Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

Performance Analysis of Reconstruction Techniques for Frequency-Domain Optical-Coherence Tomography

We address the issue of noise robustness of reconstruction techniques for frequency-domain optical-coherence tomography (FDOCT). We consider three reconstruction techniques: Fourier, iterative phase recovery, and cepstral techniques. We characterize the reconstructions in terms of their statistical bias and variance and obtain approximate analytical expressions under the assumption of small noise. We also perform Monte Carlo analyses and show that the experimental results are in agreement with the theoretical predictions. It turns out that the iterative and cepstral techniques yield reconstructions with a smaller bias than the Fourier method. The three techniques, however, have identical variance profiles, and their consistency increases linearly as a function of the signal-to-noise ratio.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Quadrature generation techniques for frequency multiplication based oscillators

Frequency multiplication (FM) can be used to design low power frequency synthesizers. This is achieved by running the VCO at a much reduced frequency, while employing a power efficient frequency multiplier, and also thereby eliminating the first few dividers. Quadrature signals can be generated by frequency- multiplying low frequency I/Q signals, however this also multiplies the quadrature error of these signals. Another way is generating additional edges from the low-frequency oscillator (LFO) and develop a quadrature FM. This makes the I-Q precision heavily dependent on process mismatches in the ring oscillator. In this paper we examine the use of fewer edges from LFO and a single stage polyphase filter to generate approximate quadrature signals, which is then followed by an injection-locked quadrature VCO to generate high- precision I/Q signals. Simulation comparisons with the existing approach shows that the proposed method offers very good phase accuracy of 0.5deg with only a modest increase in power dissipation for 2.4 GHz IEEE 802.15.4 standard using UMC 0.13 mum RFCMOS technology.

Robust heuristics for scalable optimization of complex SQL queries

Modern database systems incorporate a query optimizer to identify the most efficient "query execution plan" for executing the declarative SQL queries submitted by users. A dynamic-programming-based approach is used to exhaustively enumerate the combinatorially large search space of plan alternatives and, using a cost model, to identify the optimal choice. While dynamic programming (DP) works very well for moderately complex queries with up to around a dozen base relations, it usually fails to scale beyond this stage due to its inherent exponential space and time complexity. Therefore, DP becomes practically infeasible for complex queries with a large number of base relations, such as those found in current decision-support and enterprise management applications. To address the above problem, a variety of approaches have been proposed in the literature. Some completely jettison the DP approach and resort to alternative techniques such as randomized algorithms, whereas others have retained DP by using heuristics to prune the search space to computationally manageable levels. In the latter class, a well-known strategy is "iterative dynamic programming" (IDP) wherein DP is employed bottom-up until it hits its feasibility limit, and then iteratively restarted with a significantly reduced subset of the execution plans currently under consideration. The experimental evaluation of IDP indicated that by appropriate choice of algorithmic parameters, it was possible to almost always obtain "good" (within a factor of twice of the optimal) plans, and in the few remaining cases, mostly "acceptable" (within an order of magnitude of the optimal) plans, and rarely, a "bad" plan. While IDP is certainly an innovative and powerful approach, we have found that there are a variety of common query frameworks wherein it can fail to consistently produce good plans, let alone the optimal choice. This is especially so when star or clique components are present, increasing the complexity of th- e join graphs. Worse, this shortcoming is exacerbated when the number of relations participating in the query is scaled upwards.

Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for Reduced Harmonic Distortion and Switching Loss

Novel switching sequences can be employed in spacevector-based pulsewidth modulation (PWM) of voltage source inverters. Differentswitching sequences are evaluated and compared in terms of inverter switching loss. A hybrid PWM technique named minimum switching loss PWM is proposed, which reduces the inverter switching loss compared to conventional space vector PWM (CSVPWM) and discontinuous PWM techniques at a given average switching frequency. Further, four space-vector-based hybrid PWM techniques are proposed that reduce line current distortion as well as switching loss in motor drives, compared to CSVPWM. Theoretical and experimental results are presented.

The reaction of the nuclei of the vegetative cells and zygotes of Saccharomyces carlsbergensis to some staining techniques

Vegetative cells and zygotes of Saccharomyces carlsbergensis fixed in iodine formaldehyde acetic acid solution and stained after acid hydrolysis in hæmatoxylin, Feulgen and Giemsa show a remarkable similarity in the size and orientation of the structures in the nuclear matrix with reference to the nuclear membrane. The nucleolus described by Guilliermond may either be the chromocenter or the nucleolar equivalent.

