Modulation resonance enhancement in SCH quantum-well lasers with anexternal Bragg reflector

The modulation response of a semiconductor laser can be enhanced by coupling it to an external cavity with frequency-selective feedback. This creates a comb of transmission bands where the modulation response is high, at the cavity round-trip frequency and its harmonics. In a previous publication, we related the bandwidths of these bands to the material and structural parameters of a bulk laser. We showed that a nonzero linewidth enhancement factor together with a nonzero intermediate facet reflectivity lead to deep nulls close to the peaks of these transmission bands. This suggests that quantum-well (QW) lasers, which have a low linewidth enhancement factor, may give a better performance than bulk lasers. To test this hypothesis, we have extended our analysis to model QW lasers coupled to a fiber grating. Carrier transport, carrier heating, intraband carrier fluctuations, and nonparabolic band structures are considered. We show that electron carrier transport and amplitude-phase coupling in the separate-confinment-heterostructure (SCH) layer contribute to the nulls in the modulation response. Therefore, the apparent advantage of having a reduced linewidth enhancement factor that we found in our previous analysis cannot be fully realized by using QW lasers     

Predicting Missing Items in Shopping Carts

Existing research in association mining has focused mainly on how to expedite the search for frequently co-occurring groups of items in “shopping cart” type of transactions; less attention has been paid to methods that exploit these “frequent itemsets” for prediction purposes. This paper contributes to the latter task by proposing a technique that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by uncertainty processing techniques, including the classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination.     

Raman-Mediated Nonlinear Interactions in Silicon Waveguides: Copropagating and Counterpropagating Pulses

This letter focuses on interaction of copropagating and counterpropagating pulses inside silicon-on-insulator waveguides using finite-difference time-domain (FDTD) simulations. To the best of our knowledge, this is the first time that copropagating and counterpropagation regimes have been analyzed and compared within an extended FDTD model that takes into account linear dispersion of silicon as well as the effect of stimulated Raman scattering. Our analysis shows unambiguously that the second-order Stokes and anti-Stokes sidebands of sufficiently high intensities can develop from noise when the two pulses are copropagating, but these sidebands are absent owing to an inherent phase mismatch when these pulses counterpropagate. We study the evolution of interacting pulses in the temporal and frequency domains and compare the FDTD results with those obtained by integrating a generalized nonlinear Schrodinger equation.     

Identification and decarboxylase activities of bacteria isolated from decomposed mahimahi (Coryphaena hippurus) after incubation at 0 and 32°C

Identification and amine-forming ability of bacterial isolates from decomposed mahimahi (Coryphaena hippurus) were studied in order to identify spoilage microflora and determine the potential for a chemical index of spoilage based on the metabolites produced. Mesophilic bacteria isolated from a fish incubated at 32°C for 24 h were essentially all Gram-negative rods; 89% of these were Vibrio alginolyticus. Strong histamine-forming (> 100 mg/100 ml) mesophiles consisted of eight cultures of Morganella morganii and one of Proteus mirabilis. Weak histamine-forming (< 10 mg/100 ml) mesophiles were all V. alginolyticus, and these comprised 15% of the isolates assigned to that species. Decarboxylation of ornithine and lysine occurred in 38 and 92%, respectively, of the mesophilic isolates. Psychrotrophic isolates obtained from a fish incubated 14 days at 0°C were predominantly Gram-negative genera. Of these, 9% were histamine-forming cultures of Alteromonas putrefaciens, a weak histamine former that produced < 1 mg/100 ml at 5 and 20°C. Decarboxylation of ornithine and lysine occurred in 13 and 15%, respectively, of the psychrotrophic isolates.     

On the validity of unintentional doping densities extracted using Mott–Schottky analysis for thin film organic devices

The organic electronic devices are often understood invoking the concept of ‘unintentional doping’. However, the applicability and usefulness of this controversial concept is not very clear and is under much recent debate. In this work, we revaluate the validity of this concept through careful experiments and detailed numerical simulations. Specifically, we use the Capacitance Voltage (CV) measurement of pentacene devices as a testbed to unravel the role of injecting electrodes and unintentional doping (if any). Indeed, our results indicate that the CV of pentacene capacitors can be solely understood in terms of properties of the contact electrodes. The unintentional doping, if present, has an inconsequential role in device performance. Our conclusions indicate that, often, an incorrect interpretation of CV results would lead to unphysical values of unintentional doping and have obvious implications towards the fundamental understanding of organic semiconductor device physics, modeling, and characterization; thus resolving many ambiguities in literature by providing a consistent interpretation through a coherent conceptual framework.     

Rule mining and classification in a situation assessment application: a belief-theoretic approach for handling data imperfections.

Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.