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generatorsingle · 4 years ago

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matlabhwexperts-blog · 7 years ago

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Generate Matlab Waveforms Assignment Help

https://www.matlabhomeworkexperts.com/using-matlab-to-generate-waveforms.php

Waveform Generator uses Direct Digital Frequency Synthesis (DDS) technology to generate high accuracy and high stability waveforms. It can also generate programmable pulse signals as well as the standard wave functions. Electronic designs require a variety of stimulus signals during test. Additionally, the arbitrary waveform feature allows engineers to generate any desired waveform with Ultra Wave, our free waveform editing software. Actual signals can also be captured through an oscilloscope, and then downloaded to a signal generator for output. The digital sampling technology and the Direct Digital Frequency Synthesis technology enable engineers to build any required waveforms for circuit design verification. MATLAB can also be used to generate waveforms,. In most branches of Electronics Engineering, waves are studied both in the time domain and the frequency domain simultaneously. Our talented pool of Waveform Generator experts, Waveform Generator assignment tutors, Waveform Generator solvers, Waveform Generator professionals and Waveform Generator homework tutors can cater to your entire needs in the area of Digital Signal Processing such as Waveform Generator Homework Help, Undergraduate Waveform Generator Assignment Help and Graduate Waveform Generator Assignment Help, MATLAB Waveform Generator Project Paper Help and Waveform Generator Exam Preparation Help

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derrickcodes · 6 years ago

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Hi, can you help me, if you have some time? I’m in college and I’m supposed to choose my specialty in like a month, but I still don’t know what do I want to do. I feel like there’s so much to learn and I don’t want to miss out on anything. Can you tell me what should I expect from working with different languages? (I’ve tried only like two so far) or do you have any tips which would help me figure it out? Please, you’d literally help save my future (dramatic, I know, sorry xD).

Sure! I understand your sentiment completely. Computer Science is such a vast field, it can feel overwhelming with how much there is to learn. I was in that same boat for the first three years of my comp sci degree and I still don’t fully know what I want to do.

The great thing about computer science is that while it is a relatively new field, it has spread its wings and has branched out in so many ways and has even affected other areas of study. Here are 10 common specializations, what they do, and what some code might look like (when possible):

Software development is what people tend to think of when studying computer science. This typically involves wanting to work in the industry as someone who develops code based on what a client or company wants. You will take courses about the software development process, such as software testing and agile development. There aren’t really any languages I would recommend, since this is such a broad field, but good places to start are C++, Java, C#, and Python. If anything, I would suggest reading further, since software development can be broken down into the other categories. An example of Java code can be seen below (and C++ and C# basically look like this as well).

Game development is another topic people think of with computer science. A lot of our generation grew up playing video games and somewhere along the line thought that they would want to develop games as well. Game developers need to have a good understanding of computer graphics (such as using OpenGL), physics, and computer programming in C++ and C#. A great place to start is looking into Unity. It’s free, it’s easy to use, and it’s what a lot of industry people use today.

Web development has been, currently is, and will always be in high demand. Most interactions people have with computers are through websites, so of course there’s a lot of companies whose development revolves around websites. The standard languages to learn are HTML, CSS, and JavaScript, although if you want an edge up, look into JavaScript libraries and frameworks, like Angular and Node.js. Also, W3Schools will be your best friend. It’s hard to show examples of this that aren’t hundreds of lines long, so here’s a little example showing HTML, CSS, and JavaScript similar to a W3Schools example.

Data science is exploding right now. The world has so much data and we’re just now beginning to analyze all of it. Say you have the history of every user that has ever been shown your ad and who clicked on it and when. Could you use that to determine anything about the effectiveness of the ad, time of day, where it’s displayed, and if they’ll click again? That’s data science. Typical courses include Statistical Computing, Data Mining, and Machine Learning. Typical languages for data science include R and Python. One subtopic that’s really big is machine learning. Can you take the data that you have and have a program “learn” off that data and make predictions about the future? Take a look at this Python code that analyzes a data set and is able to predict whether or not breast cancer is present based on a few attributes:

Information systems is the foundation of both web development and data science, as it involves how and where we store our information and data. You’ll study database management and possibly some cloud storage, since this is usually where we store things. You will want a strong understanding of data structures if you really want to learn the best ways to store things (I’ll give you a hint, databases usually use a variation of Binary Search Trees). You’ll also learn how to retrieve and manipulate the data that is stored. The languages you’ll want is SQL (rather MySQL or NoSQL) and PHP. Some MySQL code for creating a schema with tables will look like this.

