Yearly Archives: 2012

The introduction of Gimbal™, a context aware mobile platform and SDK recently introduced by Qualcomm, heralds more intuitive communications between consumers and their smart mobile devices. No longer must users tediously program in all their preferences and rules. Now, mobile devices will be able to track and learn the habits of their users, understand their interest, and adapt to their actions. Gimbal currently features location awareness through GPS-based geo-fencing (a virtual electronic perimeter), as well as electronic information such as purchasing history, to profile users’ interests. But that is just a starting point. There are many more pieces of information already available in a smartphone that can and will be leveraged by these devices. For example, using geo-fencing a store … Continue reading →

Sensor fusion has become a popular topic for designers integrating inertial sensors into smartphones and tablets. The idea of sensor fusion is to take measurements from different sensors to estimate the internal state of a system. In the case of the smartphone, we use inertial sensors (accelerometers, magnetometers, gyroscopes, and barometers) which do not directly measure orientation or position, to compute the orientation and position of the mobile device. Sensor fusion, however, is as old as the space program. The most practiced approach to this estimation problem was first proposed by Rudolf E. Kalman in a paper published in 1960, the so-called Kalman filter. It is an optimal, recursive Bayesian estimator. “Optimal” means that it produces the best estimate of … Continue reading →

Many datasheets serve both as technical specificationsand promotional collateral. As such, “specsmanship” often makes comparing two sensor components on paper complicated. This article focuses on a few key parameters that system designers should consider. Measurement Range Measurement range refers to the span of measurement values the sensor can record between the lowest and highest possible values. Dynamic range of a sensor refers to the span between the largest and smallest values two consecutive samples can take. Some sensors have dynamic ranges that cover their whole measurement range; for others, dynamic range is a subset of the total measurement range (see figure). In the latter cases, the bias can be charged either through register settings or be adjusted automatically by an … Continue reading →

Many system designers have faced the challenge to optimize sensor components selection based on use cases they plan to support. This article examines the key classes of use cases and discusses their demands on the resolution, bandwidth and dynamic range of various sensors. The resolution of a sensor refers to the finest measurement it can discern. This depends on two factors: the number of quantization levels available to a digital measurement and the noise experienced by the sensor. For example, a 12-bit sensor can cover a 72dB range while a 16-bit sensor covers a 96dB range. But if background noise for a sensor is higher than -72dB, a 16-bit sample would not capture any more useful information than a 12-bit … Continue reading →