Monday 27 May 2019

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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Wednesday 1 May 2019

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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