Regularly Sampled Research Articles

Text as signal. A tutorial with case studies focusing on social media (Twitter)

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

Transformation cost spectrum for irregularly sampled time series

Irregularly sampled time series analysis is a common problem in various disciplines. Since conventional methods are not directly applicable to irregularly sampled time series, a common interpolation approach is used; however, this causes data distortion and consequently biases further analyses. We propose a method that yields a regularly sampled time series spectrum of costs with minimum information loss. Each time series in this spectrum is a stationary series and acts as a difference filter. The transformation costs approach derives the differences between consecutive and arbitrarily sized segments. After obtaining regular sampling, recurrence plot analysis is performed to distinguish regime transitions. The approach is applied to a prototypical model to validate its performance and to different palaeoclimate proxy data sets located around Africa to identify critical climate transition periods during the last 5 million years and their characteristic properties.

Noise Reduction with Fuzzy Inference Based on Generalized Mean and Singleton Input–Output Rules: Toward Fuzzy Rule Learning in a Unified Inference Platform

A method is proposed for reducing noise in learning data based on fuzzy inference methods called α-GEMII (α-level-set and generalized-mean-based inference with the proof of two-sided symmetry of consequences) and α-GEMINAS (α-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points). It is particularly effective for reducing noise in randomly sampled data given by singleton input–output pairs for fuzzy rule optimization. In the proposed method, α-GEMII and α-GEMINAS are performed with singleton input–output rules and facts defined by fuzzy sets (non-singletons). The rules are initially set by directly using the input–output pairs of the learning data. They are arranged with the facts and consequences deduced by α-GEMII and α-GEMINAS. This process reduces noise to some extent and transforms the randomly sampled data into regularly sampled data for iteratively reducing noise at a later stage. The width of the regular sampling interval can be determined with tolerance so as to satisfy application-specific requirements. Then, the singleton input–output rules are updated with consequences obtained in iteratively performing α-GEMINAS for noise reduction. The noise reduction in each iteration is a deterministic process, and thus the proposed method is expected to improve the noise robustness in fuzzy rule optimization, relying less on trial-and-error-based progress. Simulation results demonstrate that noise is properly reduced in each iteration and the deviation in the learning data is suppressed considerably.

Compensating Baseband Distortion of Regularly Sampled Pulsewidth Modulators for High-Precision Power Converters

This paper presents a straight-forward method to compensate baseband distortion for regularly sampled pulsewidth modulation, for both sawtooth and triangular carrier, aimed at high-precision power electronics applications such as medical MRI and semiconductor lithography. The method adds correction signals to the modulator input to mitigate the distortion, which is computationally lightweight, and pseudo-code is given to simplify implementation. Measurements are conducted, which confirm the improvements offered by the method. It is shown that baseband distortion can be reduced by upto 60 dB. This improves the linearity and reduces audible noise of power converters.

One-Dimensional Spectral Analysis of Complex PWM Waveforms Using Superposition

This paper takes a new look at the mechanisms underlying the double-edge pulse-width modulation (PWM) process. It presents a novel way of deriving equations for the spectrum of double-edge PWM using basic mathematical techniques. In the process the underlying nonlinearities that generate the PWM sidebands are identified. Unlike the classical double Fourier series approach, the proposed method of deriving the PWM spectrum does not require the construction of the so-called unit cell. The interaction between this new model of the pulse-width modulator and the regular sampling process is studied, and generalized equations for the Fourier transforms of regularly sampled PWM waveforms are derived. A general solution to the important question of what happens to the PWM spectrum when the PWM reference consists of a summation of signals is presented. It is shown that the addition of reference signals in the time domain results in a double convolution of the PWM sidebands in the frequency domain. As an application of this result, it is shown how new analytic equations for the harmonics of third-harmonic injection PWM and space vector modulation can easily be derived. Finally, the new theoretical results are benchmarked against results from the well-established double Fourier series method.

Effective management of heat transfer fluid flash point temperatures using a light-ends removal kit (LERK)

Heat transfer fluids (HTF) need to be regularly sampled to assess the extent of thermal degradation, oxidative state, the accumulation of short-chained light-ends and contamination by intrinsic or extrinsic particles. The build-up of light-ends in a HTF system presents a potential fire hazard. A light-ends removal kit (LERK) enables light-ends to be removed continuously, helping to push-up flash point temperatures. In the current case, the concentration of light-ends started to build-up in the client’s system and a LERK was subsequently installed. Data is presented that shows how effective the LERK was in restoring mean closed flash point temperature to stable levels, similar to those seen for a virgin HTF. Closed flash point temperature was, in this case, more variable than open flash point temperature. This highlights the need to make direct measurements of closed flash point temperature as opposed to indirect measurements of open flash point temperature. This case emphasises the need for regular HTF sampling and analysis, and that the installation of a LERK can help maintain the condition and life of a HTF.

