Traffic classification and scheduling in ATM networks

Virtual paths (VPs) are an integral part of the resource management and control hierarchy of ATM (asynchronous transfer mode) networks. To improve the utilization of network resources and facilitate management and control, source types are organized into traffic classes. Each traffic class is transported by its own virtual path subnetwork. In this paper, we consider issues related to the design of traffic classes. We consider an ATM switch node to which cells arrive from a diverse set of source types. Traffic classes are assumed to be served according to a weighted round robin policy, while cells belonging to a given traffic class are served in first-come-first-served order. We consider the problem of determining the optimal set of traffic classes. Under suitable simplifying assumptions, it is shown that the above problem can be modeled as a set-partitioning problem. The structure of the problem at hand is then exploited to develop an efficient heuristic. Several examples are given to illustrate the developed methodology.Supported partially through NSF Grant NCR-891447 and AT&T Grant 5-23690.     

Modified tracking loop for DS spread spectrum systems

A nonconventional pseudo-noise (PN) code tracking loop for direct sequence (DS) spread spectrum systems is presented. It solves problems of component imbalance while maintaining hardware simplicity.<>     

Cracking orientation and induced anisotropy of a ceramic matrix composite under off-axis loading

The effect of matrix microcracking on the stiffnesses of a carbon-fibre/SiC-matrix woven composite is studied by means of an ultrasonic method. It provides the whole set of the stiffness tensor coefficients which are inaccessible by classical strain measurements and which are required to identify anisotropic damage. The induced anisotropy depends on the loading direction. If a tensile solicitation in a fibre direction leads to stiffnesses decreases without any rotation of principal axes, a tensile solicitation of 45° from a fibre direction creates microcracks with a predominant orientation that does not coincide with the elastic symmetry axes, and induce a fully anisotropic elastic degradation.     

Transport of arginine and ornithine into isolated mitochondria of Saccharomyces cerevisiae

In this work we have characterised the transport of L-arginine and L-ornithine into mitochondria isolated from a wild-type Saccharomyces cerevisiae strain and an isogenic arg11 knock-out mutant. The Arg11 protein (Arg11p) is a mitochondrial carrier required for arginine biosynthesis [Crabeel, M., Soetens, O., De Rijcke, M., Pratiwi, R. & Pankiewicz, R. (1996) J. Biol. Chem. 271, 25011-25019]. Reconstitution experiments have confirmed that it is an L-ornithine carrier also transporting L-arginine and L-lysine by order of decreasing affinity, but not L-histidine [Palmieri, L., De Marco, V., Iacobazzi, V., Palmieri, F., Runswick, M. & Walker, J. (1997) FEBS Lett. 410, 447-451]. Evidence is presented here that the mitochondrial inner membrane contains an L-arginine and L-ornithine transporting system distinct from Arg11p, in keeping with the arginine leaky phenotype of arg11 knock-out mutants. The newly characterised carrier, which we propose to name Bac1p (basic amino acid carrier), behaves as an antiporter catalysing the electroneutral exchange of the basic amino acids L-arginine, L-lysine, L-ornithine and L-histidine and displays the highest affinity for L-arginine (Km of 30 microM). L-Arginine uptake has a pH optimum in the range of 7.5-9 and is inhibited by several sulphydryl reagents, by pyridoxal 5'-phosphate and by cations.     

Transmission line analysis of nonlinear slot coupled microstrip antenna

The transmission line method is used for the analysis of microstrip antennas fed by several nonlinear slots with 'H' shapes or new geometries. This analysis allows the input impedance of nonlinear slot coupled microstrip antennas to be calculated for the four different structures.<>     

Single-sided ventilation — Part II. further considerations

This paper extends an earlier report of measurements and computer predictions of flows in a cubical cavity exposed on one side to the scavenging effect of an external stream. Further measurements and photographs are presented; they are consistent with each other and with the predictions of the computational model. Specifically, comparisons are made between measured and predicted mean velocities and turbulent shear stresses, and photographs are used to elucidate features of the shear layers generated by accelerating and decelerating external flows. The computer model is used to deduce approximate relations between internal turbulent activity (measured by mean velocity, turbulence energy and shear stress) and external velocity, percentage ‘window’ opening and relative position of the plane walls upstream and downstream of the cavity.     

Extended-spectrum beta-lactamase-producing Klebsiella pneumoniae strains causing nosocomial outbreaks of infection in the United Kingdom

Representative isolates from 10 distinct extended-spectrum beta-lactamase-producing strains of Klebsiella pneumoniae that caused hospital outbreaks in the United Kingdom from 1991 to 1994 were examined for relationships between their enzymes and plasmids. The beta-lactamases were identified by a combination of isoelectric focusing and gene sequencing. SHV-2 beta-lactamase was produced by isolates from four outbreaks, SHV-5 was involved in three, and SHV-4, TEM-15, and TEM-26 were involved in one outbreak each. All of the extended-spectrum beta-lactamases were encoded by self-transmissible plasmids, with sizes ranging from about 70 to 160 kb. No similarities between the restriction digest patterns of the extended-spectrum beta-lactamase-encoding plasmids were detected, except to some extent between those that produced TEM-15 and TEM-26. Thus, outbreaks of hospital infection with these organisms in the United Kingdom from 1991 to 1994 involved distinct organisms and resistance plasmids and appeared to be unrelated.     

Energy inefficiency diagnosis in industrial process through one-class machine learning techniques

In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

     

Intrinsic nonlinear multiscale image decomposition: A 2D empirical mode decomposition-like tool

Many works have been achieved for analyzing images with a multiscale approach. In this paper, an intrinsic and nonlinear multiscale image decomposition is proposed, based on partial differential equations (PDEs) and the image frequency contents. Our model is inspired from the 2D empirical mode decomposition (EMD) for which a theoretical study is quite nonexistent, mainly because the algorithm is based on heuristic and ad hoc elements making its mathematical study hard. This work has three main advantages. Firstly, we prove that the 2D sifting process iterations are consistent with the resolution of a nonlinear PDE, by considering continuous morphological operators to build local upper and lower envelopes of the image extrema. In addition to the fact that now differential calculus can be performed on envelopes, the introduction of such morphological filters eliminates the interpolation dependency that also terribly suffers the method. Also, contrary to former 2D empirical modes, precise mathematical definition for a class of functions are now introduced thanks to the nonlinear PDE derived from the consistency result, and their characterization on the basis of Meyer spaces. Secondly, an intrinsic multiscale image decomposition is introduced based on the image frequency contents; the proposed approach almost captures the essence and philosophy of the 2D EMD and is linked to the well known Absolutely Minimizing Lipschitz Extension model. Lastly, the proposed multiscale decomposition allows a reconstruction of images. The filterbank capability of the new multiscale decomposition algorithm is shown both on synthetic and real images, and results show that our proposed approach improves a lot on the 2D EMD. Moreover, the complexity of the proposed multiscale decomposition is very reduced compared to the 2D EMD by avoiding the surface interpolation approach, which is the core of all 2D EMD algorithms and is very time consuming. For that purpose also, our work will then be a great benefit; especially, in higher dimension spaces.