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Saturday, 17 August 2019
How to prepare Literature surveys for Projects
Thursday, 8 August 2019
Effective Thesis writing and IEEE paper writing
Tuesday, 16 July 2019
Gps signal analysis and Target detection
GPS Signal Analysis & target Detection
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Saturday, 15 June 2019
Get to know How multimodal Feature Extraction technique works
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Monday, 27 May 2019
Design of Respiratory Rate measurement SYSTEM using ECG through Recurrent NOTCH FILTER
TOOLS USED
MATLAB 2018
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Wednesday, 1 May 2019
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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Sunday, 21 April 2019
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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Wednesday, 17 October 2018
FIR Filter Design with Configurable LUT using 4:2 Compressor and 7:2 Compressor
Friday, 14 September 2018
MATLAB-VLSI Project :Detection of Finger vein
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Thursday, 28 June 2018
Tumor detection simple technique using Vlsi design
Vlsi projects in chennai
Vlsi based medical image processing for tumor detection
image processing is one of the trending technique which enable us to understand and design fault tolerant, critic analysis system for medical imaging.
detecting the tumor cells, analysing disease factors, morphological analysis,.
even more can be done at high speed FPGA chip through image processing techniques and digital architecture modelling.
steps involved
image preprocessing
input Image is being read from the source like dataset collected from CT scan, mri scan etc those images are resized, converted from rgbtogray,and finally converted to binary or black and white.
the image is background subtracted and blob of cancer cell is detected
high speed process is written in Vhdl behavioral model.
the detected blob is enhanced and volume is calculated
the data is transfered through USB to Ttl converter and transfered to Display system.