Computer engineering is a close friend of computer science, but is mostly focused on the hardware side of things. Computer engineering is all about how you build the computer system. You will spend a lot of time learning the physics that goes into computer design, namely electricity and magnetism. Some classes would include Circuit Analysis, Signals, and Digital Systems, but a lot of it is up to you.

Systems & Architecture is similar to computer engineering, as you’re still focused on being close to the hardware, but you’re more interested in the software side. This was my favorite section of computer science, because you get to learn about computers from a brand new perspective and see how they work down to the electricity flowing through it. Typical courses include Computer Architecture, Operating Systems, Parallel Systems, and the like. You will learn languages like C and Assembly so you can get a good grasp of how fast and powerful a computer can be since you’re almost talking directly to it. For example, this C code is typical practice for interacting with dynamic libraries.

Theoretical computer science is a very intriguing study. Instead of learning about how all these different languages can be applied, you look into what computers are actually capable of. The main courses in any theoretical computer science section are Programming Language Theory, looking into how can you design and classify a programming language, Algorithm Analysis and Design, the different paradigms used to solve different problems, and Theory of Computation, studying what problems can be solved by computers and how quickly can they be solved. Studying this is a good way to get a job in the government, as organizations like the NSA are always looking for people to work on cryptography, which has a strong background in theory.

Scientific computing is the mix of computer science and applied mathematics. You take your understanding of programming and mathematical theory to create computer algorithms to solve problems as fast as they can (and maybe faster than ever before)! You’ll want to have a very strong understanding of linear algebra (the study of matrices), since a lot of computational tasks can be done effectively and efficiently using matrices. Typical courses include Numerical Linear Algebra, Numerical Analysis, and Partial Differential Equations. For this, languages like MATLAB (or its free counterparts Octave or Scilab), Mathematica, and even Fortran are your best bets. A typical career can involve becoming a researcher, or working for a company that relies on the most optimized mathematical code, such as a government agency or somewhere in the finance world. Here’s an example of some code written in Octave to analyze a waveform and reproduce it as a series of numbers (hey, I did a post about this earlier!)

Bioinformatics is the love child of computer science and biology. In this study, you will use what you know about computer science and programming to better understand biological data. You can use this to study the human body, such as the human genome, so we as humans can have a better understanding of what makes us human, or you can apply it and develop medical software. One of my friends got a PhD in bioinformatics and she now writes the software for heart monitors. Since this is somewhat similar to data science, you’ll want to learn Python and R.

There are more specializations, like computer security or networking, but these are the 10 I’m most familiar with. I hope this helped and feel free to reach out to me if you have any more questions!

#computer science #coding #programming #advice #Ask Derrick

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rojaswinther47-blog · 6 years ago

