35 research outputs found
Application of artificial neural networks and the wavelet transform for pattern recognition, noise reduction and data compression.
Theory of Artificial Neural Networks (ANNs) could not provide an exact method of
weights training. The training is done mostly by iterative trial and error minimisation
methods which do not enable the ANNs for time incremental learning. In this thesis, it is
shown that the weights successfully produced by an error minimisation method are
nothing more than the scaled versions of their respective components of the sample
pattern and that the training methods leaves a chance for a neuron to be deceived. An
exact method of weight construction is developed in the form of a system of linear
equations. A new linear classifier ANN and a number of thresholding procedures are
developed. It is proved that the Hopfield network and the Boltzmann machine do not
qualify as the reasonable networks. A generalised multiclass linear classifier ANN is
developed which is a combination of a newly developed multiclass linear ANN and a
newly developed multiclass XOR classifier ANN. A biological neuromuscular system is
interpreted as a multiclass linear classifier ANN. A new technique for pattern recognition.
especially for images, has been presented with a software check. The technique
minimises the design topology of ANNs and enables them to classify a scaled, a mirror
image, and a noisy version of the sample pattern.
The Continuous Wavelet Transform (CWT), the Discrete Wavelet Transform, and the
Wavelet Decomposition has been connected by developing an extend-able and intensifyable
system of particular six Gaussian wavelets. A binary transform applicable for every
real function is developed. The confusing automatic nature of the CWT is explained
along with presenting a new style of defining wavelets. Application of the wavelet
transforms for noise reduction and data compression/expansion is explained and their
performance is checked through the self developed software. A modification in the CWT
is made in order to make their application easier through ANNs. The ANNs are
developed and their performance is checked against the self developed software. A new
multiresolution zoom-out wavelet transform is developed which expands data without
smoothing it. A new wavelet is deduced from the smoothing average filter. Some twodimensional
wavelets for noise reduction and data compression/expansion are developed
on the same style and their performance is checked through the self developed software.
An ANN for CWT using a newly developed two-dimensional wavelet is developed and
its activation is explained. Data compression by locating peaks and bottoms of data and
setting other elements equals zero is done with the guarantee of reconstruction. The new
wavelet transform is modified to reconstruct the data between peaks and bottoms. Peaks
and bottoms detecting ANNs are developed and their performance is checked against the
self developed software. Procedures for classification are presented with self developed
software check. The theory of ANNs requires bit-wise parallel adders and multiplexors. A
parallel adder circuit is developed by combining some newly developed basic units for
the purpose
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In
recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence
Forschungsbericht 1997 : Berichtszeitraum 1995-1996
Berichtszeitraum 1995-199
Improved methods for functional neuronal imaging with genetically encoded voltage indicators
Voltage imaging has the potential to revolutionise neuronal physiology, enabling high temporal and spatial resolution monitoring of sub- and supra-threshold activity in genetically defined cell classes. Before this goal is reached a number of challenges must be overcome: novel optical, genetic, and experimental techniques must be combined to deal with voltage imaging’s unique difficulties.
In this thesis three techniques are applied to genetically encoded voltage indicator (GEVI)
imaging. First, I describe a multifocal two-photon microscope and present a novel source localisation control and reconstruction algorithm to increase scattering resistance in functional
imaging. I apply this microscope to image population and single-cell voltage signals from voltage sensitive fluorescent proteins in the first demonstration of multifocal GEVI imaging. Second, I show that a recently described genetic technique that sparsely labels cortical pyramidal
cells enables single-cell resolution imaging in a one-photon widefield imaging configuration.
