Image processing on 3d brain images using interpixel slicing technique

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How to prepare Literature surveys for Projects

 WRITING LITERATURE SURVEYS

Any kind of research work will be initiated with the help of background study. Literature survey help us to get clarity about the extension of the present work, the challenges faced in the previous research work, the solutions found in the previous research etc. The scope of the research work will be examined through the literature surveys. The study also clarify the advantages and drawbacks of the conducted research work.

Simplified Steps to write effective Literature Survey is pointed out here.

1.    Take the base paper or the research paper which sparkled out your initial research work in popular journals such as IEEE, Elsevier, and Springier etc.

2.    Go through the Abstract and do the clear study of the paper.

3.    Take the reference papers from the final page and download the same from journal websites

4.    For better understanding minimum of 10 years research survey would be required

5.    Node down the year of publishing, the journal name and any supporting university etc.

6.    While writing the literature survey mention the author name with year of publishing and university name followed by a clear declaration of how the author concluded the work.

7.    Node down the points on the algorithm developed by the author, the advantages of his research work, the drawbacks of the presented algorithm too

8.    Put a table or Bullet points mentioning the short depicting content of the papers one by one in a elaborated way

Sample Format of Literature Survey is Shown below

Paper Title
Year of Publishing
Author Name
Research Findings	·            ·             ·             ·            ·            ·
Advantages
Drawbacks
Methodology Adopted

Effective Thesis writing and IEEE paper writing

TECHNICAL WRITING

THESIS Writing Works

The research work is concluded with a positive note with the help of an effective and deep scrutinized contents on research area. The effective content strongly focused on the root of the research and future scope, extendibility which is applicable for intellectual property. We focused on writing effective research thesis work with deeply explained contents on VLSI Design, image processing, neural networks, data science, neural networks, machine learning, and medical systems for all electronics, communication, instrumentation and computer science domains

IEEE Paper

IEEE papers have the certain principles which is to be followed well when writing the paper content deeply explains the research concepts and analyze the research background. Every IEEE paper creates a big impact to engineers on creating further research work on the specialized area. We focused on provided quality contents for Papers, conference and study papers for all research scholars with guidance

Plagiarism Free Contents

We write plagiarism free paper contents by understanding the deep concepts behind the research work and validation checks will be frequently done line by done through experts and professionals. 0% plagiarism will be expected and grammars will be checked thoroughly. We specialized in writing unique contents in all areas.

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Gps signal analysis and Target detection

GPS Signal Analysis & target Detection




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Get to know How multimodal Feature Extraction technique works

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Design of Respiratory Rate measurement SYSTEM using ECG through Recurrent NOTCH FILTER

Design of Respiratory Rate measurement SYSTEM using ECG through Recurrent NOTCH FILTER

Notch filters are a highly discriminating, high-Q, method of the band stop filter which can be used to discard a single or very small band of frequencies slightly than a whole bandwidth of dissimilar frequencies. For example, it may be essential to reject or reduce a specific frequency generating electrical noise (such as mains hum) which has been prompted into a circuit from inductive loads such as motors or ballast lighting, or the exclusion of harmonics, etc.

But as well as clarifying, variable notch filters are also used by musicians in sound apparatus such as graphic equalizers, synthesizers and electronic borders to deal with narrow points in the sound response of the music. Then we can see that notch filters are broadly used in much the same way as low-pass and high-pass filters.

Notch filters by design have a very slender and very deep stop band around their center frequency with the width of the notch being labeled by its selectivity Q in exactly the same way as resonance frequency peaks in RLC circuits.

Ultimately most of the notch filter design is the twin-T notch filter network. In its basic form, the twin-T, also called a parallel-tee, formation consists of two RC branches in the form of two tee sections, that use three resistors and three capacitors with opposite and opposing R and C elements in the tee part of its design as shown, creating a deeper notch.

Notch filters act as Band pass filters in which the obligatory ECG & PPG frequency alone pass the filter initially. Regularity and amplitude is desirable to filter the ECG & PPG signals.

To measure the R Rate the system need to be picking up the peak values from the given PPG & ECG Data sets.  ECG signals are very low level signals which contains mvolt of amplitude apart from PQRST Peaks.The peaks also influence noise.

RESULTS

TOOLS USED
MATLAB 2018 



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SCENE CLASSIFICATION USING Recurrent CNN Technique

SCENE CLASSIFICATION USING Recurrent CNN Technique

Summary

Global Positioning systems are now moving forward with Vision based positioning systems. Camera is being used in all places for safety and security reasons. The research on image based data extraction is always adaptive in which scene classification is one of the criteria.

Scene classification is nothing but extracting the maximum information for the input captured scene image( Casual image captured with Landmark or Text info etc) The maximum information we mean it as text info from the name board of the coffee shop, number plate of the car, name board of the bus stop etc any text.Image info also consists of Land mark of the place such as a temple, a famous building etc

Image processing technique is used basically to extract the information about the input image such as shape, color, text, unique features such as corners, lines, any geometrical shapes etc Extracting those info we call as pre-processing techniques.

Next step is segmentation of the feature extracted info such as any particular shape present then it is to be segmented from the background to analyze it Separately.The text segmentation is another work. extracting the text component alone from the image and converting it into sharpened text image and finally comparing the image components using OCR technique to get the text info completely.

