Digital Signal Processing

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Having over 6 projects in Digital Signal Processing

ECG Systems Analysis using DWT

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68

The investigation of the ECG has been extensively used for diagnosing many cardiac diseases. The ECG is a realistic record of the direction and magnitude of the electrical commotion that is generated by depolarization and re-polarization of the atria and ventricles. The majority of the clinically useful information in the ECG is originated in the intervals and amplitudes defined by its features (characteristic wave peaks and time durations). The improvement of precise and rapid methods for automatic ECG feature extraction is of chief importance, particularly for the examination of long recordings. In this project we will implement a system which will firstly extract the characteristics of ECG and on basis of that we will find the location and amplitude of details of ECG signal so that we can find the problem that cause to patient we will have ECG signal from static database of patients.
ECG Feature Extraction | ST Segment Detection | Disease Detection_Img
Audio Steganography Using LSB methodology

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68

Information hiding is a part of information Security. Steganography is a technique of information hiding that focuses on hiding the existence of secret messages. The aim of steganographic methods is to hide the existence of the communication and therefore to keep any third -party unaware of the presence of the Steganographic exchange In our project we will implement an algorithm to hide text messages in audio signal which can be used for message communication and will decoded at other user end with the decoder software designed with inverse of algorithm to decode messages from audio file send. Embedding secret messages into digital sound is known as audio Steganography. It is usually amore difficult process than embedding messages in other media. Audio Steganography methods can embed messages in WAV, AU, and even MP3 sound files.
LSB based audio steganography_Img
Video Steganography for Data Hiding

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68

Due to increased security threats experienced today, confidential information, such as medical records and banking data, are at risk. Thus, multiple layers of data protection beyond conventional encryption must be considered. The current study presents the design and implementation of a steganographic protocol and a suite of tools that can automatically analyze a flash video (FLV) format and effectively hide information within it for application in a digital records environment. This study proposes several methods of hiding information within an FLV and discusses the corresponding advantages and disadvantages of such methods. The proposed steganographic methods are analyzed qualitatively by using auditory-visual perception tests and quantitatively by using video tags evolution graphs and histograms and RGB averaging analysis. This study assumes a system where sensitive data is embedded inside FLVs and then transmitted to several recipients who hold varying access authorization levels.
Data hiding in video | Video steganography_Img
Digital Filter Designing using Genetic Algorithm

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68

In this project, a new technique is presented for the design and optimization of digital FIR filters and IIR filters with coefficients that are presented in canonic signed-digit (CSD) format. Since such implementation requires no multipliers, it reduces the hardware cost and lowers the power consumption. The proposed technique considers three goals, the optimum number of coefficients, the optimum word length, and the optimum set of coefficients which satisfies the desirable frequency response and ensures the minimum hardware cost by minimizing the number of nonzero digits in CSD representation of the coefficients using Genetic Algorithms (GA). Comparing with equip ripple method, the proposed technique results in about 30-40 percent reduction in hardware cost. Here, to improve SNR of signal in communication system FIR or IIR filter designing is done, it is part of digital filter designing for signal enhancement several recently proposed optimization-based algorithms for the design of FIR and IIR filters and filter banks are reviewed for linear-phase FIR filters, IIR filters.
Digital filter designing and optimization_Img
ADPCM based Coding And Compression

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68

The specification of ADPCM opens the door to a host of applications in telecommunication networks. These applications can be divided into three categories: telephone company use, end customer applications, and new service offerings. An adaptive quantizer and an adaptive predictor. The relation between the encoder and the decoder is also depicted. The decoder is simply a subset of the encoder and transmits r(n) as its output instead of c(n). The adaptive predictor, which is composed of two poles and six zeros, computes an input signal estimate ?(n) which is subtracted from input signal s(n) resulting in a difference signal d(n). The adaptive quantizer codes d(n) into codeword c(n) which is sent over the transmission facility. At the receiving end, an ADPCM decoder uses c(n) to attempt to reconstruct the original signal s(n). Actually, only r(n) can be reconstructed which is related to the original input signal s(n)
Adpcm based Coding And Compression | Audio Coding_Img
LMS based Audio Signal Enhancement

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68

Signal processing is an operation designed for extracting, enhancing, storing, and transmitting useful information. Hence signal processing tends to be application dependent. In contrast to the conventional filter design techniques, adaptive filters do not have constant filter coefficients and no priori information is known. Such a filter with adjustable parameters is called an adaptive filter. Adaptive filter adjust their coefficients to minimize an error signal and can be realized as finite impulse response (FIR), infinite impulse response (IIR), lattice and transform domain filter. The most common form of adaptive filter is the transversal filter using least mean square (LMS) algorithm In this project LMS algorithm is implemented in which step followed for implementation are as • Firstly have audio signal from user • Signal will be mixed with noise • Noisy signal is given to adaptive filter (LMS) • Filtration is performed by adaptive filter • Final de-noised signal is obtained • Analysis is done by comparing de-noised signal with original signal
Adaptive Filters | Noise Removal | Filteration of audio signal_Img