This paper addresses the highly challenging problem of vehicle detection from high resolution remote sensing imagery by introducing a novel medium size annotated dataset named Satellite Imagery Multi-vehicles Dataset (SIMD) along with an adapted single pass deep multi-scale object detection framework with the aim to detect multi sized/type objects for catering above-ground perspective of vehicles. The dataset images are acquired from multiple locations in the EU/US regions available in the public Google Earth satellite imagery. Specifically, it comprises 5,000 images of resolution 1024 x 768 and collectively contains 45,096 objects in 15 different classes of vehicles including cars, trucks, buses, long vehicles, various types of aircrafts and boats. In the proposed architecture, we demonstrate the relevant modifications needed to translate the state-of-the-art object detection frameworks to solve the object detection problem from remote sensing imagery. The proposed architecture has been evaluated on SIMD and a public dataset VEDAI. The comparative analysis has been performed with existing off-theshelf single-shot object detection models including YOLO and YOLT yielding superior performance measured with standard evaluation strategies. To ignite further research in this domain, the introduced SIMD dataset and the corresponding architecture is publicly available at this link: http://vision.seecs.edu.pk/simd.

A strip-shaped sensor network is considered, where randomly placed nodes communicate cooperatively by forming an opportunistic large array (OLA). The transmission from a group of cooperating nodes to another group of nodes is modeled with a quasi-stationary Markov chain, where the transmission channel is assumed to experience lognormal shadowing and Rice fading. The distribution of received power at a node is calculated as a three-step process, which includes finding the distribution of random distance between nodes in addition to other channel impairments, that is, fading and shadowing. It is shown that, in the presence of all three channel impairments, the received power at a node follows a lognormal distribution. This approximation uses a series of steps that involves techniques such as moment matching and moment generating function (MGF). Using the underlying Markov chain properties, the one-hop success probability of the network is derived. The system performance and coverage range of the network are quantified as a function of various network parameters and node topologies. The theoretical results are validated by performing computer simulations.

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