Performance limitation due to statistical Raman crosstalk in a WDM system with multiple-wavelength bidirectionally pumped Raman amplification

A general theoretical model of statistical Raman crosstalk and its impact on system performance in a multiwavelength bidirectionally pumped Raman fiber amplifier (RFA) is developed for the first time, where we have taken modulation statistics, dispersion-induced pulse walk-off and signal-induced pump depletion into account. Two kinds of statistical Raman crosstalk, from signal-induced forward-pump depletion and from signal-signal Raman interaction, are included in one model. Formulas for normalized Raman crosstalk, Raman crosstalk-induced relative intensity noise spectral density, and its variance and system performance impact in terms of Q penalty are presented for both a single-span system and a dispersion-compensated multispan wavelength-division-multiplexed (WDM) link. Based on these formulas, we numerically investigate the impact of Raman crosstalk on system performance in a three-wavelengths bidirectionally pumped 40 /spl times/ 40-Gb/s WDM system for various fiber types. In addition, Raman crosstalk in a four-wavelength bidirectionally pumped RFA was experimentally measured. The results agree well with our theory.     

Video monitoring of pulverized coal injection in the blast furnace

A novel approach to monitoring and control of the coal powder injection in a blast furnace is presented and discussed. Image analysis of video recordings is used as a means to estimate the instantaneous coal flow. Initial experiments at blast furnace number 3 of SSAB Tunnplat AB, Lulea, Sweden, are reported and firsthand results on modeling and control of a single injection line are given     

Time jitter analysis of 1-bit sigma-delta synthesizer

This letter presents a general analysis of time jitter in sigma-delta synthesizer. One of the practical applications of 1-bit sigma-delta systems is a generation of high-resolution local oscillator (LO) signals. In order to generate LO signals, one may use a single-bit sigma-delta technique to encode sine waves. Today, direct generation of high-frequency sine wave using a clock with higher frequency, is possible. In this letter, we will analyze different LO signal generators and develop a practical way of computation to decide how to choose the best configuration under minimum degradation of signal-to-noise ratio (SNR) at the output.     

Effect of the add-back process on the free amino acid pool of potatoes

Summary The major losses in nitrogen and free amino acids (FAA) of the potato during the add-back (A-B) process were in the pre-cooking and mash-mixing steps (Fig. 1). These losses were due to leaching of nitrogen compounds into the processing water and heating.The FAA pool of the potato is composed mainly of the amides (asparagine and glutamine), aspartic and glutamic acids (approx. 53%). These amino acids were degraded substantially during the add-back process. There was also a significant loss in valine, threonine, serine, leucine, phenylalanine, and arginine, but no change in -aminobutyric acid (GABA).

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Einfluß des Add-Back Prozesses auf die freie Aminosäuremenge von Kartoffeln

Zusammenfassung Die Hauptverluste an Stickstoff und freien Aminosäuren werden bei der Kartoffel während des Add-back-Prozesses (A-B) beim Vorkochen und bei der Zerkleinerung (Abb. 1) festgestellt. Die Verluste sind durch das Auswaschen der Stickstoffverbindungen mit dem für die Herstellung erforderlichen Wasser und durch die Erhitzung bedingt. Die freien Aminosäuren der Kartoffel setzen sich im wesentlichen aus Amiden (Asparagin und Glutamin), Asparagin- und Glutaminsäuren (ca. 53%) zusammen. Die Amisnosäuren werden hauptsächlich während des Add-Back-Prozesses abgebaut. Ebenso nehmen Valin, Threonin, Serin, Leucin, Phenylalanin und Arginin im Verlauf des Prozesses signifikant ab, jedoch veränderte sich der -Aminobuttersäuregehalt (GABA) nicht.

     

Exploring similarities and differences in teamwork across diverse healthcare contexts using communication analysis

Teamwork is prevalent in many work contexts. This study explored the similarities and differences in teamwork processes across different healthcare work contexts with the aim of assessing knowledge transfer feasibility. The research approach was to aggregate team communication analyses from four healthcare contexts to uncover teamwork similarities and differences. The four healthcare contexts included two handoffs and two surgery contexts. The communication analysis segmented communication into meaningful sequences. It categorized utterances into content categories and verbal behaviors. There were a few similar content categories across the four contexts. A clear information structure emerged in the two handoff contexts. In addition, there were more dialogues and requests in the surgeries compared to more reports in the handoffs. The content similarities suggest that some knowledge is transferable among the contexts. However, the differences in communication patterns reflect fundamental differences between handoff and surgery contexts in some teamwork processes. This research demonstrated that using communication analysis can uncover similarities and differences in team cognition and teamwork processes across work contexts. This in turn can help determine what knowledge and methods pertaining to team training, procedures, and technology are transferable across the contexts.     

Derandomized graph products

Berman and Schnitger gave a randomized reduction from approximating MAX-SNP problems within constant factors arbitrarily close to 1 to approximating clique within a factor ofn (for some ). This reduction was further studied by Blum, who gave it the namerandomized graph products. We show that this reduction can be made deterministic (derandomized), using random walks on expander graphs. The main technical contribution of this paper is in proving a lower bound for the probability that all steps of a random walk stay within a specified set of vertices of a graph. (Previous work was mainly concerned with upper bounds for this probability.) This lower bound extends also to the case where different sets of vertices are specified for different time steps of the walk.     

Agent decision-making in open mixed networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.     

Automatic boosting of cross-product coverage using Bayesian networks

Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage in the early stages of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method.