This article presents in-pixel (of a CMOS image sensor (CIS)) temperature sensors with improved accuracy in the spatial and the temporal domain. The goal of the temperature sensors is to be used to compensate for dark (current) fixed pattern noise (FPN) during the exposure of the CIS. The temperature sensors are based on substrate parasitic bipolar junction transistor (BJT) and on the nMOS source follower of the pixel. The accuracy of these temperature sensors has been improved in the analog domain by using dynamic element matching (DEM), a temperature independent bias current based on a bandgap reference (BGR) with a temperature independent resistor, correlated double sampling (CDS), and a full BGR bias of the gain amplifier. The accuracy of the bipolar based temperature sensor has been improved to a level of ±0.25 °C, a 3σ variation of ±0.7 °C in the spatial domain, and a 3σ variation of ±1 °C in the temporal domain. In the case of the nMOS based temperature sensor, an accuracy of ±0.45 °C, 3σ variation of ±0.95 °C in the spatial domain, and ±1.4 °C in the temporal domain have been acquired. The temperature range is between −40 °C and 100 °C.
Our group has designed, sourced and constructed a radiosonde/ground-station pair using inexpensive opensource hardware. Based on the Arduino platform, the easy to build radiosonde allows the atmospheric science community to test and deploy instrumentation packages that can be fully customized to their individual sensing requirements. This sensing/transmitter package has been successfully deployed on a tethered-balloon, a weather balloon, a UAV airplane, and is currently being integrated into a UAV quadcopter and a student-built rocket. In this paper, the system, field measurements and potential applications will be described. As will the science drivers of having full control and open access to a measurement system in an age when commercial solutions have become popular but are restrictive in terms of proprietary sensor specifications, "black-box" calibration operations or data handling routines, etc. The ability to modify and experiment with both the hardware and software tools is an essential part of the scientific process. Without an understanding of the intrinsic biases or limitations in your instruments and system, it becomes difficult to improve them or advance the knowledge in any given field.
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