Formal Verification Successes at Motorola

Formal tools are either too labor intensive or are completely impractical for industrial-size problems. This paper describes two formal verification tools used within Motorola, Versys2 and CBV, that challenge this assertion. The two tools are being used in current design verification flows and have shown that it is possible to seamlessly integrate formal tools into existing design flows.     

Power supplies for plasma column control in the COMPASS tokamak

The main magnetic fields in COMPASS – i.e. The Toroidal, Magnetising, Equilibrium, and Shaping Fields – are created by a set of four corresponding thyristor power supplies controlled in a 0.5 ms loop.The plasma position has to be controlled both radially and vertically by two additional magnetic fields provided by two fast amplifiers (FAs) based on MOSFET technology, each supplying ±100 V and up to ±5 kA.Currently, an ongoing project aims at ELM triggering by fast changes of the vertical position of the plasma column, also referred to as vertical kicks. For this purpose, a new Vertical Kicks Power Supply (VKPS) capable of quick change of vertical plasma position is being constructed. This power supply should operate at up to 1.2 kV with switching frequency up to 5 kHz. It is designed as a H-bridge but based on IGBT transistors which can be operated at higher voltages than MOSFETs.In this paper, we focus on the FAs and VKPS engineering design and required output parameters. Both the power supplies are based on modern components with highest available ratings in their categories. Unique design of the power supplies takes advantage of the short duration of the COMPASS discharge by overloading the transistors above their maximal steady-state rating.The FA is regularly operating, so that in addition to describing its design, we also describe the achieved performance parameters. Finally, the common controller unit, communication, and error handling is described.     

Manipulation of body odour alters men's self-confidence and judgements of their visual attractiveness by women

Human body odour is important in modulating self-perception and interactions between individuals. Artificial fragrances have been used for thousands of years to manipulate personal odour, but the nature and extent of influences on person perception are relatively unexplored. Here we test the effects of a double-blind manipulation of personal odour on self-confidence and behaviour. We gave to male participants either an aerosol spray containing a formulation of fragrance and antimicrobial agents or an otherwise identical spray that lacked these active ingredients. Over several days, we found effects between treatment groups on psychometric self-confidence and self-perceived attractiveness. Furthermore, although there was no difference between groups in mean attractiveness ratings of men's photographs by a female panel, the same women judged men using the active spray as more attractive in video-clips, suggesting a behavioural difference between the groups. Attractiveness of an individual male's non-verbal behaviour, independent of structural facial features, was predicted by the men's self-reported proclivity towards the provided deodorant. Our results demonstrate the pervasive influence of personal odour on self-perception, and how this can extend to impressions on others even when these impressions are formed in the absence of odour cues.     

Multidimensional quasi-eigenfunction approximations andmulticomponent AM-FM models

We develop multicomponent AM-FM models for multidimensional signals. The analysis is cast in a general n-dimensional framework where the component modulating functions are assumed to lie in certain Sobolev spaces. For both continuous and discrete linear shift invariant (LSI) systems with AM-FM inputs, powerful new approximations are introduced that provide closed form expressions for the responses in terms of the input modulations. The approximation errors are bounded by generalized energy variances quantifying the localization of the filter impulse response and by Sobolev norms quantifying the smoothness of the modulations. The approximations are then used to develop novel spatially localized demodulation algorithms that estimate the AM and FM functions for multiple signal components simultaneously from the channel responses of a multiband linear filterbank used to isolate components. Two discrete computational paradigms are presented. Dominant component analysis estimates the locally dominant modulations in a signal, which are useful in a variety of machine vision applications, while channelized components analysis delivers a true multidimensional multicomponent signal representation. We demonstrate the techniques on several images of general interest in practical applications, and obtain reconstructions that establish the validity of characterizing images of this type as sums of locally narrowband modulated components.     

Oriented texture completion by AM-FM reaction-diffusion

We provide an automated method to repair broken, occluded oriented image textures. Our approach is based on partial differential equations (PDEs) and AM-FM image modeling. Reconstruction of the texture occurs via simultaneous PDE-generated diffusion and reaction. In the diffusion process, the image is adaptively smoothed, preserving important boundaries and features. The reaction process produces the reconstructed textural information in the occluded image regions. Gabor (1946) filters are designed and used in the reaction process using an AM-FM dominant component analysis. An AM-FM model of the texture image is constructed, making it possible to localize the reaction filters spatio-spectrally. In contrast to previous disocclusion techniques that depend on interpolation, on continuity of the connected components within the image level sets, or on texture estimation, the reaction-diffusion process proposed here yields a seamless transition between the recreated region and the unoccluded image regions. Using AM-FM dominant component analysis, we avoid the ad hoc parameter selection typified with other reaction-diffusion approaches. As a useful example, we focus on the repair of broken, occluded fingerprints. We also treat several exemplary natural textures to demonstrate the technique's generality     

Neighborhood-level learning techniques for nonparametric scene models

A new stochastic learning algorithm for use in nonparametric pixel-level background models is presented in this paper. For the first time, we propose the use of kernel density estimation techniques in the model update step to identify outliers within the pixel-level sample collections and replace them with recently observed background pixel values. A neighborhood diffusion process that improves on recently reported scene model learning techniques is presented, wherein information sharing between similarly structured adjacent background models is encouraged to promote spatial consistency within localized image regions. We demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art visual background extraction system using the well-known percentage correct classification statistic and a new figure of merit, probability correct classification, presented here for the first time.     

Effect of the violation of assumptions upon significance levels of the Pearson r.

Conducted a study to determine empirically the effects of the violation of assumptions of normality and of measurement scales on the Pearson product-moment correlation coefficient. The effects of such violations were studied separately and in combination for samples varying in size from 5 to 60. Monte Carlo procedures were used to generate populations of scores for 4 basic distributions: normal, positively skewed, negatively skewed, and leptokurtic. Samples of varying sizes were then randomly selected from specific populations. Results were based on distributions of r , which were calculated on 5,000 sets of samples of n = 5 or n = 15 and 3,000 sets of samples of n = 30 or n = 60. Results indicate that the Pearson r is insensitive to extreme violations of the basic assumptions of normality and of the type of measurement scale. Failure to meet the basic assumptions separately or in combination had little effect on the obtained distributions of r s based on rho of zero. (18 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)