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This book is a comprehensive guide to new design for testability (DFT) methods that will show the readers how to design a testable and quality product, drive down test cost, improve product quality and yield, and speed up time-to-market and time-to-volume. Key features include up-to-date coverage of design for testability, coverage of industry practices commonly found in commercial DFT tools but not discussed in other books, and numerous, practical examples in each chapter illustrating basic VLSI test principles and DFT architectures. Practitioners/Researchers in VLSI design and testing; design or test engineers, as well as research institutes will benefit from this book. This book is also appropriate for undergraduate and graduate-level courses in electronic testing, digital systems testing, digital logic test and simulation, and VLSI design.

Scale is a critical factor when studying patterns and the processes that cause them. A variety of approaches have been used to define the concept of scale but confusion and ambiguities remain regarding scale types and their definitions. The objectives of this study were therefore (1) to review existing types and definitions of scale, and (2) to systematically investigate the ambiguities in scale definitions and to determine the applicability of the various scale types and definitions. Through a comprehensive literature review, we identified seven types of scales and designed a survey for the seven definitions of scale and interviewed 150 scientists. The results show that the more cartography related types of scale are relatively well known while the more abstract dimensions are less known and are most ambiguous. Based on graphical examples, participants were asked which spatial scales are most relevant for their work. Surprisingly, composite objects such as a forest stand were most relevant followed by individual objects such as single trees and, lastly, more generalized categorizes or meta-objects such as \"forested area\". We have drawn some conclusions that will help to clarify the different types of scale in regard to their practical use.

Several scales are commonly used for assessing pain intensity. Among them, the numerical rating scale (NRS), visual analog scale (VAS), and verbal rating scale (VRS) are often used in clinical practice. However, no study has performed psychometric analyses of their reliability and validity in the measurement of osteoarthritic (OA) pain. Therefore, the present study examined the test-retest reliability, validity, and minimum detectable change (MDC) of the VAS, NRS, and VRS for the measurement of OA knee pain. In addition, the correlations of VAS, NRS, and VRS with demographic variables were evaluated. The study included 121 subjects (65 women, 56 men; aged 40-80 years) with OA of the knee. Test-retest reliability of the VAS, NRS, and VRS was assessed during two consecutive visits in a 24 h interval. The validity was tested using Pearson's correlation coefficients between the baseline scores of VAS, NRS, and VRS and the demographic variables (age, body mass index [BMI], sex, and OA grade). The standard error of measurement (SEM) and the MDC were calculated to assess statistically meaningful changes. The intraclass correlation coefficients of the VAS, NRS, and VRS were 0.97, 0.95, and 0.93, respectively. VAS, NRS, and VRS were significantly related to demographic variables (age, BMI, sex, and OA grade). The SEM of VAS, NRS, and VRS was 0.03, 0.48, and 0.21, respectively. The MDC of VAS, NRS, and VRS was 0.08, 1.33, and 0.58, respectively. All the three scales had excellent test-retest reliability. However, the VAS was the most reliable, with the smallest errors in the measurement of OA knee pain.

Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010 , under revision) and comprising: an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, complementary post-smoothing after the computation of non-linear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale. It is shown how the selected scales of different linear and non-linear interest point detectors can be analyzed for Gaussian blob models. Specifically it is shown that for a rotationally symmetric Gaussian blob model, the scale estimates obtained by weighted scale selection will be similar to the scale estimates obtained from local extrema over scale of scale normalized derivatives for each one of the pure second-order operators. In this respect, no scale compensation is needed between the two types of scale selection approaches. When using post-smoothing, the scale estimates may, however, be different between different types of interest point operators, and it is shown how relative calibration factors can be derived to enable comparable scale estimates for each purely second-order operator and for different amounts of self-similar post-smoothing. A theoretical analysis of the sensitivity to affine image deformations is presented, and it is shown that the scale estimates obtained from the determinant of the Hessian operator are affine covariant for an anisotropic Gaussian blob model. Among the other purely second-order operators, the Hessian feature strength measure I has the lowest sensitivity to non-uniform scaling transformations, followed by the Laplacian operator and the Hessian feature strength measure II. The predictions from this theoretical analysis agree with experimental results of the repeatability properties of the different interest point detectors under affine and perspective transformations of real image data. A number of less complete results are derived for the level curve curvature operator.

