Method for Determining the Estimated Timing Uncertainty for Digital Sampling Instruments

The overall uncertainty in digital captured data points is often misunderstood in our organization and is typically accepted as only the manufacturer uncertainty specification of the time base clock typically on the order of 10-100 parts per million. The time base clock of digital sampling technologies is critically important to maintain timing control of the internal electronics and to achieve the specified sampling rate of the instrument. The time base clock must remain within the manufacturer specification tolerance throughout the calibration interval to assure accurate performance. However, the time base uncertainty does not adequately account for the additional measurement errors accompanying the capture and evaluation of the time values for any cardinal points of interest when periodically sampling analog waveforms generated by other instruments or Units Under Test (UUTs). The proposed methodology described here details a general approach used to estimate the magnitude of the digital instrument sampling error when capturing analog waveforms based upon the instrument sampling rate, the frequency of a nominally equivalent sinusoidal waveform, as well as, whether the time value of any cardinal points is selected by a ‘Next Point After’ or Interpolation method for our purposes. Finally, the overall estimated timing uncertainty is quantified by arithmetically combining the error contributions for the sampling rate, the cardinal point selection method, and the instrument time base specification. The results of this method aid in selecting the appropriate digital sampling technology based upon waveform rise time requirements and provide general engineering guidance. Since the estimated error is a portion of the sampling timestep interval, the percentage error could be significant based upon the measured rise time. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA0003525.

Digital Structure

In this chapter, I consider whether digital images are digital in the strongest sense; namely, qua images. Assuming that a digital image is one that is made and screened digitally, there is a further question as to whether the representational scheme to which the image belongs has a fundamentally digital structure. Answering this question requires close analysis of Nelson Goodman’s classical account of the analog/digital distinction. It also requires a response to Goodman’s insistence on the essential analogicity of the pictorial. Such a response points to the uses of digital sampling and quantization technology to impose digital structure on encoded, replicable images.

COMPRESSIVE SENSING BASED OPTICAL SPECTROMETER FOR DOWNHOLE FLUID ANALYSIS

Downhole fluid analysis has the potential to resolve ambiguity in very complex reservoirs. Downhole fluid spectra contain a wealth of information to fingerprint a fluid and help to assess continuity. Commonly, a narrowband spectrometer with limited number of channels is used to acquire optical spectra of downhole fluid. The spectral resolution of this type of spectrometer is low due to limited number of narrowband channels. In this paper, we demonstrate a new type, compressive sensing (CS) based broadband spectrometer that provides accurate and high-resolution spectral measurement. Several specially designed broadband filters are used to simplify the mechanical, electrical, optical, and computational construction of a spectrometer, therefore provides measurement of fluid spectrum with high signal-to-noise ratio, robustness, and a broader spectral range. The compressive sensing spectrometer relies on reconstruction technique to compute the optical spectrum. Based on a large spectral database, containing more than 10000 spectra of various fluids at different temperature and pressure conditions, which were collected using conventional high resolution spectrometer in a lab, the basis functions of the optical spectra of three types of fluids (water, oil and gas/condensate) can be extracted. The reconstruction algorithm first classifies the fluid into one of three fluid types based on multichannel CS spectrometer measurements, the optical spectrum is reconstructed by using linear combination of the basis functions of corresponding fluid type, with weighting coefficients determined by minimizing the difference between calculated detector responses and measured detector responses across multiple optical channels. The reconstructed data may then be used for purposes such as contamination measurement, fluid property trends for reservoir continuity assessment, and digital sampling. Digital sampling is the process of extrapolating clean fluid properties from formation fluids not physically sampled. The reconstruction spectrum covers wavelengths from 500 nm to 3300 nm, which is a wider spectral region than has historically been accessible to formation testers. The expanded wavelength range allows access of the mid-infrared spectral region for which synthetic drilling-fluid components typically have higher optical absorbance. This reconstruction spectra may allow contamination to be directly determined. This paper will discuss the CS optical spectrometer design, fluid classification and spectral reconstruction algorithm. In addition, the applicability of the technique to fluid continuity assessment, sample contamination assessment and digital sampling will also be discussed.

Drop that Beat! Digital Sampling with Ableton Live in Higher Education (Intermediate)

The aim of this chapter is to get students comfortable with the basics of Ableton live. The lessons described in this chapter were designed for a combined undergraduate and graduate Digital Music Production course. Students engage with Ableton Live and a MIDI keyboard to create original beats and sample audio using online databases. Students identify, sample, edit, and manipulate a kick drum, snare drum, hi-hat, and additional percussive sounds using waveform mediums. These lessons use a project-based and teacher-facilitated approach, which allows for scaffolding, modification, and adaptation as needed. Each sequential step outlined can be time-modified based on student needs. This also allows for flexibility of student progress, questions, or challenges that may arise, and individual adaptations for learning.