(c) Vehicles, such as SUVs.2.2. Sparse Detector ModelOur prototype sparse detector imaging sensor system can be viewed as a near-infrared implementation of the model described by our colleagues at the Center for Advanced Sensors as shown in selleck chemical Tipifarnib Figure 2. As modeled in Robinson et al. [8], the sparse detector system consists of an array of sensing elements www.selleckchem.com/products/Bortezomib.html deployed in a vertical Inhibitors,Modulators,Libraries configuration on a transmitting/receiving platform. The sensor system also has a reflecting platform that is placed at a distance represented by widthsys. Each sensing element has Inhibitors,Modulators,Libraries a detector and a dedicated collecting lens component. Each sensing element is arranged such that its optical axis is perpendicular to the plane of the vertical array Inhibitors,Modulators,Libraries comprised of N sensing elements that are placed at a distance of dpitch apart.

The range to the object of interest along the optical Inhibitors,Modulators,Libraries axis is Inhibitors,Modulators,Libraries represented by R. The field of view (FOV) of each sensing element is calculated as a function of the detector area and optics of each sensing element. The Inhibitors,Modulators,Libraries overall height of the sensor system is represented by heightsys.Figure 2.Sparse detector system model configuration.Robinson et al. [8] have developed and used this model for rough trade-off analyses, which include the effects of Inhibitors,Modulators,Libraries the optics, atmosphere, detectors, object-of-interest characteristics, and system characteristics, such as detector pitch and normalized detectivity. As further described in [8], the sensor can be classified as either having a staring or a scanned system type [9].

Since it consists of a stationary sparse detector array with no device for Inhibitors,Modulators,Libraries scanning the image across the detectors it could be viewed as a staring system in which the vertical resolution is dependent upon the deployment of the individual sensing elements, which comprise the overa
With more than 200 million users since its release Anacetrapib in June 2005 [1], Google Earth (GE) has recently been recognized for its potential to significantly improve the visualization and dissemination of scientific data [2-4]. Yet the imagery which underlies GE has potential applications that extend beyond visualization; the archive could contribute directly to land-cover and land-use change science (LCLUC). GE now hosts high-resolution (< 2.

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