A self-calibration circuit for a neural spike recording channel

This paper presents a self-calibration circuit for a neural spike recording channel. The proposed design tunes the bandwidth of the signal acquisition Band-Pass Filter (BPF), which suffers from process variations corners. It also performs the adjustment of the Programmable Gain Amplifier (PGA) gain to maximize the input voltage range of the analog-to-digital conversion. The circuit, which consists on a frequency-controlled signal generator and a digital processor, operates in foreground, is completely autonomous and integrable in an estimated area of 0.026mm 2 , with a power consumption around 450nW. The calibration procedure takes less than 250ms to select the configuration whose performance is closest to the required one.Ministerio de Ciencia e Innovación TEC2009-08447Junta de Andalucía TIC-0281

Offset-calibration with Time-Domain Comparators Using Inversion-mode Varactors

This paper presents a differential time-domain comparator formed by two voltage controlled delay lines, one per input terminal, and a binary phase detector for comparison solving. The propagation delay through the respective lines can be adjusted with a set of digitally-controlled inversion-mode varactors. These varactors provide tuning capabilities to the comparator; feature which can be exploited for offset calibration. This is demonstrated with the implementation of a differential 10-bit SAR-ADC. The design, fabricated in a 0.18μm CMOS process, includes an automatic mechanism for adjusting the capacitance of the varactors in order to calibrate the offset of the whole converter. Correct functionality was measured in all samples.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N0001414135

Towards dynamic camera calibration for constrained flexible mirror imaging

Flexible mirror imaging systems consisting of a perspective camera viewing a scene reflected in a flexible mirror can provide direct control over image field-of-view and resolution. However, calibration of such systems is difficult due to the vast range of possible mirror shapes and the flexible nature of the system. This paper proposes the fundamentals of a dynamic calibration approach for flexible mirror imaging systems by examining the constrained case of single dimensional flexing. The calibration process consists of an initial primary calibration stage followed by in-service dynamic calibration. Dynamic calibration uses a linear approximation to initialise a non-linear minimisation step, the result of which is the estimate of the mirror surface shape. The method is easier to implement than existing calibration methods for flexible mirror imagers, requiring only two images of a calibration grid for each dynamic calibration update. Experimental results with both simulated and real data are presented that demonstrate the capabilities of the proposed approach

Copula Calibration

We propose notions of calibration for probabilistic forecasts of general multivariate quantities. Probabilistic copula calibration is a natural analogue of probabilistic calibration in the univariate setting. It can be assessed empirically by checking for the uniformity of the copula probability integral transform (CopPIT), which is invariant under coordinate permutations and coordinatewise strictly monotone transformations of the predictive distribution and the outcome. The CopPIT histogram can be interpreted as a generalization and variant of the multivariate rank histogram, which has been used to check the calibration of ensemble forecasts. Climatological copula calibration is an analogue of marginal calibration in the univariate setting. Methods and tools are illustrated in a simulation study and applied to compare raw numerical model and statistically postprocessed ensemble forecasts of bivariate wind vectors

Signal inference with unknown response: Calibration-uncertainty renormalized estimator

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of CURE, developed in the framework of information field theory, is starting with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify CURE by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov Chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a non-iterative alternative to it

Improving self-calibration

Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on the calibration parameters. The common practice is to augment an external calibration solution using a known reference signal with an internal calibration on the unknown measurement signal itself. Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements. This can be understood in terms of maximizing the joint probability of signal and calibration. However, the full uncertainty structure of this joint probability around its maximum is thereby not taken into account by these schemes. Therefore better schemes -- in sense of minimal square error -- can be designed by accounting for asymmetries in the uncertainty of signal and calibration. We argue that at least a systematic correction of the common self-calibration scheme should be applied in many measurement situations in order to properly treat uncertainties of the signal on which one calibrates. Otherwise the calibration solutions suffer from a systematic bias, which consequently distorts the signal reconstruction. Furthermore, we argue that non-parametric, signal-to-noise filtered calibration should provide more accurate reconstructions than the common bin averages and provide a new, improved self-calibration scheme. We illustrate our findings with a simplistic numerical example.Comment: 17 pages, 3 figures, revised version, title change