Nazmul H. Siddique

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Femtocells use common cellular air access technologies, but claim to improve system capacity according to Shannon's law by reducing distance between transmitter and receiver and thus improving the signal-to-noise ratio (SNR). Femtocells, however, use the IP Network as backhaul architecture instead of the conventional cellular network infrastructure. Thus,(More)
In industr?, t o d q many producfs are soldfor their efficacy rather than their chemical composition. There are several key attributes within the coating industrv such as, Anchorage, Seal strength etc., which characterize the quality of the final product and are features used by the company to promote the sale of the product. Such qualify variables(More)
The limitations of conventional model-based control mechanisms for flexible manipulator systems have stimulated the development of intelligent control mechanisms incorporating fuzzy logic and neural networks. Problems have been encountered in applying the traditional PD-, PI-, and PID-type fuzzy controllers to flexible-link manipulators. A PD-PI-type fuzzy(More)
This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of(More)
This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions(More)
This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to(More)
Abstract The term clustering refers to the identification of natural groups within a data set such that instances in the same group are more similar than instances in different groups. Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect(More)