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My Favorite Stethoscopes

Littmann Classic III

Digital stethoscopes are computer aided products which can read sound, vibration, or acoustic signals. The list here don't actually only apply to medical students; it also applies to PGIs (post graduate interns) and even resident physicians. Since these things will go a long way from the time you start med school up until your early years of practicing the medical profession, it's smart to invest in quality items. Like for example, when it comes to stethoscopes you might want to consider investing in a 3M Littmann Classic II SE Stethoscope or the Master Cardiology stethoscope This is because this particular brand is very durable and could really go a long way. In fact, some of today's renowned doctors are even using the same Littmann units that they've been working with since med school. This entry-level Littmann Lightweight II S.E. Stethoscope is equipped with double-sided chest pieces and tunable diaphragm. Available in a variety of colors, including black, cell blue, green, lilac and sea foam green, the Littmann Lightweight II S.E. and similar to the Littmann II S.E. stethoscope, features a non-chill rim and diaphragm. This is one of the most commonly used stethoscopes among nurses and it is largely considered as one of the best stethoscope out there. The MATLAB user interface also met expectations, displaying cardiac waveforms in real time and when a stored waveform was played back as illustrated in the figure below. Several people were tested using the stethoscope to determine the accuracy of the BPM measurement algorithm. After obtaining a measurement using the digital stethoscope, the volunteers also determined their heart rate by holding two fingers to their necks and recording the number of seconds required for 10 heart beats. The results are included in the table below. The largest error between the two measurement techniques for the samples taken was around 6%, suggesting that the BPM measurements displayed on the plot were fairly accurate. It should be noted that there is a degree of uncertainty in measuring heart rate using the method with two fingers. Its flagship part is the sphygmomanometer which checks your blood pressure manually. You simply have to place it at your heart and breathe naturally. The rest will simply follow. Being devoid of any complex controls, it is likely to elicit any inconveniences at all. A vital element of the hospital beds mentioned above, are the mattress itself. One highly popular type of mattress available is the egg mattress These mattresses, so called due to their similarity in appearance to an egg box or carton, are placed on top of a standard mattress to decrease pressure on the skin, helping prevent sores and helping the blood flow of patients who spend a lot of time confined to bed. Their design also helps patients remain cool by allowing increased airflow around the patient. The stethoscope (from Greek στηθοσκόπιο, of στήθος, stéthos - chest and σκοπή, skopé - examination) is an acoustic medical device for auscultation, or listening to the internal sounds of an animal body. It is most often used to listen to heart sounds and breathing. It is also used to listen to intestines and blood flow in arteries and veins. Less commonly, "mechanic's stethoscopes" are used to listen to internal sounds made by machines, such as diagnosing a malfunctioning automobile engine by listening to the sounds of its internal parts. Stethoscopes can also be used to check scientific vacuum chambers for leaks, and for various other small-scale acoustic monitoring tasks.

25 Greates medical accessories To Start medical studies

Of course disposable stethoscopes have been around for some time, specifically for high risk areas. But at a cost of $3 or so, they are a somewhat expensive solution, and also for that cost, their quality (particularly acoustic quality, a key to Littmann brand performance) is quite poor. The main distinguishing design features in cardiology stethoscopes vs regular is the difference in chest piece (head design) and thicker tube. The tubing is made thicker to minimize interference as the sound waves are transmitted between the chest piece and the ear piece. The thickness is also meant to promote amplification of the sound as it travels. Product Details The 3M Littmann L5807RB-CAR Classic III Rainbow Finish Stethoscope features a Rainbow finish chestpiece and Brass finish eartubes. The single-piece diaphragm is easy to attach and easy to clean, with a smooth, crevice-free surface. littmann classic 3 reviews There are tunable diaphragms on both the adult and pediatric sides of the chestpiece. The pediatric side can convert to an open bell by just removing the single-piece diaphragm and replacing it with a non-chill rim. The open bell stays clear of dirt and debris when covered by the small diaphragm. The next-generation tubing lasts longer, with improved resistance to skin oils and alcohol. No natural rubber latex or phthalate plasticizers used in the tubing or any other component. Comes with a 5-year warranty. This dependable stethoscope comes from renown brand MDF. This stethoscope is widely popular with the medically related person not because of its high price but for its great services. It is made up such a way patient feel optimum comfort as well as a nurse also by using the stethoscope. Everybody's external acoustic meatus is not the same size and thinking this important fact three pairs of different sizes comfortSeal ear tips come with the package. The Listen-In Mobile App can improve the confidence of both the instructor and the students by ensuring that the sounds being heard Monitoring Stethoscope are accurate. Additionally, the application enforces correct anatomical positioning through both observation and interaction. The stethoscope traditionally used is the Sprague, developed more than 45 years ago. It is still considered to be the gold standard acoustic stethoscope, with an outstanding high to low range. However, that being said, once you have learnt to recognize what is simply the tube sound, you'd be hard pressed to get better acoustics from any scope.

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simone0123 · 4 years ago

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Signal Processing Assignment Help

What is a signal?

A signal can be defined as an envelope or stream of information encoded in the form of waves. It is generally in the form of continuous signals or electrical pulses. The branch of Electrical and Electronics Engineering has evolved which is basically designed to focus on how the information extraction and information propagation takes place. This branch is sometimes called as Signal Processing.

