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.

Simulation of wavelength/time multiple-pulses-per-row fiber-optic CDMA network

Passive wavelength/time fiber-optic code division multiple access (WIT FO-CDMA) network is a viable option for highspeed access networks. Constructions of 2-D codes, suitable for incoherent WIT FO-CDMA, have been proposed to reduce the time spread of the 1-D sequences. The 2-D constructions can be broadly classified as 1) hybrid codes and 2) matrix codes. In our earlier work [141, we had proposed a new family of wavelength/time multiple-pulses-per-row (W/T MPR) matrix codes which have good cardinality, spectral efficiency and at the same time have the lowest off-peak autocorrelation and cross-correlation values equal to unity. In this paper we propose an architecture for a WIT MPR FO-CDAM network designed using the presently available devices and technology. A complete FO-CDMA network of ten users is simulated, for various number of simultaneous users and shown that 0 --> 1 errors can occur only when the number of interfering users is at least equal to the threshold value.

Comparative analysis of thermophilic and mesophilic proteins using Protein Energy Networks

Background: Thermophilic proteins sustain themselves and function at higher temperatures. Despite their structural and functional similarities with their mesophilic homologues, they show enhanced stability. Various comparative studies at genomic, protein sequence and structure levels, and experimental works highlight the different factors and dominant interacting forces contributing to this increased stability. Methods: In this comparative structure based study, we have used interaction energies between amino acids, to generate structure networks called as Protein Energy Networks (PENs). These PENs are used to compute network, sub-graph, and node specific parameters. These parameters are then compared between the thermophile-mesophile homologues. Results: The results show an increased number of clusters and low energy cliques in thermophiles as the main contributing factors for their enhanced stability. Further more, we see an increase in the number of hubs in thermophiles. We also observe no community of electrostatic cliques forming in PENs. Conclusion: In this study we were able to take an energy based network approach, to identify the factors responsible for enhanced stability of thermophiles, by comparative analysis. We were able to point out that the sub-graph parameters are the prominent contributing factors. The thermophiles have a better-packed hydrophobic core. We have also discussed how thermophiles, although increasing stability through higher connectivity retains conformational flexibility, from a cliques and communities perspective.

Comparative analysis of thermophilic and mesophilic proteins using Protein Energy Networks

Background: Thermophilic proteins sustain themselves and function at higher temperatures. Despite their structural and functional similarities with their mesophilic homologues, they show enhanced stability. Various comparative studies at genomic, protein sequence and structure levels, and experimental works highlight the different factors and dominant interacting forces contributing to this increased stability. Methods: In this comparative structure based study, we have used interaction energies between amino acids, to generate structure networks called as Protein Energy Networks (PENs). These PENs are used to compute network, sub-graph, and node specific parameters. These parameters are then compared between the thermophile-mesophile homologues. Results: The results show an increased number of clusters and low energy cliques in thermophiles as the main contributing factors for their enhanced stability. Further more, we see an increase in the number of hubs in thermophiles. We also observe no community of electrostatic cliques forming in PENs. Conclusion: In this study we were able to take an energy based network approach, to identify the factors responsible for enhanced stability of thermophiles, by comparative analysis. We were able to point out that the sub-graph parameters are the prominent contributing factors. The thermophiles have a better-packed hydrophobic core. We have also discussed how thermophiles, although increasing stability through higher connectivity retains conformational flexibility, from a cliques and communities perspective.

Power Optimal Network-on-Chip Interconnect Design

A large part of today's multi-core chips is interconnect. Increasing communication complexity has made essential new strategies for interconnects, such as Network on Chip. Power dissipation in interconnects has become a substantial part of the total power dissipation. Techniques to reduce interconnect power have thus become a necessity. In this paper, we present a design methodology that gives values of bus width for interconnect links, frequency of operation for routers, in Network on Chip scenario that satisfy required throughput and dissipate minimal switching power. We develop closed form analytical expressions for the power dissipation, with bus width and frequency as variables and then use Lagrange multiplier method to arrive at the optimal values. We present a 4 port router in 90 nm technology library as case study. The results obtained from analysis are discussed.

Generic Routing Rules and a Scalable Access Enhancement for the Network-on-Chip RECONNECT

RECONNECT is a Network-on-Chip using a honeycomb topology. In this paper we focus on properties of general rules applicable to a variety of routing algorithms for the NoC which take into account the missing links of the honeycomb topology when compared to a mesh. We also extend the original proposal [5] and show a method to insert and extract data to and from the network. Access Routers at the boundary of the execution fabric establish connections to multiple periphery modules and create a torus to decrease the node distances. Our approach is scalable and ensures homogeneity among the compute elements in the NoC. We synthesized and evaluated the proposed enhancement in terms of power dissipation and area. Our results indicate that the impact of necessary alterations to the fabric is negligible and effects the data transfer between the fabric and the periphery only marginally.

