Asok Bhattacharyya

A new mixed mode biquad using reduced number of active and passive elements

The configuration that can work in mixed mode may be useful from IC realization viewpoint and application adaptability. This paper proposes a generalized mixed mode universal filter configuration that may be used in all possible modes i.e. voltage mode, current mode, trans-impedance mode and trans-admittance mode. The architecture uses minimum number of active and passive components reported till date and can realize all generic filter functions: low pass, band pass, high pass, notch and all pass. PSPICE simulation results agree well with experimental values.

Optimal sensor placement for surveillance of large spaces

Visual sensor network design facilitates applications such as intelligent rooms, video surveillance, automatic multi-camera tracking, activity recognition etc. These applications require an efficient visual sensor layout which provides a minimum level of image quality or image resolution. This paper addresses the practical problem of optimally placing the multiple PTZ cameras to ensure maximum coverage of user defined priority areas with optimum values of parameters like pan, tilt, zoom and the locations of the cameras. The proposed algorithm works offline and does not require camera calibration. We mapped this problem as an optimization problem using Genetic Algorithm, by defining, coverage matrix as a set of sensor parameters and the space model parameters like priority areas, obstacles and feasible locations of the sensors, and by modelling discrete spaces using probabilistic frame work. We minimized the probability of occlusion due to randomly moving objects by covering each priority area using multiple cameras. The proposed method will be applicable for surveillance of large spaces with discrete priority areas like a hall with more than one entrance or many events happening at different locations in a hall eg.Casino. As we are optimizing the parameters like pan, tilt, zoom and even the locations of the cameras, the coverage provided by this approach will assure good resolution, which improves the QOS of the visual sensor network.

A novel current controlled current mode universal filter: SITO approach

A novel electronically tunable single-input three-output (SITO) universal filter employing three current controlled conveyors and two grounded capacitors is presented. The proposed filter offers the following advantageous features: low input impedance and high output impedance- a desirable property of current mode filters, realization of low pass, band pass, high pass, notch and all pass signals from the same configuration, no matching constraint, low sensitivity performance and use of grounded capacitors ideal for integration. The validity of the proposed filter is verified through PSPICE simulations.

Handcrafted fuzzy rules for tissue classification.

This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.

On improving the performance of Traff's comparator

In this paper a new current comparator is proposed which offers high speed and high resolution while maintaining low power dissipation. The design improves upon previous Traff current comparator by modifying the given gain stage which leads to up to 83% improvement in delay. Simulation results performed on SPICE using TSMC 0.18μm CMOS technology demonstrate that proposed current comparator has a resolution of ± 10nA and delay of 0.86ns at ± 1μA input current. Performance for lower supply voltages is also reported.

An insensitive current mode universal biquad: multi-input multi-output

A current mode multi-input multi-output (MIMO) universal biquad using multi-output CCCII (MO-CCCII) is presented. The filter offers the following attractive features: orthogonal control of Q/sub 0/ and /spl omega//sub 0/, electronic control of /spl omega//sub 0/, Q/sub 0/, /spl omega//sub 0//Q/sub 0/ and band pass gain via bias currents of MO-CCCII, convenience for monolithic integration because of all grounded passive elements, high output impedance and low sensitivity. The theoretical and simulated results are found to be well in agreement. The circuit is also analyzed for nonideal current conveyors to show that it still functions as a MIMO universal biquad but with slightly altered values of /spl omega//sub 0/ and Q/sub 0/.

Tissue classification in magnetic resonance images through the hybrid approach of Michigan and Pittsburg genetic algorithm

Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable.

Soft-computing based diagnostic tool for analyzing demyelination in magnetic resonance images

This paper proposes a soft-computing based diagnostic tool for analyzing (white matter changes) demyelination due to radiation therapy given to brain tumor cases. The tool exploits the pattern of changes in gray level distribution using a temporal sequence of magnetic resonance (MR) images. Appearance of white matter changes due to demyelination varies from patient to patient. Further, there exists inherent impreciseness in the white matter change patterns. These characteristics make use of fuzzy features well suited for describing image based temporal patterns. Correlation between these temporal patterns and actual onset of demyelination can be captured by fuzzy rules because of the inherent uncertainty associated with changes in gray level pattern in the image and occurrence of the disease. The tool is based on hybrid approach of two popular approaches of genetic algorithm based machine learning (GBML) techniques namely Michigan and Pittsburgh approach. The genetic algorithm (GA) based machine learning tool generates an optimized rule set to indicate positive (P), negative (N) or doubtful (D) cases of demyelination.

Delay area efficient low voltage FVF based current comparator

This paper presents an improved current comparator using flipped voltage follower (FVF) to obtain the single supply voltage. This circuit exhibits short propagation delay and occupies a small chip area. The proposed circuit has been simulated employing PSpice simulator for 0.18 μm CMOS technology and a comparison has been performed with its non FVF counterpart to contrast its effectiveness, simplicity, compactness and low power consumption.

Bio-Inspired Distributed Sensing Using a Self-Organizing Sensor Network

Nature offers several examples of self-organizing systems that automatically adjust to changing conditions without adversely affecting the system goals. We propose a self-organizing sensor network that is inspired from real-life systems for sampling a region in an energy-efficient manner. Mobile nodes in our network execute certain rules by processing local information. These rules enable the nodes to divide the sampling task in a manner such that the nodes self-organize themselves to reduce the total power consumed and improve the accuracy with which the phenomena are sampled. The digital hormone-based model that encapsulates these rules, provides a theoretical framework for examining this class of systems. This model has been simulated and implemented on cricket motes. Our results indicate that the model is more effective than a conventional model with a fixed rate sampling.