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juniperpublishers-etoaj · 6 years ago

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Juniper Publishers - Open Access Journal of Engineering Technology

Removal of the Power Line Interference from ECG Signal Using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing Data Points of ECG in Telecardiology System

Authored by : Shekh Md Mahmudul Islam

Abstract

Maintaining one's health is a fundamental human right although one billion people do not have access to quality healthcare services. Telemedicine can help medical facilities reach their previously inaccessible target community. The Telecardiology system designed and implemented in this research work is based on the use of local market electronics. In this research work we tested three algorithms named as LMS (Least Mean Square), NLMS (Normalized Mean Square), and RLS (Recursive Least Square). We have used 250 mV amplitude ECG signal from MIT- BIH database, 25mV (10% of original ECG signal) of random noise, white Gaussian noise and 100mV (40% of original ECG signal) of 50 Hz power signal noise is added with ECG signal in different combinations and Adaptive filter with three different algorithms have been used to reduce the noise that is added during transmission through the telemedicine system. Normalized mean square error was calculated and our MATLAB simulation results suggest that RLS performs better than other two algorithms to reduce the noise from ECG. During analog transmission of ECG signal through existing Telecommunication network some data points may be lost and we have theoretically used Cubic Spline interpolation to regain missing data points. We have taken 5000 data points of ECG Signal from MIT -BIH database. The normalized mean square error was calculated for regaining missing data points of the ECG signal and it was very less in all the conditions. Cubic Spline Interpolation function on MATLAB platform could be a good solution for regaining missing data points of original ECG signal sent through our proposed Telecardiology system but practically it may not be efficient one.

Keywords: Telemedicine; Power line interference (PLI); ECG; Adaptive filter; LMS; NLMS; RLS

Abbreviations: EMG: Electromyography; ECG: Electrocardiogram; EEG: Electroencephalogram; NLMS: Normalized Least Mean Square; RLS: Recursive Least Square; SD: Storage Card

Introduction

The ECG signal measured with an electrocardiograph, is a biomedical electrical signal occurring on the surface of the body related to the contraction and relaxation of the heart. This signal represents an extremely important measure for doctors as it provides vital information about a patient cardiac condition and general health. Generally the frequency band of the ECG signal is 0.05 to 100 Hz [1]. Inside the heart there is a specialized electrical conduction system that ensures the heart to relax and contracts in a coordinated and effective fashion. ECG recordings may have common artifacts with noise caused by factors such as power line interference, external electromagnetic field, random body movements, other biomedical signals and respiration. Different types of digital filters may be used to remove signal components from unwanted frequency ranges [2].

The Power line interference 50/60 Hz is the source of interference in bio potential measurement and it corrupt the biomedical signal's recordings such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG) which are extremely important for the diagnosis of patients. It is hard to find out the problem because the frequency range of ECG signal is nearly same as the frequency of power line interference. The QRS complex is a waveform which is most important in all ECG's waveforms and it comes into view in usual and unusual signals in ECG [3]. As it is difficult to apply filters with fixed coefficients to reduce biomedical signal noises because human behaviour is not exact depending on time, an adaptive filtering technique is required to overcome the problem. Adaptive filer is designed using different algorithms such as least mean square (LMS), Normalized least mean square (NLMS), Recursive least square (RLS) [4]. Least square algorithms aims at minimization of the sum of the squares of the difference between the desired signal and model filter output when new samples of the incoming signals are received at every iteration, the solution for the least square problem can be computed in recursive form resulting in the recursive least square algorithm. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts so as to present an ECG that facilitates an easy and accurate interpretation.

The basic idea for adaptive filter is to predict the amount of noise in the primary signal and then subtract noise from it. In this research work a Telecardiology system has been designed and implemented using instrumentation amplifier, band pass filter and Arduino interfacing between Smartphone and Arduino board. First of all raw ECG signal has been amplified and filtered by Band pass filter. Analog signal has been digitized using Arduino board and then interfacing between Arduino board and smart phone has been implemented and Digitized value of analog signal has been sent from Arduino board to smart phone and digitized value of analog signal has been stored in SD storage card of smart phone. Using Bluetooth or existing Telecommunication Network. As sinusoidal signals are known to be corrupted during transmission it is expected that similarly an ECG signal will be corrupted.

