Generic Visual Perception Processor Essay Example for Free

Generic Visual Perception Processor EssayThe generic visual perception processor (GVPP) has been developed after 10 long days of scientific effort . Generic Visual Perception Processor (GVPP) can automatically detect quarrys and track their movement in real-time. The GVPP, which crunches 20 one million million instructions per second (BIPS), models the gentle perceptual process at the hardware level by mimicking the separate temporal and spatial functions of the eye-to-brain system.The processor sees its environsment as a stream of histograms regarding the location and velocity of objects. GVPP has been demonstrated as capable of learning-in-place to solve a variety of pattern recognition problems. It boasts automatic normalization for varying object size, orientation and lighting conditions, and can function in daylight or darkness. This electronic eye on a flake can now exert most tasks that a normal kind eye can. That includes driving safely, selecting ripe fruits, rea ding and recognizing things.Sadly, though modeled on the visual perception capabilities of the human brain, the chip is not really a medical marvel, poised to cure the blind Introduction ofGVPP The GVPP tracks an object, defined as a certain set of hue, luminance and impregnation set in a specific shape, from frame to frame in a video stream by anticipating where its leading and trailing edges make differences with the background. That direction it can track an object through varying light sources or changes in size, as when an object gets closer to the viewer or moves farther away.The GVPPS major performance strength over current-day vision systems is its setation to varying light conditions. Todays vision systems dictate uniform shadow less illumination ,and even contiguous generation prototype systems, designed to work under normal lighting conditions, can be used only dawn to dusk. The GVPP on the other hand, adapt to real time changes in lighting without recalibration, day or light. For many decades the field of computing has been trapped by the limitations of the traditional processors.Many futuristic technologies maintain been bound by limitations of these processors . These limitations stemmed from the basic architecture of these processors. Traditional processors work by slicing each and every complex program into simple tasks that a processor could execute. This requires an existence of an algorithm for solution of the particular problem. But there are many situations where there is an inexistence of an algorithm or inability of a human to understand the algorithm. Even in these extreme cases GVPP performs well.It can solve a problem with its nervous learning function. Neural networks are extremely fault tolerant. By their design even if a group of neurons get, the neural network only suffers a smooth degradation of the performance. It wont abruptly fail to work. This is a all important(p) difference, from traditional processors as they fail to work even if a few components are damaged. GVPP recognizes stores , matches and process patterns. Even if pattern is not recognizable to a human programmer in input the neural network, it will dig it out from the input.Thus GVPP becomes an efficient tool for applications like the pattern matching and recognition HOW IT plant life Basically the chip is made of neural network modeled resembling the structure of human brain. The basic element here is a neuron. There are vainglorious number of input lines and an output line to a neuron. Each neuron is capable of implementing a simple function. It takes the weighted sum of its inputs and produces an output that is fed into the near layer. The weights assigned to each input are a variable quantity.A large number of such neurons interconnected form a neural network. each input that is given to the neural network gets transmitted over entire network via direct connections called synaptic connections and feed back paths. Thus the sign al ripples in the neural network, every time changing the weighted values associated with each input of every neuron. These changes in the ripples will naturally direct the weights to modify into those values that will become stable .That is, those values does not change. At this point the information about the signal is stored as the weighted values of inputs in the neural network. A neural network geometrizes computation. When we draw the state diagram of a neural network, the network activity burrows a trajectory in this state space. The trajectory begins with a computation problem. The problem specifies initial conditions which define the beginning of trajectory in the state space.