A prototype 3D optically interconnected neural network

We report the implementation of a prototype three-dimensional (3D) optoelectronic neural network that combines free-space optical interconnects with silicon-VLSI-based optoelectronic circuits. The prototype system consists of a 16-node input, 4-neuron hidden, and a single-neuron output layer, where the denser input-to-hidden-layer connections are optical. The input layer uses PLZT light modulators to generate optical outputs which are distributed over an optoelectronic neural network chip through space-invariant holographic optical interconnects. Optical interconnections provide negligible fan-out delay and allow compact, purely on-chip electronic H-tree type fan-in structure. The small prototype system achieves a measured 8-bit electronic fan-in precision and a calculated maximum speed of 640 million interconnections per second. The system was tested using synaptic weights learned off system and was shown to distinguish any vertical line from any horizontal one in an image of 4×4 pixels. New, more efficient light detector and small-area analog synapse circuits and denser optoelectronic neuron layouts are proposed to scale up the system. A high-speed, feed-forward optoelectronic synapse implementation density of up to 10⁴/cm² seems feasible using new synapse design. A scaling analysis of the system shows that the optically interconnected neural network implementation can provide higher fan-in speed and lower power consumption characteristics than a purely electronic, crossbar-based neural network implementation     

Inelastic post-buckling analysis of truss structures by dynamic relaxation method

The paper presents the inelastic post-buckling analysis of truss structures by the Dynamic Relaxation (DR) method. A simplified inelastic finite element formulation for truss element and new algorithms are proposed for Elastic Post-Buckling (EPB) analysis and Inelastic Post-Buckling (IEPB) analysis using the DR method. The post-buckling paths for elastic, EPB and IEPB analyses are completely traced using the variable-arc-length method. Four numerical examples are presented to illustrate the application of the proposed algorithms.     

Microstructural developments in Mg-Ti-PSZ systems

The microstructure of titania-added Mg-partially-stabilized zirconia (PSZ) is dramatically influenced by thermal treatments. Effects of various sintering, heat-treatment and thermal shock cycling parameters on the microstructure of the Mg-Ti-PSZ system are described. Conditions favourable for the growth of needle-like Ti-rich reinforcements in highly thermalshock-resistant Mg-Ti-PSZ ceramics are identified. TiO₂ seems to play a catalytic role in the formation of Zr-rich networks during high-temperature (1700°C) sintering of the Mg-Ti-PSZ system, quite similar to those found in Mg-PSZ, heat-treated above 1300 °C.     

Pricing strategies with reference effects in competitive industries

This paper examines the effect of reference prices on companies operating within competitive industries. We confirm that even with competition, firms optimally price high in the short term to generate a high reference price and then decrease this price over time. Competitors' prices converge toward each other over time, emphasizing the short‐term nature of reference prices. We then show that pricing optimally to take advantage of reference prices generates a positive externality for other firms in an industry, such that competitors may generate higher profit. The longer the focus of a given firm, the more profit the firm generates, but less relative to its competitors. This arises because the externalities created through pricing high to increase reference prices outweigh the benefits of the higher reference prices themselves. If pricing managers are compensated relative to their competition, this suggests that short‐termism may be implicitly encouraged to the detriment of profit.     

Photonic content-addressable memory system that uses a parallel-readout optical disk

We describe a high-performance associative-memory system that can be implemented by means of an optical disk modified for parallel readout and a custom-designed silicon integrated circuit with parallel optical input. The system can achieve associative recall on 128 × 128 bit images and also on variable-size subimages. The system's behavior and performance are evaluated on the basis of experimental results on a motionless-head parallel-readout optical-disk system, logic simulations of the very-large-scale integrated chip, and a software emulation of the overall system.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models

This article is about testing the equality of several normal means when the variances are unknown and arbitrary, i.e., the set up of the one-way ANOVA. Even though several tests are available in the literature, none of them perform well in terms of Type I error probability under various sample size and parameter combinations. In fact, Type I errors can be highly inflated for some of the commonly used tests; a serious issue that appears to have been overlooked. We propose a parametric bootstrap (PB) approach and compare it with three existing location-scale invariant tests—the Welch test, the James test and the generalized F (GF) test. The Type I error rates and powers of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test is the best among the four tests with respect to Type I error rates. The PB test performs very satisfactorily even for small samples while the Welch test and the GF test exhibit poor Type I error properties when the sample sizes are small and/or the number of means to be compared is moderate to large. The James test performs better than the Welch test and the GF test. It is also noted that the same tests can be used to test the significance of the random effect variance component in a one-way random model under unequal error variances. Such models are widely used to analyze data from inter-laboratory studies. The methods are illustrated using some examples.     

The importance of public support in the implementation of green transportation in the smart cities

Smart cities are increasingly evolving, initializing new strategies, and programs that have a significant influence on policy making and scheduling while coexisting with urban facilities. To recognize urban planning offers to a smarter city context, it is now necessary to understand the contribution of the smart city in overall urban planning and vice versa. Currently, transportation has been seen as a connection to all aspects of life across the world. This article presents the results of a survey testing into whether the public supports the concept of green transportation in the smart cities. The green urban mobility model has been proposed to investigate urban traffic information to characterize important features of smart mobility in the smart cities. The development of intelligent transportation systems using the proposed model that makes traffic easier in the city to transport safe and more comfortable. The experimental results suggest the required factors of green transportation and realistic behavior of smart mobility.     

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.