Probabilistic evaluation of seismic soil liquefaction potential based on SPT data

The performance-based liquefaction potential analysis was carried out in the present study to estimate the liquefaction return period for Bangalore, India, through a probabilistic approach. In this approach, the entire range of peak ground acceleration (PGA) and earthquake magnitudes was used in the evaluation of liquefaction return period. The seismic hazard analysis for the study area was done using probabilistic approach to evaluate the peak horizontal acceleration at bed rock level. Based on the results of the multichannel analysis of surface wave, it was found that the study area belonged to site class D. The PGA values for the study area were evaluated for site class D by considering the local site effects. The soil resistance for the study area was characterized using the standard penetration test (SPT) values obtained from 450 boreholes. These SPT data along with the PGA values obtained from the probabilistic seismic hazard analysis were used to evaluate the liquefaction return period for the study area. The contour plot showing the spatial variation of factor of safety against liquefaction and the corrected SPT values required for preventing liquefaction for a return period of 475 years at depths of 3 and 6 m are presented in this paper. The entire process of liquefaction potential evaluation, starting from collection of earthquake data, identifying the seismic sources, evaluation of seismic hazard and the assessment of liquefaction return period were carried out, and the entire analysis was done based on the probabilistic approach.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Protein sequence design on the basis of topology optimization techniques -Using continuous modeling of discrete amino acid types

The notion of optimization is inherent in protein design. A long linear chain of twenty types of amino acid residues are known to fold to a 3-D conformation that minimizes the combined inter-residue energy interactions. There are two distinct protein design problems, viz. predicting the folded structure from a given sequence of amino acid monomers (folding problem) and determining a sequence for a given folded structure (inverse folding problem). These two problems have much similarity to engineering structural analysis and structural optimization problems respectively. In the folding problem, a protein chain with a given sequence folds to a conformation, called a native state, which has a unique global minimum energy value when compared to all other unfolded conformations. This involves a search in the conformation space. This is somewhat akin to the principle of minimum potential energy that determines the deformed static equilibrium configuration of an elastic structure of given topology, shape, and size that is subjected to certain boundary conditions. In the inverse-folding problem, one has to design a sequence with some objectives (having a specific feature of the folded structure, docking with another protein, etc.) and constraints (sequence being fixed in some portion, a particular composition of amino acid types, etc.) while obtaining a sequence that would fold to the desired conformation satisfying the criteria of folding. This requires a search in the sequence space. This is similar to structural optimization in the design-variable space wherein a certain feature of structural response is optimized subject to some constraints while satisfying the governing static or dynamic equilibrium equations. Based on this similarity, in this work we apply the topology optimization methods to protein design, discuss modeling issues and present some initial results.

Probabilistic Analysis of Cracking Moment of Reinforced Concrete Beams

Probabilistic analysis of cracking moment from 22 simply supported reinforced concrete beams is performed. When the basic variables follow the distribution considered in this study, the cracking moment of a beam is found to follow a normal distribution. An expression is derived, for characteristic cracking moment, which will be useful in examining reinforced concrete beams for a limit state of cracking.

Synergistic use of thermogravimetric and electrochemical techniques for thermodynamic study of TiOx (1.67 <= x <= 2.0) at 1573 K

A thermodynamic study of the Ti-O system at 1573 K has been conducted using a combination of thermogravimetric and emf techniques. The results indicate that the variation of oxygen potential with the nonstoichiometric parameter delta in stability domain of TiO2-delta with rutile structure can be represented by the relation, Delta mu o(2) = -6RT In delta - 711970(+/-1600) J/mol. The corresponding relation between non-stoichiometric parameter delta and partial pressure of oxygen across the whole stability range of TiO2-delta at 1573 K is delta proportional to P-O2(-1/6). It is therefore evident that the oxygen deficient behavior of nonstoichiometric TiO2-delta is dominated by the presence of doubly charged oxygen vacancies and free electrons. The high-precision measurements enabled the resolution of oxygen potential steps corresponding to the different Magneli phases (Ti-n O2n-1) up to n = 15. Beyond this value of n, the oxygen potential steps were too small to be resolved. Based on composition of the Magneli phase in equilibrium with TiO2-delta, the maximum value of n is estimated to be 28. The chemical potential of titanium was derived as a function of composition using the Gibbs-Duhem relation. Gibbs energies of formation of the Magneli phases were derived from the chemical potentials of oxygen and titanium. The values of -2441.8(+/-5.8) kJ/mol for Ti4O7 and -1775.4(+/-4.3) kJ/mol for Ti3O5 Obtained in this study refine values of -2436.2(+/-26.1) kJ/mol and-1771.3(+/-6.9) kJ/mol, respectively, given in the JANAF thermochemical tables.

Nature of nitrogen adsorbed on transition metal surfaces as revealed by electron spectroscopy and cognate techniques

The nature of the chemisorbed states of nitrogen on various transition metal surfaces is discussed comprehensively on the basis of the results of electron spectroscopic investigations augmented by those from other techniques such as LEED and thermal desorption. A brief discussion of the photoemission spectra of free N2, a comparison of adsorbed N2 and CO as well as of physisorption of N2 on metal surfaces is also presented. We discuss the chemisorption of N2 on the surfaces of certain metals (e.g. Ni, Fe, Ru and W) in some detail, paying considerable attention to the effect of electropositive and electronegative surface modifiers. Features of the various chemisorbed states (one or more weakly chemisorbed gamma-states, strongly chemisorbed alpha-states with bond orders between 1 and 2. and dissociative chemisorbed beta-states) on different surfaces are described and relations between them indicated. While the gamma-state could be a precursor of the alpha-state, the alpha-state could be the precursor of the beta-state and this kind of information is of direct relevance to ammonia synthesis. The nature of adsorption of N2 on the surfaces of some metals (e.g. Cr, Co) deserves further study and such investigations might as well suggest alternative catalysts for ammonia synthesis.