Software component certification

Although the stringent requirements of some critical applications may require independent certification, the authors see software developer self-certification as a viable alternative in many other cases. They accept that using software certification laboratories (SCLs) may work well for certain software distribution models, but they cannot be applied to all types of software development. The approach has several drawbacks. For example, an SCL may work well for larger software houses that ship mass-marketed software applications to the public, but it is less satisfactory for smaller developers who make reusable components or safety-critical software or for developers who belong to the freeware community     

Per-queue stability analysis of a random access system

We have extended our previous studies (2000) of the system stability of buffered ALOHA systems to study an individual queue's stability, i.e., per-queue stability. The main result obtained in this work is a necessary and sufficient per-queue stability condition, which can be computed analytically only for several cases. For other incomputable cases, we evaluated several inner and outer bounds. They are generally quite tight for not-so-asymmetric systems     

T c enhancement in superconductor-semiconductor arrays

Subsequent to our observation that the Tl- and Bi-based cuprate high-T _c superconductors are built of superconductor-semiconductor arrays (P. C. W. Fung and W. Y. Kwok,J. Superconduct., this issue), we investigate in this paper the possibility ofT _c enhancement arising from the effect of change of phonon spectrum and the effect of size quantization when one or more semiconductor blocks is attached to the basic superconductor in the unit cell.     

Identification of nitrate sources in Taihu Lake and its major inflow rivers in China, using δ(15)N-NO(3)(-) and δ(18)O-NO(3)(-) values

In the present study, δ(15)N and δ(18)O-NO(3)(-) values, as well as concentrations of some major ion tracers were determined in seasonal water samples from Taihu Lake and major watersheds to investigate the temporal and spatial variations of nitrate sources and assess the underlying nitrogen (N) biogeochemistry process. The results lead to the conclusion that the nitrate concentrations in Taihu Lake are lower in summer than that in winter due to the dilution effect of wet deposition. In winter, sewage and manure were the primary nitrate sources in major inflow rivers and North Taihu Lake (NTL), while nitrate sources in East Taihu Lake (ETL) probably derived from soil organic N. In summer, atmospheric deposition and sewage/manure inputs appear to play an important role in controlling the distribution of nitrates in the whole lake. The δ(18)O-NO(3)(-) values suggest that the nitrate produced from microbial nitrification is another major nitrate source during both winter and summer months. The variations in isotopic values in nitrate suggest denitrification enriched the heavier isotopes of nitrate in NTL in winter and in ETL in summer.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

A variational formulation for fingerprint orientation modeling

Orientation pattern is an important feature for characterizing fingerprint and plays critical roles in fingerprint recognition and fingerprint classification. This paper proposes a framework for modeling the fingerprint orientation field based on the variational principle, where the orientation pattern can be estimated through solving the associated Euler–Lagrange equation. Compared with existing methods, our proposed method has the following features. Firstly, it does not require any prior information about the structure of the acquired fingerprint, such as location of singular point(s). Secondly, it explicitly provides freedom for modeling the singularity in the orientation field. Thirdly, it has less number of parameters. Comparison has been made with respect to state-of-the-arts in fingerprint orientation modeling in terms of modeling accuracy, fingerprint enhancement and singular point detection. Advantages of the proposed method are demonstrated.     

The non-constant CTOD/CTOA in stable crack extension under plane-strain conditions

Contrary to the previous work that successfully applied the constant CTOD/CTOA fracture criteria to relatively thin structures, this paper demonstrates that the initial non-constant portion of the CTOD/CTOA plays an essential role in predicting fracture behavior under plane-strain conditions. Three- and two-dimensional finite element analyses indicate that a severe underestimation of the load would occur as the crack extends if a constant CTOD/CTOA criterion were used. However, the use of a simplified, bilinear CTOD/CTOA criterion to approximate its non-constant portion will closely duplicate the test data. Furthermore, using the experimental data from J-integral tests with various crack length to specimen width ratios (a/W), it is demonstrated that the critical CTOD/CTOA is crack tip constraint dependent. The initial high values of the CTOD/CTOA are in fact a natural consequence of crack growth process that is reflected by, and consistent with, the J-resistance (J-R) curve and its slope (tearing modulus).     

Optimization of photomask design for reducing aberration-induced placement error

In semiconductor manufacturing, the accurate placement of circuit components ensures the proper functioning of microelectronic circuits. This is often subject to photolithography, an optical technique that transfers circuit patterns from photomasks to silicon wafers. Sources of placement error include aberration and misalignment between different levels, and we focus on the former. Aberration is an optical phenomenon that often degrades imaging system performance. Since aberration differs from one imaging system to another, a photomask design that minimizes the aberration-induced placement error is desired. In this paper, we discuss the optimization process of a general one-dimensional mask pattern under a general illumination condition. The constraint is a known population mean of the root mean square aberrations for the imaging systems under consideration. To apply the theory, we search for the optimal parameters for two common mask designs: alternating phase-shifting masks (PSMs) and attenuated PSMs. The theoretical results are compared with those from a Monte Carlo analysis on a large set of imaging systems. These results are indicative to mask manufacturers and circuit designers of increasing manufacturability of circuits.