Siddharth Pal

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Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently developed metaheuristic algorithm, known as the Invasive Weed Optimization (IWO), to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum(More)
We consider ensemble classification for the case where there is no common labeled training data for jointly designing the individual classifiers and the function that aggregates their decisions. This problem, which we call distributed ensemble classification, applies when individual classifiers operate (perhaps remotely) on different sensing modalities and(More)
Time modulated antenna arrays attracted the attention of researchers for the synthesis of low/ultra-low side lobes in recent past. In this article we propose an improved variant of a recently developed ecologically inspired metaheuristic, wellknown as Invasive Weed Optimization (IWO), to solve the real parameter optimization problem related to the design of(More)
Concentric Circular Antenna Array (CCAA) has several interesting features that makes it indispensable in mobile and communication applications. Here we have considered a uniform arrangement of elements where the inter-element spacing is kept half a wavelength. The main aim is to reduce the sidelobe levels and the primary lobe beamwidth as much as possible.(More)
We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet R ) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy(More)
In this article we describe an optimization-based design method for non-uniform, planar, and circular antenna arrays with the objective of achieving minimum side lobe levels for a specific first null beamwidth and also a minimum size of the circumference. Central to our design is a hybridization of two prominent metaheuristics of current interest namely the(More)
We propose a simple learning rule in games. The proposed rule only requires that (i) if there exists at least one strictly better reply, an agent switches its action to each strictly better reply with positive probability or stay with the same action (with positive probability), and (ii) when there is no strictly better reply, the agent either stays with(More)