Influence of Cobalt Doping on the Physical Properties of Zn<Subscript>0.9</Subscript>Cd<Subscript>0.1</Subscript>S Nanoparticles

Zn_0.9Cd_0.1S nanoparticles doped with 0.005–0.24 M cobalt have been prepared by co-precipitation technique in ice bath at 280 K. For the cobalt concentration >0.18 M, XRD pattern shows unidentified phases along with Zn_0.9Cd_0.1S sphalerite phase. For low cobalt concentration (≤0.05 M) particle size, d _XRD is ~3.5 nm, while for high cobalt concentration (>0.05 M) particle size decreases abruptly (~2 nm) as detected by XRD. However, TEM analysis shows the similar particle size (~3.5 nm) irrespective of the cobalt concentration. Local strain in the alloyed nanoparticles with cobalt concentration of 0.18 M increases ~46% in comparison to that of 0.05 M. Direct to indirect energy band-gap transition is obtained when cobalt concentration goes beyond 0.05 M. A red shift in energy band gap is also observed for both the cases. Nanoparticles with low cobalt concentrations were found to have paramagnetic nature with no antiferromagnetic coupling. A negative Curie–Weiss temperature of −75 K with antiferromagnetic coupling was obtained for the high cobalt concentration.     

On denoising and compression of DNA microarray images

We address the problems of noise and huge data sizes in microarray images. First, we propose a mixture model for describing the statistical and structural properties of microarray images. Then, based on the microarray image model, we present methods for denoising and for compressing microarray images. The denoising method is based on a variant of the translation-invariant wavelet transform. The compression method introduces the notion of approximate contexts (rather than traditional exact contexts) in modeling the symbol probabilities in a microarray image. This inexact context modeling approach is important in dealing with the noisy nature of microarray images. Using the proposed denoising and compression methods, we describe a near-lossless compression scheme suitable for microarray images. Results on both denoising and compression are included, which show the performance of the proposed methods. Further experiments using the results of the proposed near-lossless compression scheme in gene clustering using cell-cycle microarray data for S. cerevisiae showed a general improvement in the clustering performance, when compared with using the original data. This provides an indirect validation of the effectiveness of the proposed denoising method.     

FireFly: a cross-layer platform for real-time embedded wireless networks

We propose a cross-layer approach with tightly-coupled time synchronization for real-time support and predictable lifetime in battery-operated sensor networks. Our design spans a sensor hardware platform with hardware-based global time synchronization, a TDMA link layer protocol with collision-free multi-hop support and node scheduling algorithms for maximum concurrency and streaming. Our dual-radio sensor platform, FireFly, features an IEEE 802.15.4 transceiver and supports global time synchronization indoors by using an AM radio carrier-current method and an atomic clock receiver for outdoors. A TDMA-based link protocol, RT-Link, leverages the hardware for fixed and mobile nodes with a near-optimal and predictable node lifetime of over 2 years. It outperforms comparable sensor network link protocols such as B-MAC and S-MAC in terms of end-to-end latency and throughput and node lifetime across all duty cycle ratios. Operating over RT-Link is MAX, a scheduling framework which offers optimal transmission concurrency and bandwidth management for networks with regular structure. Through analysis and experiments we show that global time sync is a robust, economical and scalable alternative to in-band software-based techniques. To illustrate the capabilities and flexibility of our platform, we describe our experiences with two-way voice streaming over multiple hops. We have deployed a 42-node network with sub-100 μs synchronization accuracy in the NIOSH experimental coal mine for people-tracking and voice communication.

Synthesis and characterization of zirconia-based catalyst for the isomerization of n-hexane

Sulfated zirconia is a very strong solid acid catalyst which can be utilized for various reactions. The present study focuses on synthesis of zirconia-based catalyst with high acidity and high surface area, particularly for isomerization reaction. Sulfated zirconia has been obtained by sulfation of zirconia prepared by hydrothermal route. The catalyst was developed by impregnating tungstophosphoric acid on sulfated zirconia by wet incipient method. The catalyst was characterized through Brunauer–Emmett–Teller (BET) surface area, temperature-programmed desorption of ammonia, temperature program reduction of hydrogen, Fourier transmission infrared spectroscopy, and thermogravimetric analysis. The results revealed that the catalyst is crystalline in nature with surface area 190–225?m² g^?1 and acidity 0.135–0.558?mmol?g^?1. Twenty-five percent conversion was obtained (as confirmed by gas chromatography) at 225°C using n-hexane as model hydrocarbon in fixed-bed microreactor.     

HA/β-TCP plasma sprayed coatings on Ti substrate for biomedical applications

The present work focuses on the fabrication of βTCP (β-tricalcium phosphate) and HA/βTCP (hydroxyapatite/β-tricalcium phosphate) composite coatings by plasma spraying. The starting powders were produced via solid-state method using 2 wt% MgO to stabilize βTCP phase. The synthesized powders were preliminarily granulated to be used by the plasma spray process. Coatings obtained on titanium substrates are uniform and well adherent but due to the high temperature and cooling rate typical for plasma spraying process, βTCP phase is almost totally transformed into the α allotrope. Thermal treatment at 800 °C allows the reconversion of the phase αTCP→ βTCP. It is therefore possible to produce coatings with tuneable dissolution properties by selecting the proper initial powder mixture and the specific thermal treatment.     

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.     

Rapidly exploring random graphs: motion planning of multiple mobile robots

Rapidly exploring random trees (RRT) and probabilistic roadmaps (PRM) are sampling-based techniques being extensively used for robot path planning. In this paper, the tree structure of the RRT is generalized to a graph structure which enables a greater exploration. Exploration takes place simultaneously from multiple points in the map, all explorations fusing at multiple points producing well-connected graph architecture. Initially, in a typical RRT manner, the search algorithm attempts to reach the goal by expansions, and thereafter furtherer areas are explored. With some additional computation cost, as compared to RRT with a single robot, the results can be significantly improved. The so-formed graph is similar to roadmap produced by PRM. However as compared to PRM, the proposed algorithm has a more judicious search strategy and is adaptable to the number of nodes as a parameter. Experimental results are shown with multiple robots planned using prioritization scheme. Results show the betterment of the proposed algorithm as compared to RRT and PRM techniques.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Reduced order modelling and predictive control of multivariable nonlinear process

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.