Performance Analysis of Analog Butterworth Low Pass Filter as Compared to Chebyshev Type-I Filter, Chebyshev Type-II Filter and Elliptical Filter

A signal is the entity that carries information. In the field of communication signal is the time varying quantity or functions of time and they are interrelated by a set of different equations, but some times processing of signal is corrupted due to adding some noise in the information signal and the information signal become noisy. It is very important to get the information from corrupted signal as we use filters. In this paper, Butterworth filter is designed for the signal analysis and also compared with other filters. It has maximally flat response in the pass band otherwise no ripples in the pass band. To meet the specification, 6th order Butterworth filter was chosen because it is flat in the pass band and has no amount of ripples in the stop band.

MLEA： A Solution for Users of Android in UTPVirtual

This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment （Moodle）, using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture （SOA） methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer （REST）, as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.

Impact of Photovoltaics

Photovoltaics(PV)can convert sunlight into electricity by making use of the photovoltaic effect.Solar panels consist of photovoltaic cells made of semiconductor materials(such as silicon)to utilise the photovoltaic effect and convert sunlight into direct current(DC)electricity.Nowadays,PV has become the cheapest electrical power source with low price bids and low panel prices.The competitiveness makes it a potential path to mitigate the global warming.In this paper,we investigate the relationship of PC array output with irradiance and temperature,the performance of PV array over 24 hours period,and the simulation of PV micro grid by MATLAB simulation.

Analytical Engineering for Data Stream

The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.

Vector Control of Three-Phase Solar Farm Converters Based on Fictive-Axis Emulation

In this paper,a new method for adjusting the current of three-phase voltage source DC-AC converter in orthogonal(DQ)reference frame is presented.In the DQ reference system,AC variable appears in the constant form of DC,making the controller design the same as the DC-DC converter[1].It provides controllable gain benefits at the steady-state operating point,and finally realizes zero steady-state error[2].In addition,the creative analytical model is dedicated to building up a series of virtual quantities orthogonal to the actual single-phase system.In general,orthogonal imaginary numbers get the reference signal by delaying the real quantity by a quarter period.However,the introduction of such time delay makes the dynamic response of the system worse.In this paper,orthogonal quantities are generated from a virtual axis system parallel to the real axis,which can effectively improve the dynamic performance of traditional methods without increasing the complexity of controller structure.Through PSCAD simulation,the ideal experimental results are obtained.

ARTICLE Analyzing the nonlinear system by designing an optimum digital filter named Hermitian-Wiener filter

The classical Wiener filter was engaged into identifying the linear structures,resulting in clear and incredible drawbacks in working with nonlinear integrated system.Currently,the Hermitian-Wiener system are suitable for unpredicted sub-system that consists of numerous and complex inputs.The system introduces a two-stage to analyze the subintervals where the output nonlinearities are noninvertible,through using the unknown orders and parameters.Finally,a practical strategy would be discussed to analyze the nonlinear parameters.

A 4-element Rectangular Microstrip Patch Antenna Array

Patch antennas are small in size and suitable for microwave transmission,so they are widely used in small portable wireless devices.Multiple patch antennas are connected together to form an array antenna.Compared with the patch antenna,the array antenna has a higher directivity gain and can achieve better transmission performance.In this project,I will test the single patch antenna first,and then move to 2×1 antenna array.Finally,built a 2×2 antenna array,test and record their performance respectively.

Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System

Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.

History and recent progress in ferroelectrets produced in Brazil

More than 30 years ago,a group of researchers in Tampere-Finland developed a thin foamed polymeric material for capacitive sensors.Such soft-voided films exhibited electrical charging characteristics,forming a powerful combination,which resulted in a smart-material with ferroelectric properties.The discovery of the electro-thermo mechanical film(ETMF)has sparked the curiosity of the electret community,leading to the development of several studies.At that time,ETMF became known as cellular electrets and,later,as ferroelectrets or piezoelectrets regarding their electromechanical properties.This paper provides a timeline review of the research on ferroelectrets produced in Brazil,between the years 1990 and 2020,towards demonstrating how the interest in the electret electrical charging mechanism has resulted in the use of ferroelectrets with well-controlled cavities for ultrasound applications.

Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

Nowadays,the demand for advanced functional materials in transducer technology is growing rapidly.Piezoelectric materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice versa.They are interesting from both fundamental and application points of view.Ferrooelectrets(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric materials.They exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to now.In this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of ferroelectrets.We will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.