arXiv subjects

Derek Abbott

Publications and source records attributed to Derek Abbott.

At least 91 records · Page 5Linked to original sources

arXiv2008

Low loss, low dispersion and highly birefringent terahertz porous fibers

We demonstrate that porous fibers have low effective material loss over an extended frequency range, 4.5 times larger bandwidth than that can be achieved in sub-wavelength solid core fibers. We also show that these new fibers can be designed to have near zero dispersion for 0.5-1 THz resulting to overall less terahertz signal degradation. In addition, it is demonstrated that the use of asymmetrical sub-wavelength air-holes within the core leads to high birefringence ~0.026. This opens up the potential for realization of novel polarization preserving fibers in the terahertz regime.

physics.optics↗

arXiv2007

Numerical removal of water-vapor effects from THz-TDS measurements

One source of disturbance in a pulsed T-ray signal is attributed to ambient water vapor. Water molecules in the gas phase selectively absorb T-rays at discrete frequencies corresponding to their molecular rotational transitions. This results in prominent resonances spread over the T-ray spectrum, and in the time domain the T-ray signal is observed as fluctuations after the main pulse. These effects are generally undesired, since they may mask critical spectroscopic data. So, ambient water vapor is commonly removed from the T-ray path by using a closed chamber during the measurement. Yet, in some applications a closed chamber is not applicable. This situation, therefore, motivates the need for another method to reduce these unwanted artifacts. This paper presents a study on a computational means to address the problem. Initially, a complex frequency response of water vapor is modeled from a spectroscopic catalog. Using a deconvolution technique, together with fine tuning of the strength of each resonance, parts of the water-vapor response are removed from a measured T-ray signal, with minimal signal distortion.

cs.CE↗

arXiv2007

Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance

Suprathreshold stochastic resonance (SSR) is a form of noise enhanced signal transmission that occurs in a parallel array of independently noisy identical threshold nonlinearities, including model neurons. Unlike most forms of stochastic resonance, the output response to suprathreshold random input signals of arbitrary magnitude is improved by the presence of even small amounts of noise. In this paper the information transmission performance of SSR in the limit of a large array size is considered. Using a relationship between Shannon's mutual information and Fisher information, a sufficient condition for optimality, i.e. channel capacity, is derived. It is shown that capacity is achieved when the signal distribution is Jeffrey's prior, as formed from the noise distribution, or when the noise distribution depends on the signal distribution via a cosine relationship. These results provide theoretical verification and justification for previous work in both computational neuroscience and electronics.

cond-mat.stat-mech↗

arXiv2006

Noise improvement of SNR gain in parallel array of bistable dynamic systems by array stochastic resonance

We report the regions where a signal-to-noise ratio (SNR) gain exceeding unity exists in a parallel uncoupled array of identical bistable systems, for both subthreshold and suprathreshold sinusoids buried in broadband Gaussian white input noise. Due to independent noise in each element of the parallel array, the SNR gain of the collective array response approaches its local maximum exhibiting a stochastic resonant behavior. Moreover, the local maximum SNR gain, at a non-zero optimal array noise intensity, increases as the array size rises. This leads to the conclusion of the global maximum SNR gain being obtained by an infinite array. We suggest that the performance of infinite arrays can be closely approached by an array of two bistable oscillators operating in different noisy conditions, which indicates a simple but effective realization of arrays for improving the SNR gain. For a given input SNR, the optimization of maximum SNR gains is touched upon in infinite arrays by tuning both array noise levels and an array parameter. The nonlinear collective phenomenon of SNR gain amplification in parallel uncoupled dynamical arrays, i.e. array stochastic resonance, together with the possibility of the SNR gain exceeding unity, represent a promising application in array signal processing.

cond-mat.other↗

arXiv2005

Nonlinear aspects of the EEG during sleep in children

Electroencephalograph (EEG) analysis enables the neuronal behavior of a section of the brain to be examined. If the behavior is nonlinear then nonlinear tools can be used to glean information on brain behavior, and aid in the diagnosis of sleep abnormalities such as obstructive sleep apnea syndrome (OSAS). In this paper the sleep EEGs of a set of normal and mild OSAS children are evaluated for nonlinear behaviour. We consider how the behaviour of the brain changes with sleep stage and between normal and OSAS children.

q-bio.NC↗

arXiv2005

Frequency-shift keying signal detection via short time stochastic resonance

A series of short time stochastic resonance (SR) phenomena, realized in a bistable receiver, can be utilized to convey train of information represented by frequency-shift keying (FSK) signals. It is demonstrated that the SR regions of the input noise intensity are adjacent for input periodic signals that differ in frequency appropriately. This establishes the possibility of decomposing M-ary FSK signals in bistable receivers. Furthermore, the mechanism of the M-ary FSK signal detection via short time SR effects is explicated in terms of the receiver response speed. The short time SR phenomenon might be of interest for neuronal information processing in non-stationary noisy environments, regardless of the short timescale or the frequency jitter of stimulus.

cond-mat.soft↗

arXiv2004

Quantum games with decoherence

A protocol for considering decoherence in quantum games is presented. Results for two-player, two-strategy quantum games subject to decoherence are derived and some specific examples are given. Decoherence in other types of quantum games is also considered. As expected, the advantage that a quantum player achieves over a player restricted to classical strategies is diminished for increasing decoherence but only vanishes in the limit of maximum decoherence.

quant-ph↗

arXiv2004

Evaluation of bistable systems versus matched filters in detecting bipolar pulse signals