Objective: To assess apathy in patients with Parkinson’s disease and its relation to disability, mood, personality, and cognition. Methods: Levels of apathy in 45 patients with Parkinson’s disease were compared with a group of 17 similarly disabled patients with osteoarthritis. Additional neuropsychiatric data were collected concerning levels of depression, anxiety, and hedonic tone. Personality was assessed with the tridimensional personality questionnaire. Cognitive testing included the mini-mental state examination, the Cambridge examination of cognition in the elderly, and specific tests of executive functioning. Results: Patients with Parkinson’s disease had significantly higher levels of apathy than equally disabled osteoarthritic patients. Furthermore, within the Parkinson sample, levels of apathy appear to be unrelated to disease progression. The patients with Parkinson’s disease with the highest levels of apathy where not more likely to be depressed or anxious than those with the lowest levels of apathy, though they did show reduced hedonic tone. No differences in personality traits were detected in comparisons between patients with Parkinson’s disease and osteoarthritis, or between patients in the Parkinson group with high or low levels of apathy. As a group, the patients with Parkinson’s disease tended not to differ significantly from the osteoarthritic group in terms of cognitive skills. However, within the Parkinson’s disease sample, the high apathy patients performed significantly below the level of the low apathy patients. This was particularly evident on tests of executive functioning. Conclusions: Apathy in Parkinson’s disease is more likely to be a direct consequence of disease related physiological changes than a psychological reaction or adaptation to disability. Apathy in Parkinson’s disease can be distinguished from other psychiatric symptoms and personality features that are associated with the disease, and it is closely associated with cognitive impairment. These findings point to a possible role of cognitive mechanisms in the expression of apathy.

In order to solve the safety problem of equipment fastening during the transit of the mother ship, the scale model test technology of the shipborne equipment fastening system is studied in this paper. This paper designed and carried out scaling tests of equipment safety fastening under different sea conditions and various motion states (single-freedom and multi-freedom motion), revealed the changing rules of equipment safety fastening loads, formed the analysis and evaluation techniques of equipment fastening methods, and the research results can provide guidance methods for the design of phase relation fastening equipment.

Scaling up industrial microbial processes for commercial production is a high-stakes endeavor, requiring time and investment often exceeding that for laboratory microbe and process development. Omissions, oversights and errors can be costly, even fatal to the program. Approached properly, scale-up can be executed successfully. Three guiding principles are provided as a basis: begin with the end in mind; be diligent in the details; prepare for the unexpected. A detailed roadmap builds on these principles. There is a special emphasis on the fermentation step, which is usually the costliest and also impacts downstream processing. Examples of common scale-up mistakes and the recommended approaches are given. It is advised that engineering resources skilled in integrated process development and scale-up be engaged from the very beginning of microbe and process development to guide ongoing R&D, thus ensuring a smooth and profitable path to the large-scale commercial end.

The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the choice of associated image descriptors. This paper demonstrates advantages of using generalized scale-space interest point detectors in this context for selecting a sparse set of points for computing image descriptors for image-based matching. For detecting interest points at any given scale, we make use of the Laplacian ∇ n o r m 2 L , the determinant of the Hessian det H n o r m L and four new unsigned or signed Hessian feature strength measures D 1 , n o r m L , D ~ 1 , n o r m L , D 2 , n o r m L and D ~ 2 , n o r m L , which are defined by generalizing the definitions of the Harris and Shi-and-Tomasi operators from the second moment matrix to the Hessian matrix. Then, feature selection over different scales is performed either by scale selection from local extrema over scale of scale-normalized derivates or by linking features over scale into feature trajectories and computing a significance measure from an integrated measure of normalized feature strength over scale. A theoretical analysis is presented of the robustness of the differential entities underlying these interest points under image deformations, in terms of invariance properties under affine image deformations or approximations thereof. Disregarding the effect of the rotationally symmetric scale-space smoothing operation, the determinant of the Hessian det H n o r m L is a truly affine covariant differential entity and the Hessian feature strength measures D 1 , n o r m L and D ~ 1 , n o r m L have a major contribution from the affine covariant determinant of the Hessian, implying that local extrema of these differential entities will be more robust under affine image deformations than local extrema of the Laplacian operator or the Hessian feature strength measures D 2 , n o r m L , D ~ 2 , n o r m L . It is shown how these generalized scale-space interest points allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. The best results are obtained using interest points computed with scale linking and with the new Hessian feature strength measures D 1 , n o r m L , D ~ 1 , n o r m L and the determinant of the Hessian det H n o r m L being the differential entities that lead to the best matching performance under perspective image transformations with significant foreshortening, and better than the more commonly used Laplacian operator, its difference-of-Gaussians approximation or the Harris–Laplace operator. We propose that these generalized scale-space interest points, when accompanied by associated local scale-invariant image descriptors, should allow for better performance of interest point based methods for image-based matching, object recognition and related visual tasks.

This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, or other permutation-invariant pooling over scales, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNIST Large Scale dataset, which contains rescaled images from the original MNIST dataset over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not spanned by the training data.