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in Shot noise and thermal noise have long been considered the results of two distinct mechanisms, but they aren't White Noise MOS Ti.ansistors and Resistors e live in a very energy-conscious era. In the electrical engineering community, energy-consciousness has manifested itself in an increasing focus on low-power circuits. Low-power circuits imply low current and/or voltage levels and are thus more susceptible to the effects of noise. Hence, a good understanding of noise is timely.Most people find the subject of noise mysterious, and there is, understandably, much confusion about it. Although the fundamental physical concepts behind noise are simple. much of this simplicity is often obscured by the mathematics invoked to compute expressions for the noise.The myriads of random events that happen at microscopic scales cause fluctuations in the values of macroscopic variables such as voltage, current, and charge. These fluctuations are referred to as noise.

In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera. This provides a ground-truth to which we can compare algorithms that measure optical flow by means of motion cues. An analysis of error sources led to the use of a refractory period, more accurate numerical derivatives and a Savitzky-Golay filter to achieve significant improvements in accuracy. Our pure Java implementations of two recently published algorithms reduce computational cost by up to 29% compared to the original implementations. Two of the algorithms introduced in this paper further speed up processing by a factor of 10 compared with the original implementations, at equal or better accuracy. On a desktop PC, they run in real-time on dense natural input recorded by a DAVIS camera.