

In this paper, we study a posteriori error estimates for finite element approximation of stochastic partial differential delay equations containing a noise. We derive an energy norm a posteriori bounds for an Euler time-stepping method combined with a standard Galerkin schemes for the problems. For accessibility, we first address the spatially semidiscrete case and then move to the fully discrete scheme.
Sampling Errors in the Measurement of Rainfall Parameters Using the Precipitation Occurrence Sensor System (POSS)

Abstract The Precipitation Occurrence Sensor System (POSS) is a small Doppler radar originally designed by the Meteorological Service of Canada (MSC) to report the occurrence, type, and intensity of precipitation in automated observing stations. It is also used for real-time estimation of raindrop size distributions (DSDs). From the DSD, various rainfall parameters can be calculated and relationships established, such as between the radar reflectivity factor (Z) and the rainfall rate (R). Earlier work presented first-order estimates of the sampling errors for some POSS rainfall parameter estimates. This work combines a Monte Carlo simulation and “inverse problem” analysis to better estimate errors due to the specific sampling problems of this disdrometer type. The uncertainties are necessary to determine the statistical significance of differences between DSD estimates by the POSS and other collocated disdrometers, or between POSS measurements in different climatologies. Additionally, confidence limits ca.
An Improved K-Type Seven-Level Converter Topology for Direct Grid Integration of Solar Photovoltaic Plant

This paper presents an improved K-type (IK-7) converter, which uses three solar arrays (per phase) to generate the multilevel waveforms in staircase form. This topology of the multilevel converter uses symmetric voltage sources. Hence, it helps to harness more power from the solar photovoltaic system. The proposed IK-7 configuration of the converter generates seven voltage levels with three similar PV sources and eight solid-state switches. Operating modes are well explained for the proposed converter with a proper conduction path. The presented system is operated in grid-tied mode and employs closed-loop control for converter voltage and current in dynamic solar conditions. The specific harmonic elimination strategy is applied for switching the converter to have minimum converter voltage THD. System is simulated in MATLAB and its dynamic performance is tested in different modes of solar irradiance.
Toroidal-EE-Based Integrated Common-mode Choke for dc-side EMI filter

In literature, the use of passive electromagnetic interference (EMI) filters is extensively researched to not only restrain the common-mode (CM) noise but also the differential-mode (DM) noise. Although the CM and DM chokes are simple to design, reliable, and do not need an auxiliary power supply, they increase the overall volume and reduce the power density of a power converter. Hence, this paper presents a novel integrated choke that offers higher DM impedance than the traditional CM choke by maintaining the same volume, when compared to the traditional CM choke. The calculation of CM inductance, as well as DM inductance of the proposed integrated filter topology, is presented along with a small signal measurement using an impedance analyzer. The proposed EMI filter performance is verified on a three-phase silicon carbide (SiC) fed brushless dc (BLdc) motor.
A Tailor-made Billing Model for EV Charging in a Community Living

In recent past, Electric Vehicles have become on par in terms of performance, if not exceeded their internal combustion engine counterparts. Though EV s have long been regarded as a game-changer in India's automotive industry, a proper charging infrastructure is critical for the proliferation of EV s alongside the integration of renewable energy sources into the grid to develop a low-carbon society. The availability of decentralised small-scale renewable energy sources and the uncertainties surrounding EV charging operations make the process of managing energy and setting fair prices at the grid level more difficult. In this paper, an ecosystem blueprint for an EV charging station has been proposed, which includes a dynamic pricing model for charging Electric Vehicles in any office or college space which acts as a hybrid microgrid with renewable energy sources. This work has been validated for NIT Trichy as a case study and solar PV is taken as the main renewable resource. However, the proposed model can accommodate the future addition of any renewable source and can formulate the prices accordingly. The proposed pricing model aims to maximise user satisfaction by keeping the charging bills low, as well as maximise the utilisation of renewable energy to lower the dependency on the grid. The blueprint also includes a comprehensive business plan to discuss its feasibility. Furthermore, the blueprint also involves building a suitable interface for EV users through a mobile application to efficiently book slots and charge their EVs and an admin dashboard supervises all the operations and helps keep an eye on the operational constraints.
Half Bridge Based Multi Leg Converter for Dynamic EV Charging System

Resonant Inductive Power Transmission (RIPT), a wireless power transfer technology, is a cutting-edge option for Electric Vehicle (EV) battery charging systems. RIPT is a safe and practical method of charging electric cars (EVs). RIPT systems must have high-power transmission, dependability, robustness, and cost in order to achieve highway charging. This study illustrates a multi-legged dynamic wireless charging system wherein each leg of the inverter is utilized separately for every transmitter coil to bring down the system cost. Each space between two transmitters coils a sensor attached to the road pavement that serves as a trigger when a receiver coil passes over a path used for charging. This work's transmitter part includes a proposal for a multi-leg converter. To drive a transmitter coil, each leg of the converter acts as a half-bridge high frequency inverter circuit. The proposed system achieved 76% in experimental analysis.
Design of Low Power N-Bridge Master and P-Bridge Slave Topologically Arranged Flip-Flop

This work presents a new flip-flop design. This design is achieved using two bridge structures. Master of the flip-flop consists N-bridge and slave of the flip-flop includes P-bridge. This design is constructed using 22 transistors symmetrically. Master and slave both have 11 and 11 transistors respectively. By using this design power consumption is reduced significantly. Delay of the circuit optimized for high speed application. Percent improved with respect to Clock to Output (Q) delay and Power consumption are 7.01% and 14.8% respectively, with respect to S2CFF.
Predictive Analysis of Malware using Machine Learning Techniques

As the number of cyberattacks grows exponentially, it is crucial to identify and categorise these attacks. One significant cyber hazard that has a significant impact on many different industries is malware. The paper examines the already published literary works. The suggested solution utilises a malware dataset containing 210352 samples that was gathered from Kaggle. Eight different classifiers, including support vector machines, K-nearest neighbours, Naive Bayes, logistic regression, and tree-based methods like random forests and decision trees, are used with separate training and testing sets of data. The outcomes are also contrasted with ensemble learning strategies like gradient boosting and adaboost. For experimental work, many classifier types are chosen. The outcome demonstrates that the random forest classifier outperforms the other classification methods by providing 98.02% accuracy. As a result, the paper uses machine learning methods to categorise malware, and the authors are presently attempting to enhance the system's performance by creating convolution neural networks, making the system more flexible to new infections.