Maia Angelova

Effective Denoising and Adaptive Equalization of Indoor Optical Wireless Channel With Artificial Light Using the Discrete Wavelet Transform and Artificial Neural Network

Indoor diffuse optical wireless (OW) communication systems performance is limited due to a number of effects; interference from natural and artificial light sources and multipath induced intersymbol interference (ISI). Artificial light interference (ALI) is a periodic signal with a spectrum profile extending up to the MHz range. It is the dominant source of performance degradation at low data rates, which can be removed using a high-pass filter (HPF). On the other hand, ISI is more severe at high data rates and an equalizing filter is incorporated at the receiver to compensate for the ISI. This paper provides the simulation results for a discrete wavelet transform (DWT)-artificial neural network (ANN)-based receiver architecture for on-and-off keying (OOK) non-return-to-zero (NRZ) scheme for a diffuse indoor OW link in the presence of ALI and ISI. ANN is adopted for classification acting as an efficient equalizer compared to the traditional equalizers. The ALI is effectively reduced by proper selection of the DWT coefficients resulting in improved receiver performance compared to the digital HPF. The simulated bit error rate (BER) performance of proposed DWT-ANN receiver structure for a diffuse indoor OW link operating at a data range of 10-200 Mbps is presented and discussed. The results are compared with performance of a diffuse link with an HPF-equalizer, ALI with/without filtering, and a line-of-sight (LOS) without filtering. We show that the DWT-ANN display a lower power requirement when compared to the receiver with an HPF-equalizer over a full range of delay spread in presence of ALI. However, as expected compared to the ideal LOS link the power penalty is higher reaching to 6 dB at 200 Mbps data rate.

Generalized and Gaussian coherent states for the Morse potential

In this paper, we consider the one-dimensional anharmonic oscillator, which represents well the anharmonic vibrations in diatomic molecules. For the description of the associate potential we use the Morse potential, which gives a good approximation of the experimentally observed vibrational modes of molecules and hence contributes to the realistic description of the spectrum of diatomic molecules. The generalized and Gaussian coherent states are thus constructed and compared in terms of the localization of the particle in those states. We apply these results to the example of the sodium chloride molecule, 1H35Cl.

Targeted projection pursuit for visualizing gene expression data classifications

UNLABELLED We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-orthogonal projections, respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find 2D views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques. AVAILABILITY source code, additional diagrams, and original data are available from http://computing.unn.ac.uk/staff/CGJF1/tpp/bioinf.html

Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality

Data mining and knowledge discovery as an approach to examining medical data can limit some of the inherent bias in the hypothesis assumptions that can be found in traditional clinical data analysis. In this paper we illustrate the benefits of a data mining inspired approach to statistically analysing a bespoke data set, the academic multicentre randomised control trial, U.K Glucose Insulin in Stroke Trial (GIST-UK), with a view to discovering new insights distinct from the original hypotheses of the trial. We consider post-stroke mortality prediction as a function of days since stroke onset, showing that the time scales that best characterise changes in mortality risk are most naturally defined by examination of the mortality curve. We show that certain risk factors differentiate between very short term and intermediate term mortality. In particular, we show that age is highly relevant for intermediate term risk but not for very short or short term mortality. We suggest that this is due to the concept of frailty. Other risk factors are highlighted across a range of variable types including socio-demographics, past medical histories and admission medication. Using the most statistically significant risk factors we build predictive classification models for very short term and short/intermediate term mortality.

Squeezed coherent states and the one-dimensional Morse quantum system

The Morse potential one-dimensional quantum system is a realistic model for studying vibrations of atoms in a diatomic molecule. This system is very close to the harmonic oscillator one. We thus propose a construction of squeezed coherent states similar to the one of the harmonic oscillator using ladder operators. The properties of these states are analysed with respect to the localization in position, minimal Heisenberg uncertainty relation, the statistical properties and illustrated with examples using the finite number of states in a well-known diatomic molecule.

Wavelet—Artificial Neural Network Receiver for Indoor Optical Wireless Communications

The multipath-induced intersymbol interference (ISI) and fluorescent light interference (FLI) are the two most important system impairments that affect the performance of indoor optical wireless communication systems. The presence of either incurs a high optical power penalty and hence the interferences should be mitigated with suitable techniques to ensure optimum system performance. The discrete wavelet transform (DWT) and the artificial neural network (ANN)-based receiver to mitigate the effect of FLI and ISI has been proposed in the previous study for the ON-OFF keying (OOK) modulation scheme. It offers performance improvement compared to the traditional methods of employing a high-pass filter and a finite impulse response equalizer. In this paper, the investigation of the DWT-ANN-based receiver for baseband modulation techniques including OOK, pulse position modulation, and digital pulse interval modulation is reported. The proposed system is implemented using digital signal processing board and results are verified by comparison with simulation data.

Simple applications of q-bosons

A deformation of the harmonic oscillator algebra associated with the Morse potential and the SU(2) algebra is derived using the quantum analogue of the anharmonic oscillator. We use the quantum oscillator algebra or q-boson algebra, which is a generalization of the Heisenberg-Weyl algebra obtained by introducing a deformation parameter q. Further, we present a new algebraic realization of the q-bosons, for the case of q being a root of unity, which corresponds to a periodic structure described by a finite-dimensional representation. We show that this structure represents the symmetry of a linear lattice with periodic boundary conditions.

Automatic segmentation for Arabic characters in handwriting documents

The cursive and ligature nature of the Arabic script make the segmentation of words into individual characters a difficult task. Despite attempts to apply methods for cursive Latin and other scripts to Arabic script, it is generally insufficient to segment the Arabic text. This paper proposes a new segmentation algorithm for the handwritten Arabic text and the main idea consists of segmenting the word into sub-words and then computing the baseline of each sub-word. Using the descenders of sub-words and the baseline, candidate points are then calculated using a vertical projection. The algorithm has been tested using 800 handwritten Arabic words taken from the IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.

Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network

A Viterbi algorithm (VA) is the optimal decoding strategy for the convolutional code. The Viterbi algorithm is complex and requires a large memory and delay. In this paper, an alternative sub-optimal decoder based on the artificial neural network (ANN) is proposed and studied using a sliding block decoding algorithm. The ANN is trained in a supervised manner and the system parameters are optimised using computer simulations for the optimum performance. Comparative study with the Viterbi decoder is carried out. The performance of the ANN decoder is found to be comparable to the Viterbi ‘soft’ decoding with much reduced decoding length. The key advantages of the proposed ANN decoder compared with other ANN decoders are the reduced decoding and training length, adaptive decoding, no iteration required and possibility of parallel decoding.

Algebraic approach to thermodynamic properties of diatomic molecules

A simple model extending Lie algebraic techniques is applied to the analysis of thermodynamic vibrational properties of diatomic molecules. Local anharmonic effects are described by means of a Morse-like potential and the corresponding anharmonic bosons are associated with the SU(2) algebra. The total number of anharmonic bosons, fixed by the potential shape, is determined for a large number of diatomic molecules. A vibrational high-temperature partition function and the related thermodynamic functions are derived and studied in terms of the parameters of the model. The idea of a critical temperature is introduced in relation to the specific heat. A physical interpretation in terms of a quantum deformation associated with the model is given.