Improved image recovery from compressed data contaminated with impulsive noise

Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the â„“ 2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.

The reliability of muscle biopsies taken from vastus lateralis

The purpose of this study was to examine whether a single biopsy sample of vastus lateralis could provide an accurate estimate of capillary density (CD) which is indicative of the entire muscle, or whether capillary density is distributed unevenly and varies with muscle depth. Whole muscle cross sections of vastus lateralis were excised post mortem (n=11) for analysis of capillary density at three muscle depths, (superficial, mid and deep regions). Muscle thickness varied widely (17–79mm) across subjects. The distribution of CD throughout the depth of the muscle was homogeneous in 8 subjects, but in 3 subjects it was heterogeneous (p<0.05). In 3 of these subjects there was a significant (p<0.05) effect of sample depth on CD. These data indicate that tissue from a single biopsy will not adequately represent the CD of the entire vastus lateralis in some individuals. Single biopsies from unspecified muscle depth, have routinely been used to estimate CD and fibre type in vastus lateralis. The present study indicates that a more reliable method of analysis would be to use the tissue from two needle biopsies taken at the superficial and deep portions of the muscle from a group of at least 10 subjects. Sampling theory analysis supported this conclusion.

What have sampling and data collection got to do with good qualitative research?

Objective: To highlight the importance of sampling and data collection  processes in qualitative interview studies, and to discuss the contribution of  these processes to determining the strength of the evidence generated and  thereby to decisions for public health practice and policy.

Approach: This discussion is informed by a hierarchy-of-evidence-for-practice  model. The paper provides succinct guidelines for key sampling and data  collection considerations in qualitative research involving interview studies. The  importance of allowing time for immersion in a given community to become  familiar with the context and population is discussed, as well as the practical  constraints that sometimes operate against this stage. The role of theory in  guiding sample selection is discussed both in terms of identifying likely sources  of rich data and in understanding the issues emerging from the data. It is noted  that sampling further assists in confirming the developing evidence and also  illuminates data that does not seem to fit. The importance of reporting sampling  and data collection processes is highlighted clearly to enable others to assess  both the strength of the evidence and the broader applications of the findings.

Conclusion: Sampling and data collection processes are critical to determining  the quality of a study and the generalisability of the findings. We argue that  these processes should operate within the parameters of the research goal, be  guided by emerging theoretical considerations, cover a range of relevant   participant perspectives, and be clearly outlined in research reports with an  explanation of any research limitations.

Reptile responses to fire and the risk of post-disturbance sampling bias

Altered fire regimes are a driver of biodiversity decline. To plan effective management, we need to know how species are influenced by fire and to develop theory describing fire responses. Animal responses to fire are usually measured using methods that rely on animal activity, but animal activity may vary with time since fire, potentially biasing results. Using a novel approach for detecting bias in the pit-fall trap method, we found that leaf-litter dependent reptiles were more active up to 6 weeks after fire, giving a misleading impression of abundance. This effect was not discovered when modelling detectability with zero-inflated binomial models. Two species without detection bias showed early-successional responses to time since fire, consistent with a habitat-accommodation succession model. However, a habitat specialist did not have the predicted low abundance after fire due to increased post-fire movement and non-linear recovery of a key habitat component. Interactions between fire and other processes therefore must be better understood to predict reptile responses to changing fire-regimes. We conclude that there is substantial bias when trapping reptiles after fire, with species that are otherwise hard to detect appearing to be abundant. Studies that use a survey method based on animal activity such as bird calls or animal movements, likely face a similar risk of bias when comparing recently-disturbed with control sites.