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The science team at the University of California Berkeley responsible for NASA s Stardust@Home project (http://stardustathome.ssl.berkeley.edu/) is using Amazon S3 to store and deliver the 60 million images that represent the data collected from their dust particle aerogel experiment. These images will be delivered to 100,000 volunteers around the world who scan the images looking for dust particles from comet Wild2. We quickly ran into challenges when we started the project using our own infrastructure, said Andrew Westphal, project director of Stardust@Home. Using Amazon S3 has allowed us to proceed without having to worry about building out the massive storage infrastructure we realized that we needed to successfully complete the project. The fact that Amazon S3 is an Internet-connected storage service is particularly useful to us as we expect the data examination phase of the project to take only a few months. We can quickly ramp up and back down again without a huge investment.
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In a copper cable or atmospheric transmission system two key constraints govern the ability to transmit information at different data transmission rates: the Nyquist relationship and Shannon s law.
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even a nominal distance, especially in crowd surveillance, the face segmentation process tends to be more challenging than other techniques, as there is greater potential for variation. The camera s location, field of view, and background setting can introduce considerable variation in how faces occur within the frame and how easy or hard it is to automatically locate and separate the face from the nonface parts of the image frame. Location and size differences, if not too extreme, can be corrected once the face is detected and the eyes are located. Face detection is accomplished according to shapes and features in the image. Most software attempts to find face-like regions of the image by starting from the center and progressing outward. The output of the face detection process is location and registration information, consisting of a minimum bounding box for the face, the coordinates of the eyes, and perhaps also the location of the bottom of the nose and the center of the mouth. Thresholds for expected intraocular distances can be used to aid the detection process (although that distance varies according to horizontal face rotation). Also, in the case of video sequences, motion information can be used to help locate candidates faces. Face detection performance has become very good for single-face images even rotated, off-center faces can be detected and registered for encoding. However, the problem is significantly harder when generalized to crowd environments. Crowd scenes can contain multiple faces and large variation in distance, camera angle, and pose. Additional background motion (noise) can compound the problem further, making face detection performance in these environments much less reliable than in simpler, more controlled, single-face environments. Even under less than ideal conditions, reasonable results can be achieved if the software is correctly calibrated and tuned, and cameras are strategically placed in the target environment. Some, but by no means all, of the software calibrations necessary for a particular environment can be configured by knowledgeable operators and system integrators. More dedicated calibration efforts could involve modifying the software or adding custom markers in the environment. Markers provide depth queue information to the otherwise flat images and can also help disambiguate some occlusion problems that arise in crowd situations. The commercial face recognition techniques discussed here are all designed to work best on straight 2D frontal images, or mug shots. Plausible approaches to the problem were researched and explored by universities during the 1980s, and the first generation of moderately successful approaches began to surface in academic literature in the late 1980s and early 1990s. These first examples of the technology operated in minimally constrained environments and only performed well on a small number of individuals and a limited set of images. 1996 was an important year for facial recognition, as it was the year of the first government FacE REcognition Tests (FERET).1 The 1996 FERET tests were sponsored by Army Research Laboratories and designed and orchestrated by
1. If the output of a bridging ampli er is a constant ( at) signal level of 46 dBmV and feeds a cable section that is 200 ft long, calculate the signal level at the end of the cable section if the loss speci cations are 2 dB/100 ft at 750 MHz and 0.5 dB/100 ft at 50 MHz. 2. Given a four-port 26-dB tap device has an input of 44 dBmV, what is the signal level at any one of the four ports 3. If a 26-dB four-port tap has a through loss of 1.3 dB at 750 MHz and 0.5 dB at 50 MHz, calculate the output level at the through port if the input level is 36 dBmV at 750 MHz and 38 dBmV at 50 MHz. 4. If an 8-dB directional coupler has an input level of 39.5 dBmV at 750 MHz and 33 dBmV at 50 MHz, calculate the signal level at the 8-dB port. 5. If a trunk ampli er has a noise gure of 12 dB and the input signal level is 20 dBmV, calculate the output carrier-to-noise level. 6. At the end of a 20-ampli er cascade of identical ampli ers of the same type in problem 5, what will be the carrier-to-noise ratio in dB 7. If one trunk ampli er has a carrier-to-composite triple beat (C/CTB) speci cation of 72 dB, what will be the C/CTB for a 20-ampli er cascade
After the impedance is found, calculate the Z section s required width either by employing one of the many microstrip calculation programs available free on the Web (such as HP s AppCad, or AWR s TXLine, or Daniel Swanson s MWTLC), or by calculating with the microstrip formula above.
Meet for a designated length of time: for example, 1 hour or 2 hours.
principles of operation, but recordable CD equipment uses a more highly focused, more intense laser beam capable of searing impressions in the dye layer of a blank compact disc. When recording begins, the CD recorder s laser beam penetrates the plastic substrate and heats the dye as it pulses a string of data patterns to the disc. The dye rises where it is heated, forming mounds that protrude into the gold layer. The resulting mounds correspond with the pits in a conventionally recorded CD-ROM. The laser beam is de ected when it strikes them and they are read by a CD-ROM drive in the same manner as pits. Rather than confusing an entire industry, by calling them mounds or bumps, the mounds are still typically referred to as pits. Figure 2 - 4 illustrates this concept.
Vanishing point coordinates
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