Stability of O3 + N2, O3 + O2 and O3 + CO2 double hydrates: DFT study

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Comprehensive heat exchange model for a semiconductor laser diode

By measuring the total energy flow from an optical device, we can develop new design strategies for thermal stabilization. Here we present a comprehensive model for heat exchange between a semiconductor laser diode and its environment that includes the mechanisms of conduction, convection, and radiation. We perform quantitative measurements of these processes for several devices, deriving parameters such as a laser's heat transfer coefficient, and then demonstrate the feasibility of thermal probing for the nondestructive wafer-scale characterization of optical devices.     

State of polarization changes: Classification and measurement

We propose a standardization procedure that provides a convenient, quantitative and reproducible laboratory-based method for measuring the state of polarization (SOP) fluctuations produced by polarization varying devices. This method is based on the SOP distributions generated by commercial polarization scramblers. We show that these devices generate distributions of the maximum change of the SOP (in a given sample time) that follow Rayleigh statistics, which scale linearly with scrambling frequency and the sample time. We use this procedure to measure the SOP fluctuations in a short length of coiled fiber subject to mechanical perturbations.     

Optimal mean-square prediction of the mobile-radio fading envelope

Long-range prediction of the mobile-radio fading envelope is an enabling technology for many fading compensation approaches. Because the fading envelope is well modeled as a bandlimited process, it has special predictability properties. In this paper, we find a linear predictor that is optimal in the mean-square sense when the predictor impulse response is energy constrained. This solution may be used to determine the minimum mean squared error of a prediction based on past values that are corrupted with estimation errors.     

FIRE: fractal indexing with robust extensions for image databases

As already documented in the literature, fractal image encoding is a family of techniques that achieves a good compromise between compression and perceived quality by exploiting the self-similarities present in an image. Furthermore, because of its compactness and stability, the fractal approach can be used to produce a unique signature, thus obtaining a practical image indexing system. Since fractal-based indexing systems are able to deal with the images in compressed form, they are suitable for use with large databases. We propose a system called FIRE, which is then proven to be invariant under three classes of pixel intensity transformations and under geometrical isometries such as rotations by multiples of /spl pi//2 and reflections. This property makes the system robust with respect to a large class of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. Additionally, the experimental results show the effectiveness of FIRE in terms of both compression and retrieval accuracy.     

Application of convolutional error correcting codes in ultrawideband M-ary PPM signaling

The interference issues related to ultrawideband (UWB) radio pose tight restrictions on the maximum data rate of UWB radio telecommunication systems. A possible solution is to reduce the required signal to interference ratio (SIR) that gives satisfactory performance to the UWB system. In this letter, we propose coded M-ary UWB radio communication systems. Two classes of convolutional codes, namely, low-rate superorthogonal codes and high-rate punctured codes are considered for this purpose. Simulation results on the bit error rate of the proposed system indicates that the system is capable to work in lower SIR's and therefore supports higher data transmission rates in a real interference environment compared to the previously proposed UWB communication systems.     

A VQ-based blind image restoration algorithm

Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.     

Analysis of feedback for higher order polarization-mode dispersion mitigation of NRZ 40-Gb/s optical transmission

First-order polarization-mode dispersion (PMD) compensation by means of a polarization controller and a differential delay line is not sufficient to guarantee error-free transmission for 40-Gb/s channels when higher order effects severely increase signal distortion. Higher order mitigation is possible by cascading more than one first-order block. However, only two-stage or three-stage devices remain simple enough to be actually controlled. The performance of such higher order PMD compensators is evaluated by means of numerical simulations. Two different feedback signals have been used, demonstrating that first-order and higher order PMD distortion of nonreturn-to-zero (NRZ) pulses at 40 Gb/s can be strongly mitigated for instantaneous values of the differential group delay (DGD) up to the bit slot, when the compensator is properly controlled.     

End-to-end congestion control schemes: utility functions, random losses and ECN marks

We present a framework for designing end-to-end congestion control schemes in a network where each user may have a different utility function and may experience noncongestion-related losses. We first show that there exists an additive-increase-multiplicative-decrease scheme using only end-to-end measurable losses such that a socially optimal solution can be reached. We incorporate round-trip delay in this model, and show that one can generalize observations regarding TCP-type congestion avoidance to more general window flow control schemes. We then consider explicit congestion notification (ECN) as an alternate mechanism (instead of losses) for signaling congestion and show that ECN marking levels can be designed to nearly eliminate losses in the network by choosing the marking level independently for each node in the network. While the ECN marking level at each node may depend on the number of flows through the node, the appropriate marking level can be estimated using only aggregate flow measurements, i.e., per-flow measurements are not required.