234 research outputs found
Minimum Energy Information Fusion in Sensor Networks
In this paper we consider how to organize the sharing of information in a
distributed network of sensors and data processors so as to provide
explanations for sensor readings with minimal expenditure of energy. We point
out that the Minimum Description Length principle provides an approach to
information fusion that is more naturally suited to energy minimization than
traditional Bayesian approaches. In addition we show that for networks
consisting of a large number of identical sensors Kohonen self-organization
provides an exact solution to the problem of combining the sensor outputs into
minimal description length explanations.Comment: postscript, 8 pages. Paper 65 in Proceedings of The 2nd International
Conference on Information Fusio
An improved multi-dimensional CMAC neural network: Receptive field function and placement
The standard CMAC has been shown to have fast learning computation as a result of modular receptive field placement, rectangular receptive field shape and a simple weight adaptation algorithm. The standard CMAC, however, suffers from slow convergence at some critical frequency due to the rectangular receptive field shape. A linearly-tapered field, which requires a uniform placement, was used in this research. The receptive field placement of the standard CMAC becomes less uniform locally for a larger receptive field width. This dissertation suggests a new field placement which is more uniform without extra computation. Results show that the slow convergence at the critical frequency is eliminated, and the interaction of the linearly-tapered field with the new placement achieves more accurate function approximation. A theoretical bound on the receptive field width as a function of the input dimension is proposed if a uniform placement is to be achieved. Also, a procedure for adapting receptive field density to minimize the weight usage for a given approximation accuracy is suggested
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
Image Compression Using Cascaded Neural Networks
Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented
Image Compression Using Cascaded Neural Networks
Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented
Dynamics and topographic organization of recursive self-organizing maps
Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographicmaps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizingmap (SOM) for processing sequential data, recursive SOM (RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data. © 2006 Massachusetts Institute of Technology
Development of the Feature Extractor for Speech Recognition
Projecte final de carrera realitzat en col.laboració amb University of MariborWith this diploma work we have attempted to give continuity to the previous work done by
other researchers called, Voice Operating Intelligent Wheelchair – VOIC [1]. A development of
a wheelchair controlled by voice is presented in this work and is designed for physically disabled
people, who cannot control their movements. This work describes basic components of speech
recognition and wheelchair control system.
Going to the grain, a speech recognizer system is comprised of two distinct blocks, a Feature
Extractor and a Recognizer. The present work is targeted at the realization of an adequate
Feature Extractor block which uses a standard LPC Cepstrum coder, which translates the
incoming speech into a trajectory in the LPC Cepstrum feature space, followed by a Self
Organizing Map, which classifies the outcome of the coder in order to produce optimal
trajectory representations of words in reduced dimension feature spaces. Experimental results
indicate that trajectories on such reduced dimension spaces can provide reliable representations
of spoken words. The Recognizer block is left for future researchers.
The main contributions of this work have been the research and approach of a new
technology for development issues and the realization of applications like a voice recorder and
player and a complete Feature Extractor system
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