A new generalised α scale spaces quadrature filters

The α   scale spaces is a recent theory that open new possibilities of phase-based image processing. It is a parameterised class (α∈]0,1])$(\alpha \in ]0,1\left]\right)$ of linear scale space representations, which allows a continuous connection beyond the well-known Gaussian scale space (α=1$\alpha =1$). In this paper, we make use of this unified representation to derive new families of band pass quadrature filters, built from derivatives and difference of the α scale space generating kernels. This construction leads to generalised α kernel filters including the commonly known families derived from the Gaussian and the Poisson kernels. The properties of each family are first studied and then experiments on one- and two-dimensional signals are shown to exemplify how the suggested filters can be used for edge detection. This work is complemented by an experimental evaluation, which demonstrates that the new proposed filters are a good alternative to the commonly used Log-Gabor filter.     

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.     

New approaches for determining a common set of weights for a voting system

Data Envelopment Analysis (DEA) is one of the methods that have been proposed to determine the weights in a ranked voting system. DEA solves one model corresponding to each candidate and evaluates candidates with their own weights. However, sometimes solving only one model, and evaluating the candidates based on a common set of weights, is preferred. In this paper, some drawbacks of existing models are explained and new approaches for determining a common set of weights are proposed. Numerical examples are utilized to illustrate the content of the paper.     

Hot carrier reliability of RF N- LDMOS for S Band radar application

This paper presents an innovative reliability bench specifically dedicated to high RF power device lifetime tests under pulse conditions for radar application. A base-station dedicated LDMOS transistor has been chosen for RF lifetests and a complete device electric characterization has been performed. A whole review of its critical electrical parameters after accelerated ageing tests is proposed and discussed. This study tend to explain the physical degradation mechanisms occurred during RF life-tests by means of 2D ATLAS-SILVACO simulations. Finally, the paper demonstrates that N-LDMOS degradation is linked to hot carriers generated interface states (traps) and trapped electrons, which results in a build up of negative charge at Si/SiO2 interface. More interface states are created at low temperature due to a located maximum impact ionization rate at the gate edge.     

Rogue components: their effect and control using logical analysis of data

There is a small subset of any repairable component population that can develop a failure mode outside the scope of the standard repair and overhaul procedures, which makes them “rogue”. When this happens, a Darwinian-like “natural selection” phenomenon ensures that they will be placed in the most disadvantageous position in the asset management program, negatively affecting multiple aspects of the operational and maintenance organizations. Rogue components have long plagued the airline industry and created havoc in their asset management programs. In this paper, we describe how these rogues develop, outline the natural selection process that leads to their hampering the asset management program, and examine some of the negative impacts that ensue. Then we propose a Condition based maintenance approach to control the development of these components. We explore the use of a supervised learning data mining technique called Logical analysis of data (LAD) in CBM for the purpose of detecting rogues within a population of repairable components. We apply the resulting LAD based decision model on an inventory of turbo compressors belonging to an airline fleet. Finally, we evaluate the applicability of LAD to the rogue component detection problem and review its efficiency as a decision model for this type of problem.     

Product selection in Internet business: a fuzzy approach

In this paper, we propose a methodology which helps customers buy products through the Internet. This procedure takes into account the customer's level of desire in the product attributes, which are normally fuzzy, or in linguistically defined terms. The concept of fuzzy number will be used to measure the degree of similarities of the available products to that of the customer's requirements. The degrees of similarities so obtained over all the attributes give rise to the fuzzy probabilities and hence the fuzzy expected values of availing a product on the Internet as per the customer's requirement. Attribute‐wise the fuzzy expected values are compared with those of the available products on the Internet and the product that is closest to the customer's preference is selected as the best product. The multi‐attribute weighted average method is used here to evaluate and hence to select the best product.     

Chemical and structural transformations of silicon submitted to H2 or H2/CH4 microwave plasmas

Silicon substrates are often used to synthesize polycrystalline diamond films by microwave plasma assisted chemical vapour deposition technique (MPCVD). In the case of highly oriented diamond films, several steps are employed to carefully prepare the silicon surface (pre-treatment steps), to nucleate diamond crystals (nucleation step) and to thick the film (growth step). In this study, we characterize {100} silicon substrates and diamond released from its silicon substrate by electronic microscopies (TEM and SEM), by Atomic Force Microscopy (AFM) and by X-ray photoelectron spectroscopy (XPS), to follow the substrate transformations after each step, particularly the formation and the evolution of the silicon carbide and to characterise the diamond films grown on the carburised silicon. We show that according to the experimental conditions and the level of surface/gas contamination by carbon and silicon species, isolated islands or continuous β-SiC compound are formed over the silicon surface and can generate defects such as voids or strip structures that influence the subsequent diamond nucleation and growth.     

Local maximum likelihood segmentation of echocardiographic images with Rayleigh distribution

In order to interpret ultrasound images, it is important to understand their formation and the properties that affect them, especially speckle noise. This image texture, or speckle, is a correlated and multiplicative noise that inherently occurs in all types of coherent imaging systems. Indeed, its statistics depend on the density and on the type of scatterers in the tissues. This paper presents a new method for echocardiographic images segmentation in a variational level set framework. A partial differential equation-based flow is designed locally in order to achieve a maximum likelihood segmentation of the region of interest. A Rayleigh probability distribution is considered to model the local B-mode ultrasound images intensities. In order to confront more the speckle noise and local changes of intensity, the proposed local region term is combined with a local phase-based geodesic active contours term. Comparison results on natural and simulated images show that the proposed model is robust to attenuations and captures well the low-contrast boundaries.