Rough-fuzzy collaborative clustering.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.     

Density-based multiscale data condensation

A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity     

Electrochemical machining: new possibilities for micromachining

A better understanding of high rate anodic dissolution processes is urgently required for electrochemical micromachining (EMM) to become a widely employed manufacturing process in the electronic and precision manufacturing industries particularly in the micromanufacturing domain. A successful attempt has been made to develop an EMM setup for carrying out in depth independent research for achieving satisfactory control of electrochemical machining process parameters to meet the micromachining requirements. The developed EMM setup mainly consists of various sub-components and systems, e.g., mechanical machining unit, microtooling system, electrical power and controlling system and controlled electrolyte flow system, etc. All these system components are integrated in such a way that the developed EMM system setup will be capable of performing basic and fundamental research in the area of EMM fulfilling the requirements of micromachining objectives.     

Low-passband-sensitivity switched capacitor filters using a parallel connection of two structurally lossless networks

The low-sensitivity property of IIR digital filters designed using a parallel connection of two allpass sections has already been demonstrated in the past few years. This property is closely related to the use of a particular type of allpass structures, called structurally lossless networks, which remain allpass regardless of the coefficient quantization. In this paper we provide a general theory for the synthesis of low-passband-sensitivity switched capacitor (SC) filters using a similar technique. the design of stray-insensitive structurally allpass SC sections is emphasized, ensuring the passivity property for the overall filter. Design examples are presented with computer simulations and experimental results to compare the performance of the different proposed structures. Analytical sensitivity calculations are also derived yielding useful formulae for the prediction of the filter sensitivities with respect to capacitor ratios, particularly in the stopband.     

Chemically deposited zinc oxide thin film gas sensor

Zinc oxide (ZnO) thin films were prepared by a low cost chemical deposition technique using sodium zincate bath. Structural characterizations by X-ray diffraction technique (XRD) and scanning electron microscopy (SEM) indicate the formation of ZnO films, containing 0.05–0.50 m size crystallites, with preferred c-axis orientation. The electrical conductance of the ZnO films became stable and reproducible in the 300–450 K temperature range after repeated thermal cyclings in air. Palladium sensitised ZnO films were exposed to toxic and combustible gases e.g., hydrogen (H₂), liquid petroleum gas (LPG), methane (CH₄) and hydrogen sulphide (H₂S) at a minimum operating temperature of 150 °C; which was well below the normal operating temperature range of 200–400 °C, typically reported in literature for ceramic gas sensors. The response of the ZnO thin film sensors at 150 °C, was found to be significant, even for parts per million level concentrations of CH₄ (50 ppm) and H₂S (15 ppm).     

Analysis of dynamic brain imaging data

Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.     

Development and comparison of models for light-pulse transport through scattering-absorbing media

We examine the transport of short light pulses through scattering-absorbing media through different approximate mathematical models. It is demonstrated that the predicted optical signal characteristics are significantly influenced by the various models considered, such as P(N) expansion, two-flux, and discrete ordinates. The effective propagation speed of the scattered radiation, the predicted magnitudes of the transmitted and backscattered fluxes, and the temporal shape and spread of the optical signals are functions of the models used to represent the intensity distributions. A computationally intensive direct numerical integration scheme that does not utilize approximations is also implemented for comparison. Results of some of the models asymptotically approach those of direct numerical simulation if the order of approximation is increased. In this study therefore we identify the importance of model selection in analyzing short-pulse laser applications such as optical tomography and remote sensing and highlight the parameters, such as wave speed, that must be examined before a model is adopted for analysis.     

Transient radiative transfer equation applied to oceanographic lidar

We estimate the optical signal for an oceanographic lidar from the one-dimensional transient (time-dependent) radiative transfer equation using the discrete ordinates method. An oceanographic lidar directs a pulsed blue or green laser into the ocean and measures the time-dependent backscattered light. A large number of parameters affect the performance of such a system. Here the optical signal that is available to the receiver is calculated, rather than the receiver output, to reduce the number of parameters. The effects of albedo of a uniform water column are investigated. The effects of a school of fish in the water are also investigated for various school depths, thicknesses, and densities. The attenuation of a lidar signal is found to be greater than the diffuse attenuation coefficient at low albedo and close to it at higher albedo. The presence of fish in the water is found to have a significant effect on the signal at low to moderate albedo, but not at high albedo.     

An MLP-based model for identifying qEEG in depression

Manual differentiation of electroencephalography (EEG) paper recordings in cases of depression is not very helpful. So, a Multilayer Perceptron (MLP) has been used to differentiate the EEG power density spectra (qEEG) in the wakeful state from animals (control, exercised and depressed). The qEEG ranging from 1 to 30 Hz, at 1 Hz increments (30 input features) and also a slow, medium and fast activity (represented by three ranges of frequencies at the input) were used. After training with depressed and control qEEG only, the MLP has been found to distinguish successfully between the normal and the depressed rats in more than 80% of the cases, identifying, in the process, most of the exercised groups' EEG as normal. The reduction in the dimension of input features from 30 individual frequencies to 3 frequency bands has produced similar results. The rules generated for making such distinctions have been found to be similar to the clinical views.