10,217 research outputs found
Adaptive Noise Cancellation Using Recurrent Radial Basis Function Networks
Radial basis function neural network architectures are introduced for the nonlinear adaptive noise cancellation problem. Both FIR and IIR filter designs are considered and it is shown that by exploiting the duality with system identification that the nonlinear IIR filter can be configured as a recurrent radial basis function network. Details of network training which is based on a combined k-means clustering and Givens routine, the inclusion of linear dynamic network links and metrics for performance monitoring are also discussed. Examples are included to demonstrate the degree of noise suppression that can be achieved based on the new design
Hardware-Efficient Structure of the Accelerating Module for Implementation of Convolutional Neural Network Basic Operation
This paper presents a structural design of the hardware-efficient module for
implementation of convolution neural network (CNN) basic operation with reduced
implementation complexity. For this purpose we utilize some modification of the
Winograd minimal filtering method as well as computation vectorization
principles. This module calculate inner products of two consecutive segments of
the original data sequence, formed by a sliding window of length 3, with the
elements of a filter impulse response. The fully parallel structure of the
module for calculating these two inner products, based on the implementation of
a naive method of calculation, requires 6 binary multipliers and 4 binary
adders. The use of the Winograd minimal filtering method allows to construct a
module structure that requires only 4 binary multipliers and 8 binary adders.
Since a high-performance convolutional neural network can contain tens or even
hundreds of such modules, such a reduction can have a significant effect.Comment: 3 pages, 5 figure
A space-time neural network
Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given
Analysis and application of digital spectral warping in analog and mixed-signal testing
Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation
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