In this paper, we present a Compressive Sampling (CS)-based feature extraction method for audio signals. In the proposed approach, the audio signal is firstly segmented by hamming windows and the Discrete Fourier Transform (DFT) of the samples is calculated within each frame. Then, the normalized values of the DFT coefficients of each frame are accumulated. At the next step, the second DFT is applied on the vector formed from the accumulated sum in consecutive frames. Finally, considering the sparseness of the resulted vector, our proposed CS-2FFT feature vector is achieved by a random Sampling. In this research, the performance of CS-2FFT feature vector has been examined in the applications of audio classification and audio source localization. The simulation show that the proposed feature vector results in a classifier which is more accurate and less computationally complex compared to the classical classifiers. Also, it is shown that the employing CS-2FFT feature vector, the localization error will be less than 2%.

Background and Aim: ZOE has been used in different fields of dentistry for many years. A locally produced component (Zoliran) has been recently introduced to the marketwith similar characteristic to the original Zonalin. Because of a lower cost involved to use Zoliran cement and its availability, confirm reliability of its physical properties. This investigation was designed to assess the Compressive strength of Zoliran cement in comparison to Zonalin cement as the standard material. Materials and Methods: Five samples with dimension of 4mmx6mm of each cement were provided and stored in distilled water in 370C±10C for a period of 24 hours. The lowest load of force was registered as the reference to which the sample could be broken by(according to the criteria No: 30 of ANSIIADA). The value of Compressive strength was then calculated using the following formula (K =4F/πD2 ). Results: The mean Compressive strength of five samples was measuredas: 14.33 Mpa for Zoliran cement and 31.83 Mpa for Zonalin cement. The mean Compressive strength of Zonalincement was significantly higher than the mean suggested in ANSl/ADA Specification No.30. The mean Compressivestrength of Zoliran cement was also lower than the mean value registered in ANSI/ADA SpecificationNo.30. Conclusion: Compressive strength of Zoliran cement was significantlylower than that of Zonalin cement. Further tests are required to compare the other physical properties of this material before it can be clinically recommended.

In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and Compressive Sensing (CS) technique in Wireless Sensor Networks (WSNs) has been considered. It has been shown that when the dictionary obtained with the learning technique based on principal component analysis is used for CS-based data gathering of environmental signals, using a deterministic node selection method for data collection in WSNs can outperform random node selection ones. In this article, a deterministic and non-random Sampling design for use in the CS-based data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by Sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals in MATLAB software environment show that when the number of Sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference Sampling method.

Compressive Sampling (CS) is a new technique for simultaneous Sampling and compression of signals in which the Sampling rate can be very small under certain conditions. Due to the limited number of samples, image reconstruction based on CS samples is a challenging task. Most of the existing CS image reconstruction methods have a high computational complexity as they are applied on the entire image. To reduce this complexity, block-based CS (BCS) image reconstruction algorithms have been developed in which the image Sampling and reconstruction processes are applied on a block by block basis. In almost all the existing BCS methods, a fixed transform is used to achieve a sparse representation of the image. however such fixed transforms usually do not achieve very sparse representations, thereby degrading the reconstruction quality. To remedy this problem, we propose an adaptive block-based transform, which exploits the correlation and similarity of neighboring blocks to achieve sparser transform coefficients. We also propose an adaptive soft-thresholding operator to process the transform coefficients to reduce any potential noise and perturbations that may be produced during the reconstruction process, and also impose sparsity. Experimental results indicate that the proposed method outperforms several prominent existing methods using four different popular image quality assessment metrics.

Introduction: In qualitative research the Sampling process is usually determined by the methodology employed. However, this is not always evident in published qualitative research papers as many qualitative studies appear not to have a clearly defined methodological approach. Indeed, pragmatic researches focus on the need to adopt a flexible rather than rigid approach to application of qualitative methodologies. Therefore, this study was performed to review previous research to clarify qualitative Sampling. Valid articles and books were used in this review study.Method: The keywords qualitative research and Sampling were searched in Cumulative Index to Nursing and Allied Health Literature (CINAHL), ProQuest, PsycINFO, ScienceDirect, Scopus, and Medline databases.Results: Although Sampling methods in qualitative research are known as purposive Sampling, there is an extensive spectrum of Sampling methods such as quota, snowball, theoretical, critical cases, homogeneity, sequential, criterion, and combination Sampling.Conclusion: The purpose of Sampling in qualitative research is not to establish a random or representative sample draw from a population, but rather to identify specific groups of people, who either possess characteristics or live experiences relevant to the social phenomenon being studied. Informants are identified because they will enable the exploration of a particular aspect of behavior relevant to the research. The benefits of the qualitative approach to health care research are becoming increasingly recognized by both academics and clinicians. However, misunderstandings about the philosophical basis and the methodological approach remain. The process of Sampling is one of the principal areas of confusion. Sampling is a very complex issue in qualitative research. This is due to the many variations of qualitative Sampling described in the literature, and much confusion and overlapping of types of Sampling.

Background: A detailed study of hidden and hard to reach populations in order to identify their characteristics is essential because they endanger the health of the society by their dangerous behaviors. However, Sampling in these populations through conventional Sampling methods is impossible due to reasons such as being hidden and lacking a precise frame.Methods: In this article, applicable concepts and key points in the implementation of respondent driven Sampling were defined by focusing on designs that aimed to study dangerous and hidden behaviors such as dangerous sexual behavior, injecting drug abuse, and HIV infection.Findings: The merits of the respondent driven Sampling are the production of unbiased and effective estimations of necessary parameters in populations at risk of rare and hidden diseases in comparison to other chain referral and conventional Sampling methods, and the innovations of this Sampling method during its implementation.Conclusion: The application of the respondent driven Sampling method in order to achieve unbiased and precise estimates of the prevalence rate of rare and hidden diseases is recommended in health programs of the society.