We have therefore designed an adaptive filter with three different algorithms and simulated in MATLAB platform to compare the performance of denoising of ECG signal. During transmission of ECG signals through existing Telecommunication networks some data pints may be lost. In this research work we have used cubic spline interpolation to regain missing data points. If more data points are missing then reconstruction of an ECG signal becomes impossible and doctor can not accurately interpret a patient's ECG in an efficient manner. Cubic spline interpolation has been implemented for various missing data points of original ECG signal taken from MIT-BIH database. The normalized mean square error of cubic spline interpolation was very low. Cubic Spline interpolation in Matlab platform could be a better solution for regaining missing data points of ECG signal theoretically.

Related Works and Literature Review

The extraction of high-resolution ECG signals from recordings contaminated with system noise is an important issue to investigate in Telecardiology system. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts, so as to present an ECG that facilitates easy and accurate interpretation.

The work of this research paper is the development of our previous research work "Denoising ECG Signal using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing data points of ECG in Telecardiology System” [5]. Many approaches have been reported in the literature to address ECG enhancement using adaptive filters [6-9], which permit to detect time varying potentials and to track the dynamic variations of the signals. In Md. Maniruzzaman et al [7,10,11] proposed wavelet packet transform, Least-Mean-Square (LMS) normalized least-mean-square (NLMS) and recursive-least-square (RLS), and the results are compared with a conventional notch filter both in time and frequency domain. In these papers, power line interference noise is denoised by NLMS or LMS or RLS algorithms and performed by MTLAB or LABVIEW. But in our research work we have developed our previous work. We denoised ECG signal by removing power line interference, Random noise and White Gaussian noise. In our previous research paper [12] we denoised ECG signal from random noise and white Gaussian noise. In our present research work we have added power line interference with Pure ECG signal individually and added in mixed of power line interference, random noise and white Gaussian noise in different combinations. Finally in our research work we have used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

There are certain clinical applications of ECG signal processing that require adaptive filters with large number of taps. In such applications the conventional LMS algorithm is computationally expensive to implement The LMS algorithm and NLMS (normalized LMS) algorithm require few computations, and are, therefore, widely applied for acoustic echo cancellers. However, there is a strong need to improve the convergence speed of the LMS and NLMS algorithms. The RLS (recursive least-squares) algorithm, whose convergence does not depend on the input signal, is the fastest of all conventional adaptive algorithms. The major drawback of the RLS algorithm is its large computational cost. However, fast (small computational cost) RLS algorithms have been studied recently In this paper we aim to obtain a comparative study of faster algorithm by incorporating knowledge of the room impulse response into the RLS algorithm. Unlike the NLMS and projection algorithms, the RLS algorithm does not have a scalar step size.

Therefore, the variation characteristics of an ECG signal cannot be reflected directly in the RLS algorithm. Here, we study the RLS algorithm from the viewpoint of the adaptive filter because

a. The RLS algorithm can be regarded as a special version of the adaptive filter and

b. Each parameter of the adaptive filter has physical meaning.

Computer simulations demonstrate that this algorithm converges twice as fast as the conventional algorithm. These characteristics may plays a vital role in biotelemetry, where extraction of noise free ECG signal for efficient diagnosis and fast computations, high data transfer rate are needed to avoid overlapping of pulses and to resolve ambiguities. To the best of our knowledge, transform domain has not been considered previously within the context of filtering artifacts in ECG signals.

In this paper we compare the performances of LMS, NLMS and RLS algorithms to remove the artifacts from ECG. This algorithm enjoys less computational complexity and good filtering capability. To study the performance of the algorithms to effectively remove the noise from the ECG signal, we carried out simulations on MIT-BIH database. During transmission of ECG signal through existing Telecommunication network it may be corrupted or some data points may be lost. Linear Spline interpolation was popular method for regaining missing data points of ECG signal [13]. Cubic Spline interpolation has gained popularity very recently [6]. In our previous research work we used cubic spline interpolation. The development of our previous research work i.e., in the present research work, in this research paper we have also used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

Adaptive Filter Algorithms

In this research work a Telecardiology system has been implemented and proposed for sending ECG signal through smart phone (Figure 1).

