A Simple Instrumentation Calibration Technique for Electrical Impedance Tomography (EIT) Using A 16-Electrode Phantom

A simple analog instrumentation for Electrical Impedance Tomography is developed and calibrated using the practical phantoms. A constant current injector consisting of a modified Howland voltage controlled current source fed by a voltage controlled oscillator is developed to inject a constant current to the phantom boundary. An instrumentation amplifier, 50 Hz notch filter and a narrow band pass filter are developed and used for signal conditioning. Practical biological phantoms are developed and the forward problem is studied to calibrate the EIT-instrumentation. An array of sixteen stainless steel electrodes is developed and placed inside the phantom tank filled with KCl solution. 1 mA, 50 kHz sinusoidal current is injected at the phantom boundary using adjacent current injection protocol. The differential potentials developed at the voltage electrodes are measured for sixteen current injections. Differential voltage signal is passed through an instrumentation amplifier and a filtering block and measured by a digital multimeter. A forward solver is developed using Finite Element Method in MATLAB7.0 for solving the EIT governing equation. Differential potentials are numerically calculated using the forward solver with a simulated current and bathing solution conductivity. Measured potential data is compared with the differential potentials calculated for calibrating the instrumentation to acquire the voltage data suitable for better image reconstruction.

Supervisory Control for Real-Time Systems Based on Conflict-Tolerant Controllers

This paper addresses the problem of detecting and resolving conflicts due to timing constraints imposed by features in real-time and hybrid systems. We consider systems composed of a base system with multiple features or controllers, each of which independently advise the system on how to react to input events so as to conform to their individual specifications. We propose a methodology for developing such systems in a modular manner based on the notion of conflict-tolerant features that are designed to continue offering advice even when their advice has been overridden in the past. We give a simple priority-based scheme forcomposing such features. This guarantees the maximal use of each feature. We provide a formal framework for specifying such features, and a compositional technique for verifying systems developed in this framework.

RACK: RApid Clustering using K-means algorithm

The k-means algorithm is an extremely popular technique for clustering data. One of the major limitations of the k-means is that the time to cluster a given dataset D is linear in the number of clusters, k. In this paper, we employ height balanced trees to address this issue. Specifically, we make two major contributions, (a) we propose an algorithm, RACK (acronym for RApid Clustering using k-means), which takes time favorably comparable with the fastest known existing techniques, and (b) we prove an expected bound on the quality of clustering achieved using RACK. Our experimental results on large datasets strongly suggest that RACK is competitive with the k-means algorithm in terms of quality of clustering, while taking significantly less time.

Intent Based Clustering of Search Engine Query Log

The keyword based search technique suffers from the problem of synonymic and polysemic queries. Current approaches address only theproblem of synonymic queries in which different queries might have the same information requirement. But the problem of polysemic queries,i.e., same query having different intentions, still remains unaddressed. In this paper, we propose the notion of intent clusters, the members of which will have the same intention. We develop a clustering algorithm that uses the user session information in query logs in addition to query URL entries to identify cluster of queries having the same intention. The proposed approach has been studied through case examples from the actual log data from AOL, and the clustering algorithm is shown to be successful in discerning the user intentions.

Sensor network deployment for agronomical data gathering in semi-arid regions

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

Sequence design for symbol-asynchronous CDMA with power or rate constraints

Sequence design and resource allocation for a symbol-asynchronous chip-synchronous code division multiple access (CDMA) system is considered in this paper. A simple lower bound on the minimum sum-power required for a non-oversized system, based on the best achievable for a non-spread system, and an analogous upper bound on the sum rate are first summarised. Subsequently, an algorithm of Sundaresan and Padakandla is shown to achieve the lower bound on minimum sum power (upper bound on sum rate, respectively). Analogous to the synchronous case, by splitting oversized users in a system with processing gain N, a system with no oversized users is easily obtained, and the lower bound on sum power (upper bound on sum rate, respectively) is shown to be achieved by using N orthogonal sequences. The total number of splits is at most N - 1.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Metabolism of Plasmodium bergheistar, open: III. Carbon dioxide fixation and role of pyruvate and dicarboxylic acids

Formation of C4 dicarboxylic acids in Plasmodium berghei by carbon dioxide fixation reaction has been demonstrated by the use of labeled NaH14CO3. The reactions require glucose, which may be required not only as an energy source but also to contribute to the formation of pyruvate in the process of carbon dioxide fixation. Intracellular concentration of pyruvate may play an important role in the metabolism of P. berghei; an increased intracellular level of pyruvate seems to be a prerequisite before some of these reactions could be detected. The distribution of the label indicates extensive randomization of amino acids and suggests an extensive cycling of the amino acid and organic acid pools of the parasites. This investigation formed part of the thesis submitted in 1965 for the doctoral degree at the Indian Institute of Science, Bangalore 12, India, and was supported in part by the Council of Scientific and Industrial Research, India.

Metabolism of Plasmodium bergheistar, open: II. 32Pi incorporation into high-energy phosphates

Free parasites of Plasmodium berghei were found to incorporate labeled inorganic phosphate into high-energy phosphates by substrate linked and oxidative hosphorylation. But the parasites also appear to utilize the reserve ATP of the host cells when they are within the host cells which may indicate the dependence of the parasite on the host cells for provision of energy. This investigation formed part of the thesis submitted in 1965 for the doctoral degree at the Indian Institute of Science, Bangalore 12, India, and was supported in part by the Council of Scientific and Industrial Research, India.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Mahanadi streamflow: climate change impact assessment and adaptive strategies

Impacts of climate change on hydrology are assessed by downscaling large scale general circulation model (GCM) outputs of climate variables to local scale hydrologic variables. This modelling approach is characterized by uncertainties resulting from the use of different models, different scenarios, etc. Modelling uncertainty in climate change impact assessment includes assigning weights to GCMs and scenarios, based on their performances, and providing weighted mean projection for the future. This projection is further used for water resources planning and adaptation to combat the adverse impacts of climate change. The present article summarizes the recent published work of the authors on uncertainty modelling and development of adaptation strategies to climate change for the Mahanadi river in India.

Some pulse characteristics of atmospheric radio noise bursts at 3 MHz

An atmospheric radio noise burst represents the radiation received from one complete lightning flash at the frequency to which a receiver is tuned and within the receiver bandwidth. At tropical latitudes, the principal source of interference in the frequency range from 0.1 to 10 MHz is the burst form of atmospheric radio noise. The structure of a burst shows several approximately rectangular pulses of random amplitude, duration and frequency of recurrence. The influence of the noise on data communication can only be examined when the value of the number of pulses crossing a certain amplitude threshold per unit time of the noise burst is known. A pulse rate counter designed for this purpose has been used at Bangalore (12°58′N, 77°35′E) to investigate the pulse characteristics of noise bursts at 3 MHz with a receiver bandwidth of 3.3 kHz/6d B. The results show that the number of pulses lying in the amplitude range between peak and quasi-peak values of the noise bursts and the burst duration corresponding to these pulses follow log normal distributions. The pulse rates deduced therefrom show certain correlation between the number of pulses and the duration of the noise burst. The results are discussed with a view to furnish necessary information for data communication.