This paper presents a thorough evaluation of a bistable system versus a matched filter in detecting bipolar pulse signals. The detectability of the bistable system can be optimized by adding noise, i.e. the stochastic resonance (SR) phenomenon. This SR effect is also demonstrated by approximate statistical detection theory of the bistable system and corresponding numerical simulations. Furthermore, the performance comparison results between the bistable system and the matched filter show that (a) the bistable system is more robust than the matched filter in detecting signals with disturbed pulse rates, and (b) the bistable system approaches the performance of the matched filter in detecting unknown arrival times of received signals, with an especially better computational efficiency. These significant results verify the potential applicability of the bistable system in signal detection field.

cond-mat.soft↗

arXiv2004

Point singularities and suprathreshold stochastic resonance in optimal coding

Motivated by recent studies of population coding in theoretical neuroscience, we examine the optimality of a recently described form of stochastic resonance known as suprathreshold stochastic resonance, which occurs in populations of noisy threshold devices such as models of sensory neurons. Using the mutual information measure, it is shown numerically that for a random input signal, the optimal threshold distribution contains singularities. For large enough noise, this distribution consists of a single point and hence the optimal encoding is realized by the suprathreshold stochastic resonance effect. Furthermore, it is shown that a bifurcational pattern appears in the optimal threshold settings as the noise intensity increases. Fisher information is used to examine the behavior of the optimal threshold distribution as the population size approaches infinity.

cond-mat.stat-mech↗

arXiv2004

Advantage of a quantum player over a classical one in 2x2 quantum games

We study a general $2 \times 2$ symmetric, entangled, quantum game. When one player has access only to classical strategies while the other can use the full range of quantum strategies, there are ``miracle'' moves available to the quantum player that can direct the result of the game towards the quantum player's preferred result regardless of the classical player's strategy. The advantage pertaining to the quantum player is dependent on the degree of entanglement. Below a critical level, dependent on the payoffs in the game, the miracle move is of no advantage.

quant-ph↗

arXiv2004

Signal processing and statistical methods in analysis of text and DNA

A number of signal processing and statistical methods can be used in analyzing either pieces of text or DNA sequences. These techniques can be used in a number of ways, such as determining authorship of documents, finding genes in DNA, and determining phylogenetic and linguistic trees. Signal processing methods such as spectrograms provide useful new tools in the area of genomic information science. In particular, fractal analysis of DNA ``signals'' has provided a new way of classifying organisms.

physics.data-an↗

arXiv2004

Stochastic evolution and multifractal classification of prokaryotes

We introduce a model for simulating mutation of prokaryote DNA sequences. Using that model we can then evaluated traditional techniques like parsimony and maximum likelihood methods for computing phylogenetic relationships. We also use the model to mimic large scale genomic changes, and use this to evaluate multifractal and related information theory techniques which take into account these large changes in determining phylogenetic relationships.

q-bio.PE↗

arXiv2004

Mutual information for examining correlations in DNA

This paper examines two methods for finding whether long-range correlations exist in DNA: a fractal measure and a mutual information technique. We evaluate the performance and implications of these methods in detail. In particular we explore their use comparing DNA sequences from a variety of sources. Using software for performing in silico mutations, we also consider evolutionary events leading to long range correlations and analyse these correlations using the techniques presented. Comparisons are made between these virtual sequences, randomly generated sequences, and real sequences. We also explore correlations in chromosomes from different species.

q-bio.PE↗

arXiv2004

Techniques for noise removal from EEG, EOG and air flow signals in sleep patients

Noise is present in the wide variety of signals obtained from sleep patients. This noise comes from a number of sources, from presence of extraneous signals to adjustments in signal amplification and shot noise in the circuits used for data collection. The noise needs to be removed in order to maximize the information gained about the patient using both manual and automatic analysis of the signals. Here we evaluate a number of new techniques for removal of that noise, and the associated problem of separating the original signal sources.

physics.data-an↗

arXiv2004

Fluctuations and noise in cancer development

This paper explores fluctuations and noise in various facets of cancer development. The three areas of particular focus are the stochastic progression of cells to cancer, fluctuations of the tumor size during treatment, and noise in cancer cell signalling. We explore the stochastic dynamics of tumor growth and response to treatment using a Markov model, and fluctutions in tumor size in response to treatment using partial differential equations. We also explore noise within gene networks in cancer cells, and noise in inter-cell signalling.

q-bio.CB↗

arXiv2004

Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms.

cs.NE↗

arXiv2004

Optimizing genetic algorithm strategies for evolving networks

This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such networks typically contain redundancy in servers, where several servers perform the same task and pleiotropy, where servers perform multiple tasks. We explore this trade-off between pleiotropy versus redundancy on the cost versus reliability as a measure of the quality of the network.

cs.NE↗

arXiv2004

An ordinary differential equation model for the multistep transformation to cancer

Cancer is viewed as a multistep process whereby a normal cell is transformed into a cancer cell through the acquisition of mutations. We reduce the complexities of cancer progression to a simple set of underlying rules that govern the transformation of normal cells to malignant cells. In doing so, we derive an ordinary differential equation model that explores how the balance of angiogenesis, cell death rates, genetic instability, and replication rates give rise to different kinetics in the development of cancer. The key predictions of the model are that cancer develops fastest through a particular ordering of mutations and that mutations in genes that maintain genomic integrity would be the most deleterious type of mutations to inherit. In addition, we perform a sensitivity analysis on the parameters included in the model to determine the probable contribution of each. This paper presents a novel approach to viewing the genetic basis of cancer from a systems biology perspective and provides the groundwork for other models that can be directly tied to clinical and molecular data.

q-bio.CB↗