The raw ECG signal will be taken from patient electrode and passed through instrumentation amplifier and band pass filter to amplify the signal and to reduce the noise coming from electrodes. Then that amplified and filtered analog ECG signal will be converted into digital signal by using Arduino AVR microcontroller based system. Then that digital value of ECG signal will be sent to smart phone by using Arduino interfacing with smart phone and digitized signal values will be sent to smart phone SD card. After that digital value will be sent to another smart phone by using Bluetooth technology. Digitized ECG value will be received to smart phone via Bluetooth .During transmission of ECG signal through Telecommunication network it may be corrupted by random noise or white Gaussian noise of the network. Adaptive filter using different algorithms have been used to reduce noise of the transmitted ECG signal. A MATLAB coding has been done to reduce the noise of the ECG signal and for reducing noise of digitized ECG signal, transmitted noisy ECG signal needs to be loaded in MATLAB and then it is filtered suing adaptive filter with different algorithms and performances of different algorithms are measured based on their de-noising capabilities. During transmission of ECG signal some data points may be missing and MATLAB spline interpolation algorithm will get them back so that ECG signal can be transmitted reliably (Figure 2).

Least Mean Square (LMS), Normalized Mean Square Algorithm (NLMS) and Recursive Least Square Algorithm (RLS) has been designed and implemented for denoising ECG signal in MATLAB platform [4,12-14]. Cubic Spline Interpolation has been used for regaining missing data points of ECG signal during transmission through existing Telecommunication network. The normalized mean square error was calculated for regaining missing data points of ECG signal and it was very less and so Cubic spline interpolation could be a better solution in MATLAB platform for regaining missing data points of ECG signal.

Result

In this work we have taken pure ECG signal from MIT-BIH database. The amplitude of our taken ECG signal was 250 mV which is amplified from.5mV (2 % of original ECG signal), 10 mV (4% of original ECG) 15mV (6% of original ECG), 20 mV (8% of original ECG signal) and 25mV (10% of original ECG signal) of random noise and white Gaussian noise is added with ECG signal. Three different algorithms of Adaptive filter were implemented and tested their performances of denosing ECG signal. We have taken ECG signal with 250 mV amplitude and 5000 samples were taken from MIT-BIH database (Figure 3). In our simulation work we have denoised 100mV of 50 Hz power signal noise.

Recursive Least Square (RLS) Algorithms

We have taken 5000 data points of ECG signal from MIT- BIH database. In our simulation 11data points (from 689 to 699 of original data points of ECG), 201 data points (from 800 to 1000 of original data points of ECG), 300 data points (from 1600 to 1900 of original data points of ECG), 500 data points (from 2000 to 5000) and 6 data points (from 4095 to 5000) are made zero and cubic spline interpolation function was called in MATLAB platform and it could regain the original data points of ECG signal (Figure 15-20). The MATLAB coding result of Spline Interpolation is given below Table 2

The above simulation result suggests that Recursive algorithms. RLS could be the best option for Telemedicine system Least Square algorithm (RLS) performs better than other two to denoise ECG signal during transmission (Table 1).

Normalized mean square error calculation suggests that Cubic Spline performs satisfactorily for regaining missing data points of ECG signal.

Conclusion

During transmission of ECG signal it may be corrupted due to random noise and Gaussian noise. So we have tested the performances of LMS, NLMS and RLS algorithm of adaptive filter. Our simulation result suggest that RLS could be the best option for recovering ECG signal or denoising EEG signal during transmission through Telemedicine system.

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zanghonghuaer-blog · 7 years ago

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Agilent DSA90604A Digital Oscilloscope

Welcome to a Biomedical Battery specialist of the Agilent Battery

The DSA90604A is a 6 GHz, 4 Channel digital oscilloscope from Agilent. Measure voltage or current signals over time in an electronic circuit or component to display amplitude, frequency and rise times, etc. Applications include troubleshooting, production test, and design.