Influence of Oxide Particle Network Morphology on Ion Solvation and Transport in ``Soggy Sand''Electrolytes

The role of oxide surface chemical composition and solvent on ion solvation and ion transport of ``soggy sand'' electrolytes are discussed here. A ``soggy sand'' electrolyte system comprising dispersions of hydrophilic/hydrophobic functionalized aerosil silica in lithium perchlorate methoxy polyethylene glycol solution was employed for the study. Static and dynamic rheology measurements show formation of an attractive particle network in the case of the composite with unmodified aerosil silica (i.e., with surface silanol groups) as well as composites with hydrophobic alkane groups. While particle network in the composite with hydrophilic aerosil silica (unmodified) were due to hydrogen bonding, hydrophobic aerosil silica particles were held together via van der Waals forces. The network strength in the latter case (i.e., for hydrophobic composites) were weaker compared with the composite with unmodified aerosil silica. Both unmodified silica as well as hydrophobic silica composites displayed solid-like mechanical strength. No enhancement in ionic conductivity compared to the liquid electrolyte was observed in the case of the unmodified silica. This was attributed to the existence of a very strong particle network, which led to the ``expulsion'' of all conducting entities from the interfacial region between adjacent particles. The ionic conductivity for composites with hydrophobic aerosil particles displayed ionic conductivity dependent on the size of the hydrophobic chemical moiety. No spanning attractive particle network was observed for aerosil particles with surfaces modified with stronger hydrophilic groups (than silanol). The composite resembled a sol, and no percolation in ionic conductivity was observed.

Neural network approach for fault location in unbalanced distribution networks with limited measurements

This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.

An auction algorithm for procuring wireless channel in a heterogenous wireless network

In this paper, we develop a novel auction algorithm for procuring wireless channel by a wireless node in a heterogeneous wireless network. We assume that the service providers of the heterogeneous wireless network are selfish and non-cooperative in the sense that they are only interested in maximizing their own utilities. The wireless user needs to procure wireless channels to execute multiple tasks. To solve the problem of the wireless user, we propose a reverse optimal (REVOPT) auction and derive an expression for the expected payment by the wireless user. The proposed auction mechanism REVOPT satisfies important game theoretic properties such as Bayesian incentive compatibility and individual rationality.

Analysis of network architectures for zigbee sensor clusters

We provide a comparative performance analysis of network architectures for beacon enabled Zigbee sensor clusters using the CSMA/CA MAC defined in the IEEE 802.15.4 standard, and organised as (i) a star topology, and (ii) a two-hop topology. We provide analytical models for obtaining performance measures such as mean network delay, and mean node lifetime. We find that the star topology is substantially superior both in delay performance and lifetime performance than the two-hop topology.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

A neural network based CBIR system using STI features and relevance feedback

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems

Lessons from the Sparse Sensor Network Deployment in Rural India

We discuss the key issues in the deployment of sparse sensor networks. The network monitors several environment parameters and is deployed in a semi-arid region for the benefit of small and marginal farmers. We begin by discussing the problems of an existing unreliable 1 sq km sparse network deployed in a village. The proposed solutions are implemented in a new cluster. The new cluster is a reliable 5 sq km network. Our contributions are two fold. Firstly, we describe a. novel methodology to deploy a sparse reliable data gathering sensor network and evaluate the ``safe distance'' or ``reliable'' distance between nodes using propagation models. Secondly, we address the problem of transporting data from rural aggregation servers to urban data centres. This paper tracks our steps in deploying a sensor network in a village,in India, trying to provide better diagnosis for better crop management. Keywords - Rural, Agriculture, CTRS, Sparse.

Optimal Blood Glucose Regulation using Single Network Adaptive Critics

Diabetes is a serious disease during which the body's production and use of insulin is impaired, causing glucose concentration level toincrease in the bloodstream. Regulating blood glucose levels as close to normal as possible, leads to a substantial decrease in long term complications of diabetes. In this paper, an intelligent neural network on-line optimal feedback treatment strategy based on nonlinear optimal control theory is presented for the disease using subcutaneous treatment strategy. A simple mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system is considered based on the Bergman's minimal model. A glucose infusion term representing the effect of glucose intake resulting from a meal is introduced into the model equations. The efficiency of the proposed controllers is shown taking random parameters and random initial conditions in presence of physical disturbances like food intake. A comparison study with linear quadratic regulator theory brings Out the advantages of the nonlinear control synthesis approach. Simulation results show that unlike linear optimal control, the proposed on-line continuous infusion strategy never leads to severe hypoglycemia problems.