Software for integrated manufacturing systems, part 2

Part 1 presented an overview of the unified approach to manufacturing software. The specific characteristics of the approach that allow it to realize the goals of reduced cost, increased reliability and increased flexibility are considered. Why the blending of a components view, distributed languages, generics and formal models is important, why each individual part of this approach is essential, and why each component will typically have each of these parts are examined. An example of a specification for a real material handling system is presented using the approach and compared with the standard interface specification given by the manufacturer. Use of the component in a distributed manufacturing system is then compared with use of the traditional specification with a more traditional approach to designing the system. An overview is also provided of the underlying mechanisms used for implementing distributed manufacturing systems using the unified software/hardware component approach

Software for integrated manufacturing systems, part 1

For several years, a broad, unified approach to programming manufacturing cells, factory floors, and other manufacturing systems has been developed. It is based on a blending of distributed Ada, software components, generics and formal models. Among other things the machines and devices which make up the components, and the entire manufacturing cell is viewed as an assembly of software components. The purpose of this project is to reduce cost, increase the reliability and increase the flexibility of manufacturing software. An overview is given of the approach and an experimental generic factory floor controller that was developed using the approach is described. The controller is generic in the sense that it can control any one of a large class of factory floors making an arbitrary mix of parts

Dual input neural networks for positional sound source localization

In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones' coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings

Polynomial GCD using straight line program representation

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN032874 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Adaptive inverse filtering of room acoustics

Equalization techniques for high order, multichannel, FIR systems are important for dereverberation of speech observed in reverberation using multiple microphones. In this case the multichannel system represents the room impulse responses (RIRs). The existence of near-common zeros in multichannel RIRs can slow down the convergence rate of adaptive inverse filtering algorithms. In this paper, the effect of common and near-common zeros on both the closed-form and the adaptive inverse filtering algorithms is studied. An adaptive shortening algorithm of room acoustics is presented based on this study. 1

Modeling the partially coherent behavior of few-mode far-infrared grating spectrometers

Modelling ultra-low-noise far-infrared grating spectrometers has become crucial for the next generation of far-infrared space observatories. Conventional techniques are awkward to apply because of the partially coherent form of the incident spectral field, and the few-mode response of the optics and detectors. We present a modal technique for modelling the behaviour of spectrometers, which allows for the propagation and detection of partially coherent fields, and the inclusion of straylight radiated by warm internal surfaces. We illustrate the technique by modelling the behaviour of the Long Wavelength Band of the proposed SAFARI instrument on the well-studied SPICA mission.Comment: This paper is submitted to Journal Optical Society of America A. When accepted, the paper can be found here: https://opg.optica.org/josaa/home.cf

Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification

This paper studies modulation spectrum features ($\Phi$) and mel-frequency cepstral coefficients ($\Psi$) in joint speaker diarization and identification (JSID). JSID is important as speaker diarization on its own to distinguish speakers is insufficient for many applications, it is often necessary to identify speakers as well. Machine learning models are set up using convolutional neural networks (CNNs) on $\Phi$ and recurrent neural networks \unicode{x2013} long short-term memory (LSTMs) on $\Psi$, then concatenating into fully connected layers. Experiment 1 shows models on both $\Phi$ and $\Psi$ have better diarization error rates (DERs) than models on either alone; a CNN on $\Phi$ has DER 29.09\%, compared to 27.78\% for a LSTM on $\Psi$ and 19.44\% for a model on both. Experiment 1 also investigates aleatoric uncertainties and shows the model on both $\Phi$ and $\Psi$ has mean entropy 0.927~bits (out of 4~bits) for correct predictions compared to 1.896~bits for incorrect predictions which, along with entropy histogram shapes, shows the model helpfully indicates where it is uncertain. Experiment 2 investigates epistemic uncertainties as well as aleatoric using Monte Carlo dropout (MCD). It compares models on both $\Phi$ and $\Psi$ with models trained on x-vectors ($X$), before applying Kalman filter smoothing on epistemic uncertainties for resegmentation and model ensembles. While the two models on $X$ (DERs 10.23\% and 9.74\%) outperform those on $\Phi$ and $\Psi$ (DER 17.85\%) after their individual Kalman filter smoothing, combining them using a Kalman filter smoothing method improves the DER to 9.29\%. Aleatoric uncertainties are higher for incorrect predictions. Both Experiments show models on $\Phi$ do not distinguish overlapping speakers as well as anticipated. However, Experiment 2 shows model ensembles do better with overlapping speakers than individual models do.Comment: 12 pages, 7 figure

Scalar spheroidal harmonics in five dimensional Kerr-(A)dS

We derive expressions for the general five-dimensional metric for Kerr-(A)dS black holes. The Klein-Gordon equation is explicitly separated and we show that the angular part of the wave equation leads to just one spheroidal wave equation, which is also that for charged five-dimensional Kerr-(A)dS black holes. We present results for the perturbative expansion of the angular eigenvalue in powers of the rotation parameters up to 6th order and compare numerically with the continued fraction method.Comment: 11 pages, two figures, one table; vz. 2: reference added and grammar correcte