Using the obtained info as feature vectors the input images are compared with the database image and finally using an Recurrent Convolutional Neural Network method we classify the image with database to find out the best matching score.A prototypical model is developed using MATLAB software and results are shown below.

PREPROCESSING

FEATURE EXTRACTION

RCNN MODEL

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FPGA BASED BRAIN MACHINE CONTROL

Objective
To design and implement a highly powerful Network on chip configuration through Brain-Machine interface Controls

Scope of the Study
This Study helpful for us to get more knowledge on FPGA based reconfigurable platforms
This study enable us to learn more about BCI applications in Digital Platforms

 Module 1 Design of Preprocessing
This module is used to extract the useful info from the Brain wave datasets collected from the Test Peoples.The info vectors are extracted through DWT filters. The extraction of Alpha Beta Teta Delta Gamma waves are extracted separately.The frequency of the waves also determined by an cymometer designed inside.

 Module 2: Design of Neural Network Configuration
This block is used to design a Neural network platform used to analyze the input vectors with the pre-trained vectors of the database. The input vectors are compared with the trained vectors. The neural configuration is iteratively repeated with number of epocs.

 Module 3: Design of FPGA Configurations
This module focus on configuring the network on chip system with the matched vectors to process the data for controlling the available machines. The Machines are normally connected with the FPGA board in parallel manner. The Configured bits are further used for the control of machineries and other control applications.

RESULTS

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FIR Filter Design with Configurable LUT using 4:2 Compressor and 7:2 Compressor

Design of FIR FILTER with Configurable LUT using 4:2 Compressor and 7:2 Compressor

FIR filters are applications which require many multiply-and-accumulate operations which consume more space. The good digital design consists of reduced multipliers, to reduce the FPGA Accumulation cost.To achieve this kind of circuits we need to create circuits with reduced multipliers or multiplierless Distribute arithmetic concepts.

The Distributed arithmetic FIR filter can be classified as two types: the first type stores the coefficients in look-up tables (LUTs).the second part adds the outputs of these LUTs and calculates the final FIR filter output using an adder tree. 

These kind of filters have a very compact structure and can be efficiently mapped to LUT-based FPGAs, . Furthermore, DA method is very much suitable for reconfigurable designs, where the coefficients are available to be changed during run time. 

Despite the fact that the storage space grows increasingly with filter taps, this issue can be solved by replacing a large LUT with several small LUTs Few research analyzed the optimal input bit width of these small LUTs on Xilinx FPGAs and proved that a better implementation can be achieved when the input bit width is close to the number of LUT inputs in FPGAs

The proposed method comprises of 4:2 Compression and 7:2 Compression using reconfigurable LUTS where the FIR Coefficients can be varied at run time.The design can be extended further too depends upon the required application.

Simulation Results

VIDEO OUTPUT

FIR FILTER using 4:2 and 7:2 ompressor

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MATLAB-VLSI Project :Detection of Finger vein

Detection of Finger vein structure through MIURA Max-curve algorithm with improved accuracy

Aim

To design a MATLAB simulated model for Detection of Finger vein structure through Max-curve algorithm with improved accuracy

Abstract

Finger vein recognition is a method of biometric authentication that uses pattern-recognition techniques based on images of human finger vein patterns beneath the skin's surface. Finger vein recognition is one of many forms of biometrics used to identify individuals and verify their identity.Finger Vein ID is a biometric authentication system that matches the vascular pattern in an individual's finger to previously obtained data. Hitachi developed and patented a finger vein ID system.In the existing design a matlab model is developed using tri branch function to detect and analyze the finger veins.In the proposed system a accurate detection of MIURA max-curve algorithm is developed for the detection of finger vein branches. This method vary the threshold comparisons dynamically henceforth enabling the system to detect various similar output of the finger veins for better comparison. This method will try to extract the centre lines of the veins. This is done by calculating local maximum curvatures in cross-sectional profiles of a vein image. The method is supposed to be robust against varying vein widths and non-uniform brightness. Repeated line tracking,This algorithm will start at several random points and will try to track a line. If a pixel has been visited by the line tracking algorithm multiple times it is likely to be a vein

Existing System

In the existing design a matlab model is developed using tri branch function to detect and analyze the finger veins. Finger vein recognition is a method of biometric authentication that uses pattern-recognition techniques based on images of human finger vein patterns beneath the skin's surface. Finger vein recognition is one of many forms of biometrics used to identify individuals and verify their identity.Finger Vein ID is a biometric authentication system that matches the vascular pattern in an individual's finger to previously obtained data. Hitachi developed and patented a finger vein ID system

Proposed System

In the proposed system a accurate detection of MIURA max-curve algorithm is developed for the detection of finger vein branches. This method vary the threshold comparisons dynamically henceforth enabling the system to detect various similar output of the finger veins for better comparison. This method will try to extract the centre lines of the veins. This is done by calculating local maximum curvatures in cross-sectional profiles of a vein image. The method is supposed to be robust against varying vein widths and non-uniform brightness. Repeated line tracking,This algorithm will start at several random points and will try to track a line. If a pixel has been visited by the line tracking algorithm multiple times it is likely to be a vein

Block Diagram

Software Required

MATLAB 2013A

KEIL u VISION (Embedded C)

Hardware Required

PIC Microcontroller

UART Interface Driver

LCD Display

Power supply

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