This genetic technique allows simple, high signal-to-noise optical access to the primary excitatory
cells in the cerebral cortex. Third, I present the first application of lightfield microscopy
to single cell resolution neuronal voltage imaging. This technique enables single-shot capture of dendritic arbours and resolves 3D localised somatic and dendritic voltage signals. These approaches are finally evaluated for their contribution to the improvement of voltage imaging for physiology.Open Acces
Computational approaches to discovering differentiation genes in the peripheral nervous system of drosophila melanogaster
In the common fruit fly, Drosophila melanogaster, neural cell fate specification is triggered by
a group of conserved transcriptional regulators known as proneural factors. Proneural factors
induce neural fate in uncommitted neuroectodermal progenitor cells, in a process that culminates
in sensory neuron differentiation. While the role of proneural factors in early fate specification
has been described, less is known about the transition between neural specification
and neural differentiation. The aim of this thesis is to use computational methods to improve
the understanding of terminal neural differentiation in the Peripheral Nervous System (PNS) of
Drosophila.
To provide an insight into how proneural factors coordinate the developmental programme
leading to neural differentiation, expression profiling covering the first 3 hours of PNS development
in Drosophila embryos had been previously carried out by Cachero et al. [2011]. The
study revealed a time-course of gene expression changes from specification to differentiation
and suggested a cascade model, whereby proneural factors regulate a group of intermediate
transcriptional regulators which are in turn responsible for the activation of specific differentiation
target genes.
In this thesis, I propose to select potentially important differentiation genes from the transcriptional
data in Cachero et al. [2011] using a novel approach centred on protein interaction
network-driven prioritisation. This is based on the insight that biological hypotheses supported
by diverse data sources can represent stronger candidates for follow-up studies. Specifically,
I propose the usage of protein interaction network data because of documented transcriptome-interactome
correlations, which suggest that differentially expressed genes encode products
that tend to belong to functionally related protein interaction clusters.
Experimental protein interaction data is, however, remarkably sparse. To increase the informative
power of protein-level analyses, I develop a novel approach to augment publicly
available protein interaction datasets using functional conservation between orthologous proteins
across different genomes, to predict interologs (interacting orthologs). I implement this
interolog retrieval methodology in a collection of open-source software modules called Bio::
Homology::InterologWalk, the first generalised framework using web-services for “on-the-
fly” interolog projection. Bio::Homology::InterologWalk works with homology data
for any of the hundreds of genomes in Ensembl and Ensembgenomes Metazoa, and with experimental
protein interaction data curated by EBI Intact. It generates putative protein interactions
and optionally collates meta-data into a prioritisation index that can be used to help
select interologs with high experimental support. The methodology proposed represents a significant
advance over existing interolog data sources, which are restricted to specific biological
domains with fixed underlying data sources often only accessible through basic web-interfaces.
Using Bio::Homology::InterologWalk, I build interolog models in Drosophila sensory
neurons and, guided by the transcriptome data, find evidence implicating a small set of genes
in a conserved sensory neuronal specialisation dynamic, the assembly of the ciliary dendrite in
mechanosensory neurons. Using network community-finding algorithms I obtain functionally
enriched communities, which I analyse using an array of novel computational techniques. The
ensuing datasets lead to the elucidation of a cluster of interacting proteins encoded by the target
genes of one of the intermediate transcriptional regulators of neurogenesis and ciliogenesis,
fd3F. These targets are validated in vivo and result in improved knowledge of the important
target genes activated by the transcriptional cascade, suggesting a scenario for the mechanisms
orchestrating the ordered assembly of the cilium during differentiation
Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2
Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation
Algebraic Structures of Neutrosophic Triplets, Neutrosophic Duplets, or Neutrosophic Multisets
Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the neutrosophic triplets were founded, which have a similar form (x, neut(x), anti(x)), that satisfy several axioms, for each element x in a given set.This collective book presents original research papers by many neutrosophic researchers from around the world, that report on the state-of-the-art and recent advancements of neutrosophic triplets, neutrosophic duplets, neutrosophic multisets and their algebraic structures – that have been defined recently in 2016 but have gained interest from world researchers. Connections between classical algebraic structures and neutrosophic triplet / duplet / multiset structures are also studied. And numerous neutrosophic applications in various fields, such as: multi-criteria decision making, image segmentation, medical diagnosis, fault diagnosis, clustering data, neutrosophic probability, human resource management, strategic planning, forecasting model, multi-granulation, supplier selection problems, typhoon disaster evaluation, skin lesson detection, mining algorithm for big data analysis, etc