Dinesh Dileep Gaurav

Dinesh Dileep Gaurav, K. V. S. Hari

We consider a two-hop Multiple-Input Multiple-Output channel with a source, a single Amplify and Forward relay, and the destination. We consider the problem of designing precoders at the source and the relay, and the receiver matrix at the destination. In particular, we address the problem of optimal power allocation scheme at the source which minimizes the source transmit power while satisfying a given Quality of Service requirement at the destination, and a power constraint at the relay. We consider two types of receiver at the destination, a Zero Forcing receiver and an Minimum Mean Square Error receiver. Simulation Results are provided in the end which compare the performance of both the receivers.

Shreya Malani, Dinesh Gaurav, Anoop Vallabhajosyula et al.

Diverse keyword suggestions for a given landing page or matching queries to diverse documents is an active research area in online advertising. Modern search engines provide advertisers with products like Dynamic Search Ads and Smart Campaigns where they extract meaningful keywords/phrases from the advertiser's product inventory. These keywords/phrases are representative of a diverse spectrum of advertiser's interests. In this paper, we address the problem of obtaining relevant yet diverse keywords/phrases for any given document. We formulate this as an optimization problem, maximizing the parameterized trade-off between diversity and relevance constrained over number of possible keywords/phrases. We show that this is a combinatorial NP-hard optimization problem. We propose two approaches based on convex relaxations varying in complexity and performance. In the first approach, we show that the optimization problem reduces to an eigen value problem. In the second approach, we show that the optimization problem reduces to minimizing a quadratic form over an l1-ball. Subsequently, we show that this is equivalent to a semi-definite optimization problem. To prove the efficacy of our proposed formulation, we evaluate it on various real-world datasets and compare it to the state-of-the-art heuristic approaches.

Dinesh Dileep Gaurav, Renu Ramesh

This paper describes a geometry based technique for feature extraction applicable to segmentation-based word recognition systems. The proposed system extracts the geometric features of the character contour. This features are based on the basic line types that forms the character skeleton. The system gives a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked.