Theoretical quantification of shape distortion in fuzzy hough transform

We present a generalization of classical Hough transform in fuzzy set theoretic framework (called fuzzy Hough transform or FHT) in order to handle the impreciseness/ill-definedness in shape description. In addition to identifying the shapes, the methodology can quantify the amount of distortion present in each shape by suitably characterizing the parametric space. We extended FHT to take care of gray level images (gray FHT) in order to handle the gray level variation along with shape distortion. The gray FHT gives rise to a scheme for image segmentation based on the a priori knowledge about the shapes

Unsupervised feature extraction using neuro-fuzzy approach

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'<n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established

Fuzzy feature evaluation index and connectionist realization

A new feature evaluation index based on fuzzy set theory and a connectionist model for its evaluation are provided. A concept of flexible membership function incorporating weighting factors, is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. The overall importance of the features is evaluated both individually and in a group considering their dependence as well as independence. Effectiveness of the algorithms along with comparison is demonstrated on speech and Iris data

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Connectionist object recognition: methods and methodologies

An overview of different connectionist approaches for object recognition is presented. These approaches are first of all classified on the basis of functional characteristics and structural characteristics of the networks. Atthe nextlevel, these are partitioned on the basis of their underlying assumptions and the types of recognition problem that they handle. Their principles and key features are highlighted. The issue of mixed category perception (in thecontextof multipleobject recognition) is addressed. A comparison of the characteristics of various application-specific systems is provided in a tabular form. Some of the basic issues to be considered while building a connectionist system for object recognition are finally listed

Dynamics of binary Bose–Einstein condensate via Ehrenfest like equations: appearance of almost shape invariant states

We derive Ehrenfest like equations for the coupled Gross Pitaevskii equations (CGPE) which describe the dynamics of the binary Bose–Einstein condensate (BBEC) both in the free particle regime and in the regime where condensate is well trapped. Instead of traditional variational technique, we propose a new Ehrenfest based approach to explore so far unrevealed dynamics for CGPE and illustrate the possibility of almost shape invariant states in both the regimes. In the absence of a trapping potential, when all the interactions present in the system are attractive, it is possible for an initially mixed Gaussian state to propagate with almost no change in width if the proper initial condition is satisfied. Even for repulsive intra-atomic and attractive inter-atomic interaction (gαβ) one can tune |gαβ| such that the width of the propagating wave packet remains bounded within almost about 10%. We also discuss the dynamics of the initially phase separated condensate and have shown the breakdown of the Gaussian nature of the wave packets due to collisions. However, when the BEC is trapped in simple harmonic oscillator (SHO) potential, for gαβ > 0, it is possible for an initially overlapping state to retain its initial shape if gαβ is less than a critical value (gαβc). If gαβ exceeds gαβc, an overlapping state can become phase separated while keeping its shape unchanged