Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification

Detecting compositional changes in dielectric materials simulated by three-dimensional RC network models

This work discusses the detection of small compositional changes in materials that have microstructures containing conducting and dielectric phases, which can be described by networks of resistive (R) and capacitive (C) components in a three-dimensional lattice. For this purpose, a principal component analysis (PCA) method is employed to discriminate normal samples from samples with altered composition on the basis of statistics extracted from the waveform of the network response to a given excitation. This approach obviates the requirement for multivariate regression and simplifies experimental workload for model-building, since only data from normal samples are required in the development of the PCA model. Waveform variability of the excitation source is also accounted for through the use of a nominal model derived using subspace identification. This enables standardization and software based metrology transfer across different labs. The effect of network size on the capability of detecting minute compositional changes was assessed. For networks of 520 components, it was possible to identify changes in the fraction of capacitors down to 10-2 at 2 sigma confidence levels

Wavelet based detection of changes in the composition of RLC networks

The current work discusses the compositional analysis of spectra that may be related to amorphous materials that lack discernible Lorentzian, Debye or Drude responses. We propose to model such response using a 3-dimensional random RLC network using a descriptor formulation which is converted into an input-output transfer function representation. A wavelet identification study of these networks is performed to infer the composition of the networks. It was concluded that wavelet filter banks enable a parsimonious representation of the dynamics in excited randomly connected RLC networks. Furthermore, chemometric classification using the proposed technique enables the discrimination of dielectric samples with different composition. The methodology is promising for the classification of amorphous dielectrics

Entanglement measure for general pure multipartite quantum states

We propose an explicit formula for an entanglement measure of pure multipartite quantum states, then study a general pure tripartite state in detail, and at end we give some simple but illustrative examples on four-qubits and m-qubits states.Comment: 5 page

Channel equalization for indoor lighting communications networks

We consider indoors communications networks using modulated LEDs to transmit the information packets. A generic indoor channel equalization formulation is proposed assuming the existence of both line of sight and diffuse emitters. The proposed approach is of relevance to emergent indoors distributed sensing modalities for which various lighting based network communications protocols are considered

Interplay between intrinsic plasma rotation and magnetic island evolution in disruptive discharges

The behavior of the intrinsic toroidal rotation of the plasma column during the growth and eventualsaturation of m/n = 2/1 magnetic islands, triggered by programmed density rise, has been carefully investigatedin disruptive discharges in TCABR. The results show that, as the island starts to grow and rotate at aspeed larger than that of the plasma column, the angular frequency of the intrinsic toroidal rotation increasesand that of the island decreases, following the expectation of synchronization. As the island saturates at alarge size, just before a major disruption, the angular speed of the intrinsic rotation decreases quite rapidly,even though the island keeps still rotating at a reduced speed. This decrease of the toroidal rotation is quitereproducible and can be considered as an indicative of disruption

Conversion of descriptor representations to state-space form: an extension of the shuffle algorithm

This paper proposes a systematic procedure for the determination of state-space models from an available descriptor representation of a linear dynamic system. The goal is to determine a state equation, a set of algebraic equations and an output equation in terms of the state and input variables. It is shown that standard methods may fail to convert the descriptor representation to state-space form, even for simple electrical circuit models obtained from Kirchoff’s laws and constitutive element equations. A novel procedure to address this problem is then proposed as an extension of the classic shuffle algorithm combined with a singular value decomposition approach. In addition to an illustrative example involving a simple electrical circuit, the proposed method is employed in a case study involving the modeling of three-dimensional RLC networks with a large number of components

On the application of optimal wavelet filter banks for ECG signal classification

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier

Effect of biochar on the mirobial enzymes activity of soil with Eucalyptus.

Session 5- Bacterial and Plant Physiology - Poster V.5