A birth-process approach to Moranda's geometric software-reliability model

To alleviate some of the objections to the basic Jelinski Moranda (JM) model for software failures, Moranda proposed a geometric de-eutrophication model. This model assumes that the times between failures are statistically-independent exponential random variables with given failure rates. In this model the failure rates decrease geometrically with the detection of a fault. Using an intuitive approach, Musa, Iannino, Okumoto , see also Farr , derived expressions for the mean and the intensity functions of the process N (t) which counts the number of faults detected in the time interval [O, t] for the Moranda geometric de-eutrophication model. N (t) is studied as a pure birth stochastic process; its probability generating function is derived, as well as its mean, intensity and reliability functions. The expressions for the mean and intensity functions derived by MIO are only approximations and can be quite different from the true functions for certain choices of the failure rates. The exact expressions for the mean function and the intensity function of N (t) are used to find the optimum release time of software based on a cost structure for Moranda's geometric de-eutrophication model.     

Phase state stabilization of ammonium nitrate for creating an oxidizing agent for smokeless gas-generating formulations yielding no toxic combustion products

Methods for phase stabilization of ammonium nitrate were sought for in order to considerably expand the application area of this oxidizing agent in various-purpose self-combustible formulations, including that in a new generation of gas-generating formulations for automobile air bags. New methods for stabilization of ammonium nitrate were studied and, in particular, a search was made for organic compounds that can stabilize ammonium nitrate even at their low content. The mechanism of phase state stabilization of ammonium nitrate by compounds of this kind was examined.     

Equilibrium analysis of cellular neural networks

Cellular neural networks (CNNs) are dynamical systems, described by a large set of coupled nonlinear differential equations. The equilibrium-point analysis is an important step for understanding the global dynamics and for providing design rules. We yield a set of sufficient conditions (and a simple algorithm for checking them) ensuring the existence of at least one stable equilibrium point. Such conditions give rise to simple constraints, that extend the class of CNNs, for which the existence of a stable equilibrium point is rigorously proved. In addition, they are suitable for design and easy to check, because they are directly expressed in term of the template elements.     

Randomized scheduling algorithms for high-aggregate bandwidth switches

The aggregate bandwidth of a switch is its port count multiplied by its operating line rate. We consider switches with high-aggregate bandwidths; for example, a 30-port switch operating at 40 Gb/s or a 1000-port switch operating at 1 Gb/s. Designing high-performance schedulers for such switches with input queues is a challenging problem for the following reasons: (1) high performance requires finding good matchings; (2) good matchings take time to find; and (3) in high-aggregate bandwidth switches there is either too little time (due to high line rates) or there is too much work to do (due to a high port count). We exploit the following features of the switching problem to devise simple-to-implement, high-performance schedulers for high-aggregate bandwidth switches: (1) the state of the switch (carried in the lengths of its queues) changes slowly with time, implying that heavy matchings will likely stay heavy over a period of time and (2) observing arriving packets will convey useful information about the state of the switch. The above features are exploited using hardware parallelism and randomization to yield three scheduling algorithms - APSARA, LAURA, and SERENA. These algorithms are shown to achieve 100% throughput and simulations show that their delay performance is quite close to that of the maximum weight matching, even when the traffic is correlated. We also consider the stability property of these algorithms under generic admissible traffic using the fluid-model technique. The main contribution of this paper is a suite of simple to implement, high-performance scheduling algorithms for input-queued switches. We exploit a novel operation, called MERGE, which combines the edges of two matchings to produce a heavier match, and study of the properties of this operation via simulations and theory. The stability proof of the randomized algorithms we present involves a derandomization procedure and uses methods which may have wider applicability.     

Lateral ion implant straggle and mask proximity effect

Lateral scattering of retrograde well implants is shown to have an effect on the threshold voltage of nearby devices. The threshold voltage of both NMOSFETs and PMOSFETs increases in magnitude for conventional retrograde wells, but for triple-well isolated NMOSFETs the threshold voltage decreases for narrow devices near the edge of the well. Electrical data, SIMS, and SUPREM4 simulations are shown that elucidate the phenomenon.     

Dispersion-tolerant 10-gb/s duobinary system employing heterodyne detection and MLSE

We experimentally demonstrate a 10.7-Gb/s duobinary transmission system operating over a chromatic-dispersion range of /spl sim/12 000 ps/nm. Heterodyne detection and maximum-likelihood sequence estimation are employed to achieve this result.     

Unified Systolic-Like Architecture for DCT and DST Using Distributed Arithmetic

A common computing-core representation of the discrete cosine transform and discrete sine transform is derived and a reduced-complexity algorithm is developed for computation of the proposed computing-core. A parallel architecture based on the principle of distributed arithmetic is designed further for the computation of these transforms using the common-core algorithm. The proposed scheme not only leads to a systolic-like regular and modular hardware for computing these transforms, but also offers significant improvement in area-time efficiency over the existing structures. The structure proposed here is devoid of complicated input/output mapping and does not involve any complex control. Unlike the convolution-based structures, it does not restrict the transform length to be a prime or multiple of prime and can be utilized as a reusable core for cost-effective, memory-efficient, high-throughput implementation of either of these transforms     

A New Hand-Held Microsystem Architecture for Biological Analysis

This paper presents a hand-held microsystem based on new fully integrated magnetoresistive biochips for biomolecular recognition (DNA hybridization, antibody antigen interaction, etc.). Magnetoresistive chip surfaces are chemically treated, enabling the immobilization of probe biomolecules such as DNA or antibodies. Fluid handling is also integrated in the biochip. The proposed microsystem not only integrates the biochip, which is an array of 16times16 magnetoresistive sensors, but it also provides all the electronic circuitry for addressing and reading out each transducer. The proposed architecture and circuits were specifically designed for achieving a compact, programmable and portable microsystem. The microsystem also integrates a hand-held analyzer connected through a wireless channel. A prototype of the system was already developed and detection of magnetic nanoparticles was obtained. This indicates that the system may be used for magnetic label based bioassays     

The radiation resistance of an arbitrarily oriented electric dipole exciting a corner antenna or a plane screen

Rigorous analytic expressions for radiation resistance R _Σ of an electric dipole are obtained for three mutually orthogonal orientations of the dipole’s axis with respect to a plane metal screen or right-angle corner of infinite dimensions. Radiation resistance R _Σ of the dipole oriented arbitrarily in space is calculated and analyzed as a function of the distance from the screen.     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.