Khushboo Nibheriya | Rajasthan Technical University (original) (raw)
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Papers by Khushboo Nibheriya
As we know that for data compression generally Shannon – Nyquist theorem taken in to consideratio... more As we know that for data compression generally Shannon – Nyquist theorem taken in to consideration. But a severe problem which is associated with the traditional theory is the storage problem. According to this theorem the sampling rate must be twice the largest frequency component of the signal which we want to reconstruct. Due to this the data which is required to transmit a signal or to store it is too large. So to overcome this problem a new method is proposed, which is known as Compressive sensing. The sampling rate which is required reconstruct the signal is comparatively low in the compressive sensing. The various aspects about the compressive sensing and literature review with some important properties is given below. KeywordsCompressive Sensing(CS),Restricted Isometry Property (RIP) __________________________________________________*****_________________________________________________
2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018
Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signa... more Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signals using very minute observations. These minute observations are also called the number of measurement. The basic benefits of CS are that the number of measurements which are required for proper reconstruction of the compressed signal is very less than the conventional method. If we go through the literature then, we get that for proper reconstruction of signal a theory is given by Shannon. This theory states that the sampling frequency must be higher than twice the highest frequency component in that signal. So the limitation of the conventional method is that it requires so much storage to store and a large bandwidth to transmit the data. Both the things are so much scarce now days, as we know that if we have to required high resolution of signal then the storage which required to store this is also so much high. As there are various parameters in the theory of CS. But the two parameters are so much important than the others. These two parameters are basis and sensing matrices. Various types of other properties like RIP property and IID property also shows a big role in CS theory. By changing the sensing and measurement matrix the SNR value can also be enhanced. In this paper Gaussian matrix is taken as a sensing matrix & DST, DCT considered as the Basis matrices. The combination of basis and sensing matrix enhances the quality & level of compression. As the quality of compression enhanced it enhances the Signal to Noise ratio too. We cannot check the quality by using only one signal so comparison is made using Single Tone, Multi Tone and Vocal Song. 11minimization technique is used for reconstruction of compressed signal
The Oxford Journal of Intelligent Decision and Data Science
Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few ... more Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few observations or precisely very little number of measurements rather than the conventional methods. As per the theory given by Shannon for proper recovery of a signal the sampling frequency must be greater than or equal to the largest frequency component in that signal. So the storage requirement to store the data according to the Nyquist theorem is too high. So compressive sensing is used to reduce this storage requirement. There are two important parameters one is sensing matrix and another is measurement matrix by changing these two parameters we can change the quality of the recovered signal. There are various reconstruction algorithms which are used for proper reconstruction of signal. The work which is done in this paper comprises of various music signals on which the compressive sensing applied. As per the result the single tone music signal have less value of MSE than the multi tone and vocal song signal. The SNR value is quiet good for single tone than the multi tone & vocal song. This is due to the single frequency component in the single tone music signal.
As we know that for data compression generally Shannon – Nyquist theorem taken in to consideratio... more As we know that for data compression generally Shannon – Nyquist theorem taken in to consideration. But a severe problem which is associated with the traditional theory is the storage problem. According to this theorem the sampling rate must be twice the largest frequency component of the signal which we want to reconstruct. Due to this the data which is required to transmit a signal or to store it is too large. So to overcome this problem a new method is proposed, which is known as Compressive sensing. The sampling rate which is required reconstruct the signal is comparatively low in the compressive sensing. The various aspects about the compressive sensing and literature review with some important properties is given below. KeywordsCompressive Sensing(CS),Restricted Isometry Property (RIP) __________________________________________________*****_________________________________________________
2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018
Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signa... more Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signals using very minute observations. These minute observations are also called the number of measurement. The basic benefits of CS are that the number of measurements which are required for proper reconstruction of the compressed signal is very less than the conventional method. If we go through the literature then, we get that for proper reconstruction of signal a theory is given by Shannon. This theory states that the sampling frequency must be higher than twice the highest frequency component in that signal. So the limitation of the conventional method is that it requires so much storage to store and a large bandwidth to transmit the data. Both the things are so much scarce now days, as we know that if we have to required high resolution of signal then the storage which required to store this is also so much high. As there are various parameters in the theory of CS. But the two parameters are so much important than the others. These two parameters are basis and sensing matrices. Various types of other properties like RIP property and IID property also shows a big role in CS theory. By changing the sensing and measurement matrix the SNR value can also be enhanced. In this paper Gaussian matrix is taken as a sensing matrix & DST, DCT considered as the Basis matrices. The combination of basis and sensing matrix enhances the quality & level of compression. As the quality of compression enhanced it enhances the Signal to Noise ratio too. We cannot check the quality by using only one signal so comparison is made using Single Tone, Multi Tone and Vocal Song. 11minimization technique is used for reconstruction of compressed signal
The Oxford Journal of Intelligent Decision and Data Science
Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few ... more Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few observations or precisely very little number of measurements rather than the conventional methods. As per the theory given by Shannon for proper recovery of a signal the sampling frequency must be greater than or equal to the largest frequency component in that signal. So the storage requirement to store the data according to the Nyquist theorem is too high. So compressive sensing is used to reduce this storage requirement. There are two important parameters one is sensing matrix and another is measurement matrix by changing these two parameters we can change the quality of the recovered signal. There are various reconstruction algorithms which are used for proper reconstruction of signal. The work which is done in this paper comprises of various music signals on which the compressive sensing applied. As per the result the single tone music signal have less value of MSE than the multi tone and vocal song signal. The SNR value is quiet good for single tone than the multi tone & vocal song. This is due to the single frequency component in the single tone music signal.