Biblio

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

Compressive Sensing (CS) is a promising investigation field in the communication signal processing domain. It offers an advantage of compression while sampling; hence, data redundancy is reduced and improves sampled data transmission. Due to the acquisition of compressed samples, Analog to Digital Conversions (ADCs) performance also improved at ultra-high frequency communication applications. Several reconstruction algorithms existed to reconstruct the original signal with these sub-Nyquist samples. Orthogonal Matching Pursuit (OMP) falls under the category of greedy algorithms considered in this work. We implemented a compressively sensed sampling procedure using a Random Demodulator Analog-to-Information Converter (RD-AIC). And for CS reconstruction, we have considered OMP and novel Incremental Gaussian Elimination (IGE) OMP algorithms to reconstruct the original signal. Performance comparison between OMP and IGE OMP presented.

Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently.

In recent years, the big-data approach has become important within various business operations and sales judgment tactics. Contrarily, numerous privacy problems limit the progress of their analysis technologies. To mitigate such problems, this paper proposes several privacy-preserving methods, i.e., anonymization, extreme value record elimination, fully encrypted analysis, and so on. However, privacy-cracking fears still remain that prevent the open use of big-data by other, external organizations. We propose a big-data reconstruction method that does not intrinsically use privacy data. The method uses only the statistical features of big-data, i.e., its attribute histograms and their correlation coefficients. To verify whether valuable information can be extracted using this method, we evaluate the data by using Self Organizing Map (SOM) as one of the big-data analysis tools. The results show that the same pieces of information are extracted from our data and the big-data.

Dual Energy CT (DECT) has recently gained significant research interest owing to its ability to discriminate materials, and hence is widely applied in the field of nuclear safety and security inspection. With the current technological developments, DECT can be typically realized by using two sets of detectors, one for detecting lower energy X-rays and another for detecting higher energy X-rays. This makes the imaging system expensive, limiting its practical implementation. In 2009, our group performed a preliminary study on a new low-cost system design, using only a complete data set for lower energy level and a sparse data set for the higher energy level. This could significantly reduce the cost of the system, as it contained much smaller number of detector elements. Reconstruction method is the key point of this system. In the present study, we further validated this system and proposed a robust method, involving three main steps: (1) estimation of the missing data iteratively with TV constraints; (2) use the reconstruction from the complete lower energy CT data set to form an initial estimation of the projection data for higher energy level; (3) use ordered views to accelerate the computation. Numerical simulations with different number of detector elements have also been examined. The results obtained in this study demonstrate that 1 + 14% CT data is sufficient enough to provide a rather good reconstruction of both the effective atomic number and electron density distributions of the scanned object, instead of 2 sets CT data.