Agilent’s Infiniium DSA90604A oscilloscopes with battery such as Agilent A3 Battery, Agilent E6000 Battery, Agilent E6000A Battery, Agilent E6000B Battery, Agilent E6000C Battery, Agilent E6080A Battery, Acterna Battery, Acterna MTS-5000 Battery, Acterna MTS-5100e Battery, Air Shields Battery, Vickers 2HR-4UC Battery, Air Shields-Vickers JM103 Battery offer the industry’s lowest noise floor and most accurate real-time jitter measurements available on scopes of this bandwidth class. Complete with full-bandwidth probing solutions and hardware-accelerated de-embedding and equalization techniques, Agilent oscilloscopes are the best oscilloscope solution for today’s demanding high-speed measurements.

The industry’s lowest noise floor Leveraging the company expertise in RF design, Agilent has invested in key technology blocks like our proprietary Faraday caged front end to significantly reduce our inherent scope noise floor. The result: the lowest noise floor available on any real-time oscilloscope from 2.5 GHz to 12 GHz.

The industry’s deepest memory With 1 Gbyte of memory, low-frequency jitter components can be more quickly resolved in a single measurement. Statistical accuracy is improved with more data collection. Keysight’s integrated deep memory remains responsive and allows more comprehensive testing, supporting pattern lengths up to PRBS23 for accurate transmitter and receiver results.

We add full bandwidth probing and accurate de-embedding and equalization software The performance of Agilent’s oscilloscopes is matched by the superiority of our probing, de-embedding and equalization offerings. Maintain full bandwidth performance to the probe tip with our InfiniiMax probing solutions. Render waveforms anywhere in the digital serial link with our hardware accelerated N5465A InfiniiSim Waveform Transformation Toolset. Configurable system modeling allows you to remove the deleterious effects of unwanted channel elements, simulate waveforms with channel models inserted, view waveforms in physically unprobeable locations, and compensate for loading of probes and fixtures. The N5461A Serial Data Equalization software allows you to model equalization techniques in real time.

Choose from a wide range of complete compliance applications Choose from the industry’s widest range of complete applications for the Infiniium 90000 Series to ensure compliance to the leading industry standards, including SATA, PCI Express®, Ethernet, USB, and more. Comprehensive set-up wizards and full automation of the required testing take the guesswork out of demonstrating compliance quickly. Get further insight with our protocol and analysis decode available on PCI Express, SATA and USB.

Free up valuable engineering resources Set-up wizards combined with intelligent test filtering make it simple to ensure the right tests are being run. Comprehensive HTML reports with visual documentation and pass/ fail results guarantee that critical information is retained on each test. Technicians can run complete and accurate testing on their own, freeing valuable engineering resources.

Streamline your debug and analysis tasks with the industry’s widest range of application software Whether you need to trigger and decode serial buses, iron out the kinks in your memory designs, or see FFT based spectrum analysis of your signal, the Infiniium 90000 Series has application software to help. Our serial protocol views are unique to oscilloscopes, and our DDR debug tools support multiple generations of the standard. Quickly access additional features from the scope’s standard menus.

Customize your scope for even more efficiency The N5414B-1NL InfiniiScan Event Identification software makes unique capabilities like Zone Qualify and Generic Serial triggering possible. Rapidly automate any scope measurement using the N5467B-1NL User Defined Application and have it appear seamlessly in your scope’s menu. Customize your Infiniium further by taking full advantage of MyInfiniium (standard on all 90000A Series oscilloscopes). Use MyInfiniium to deliver automated measurements, execute customized scripts, save screenshots, or load your favorite setup.

Frequency Domain Analysis Infiniium’s built-in FFT allows users to quickly and easily analyze the frequency components of their signals. Both FFT magnitude and phase can be displayed and can be combined with other built-in math functions or Matlab based measurements. A resolution bandwidth of 6 kHz is supported with the standard 10 Megabytes of acquisition memory at the maximum sample rate of 40 GSa/s. With optional acquisition memory, installed resolution bandwidths of 2 kHz can be obtained. Standard windowing of Hanning, Flattop and Rectangular are supported along with cursor-based power measurements. When more powerful frequency domain measurements are required including modulation analysis, consider the Keysight 89601A Vector Signal Analyzer software.

Hardware Accelerated Differential and Common Mode Math Select the channel menu and enable differential mode to enable hardware accelerated math capability. Enjoy full channel functionality including IniniiScan triggering and jitter analysis. Save time, by using the hardware acceleration for even faster update rates with your differential and common mode math needs.

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