Gaussian Processing Research Articles

Enhanced multi-level filter algorithm and its VLSI architecture

This paper presents an enhanced multi-level filter algorithm and its Very Large Scale Integration (VLSI) architecture for infrared image processing. The modified multi-level filter algorithm resolves the splitting targets problem using Gaussian pyramid processing. Owning three filtering paths, the proposed VLSI architecture of the filter can simultaneously enhance small targets with different sizes in infrared images. Some design techniques in implementing hardwired multiplication, subsample and asynchronous FIFO have been presented. This VLSI architecture has been implemented using Semiconductor Manufacturing International Corporation (SMIC) 0.35 µm 4-layer CMOS technology. The simulation results show that it not only effectively suppresses background, eliminates noise and enhances small targets in an infrared image comparing with other small target detective methods, but also meets infrared image real-time processing requirements (5 M ∼ 10 M pixels/s). The implemented filter chip consists of 60,284 gates and 8 K Static Random Access Memory (SRAM), operates at 50 MHz.

Transition of interarrival time patterns between automated and manual configurations of assembly systems

The paradigm of hybrid automatic/manual assembly systems has recently been extended by reconfigurable systems that allow exchanging manual and automated production modules with minimum interruption of the system control and operation. However, both types of assembly systems exhibit different interarrival time patterns for layouts that employ asynchronous workpiece transport on tracks with multiple routes and loops. In this work, it has been shown analytically that for Gaussian processing time distributions the queue length follows heavy-tailed unimodal and bimodal distributions. Based on the resulting outlier probability, an analytical model for predicting the limits of allowable processing time variance for deterministic interarrival time models is proposed. This model is positively validated with simulation analysis by comparing different levels of processing time variance. It is concluded that established design practices such as cycle-time balancing and synchronization should be avoided in reconfigurable hybrid assembly system design because they introduce interarrival-time turbulence in the automated system.

Particle Size Analysis by Transmission Fluctuation Spectrometry: Experimental Results Obtained with a Gaussian Beam and Analog Signal Processing

AbstractTransmission fluctuation spectrometry (TFS) is a method for the analysis of particle size distributions based on the statistical fluctuations of a transmission signal. Complete information on the PSD and particle concentration can be retrieved by a special transformation of the transmission signal, whereby the expectancy of the transmission square (ETS) is determined after the signal has been subjected to a procedure of spatial and temporal averaging. By varying the averaging parameters over a wide range, a spectrum of ETSs is obtained and introduced into a linear equation system, which yields the PSD. In the experimental realization presented here, variable temporal averaging is realized in the frequency domain with a series of low pass filters at different cutoff frequencies while spatial averaging inevitably occurs as the particles pass through a focused Gaussian beam of finite cross section. Experimental results on spherical particles (glass beads) and non‐spherical particles (SiC) are presented. The PSDs are resolved in 30 intervals within a particle size range from 1–1000 μm, employing a modified Chahine inversion algorithm. So far, the measurements are limited to moderate particle concentrations. Some influences affecting the measurements, especially for higher particle concentrations, are discussed in detail.

98/03744 Cogasification of birch wood and Daw Mill coal in pressurized fluidized bed reactor

The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857–0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.

Image processing of computerised visual field data.

Computerised perimetry is of fundamental importance in assessing visual function. However, visual fields are subject to patient response variability which limits the detection of true visual loss. A method of improving the repeatability of visual field data was demonstrated by applying techniques used in image processing. An illustrative sample of nine normals and nine patients with field loss was used. Two successive Humphrey fields were selected for each subject. Repeatability was defined as the standard deviation of the pointwise differences between sensitivity values of the reference field and repeat field. The field data were then separately subjected to Gaussian and median image processing filters and the repeatability was compared with the unprocessed field results. Improvement in repeatability, by a factor of approximately 2, was demonstrated by both processes. These techniques may improve the reliable detection of loss of visual function using computerised perimetry.

Signal processing with fractional lower order moments: stable processes and their applications

Non-Gaussian statistical signal processing is important when signals and/or noise deviate from the ideal Gaussian model. Stable distributions are among the most important non-Gaussian models. They share defining characteristics with the Gaussian distribution, such as the stability property and central limit theorems, and in fact include the Gaussian distribution as a limiting case. To help engineers better understand the stable models and develop methodologies for their applications in signal processing. A tutorial review of the basic characteristics of stable distributions and stable signal processing is presented. The emphasis is on the differences and similarities between stable signal processing methods based on fractional lower-order moments and Gaussian signal processing methods based on second-order moments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Parallel optical pyramidal image processing

Pyramidal processing is a form of multiresolution image processing in which the image is decomposed into a sequence of images at different resolutions. Pyramidal processing aims to extract and interpret significant features of an image at different resolutions. Digital pyramidal image processing, because of the large number of convolution- type operations, is time consuming. On the other hand, optical pyramidal processors, described here, are preferable in real-time image-understanding applications because of their ease in performing convolution operations. Preliminary experimental results for optical Gaussian and Laplacian pyramidal image processing are presented.

A maximum entropy criterion of filtering and coding for stationary autoregressive signals: its physical interpretations and suggestions for its application to neural information transmission.

The operations of encoding and decoding in communication agree with filtering operations of convolution and deconvolution for Gaussian signal processing. In an analogy with power transmission in thermodynamics, an autoregressive model of information transmission is proposed for representing a continuous communication system which requires a pair of an internal noise source and a signal source to encode or decode a message. In this model transinformation (informational entropy) equals the increase in stationary nonequilibrium organization formed through the amplification of white noise by a positive feedback system. The channel capacity is finite due to the existence of inherent noise in the system. The maximum entropy criterion in information dynamics corresponds to the 2nd law of thermodynamics. If the process is stationary, the communication system is invertible, and has the maximum efficiency of transformation. The total variation in informational entropy is zero in the cycle of the invertible system, while in the noninvertible system the entropy of decoding is less than that of encoding. A noisy autoregressive coding which maximizes transinformation is optimum, but is also ideal.