Garimella Rama Murthy

Global dynamics of a class of complex valued neural networks.

In this paper activation dynamics of a complex valued neural network has been studied. Sufficient conditions for global exponential stability of a unique equilibrium are obtained. Our results show that in the serial mode of operation, the network converges to a stable state.

Solving the hidden and exposed terminal problems using directional-antenna based MAC protocol for wireless sensor networks

A critical design issue for wireless sensor networks (WSNs) is the development of medium access control (MAC) protocols that efficiently reduce power consumption. WSNs sensor nodes are generally powered by batteries which provide a limited amount of energy, and it is often difficult to recharge or replace batteries. Therefore power aware and energy efficient MAC protocols at each layer of the communications are very essential for wireless sensor networks [11]. Fairness to both the usage of a channel and messages may also be traded as for improved power consumptions. In case of classical antennas, unfair channel allocation and wastage of channels between each node can be happened, which is directly affects throughput performance. On the other hand these can bring a problem such as MAC-deadlock, hidden and exposed terminal problem. To overcome these problems a directional antennas have been extensively used in designing MAC protocols for wireless sensor networks. Directional antennas provide many advantages over the classical antennas. These advantages include spatial reuse channel and increases in coverage range distance [9]. One of the main considerations in designing MAC protocols for static wireless sensor networks is to reduce power consumption at the sensor nodes. This is usually done by imposing transmission and receiving schedules on the sensor nodes from only one side at same time. Since it is desirable for a sensor network to be self managed, these schedules need to be worked out by individual nodes in a distributed fashion. In this paper, we show that directional antennas can be used effectively to solve a common hidden and exposed terminal problem by using an energy efficient MAC protocol for wireless sensor networks. This directional Antenna could be rotated in case of base station node to avoid directional hidden terminal problem. Our MAC protocol conserves energy at the nodes by calculating a scheduling strategy at individual nodes and by avoiding packet collisions almost completely.

A Novel Routing Algorithm for Vehicular Sensor Networks

Recent advances in wireless communications are diffusing into many new applications. The tiny sensor node, which consists of sensing, data processing and communicating components, led to the idea of sensor networks. A sensor network composed of a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it. The applications envisioned for sensor networks vary from monitoring inhospitable habitats and disaster areas to operating indoors for intrusion detection and equipment monitoring. In most cases the network designer would have little control over the exact deployment of the network. Nowadays Vehicular Networks are drawing lots of attention due to the wide variety of applications that they can provide. These applications include traffic monitoring, positioning, security etc. A lot of research work is being conducted to define the standard for vehicular communication. These include frequency allocation, standards for physical and link layers, routing algorithms, security issues and new applications. In this paper we discuss the disadvantages of the traffic monitoring by traditional methods and by using GPS equipped sensors. Then we propose a new routing protocol for a fixed topology containing both stationary and mobile nodes. We also try to optimize the energy of the sensor nodes. We simulate our routing algorithm in MATLAB and evaluate it for different possible cases.

Doubly Cognitive Architecture Based Cognitive Wireless Sensor Networks

Scarcity of spectrum is increasing not only in cellular communication but also in wireless sensor networks. Adding cognition to the existing wireless sensor network WSN infrastructure has helped. As sensor nodes in WSN are limited with constraints like power, efforts are required to increase the lifetime and other performance measures of the network. In this article, the authors propose Doubly Cognitive WSN, which works by progressively allocating the sensing resources only to the most promising areas of the spectrum and is based on pattern analysis and learning. As the load of sensing resource is reduced significantly, this approach saves the energy of the nodes and reduces the sensing time dramatically. The proposed method can be enhanced by periodic pattern analysis to review the strategy of sensing. Finally the ongoing research work and contribution on cognitive wireless sensor networks in Communication Research Centre IIIT-H is discussed.

On the design of Hopfield Neural Networks: Synthesis of hopfield type associative memories

In this research paper, it is proved that it is impossible to design a Hopfield Neural Network with orthogonal stable states ( corners of hypercube ) when the total number of neurons in the network ( dimension of network ) is odd. Also linear algebraic structure of associative memory synthesized by Hopfield is discussed. Using Hadamard matrix of suitable dimension, an algorithm to synthesize real valued Hopfield neural network is discussed. The design of a certain complex Hopfield neural network is addressed and solved. Also, synthesis of real and complex Hopfield type associative memories is discussed. This synthesis enables choice of stable states and the corresponding values of energy function.

Cost effective restoration of wireless connectivity in disaster hit areas using OpenBTS

In this paper, we have proposed a cost effective method for rapid restoration of wireless connectivity in disaster hit areas, which face large scale failure of wireless infrastructure. This method includes setting of a reconfigurable Base Transceiver Station (BTS) based on Software Defined Radio (SDR) technology. The setup covers the drawbacks of existing methods for establishing connectivity in disaster hit areas such as Ham radio. It also extends the connectivity to almost every individual in the area having a GSM handset at no additional cost. This low cost deployment will encourage government to implement such models in areas struck by major catastrophe. For demonstration, we have used OpenBTS along with Universal Software Radio Peripherals (USRP) N210, to set up the BTS infrastructure. We have also analyzed scaling up of the experimental system model to set up a Macrocell network in practical scenario.

Optimization of Quadratic Forms: NP Hard Problems: Neural Networks

In this research paper, the problem of optimization of a quadratic form over the convex hull generated by the corners of hypercube is attempted and solved. It is reasoned that under some conditions, the optimum occurs at the corners of hypercube. Some results related to the computation of global optimum stable state (an NP hard problem) are discussed. A heuristic algorithm is proposed. It is hoped that the results shed light on resolving the P ≠ NP problem.

Cooperative Mesh Networks

Cooperative and mesh networks are new architectures for network convergence that solve some recent problems such as load balancing, routing, and handover. This chapter is dedicated to recent trends, proposals and architectures for cooperative and mesh networks. In many cases these networks can also be used to save energy, which also makes them energy-efficient networks.

Multi/infinite dimensional neural networks, multi/infinite dimensional logic theory.

A mathematical model of an arbitrary multi-dimensional neural network is developed and a convergence theorem for an arbitrary multi-dimensional neural network represented by a fully symmetric tensor is stated and proved. The input and output signal states of a multi-dimensional neural network/logic gate are related through an energy function, defined over the fully symmetric tensor (representing the connection structure of a multi-dimensional neural network). The inputs and outputs are related such that the minimum/maximum energy states correspond to the output states of the logic gate/neural network realizing a logic function. Similarly, a logic circuit consisting of the interconnection of logic gates, represented by a block symmetric tensor, is associated with a quadratic/higher degree energy function. Infinite dimensional logic theory is discussed through the utilization of infinite dimension/order tensors.

Existence and Synthesis of Complex Hopfield Type Associative Memories

In this research paper, a complex valued generalization of associative memory synthesized by Hopfield is considered and it is proved that it is impossible to synthesize such a neural network with desired unitary stable states when the dimension of the network (number of neurons) is odd. The linear algebraic structure of such a neural network is discussed. Using Sylvester construction of Hadamard matrix of suitable dimension, an algorithm to synthesize such a complex Hopfield neural network is discussed. Also, it is discussed how to synthesize real / complex valued associative memories with desired energy landscape (i.e. desired stable states and desired energy values of associated quadratic energy function).