Spectral analysis of irregularly-sampled data: Paralleling the regularly-sampled data approaches

The spectral analysis of regularly-sampled (RS) data is a well-established topic, and many useful methods are available for performing it under different sets of conditions. The same cannot be said about the spectral analysis of irregularly-sampled (IS) data: despite a plethora of published works on this topic, the choice of a spectral analysis method for IS data is essentially limited, on either technical or computational grounds, to the periodogram and its variations. In our opinion this situation is far from satisfactory, given the importance of the spectral analysis of IS data for a considerable number of applications in such diverse fields as engineering, biomedicine, economics, astronomy, seismology, and physics, to name a few. In this paper we introduce a number of IS data approaches that parallel the methods most commonly used for spectral analysis of RS data: the periodogram (PER), the Capon method (CAP), the multiple-signal characterization method (MUSIC), and the estimation of signal parameters via rotational invariance technique (ESPRIT). The proposed IS methods are as simple as their RS counterparts, both conceptually and computationally. In particular, the fast algorithms derived for the implementation of the RS data methods can be used mutatis mutandis to implement the proposed parallel IS methods. Moreover, the expected performance-based ranking of the IS methods is the same as that of the parallel RS methods: all of them perform similarly on data consisting of well-separated sinusoids in noise, MUSIC and ESPRIT outperform the other methods in the case of closely-spaced sinusoids in white noise, and CAP outperforms PER for data whose spectrum has a small-to-medium dynamic range (MUSIC and ESPRIT should not be used in the latter case).

An analytical technique for the determination of spectral components of multilevel carrier-based PWM methods

This paper presents an analytical approach for determining the spectral components of multilevel pulsewidth modulation (PWM) strategies, which avoids the problems of spectral leakage, roundoff, and subtle programming errors associated with the more usual approach of using discrete Fourier transforms of simulated switched waveforms. The approach uses a double Fourier integral formulation to analyze the inverter switched output, establishing the inner and outer integral limits of this formulation under conditions of natural sampling, regular sampling, and overmodulation. Closed-form and numerical integration solutions are presented for phase disposition PWM of a diode-clamped inverter to illustrate the technique, together with a comparison of theoretical and experimental spectral results for the naturally and asymmetric regularly sampled cases to confirm the validity of the approach.

Stability of the i.v. and oral formulations of etoposide in solution.

Etoposide is a widely used cytotoxic drug that requires complex formulation for both the i.v. and oral preparation to ensure drug stability. Data on the stability of the i.v. formulation when diluted in infusion fluids are contradictory, and there is little information on the stability of the oral preparation in gastric or intestinal fluids. The stability of both i.v. and oral etoposide was therefore evaluated in the present investigation. The stability of the i.v. preparation was investigated across a range of concentrations in infusion fluids, being determined by regular sampling for high-performance liquid chromatography (HPLC) analysis and by visual inspection. The stability of the oral preparation was studied in both artificial gastric and intestinal fluids, again with regular sampling for HPLC analysis, and the influence of pH, concentration and the addition of ethanol and bile salts on oral stability was determined. The i.v. preparation showed a marked decrease in stability with increasing drug concentration, but stability was additionally reduced in i.v. bags regularly sampled with a syringe and needle as compared with bags that were inspected visually only (minimal stability in sampled bags, 24 h at 0.5 mg/ml and 5 h at 1.0 mg/ml, as compared with 10 days and 18 h at the respective concentrations in unsampled bags). Stability was also greater at room temperature, 20-23 degrees C, as compared with 8-12 degrees C. Loss of stability was indicated by a decrease in etoposide concentration (measured by HPLC) and the appearance of a fine white precipitate, shown to be pure etoposide. Importantly, the appearance of precipitate was as sensitive as a specific HPLC assay in detecting loss of stability and was in many cases apparent when the etoposide concentration was within 5% of the starting concentration. The oral formulation also showed a marked concentration-dependent decrease in stability in artificial intestinal fluid at pH 7.5 (percentage of etoposide in solution after 2 h at 0.5, 1.0, 1.5 and 2.0 mg/ml, 94 +/- 2%, 80 +/- 5%, 68 +/- 13% and 41 +/- 9%, respectively). There was no concentration effect on stability in gastric fluid at pH 3.0, although stability was much greater at pH 3 and pH 5 as compared with pH 1 or in intestinal fluid at pH 7.5.(ABSTRACT TRUNCATED AT 250 WORDS)

Evaluation of the accuracy and precision of annual phosphorus load estimates from two agricultural basins in Finland

The accuracy and precision of phosphorus load estimates from two agricultural drainage basins in western Finland were evaluated, based on continuous flow measurements and frequent flow-proportional sampling of total phosphorus concentration during a 2 year period. The objective was to compare different load calculation methods and to evaluate alternative sampling strategies. An hourly data set of concentrations was constructed by linear interpolation, and these data were used in Monte Carlo runs for producing replicate data sets for calculating the accuracy and precision of load estimates. All estimates were compared with reference values computed from the complete hourly data sets. The load calculation methods based on summing the products of regularly sampled flows and concentrations produced the best precision, whereas the best accuracy was achieved using methods based on multiplying annual flow by flow-weighted annual mean concentration. When comparing different sampling strategies, concentrating sampling in high runoff periods (spring and autumn) was found to give better accuracy and precision than strategies based on regular interval sampling throughout the year. However, the best result was obtained by taking samples flow-proportionally within the highest peak flows plus additional regular interval (e.g. biweekly) samples outside these flow peaks. Using this strategy, which calls for automatic sampling equipment, accuracies better than 5% and precisions better than 10% can be achieved with only 30–50 samples year −1.