In digital signal processing (DSP), Nyquist-rate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon's sampling theorem. It is interesting to notice that most signals in reality are sparse; especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, { } 1 ψ N i i = , and a set of coefficients k x , M. M. Abo-Zahhad et al.

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The rapid development of digital wireless systems has led to a need for high-resolution and high-speed bandpass analog-to-digital converters. Continuous-time bandpass 61 modulators are very suitable for such high frequency applications. In this paper, analysis and simulation of a continuous-time bandpass 61 modulator for use in modern cellular/PCS receivers is given. The design employs undersampling relative to a radio receiver's radio frequency (RF) or intermediate frequency (IF) center frequency, while oversampling the signal bandwidth. This technique enables clocking at a frequency much lower than the RF/IF frequency, allowing use of standard CMOS technology and reducing the complexity and power consumption of subsequent digital signal processing stages. The analysis shows that it is possible to achieve a loop transfer function that matches a standard discrete-time bandpass 61 modulator while operating at sample rates significantly lower than conventional bandpass architectures.

Diagnoses of heart diseases can be done effectively on long term recordings of ECG signals that preserve the signals' morphologies. In these cases, the volume of the ECG data produced by the monitoring systems grows significantly. To make the mobile healthcare possible, the need for efficient ECG signal compression algorithms to store and/or transmit the signal efficiently has been rising exponentially. Currently, ECG signal is acquired at Nyquist rate or higher, thus introducing redundancies between adjacent heartbeats due to its quasi-periodic structure. Existing compression methods remove these redundancies by achieving compression and facilitate transmission of the patient's imperative information. Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS) has been proposed. CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the "sparse" domains through well-developed optimization procedures. In this paper, a single-lead ECG compression method has been proposed based on improving the signal sparisty through the extraction of the signal significant features. The proposed method starts with a preprocessing stage that detects the peaks and periods of the Q, R and S waves of each beat. Then, the QRS-complex for each signal beat is estimated. The estimated QRS-complexes are subtracted from the original ECG signal and the resulting error signal is compressed using the CS technique. Throughout this process, DWT sparsifying dictionaries have been adopted. The performance of the proposed algorithm, in terms of the reconstructed signal quality and compression ratio, is evaluated by adopting DWT spatial domain basis applied to ECG records extracted from the MIT-BIH Arrhythmia Database. The results indicate that average compression ratio of 11:1 with PRD1 = 1.2% are obtained. Moreover, the quality of the retrieved signal is guaranteed and the compression ratio achieved is an improvement over those obtained by previously reported algorithms. Simulation results suggest that CS should be considered as an acceptable methodology for ECG compression. M. M. Abo-Zahhad et al.

Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding.Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this paper, a comparison of two different techniques is given. The first technique used Least Significant Bit (LSB) with no encryption and no compression. In the second technique, the secret message is encrypted first then LSB technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. The LSB algorithm is implemented in spatial domain in which the payload bits are inserted into the least significant bits of cover image to develop the stego-image while DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR.

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