Calibration is an important step towards building reliable IoT systems. For example, accurate sensor reading requires ADC calibration, and power monitoring chips must be calibrated before being used for measuring the energy consumption of IoT devices. In this paper, we present ProCal, a low-cost, accurate, and scalable power calibration tool. ProCal is a programmable platform which provides dynamic voltage and current output for calibration. The basic idea is to use a digital potentiometer connected to a parallel resistor network controlled through digital switches. The resistance and output frequency of ProCal is controlled by a software communicating with the board through the SPI interface. Our design provides a simple synchronization mechanism which prevents the need for accurate time synchronization. We present mathematical modeling and validation of the tool by incorporating the concept of Fibonacci sequence. Our extensive experimental studies show that this tool can significantly improve measurement accuracy. For example, for ATMega2560, the ADC error reduces from 0.2 to 0.01 . ProCal not only costs less than 2 of the current commercial solutions, it is also highly accurate by being able to provide extensive range of current and voltage values.
Calibration can be performed multiple times after the device is fabricated. Therefore, it can be used to compensate for changes that occur over time and those influenced by environmental conditions. There are two types of calibrations: , and . Self-calibration performs the measurement-correction process within the device itself and does not require an external device. The calibration process that interrupts the ordinary data conversion process and is performed during device power up is referred to as foreground calibration or power up calibration @cite_25 @cite_9 @cite_3 @cite_23 . Although this technique results in significant error reduction, it cannot cope with temperature drift and other sources of inaccuracy such as sensor-to-ADC path loss. Alternatively, data converter can be calibrated while it is performing the regular conversion, and the result of conversion would not be affected by the calibration process. This is referred to as background calibration or runtime calibration @cite_19 @cite_1 . Background calibration encompasses temperature effect, which provides a higher absolute accuracy than foreground calibration.
@cite_9 @cite_1 @cite_3 @cite_19 @cite_23 @cite_25
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This study demonstrates for the first time the significant performance enhancement that calibration brings to folding-interpolating analog-to-digital converters (ADCs). The resulting 1.8-V ADC in 0.18- spl mu m CMOS achieves a conversion rate exceeding 1.6 GSample s, since the amplifier device sizes can be minimized to maximize speed without the restriction of device matching. At 1.6 GS s, the ADC achieves 0.15 LSB DNL, 0.35 LSB INL, 7.6 effective number of bits (ENOBs) at 100 MHz input, and 7.26 ENOB at Nyquist. At this speed, current consumption from a single 1.8-V supply is 245 mA analog, 185 mA digital, and 90 mA for the LVDS drivers. The ac performance is approximately 1.5 ENOBs higher compared to the same circuit with calibration disabled. The use of best design practices to optimize the ADC linearity prior to introducing calibration resulted in this small required dynamic calibration range, simplifying the calibrator circuitry and resulting in stable continuous performance over time without recalibration. Therefore, the fully on-chip calibration is performed automatically, just one time at power-up. A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC's) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDACs) commonly used in multistep or pipelined ADCs. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multistep or pipelined ADCs. The proposed method improves the performance of the inherently fast ADCs by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, of amp DC gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within two-thirds of the Nyquist bandwidth is recovered with 16-b accuracy using a forty-fourth order polynomial interpolation, behaving essentially as an FIR filter,. This paper considers the problem of self-calibration of a camera from an image sequence in the case where the camera's internal parameters (most notably focal length) may change. The problem of camera self-calibration from a sequence of images has proven to be a difficult one in practice, due to the need ultimately to resort to non-linear methods, which have often proven to be unreliable. In a stratified approach to self-calibration, a projective reconstruction is obtained first and this is successively refined first to an affine and then to a Euclidean (or metric) reconstruction. It has been observed that the difficult step is to obtain the affine reconstruction, or equivalently to locate the plane at infinity in the projective coordinate frame. The problem is inherently non-linear and requires iterative methods that risk not finding the optimal solution. The present paper overcomes this difficulty by imposing chirality constraints to limit the search for the plane at infinity to a 3-dimensional cubic region of parameter space. It is then possible to carry out a dense search over this cube in reasonable time. For each hypothesised placement of the plane at infinity, the calibration problem is reduced to one of calibration of a nontranslating camera, for which fast non-iterative algorithms exist. A cost function based on the result of the trial calibration is used to determine the best placement of the plane at infinity. Because of the simplicity of each trial, speeds of over 10,000 trials per second are achieved on a 256 MHz processor. It is shown that this dense search allows one to avoid areas of local minima effectively and find global minima of the cost function. Whenever we use devices to take measurements, calibration is indispensable. While the purpose of calibration is to reduce bias and uncertainty in the measurements, it can be quite difficult, expensive, and sometimes even impossible to implement. We study a challenging problem called self-calibration, i.e., the task of designing an algorithm for devices so that the algorithm is able to perform calibration automatically. More precisely, we consider the setup @math where only partial information about the sensing matrix @math is known and where @math linearly depends on @math . The goal is to estimate the calibration parameter @math (resolve the uncertainty in the sensing process) and the signal object of interest @math simultaneously. For three different models of practical relevance, we show how such a bilinear inverse problem, including blind deconvolution as an important example, can be solved via a simple linear least squares approach. As a co...