36 research outputs found
Fault tolerance issues in nanoelectronics
The astonishing success story of microelectronics cannot go on indefinitely. In fact, once
devices reach the few-atom scale (nanoelectronics), transient quantum effects are expected
to impair their behaviour. Fault tolerant techniques will then be required. The aim of this
thesis is to investigate the problem of transient errors in nanoelectronic devices. Transient
error rates for a selection of nanoelectronic gates, based upon quantum cellular automata
and single electron devices, in which the electrostatic interaction between electrons is used
to create Boolean circuits, are estimated. On the bases of such results, various fault tolerant
solutions are proposed, for both logic and memory nanochips. As for logic chips, traditional
techniques are found to be unsuitable. A new technique, in which the voting approach of
triple modular redundancy (TMR) is extended by cascading TMR units composed of
nanogate clusters, is proposed and generalised to other voting approaches. For memory
chips, an error correcting code approach is found to be suitable. Various codes are
considered and a lookup table approach is proposed for encoding and decoding. We are
then able to give estimations for the redundancy level to be provided on nanochips, so as to
make their mean time between failures acceptable. It is found that, for logic chips, space
redundancies up to a few tens are required, if mean times between failures have to be of the
order of a few years. Space redundancy can also be traded for time redundancy. As for
memory chips, mean times between failures of the order of a few years are found to imply
both space and time redundancies of the order of ten
Contributions To Automatic Particle Identification In Electron Micrographs: Algorithms, Implementation, And Applications
Three dimensional reconstruction of large macromolecules like viruses at resolutions below 8 Ã… - 10 Ã… requires a large set of projection images and the particle identification step becomes a bottleneck. Several automatic and semi-automatic particle detection algorithms have been developed along the years. We present a general technique designed to automatically identify the projection images of particles. The method utilizes Markov random field modelling of the projected images and involves a preprocessing of electron micrographs followed by image segmentation and post processing for boxing of the particle projections. Due to the typically extensive computational requirements for extracting hundreds of thousands of particle projections, parallel processing becomes essential. We present parallel algorithms and load balancing schemes for our algorithms. The lack of a standard benchmark for relative performance analysis of particle identification algorithms has prompted us to develop a benchmark suite. Further, we present a collection of metrics for the relative performance analysis of particle identification algorithms on the micrograph images in the suite, and discuss the design of the benchmark suite
Targeting Tight Junctions in Nanomedicine: a Molecular Modeling Perspective
Molecular Dynamics Simulations of Claudin Paracellular Channel
Protein microenvironments for topology analysis
Previously held under moratorium from 1st December 2016 until 1st December 2021Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined
by the properties of its surrounding residues. The term microenvironment is used
herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density,
boundaries between domains and junction complexity. These quantifications are
used to project a protein’s backbone structure into a series of scores.
The hypothesis was that these sequences of scores can be used to discover protein
domains and motifs and that they can be used to align and compare groups of
3D protein structures.
This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The
computation of the microenvironments was the most challenging aspect in terms
of performance, and the optimisations required are described.
Methods of scoring microenvironments were developed to enable the extraction
of domain and motif data without 3D alignment. The problem of allosteric site
detection was addressed with a classifier that gave high rates of allosteric site
detection.
Overall, this work describes the development of a system that scales well with
increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process
large datasets by application to allosteric site detection, a problem that has not
previously been adequately solved.Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined
by the properties of its surrounding residues. The term microenvironment is used
herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density,
boundaries between domains and junction complexity. These quantifications are
used to project a protein’s backbone structure into a series of scores.
The hypothesis was that these sequences of scores can be used to discover protein
domains and motifs and that they can be used to align and compare groups of
3D protein structures.
This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The
computation of the microenvironments was the most challenging aspect in terms
of performance, and the optimisations required are described.
Methods of scoring microenvironments were developed to enable the extraction
of domain and motif data without 3D alignment. The problem of allosteric site
detection was addressed with a classifier that gave high rates of allosteric site
detection.
Overall, this work describes the development of a system that scales well with
increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process
large datasets by application to allosteric site detection, a problem that has not
previously been adequately solved