Practical synthesis of ternary sequences for system identification

Several issues related to the practical synthesis of ternary sequences with specified spectra are addressed in this paper. Specifically, sequences with harmonic multiples of two and three suppressed are studied, given their relevance to system identification applications. In particular, the effect of non-uniform Digital to Analog Converter (DAC) levels on the spectral properties of the generated signal is analyzed. It is analytically shown that the DAC non-uniform levels result in degraded harmonic suppression performance. Moreover, a new approach is proposed for designing ternary sequences, which is flexible and can be adapted to suit different requirements. The resulting sequences, denoted as randomized constrained sequences, are compared to direct sequences already proposed in the literature. The approach is validated by numerical simulations and experimental results, showing the potential to achieve harmonic suppression performance of approximately 100 dB

Astronomical component estimation (ACE v.1) by time-variant sinusoidal modeling

Accurately deciphering periodic variations in paleoclimate proxy signals is essential for cyclostratigraphy. Classical spectral analysis often relies on methods based on (fast) Fourier transformation. This technique has no unique solution separating variations in amplitude and frequency. This characteristic can make it difficult to correctly interpret a proxy's power spectrum or to accurately evaluate simultaneous changes in amplitude and frequency in evolutionary analyses. This drawback is circumvented by using a polynomial approach to estimate instantaneous amplitude and frequency in orbital components. This approach was proven useful to characterize audio signals (music and speech), which are non-stationary in nature. Paleoclimate proxy signals and audio signals share similar dynamics; the only difference is the frequency relationship between the different components. A harmonic-frequency relationship exists in audio signals, whereas this relation is non-harmonic in paleoclimate signals. However, this difference is irrelevant for the problem of separating simultaneous changes in amplitude and frequency. Using an approach with overlapping analysis frames, the model (Astronomical Component Estimation, version 1: ACE v.1) captures time variations of an orbital component by modulating a stationary sinusoid centered at its mean frequency, with a single polynomial. Hence, the parameters that determine the model are the mean frequency of the orbital component and the polynomial coefficients. The first parameter depends on geologic interpretations, whereas the latter are estimated by means of linear least-squares. As output, the model provides the orbital component waveform, either in the depth or time domain. Uncertainty analyses of the model estimates are performed using Monte Carlo simulations. Furthermore, it allows for a unique decomposition of the signal into its instantaneous amplitude and frequency. Frequency modulation patterns reconstruct changes in accumulation rate, whereas amplitude modulation identifies eccentricity-modulated precession. The functioning of the time-variant sinusoidal model is illustrated and validated using a synthetic insolation signal. The new modeling approach is tested on two case studies: (1) a Pliocene-Pleistocene benthic delta O-18 record from Ocean Drilling Program (ODP) Site 846 and (2) a Danian magnetic susceptibility record from the Contessa Highway section, Gubbio, Italy

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Many input variables of chemical processes have a continuous-time stochastic (CTS) behavior. The nature of these variables is a persistent, time-correlated variation that manifests as process variation as the variables deviate in time from their nominal levels. This work introduces methodologies in process identification for improving the modeling of process outputs by exploiting CTS input modeling under cases where the input is measured and unmeasured. In the measured input case, the output variable is measured offline, infrequently, and at a varying sampling rate. A method is proposed for estimating CTS parameters from the measured input by exploiting statistical properties of its CTS model. The proposed approach is evaluated based on both output accuracy and predictive ability several steps ahead of the current input measurement. Two parameter estimation techniques are proposed when the input is unmeasured. The first is a derivative-free approach that uses sample moments and analytical expressions for population moments to estimate the CTS model parameters. The second exploits the CTS input model and uses the analytical solution of the dynamic model to estimate these parameters. The predictive ability of the latter approach is evaluated in the same way as the measured input case. All of the data in this work were artificially generated under the probabilistic CTS model.Reprinted (adapted) with permission from Industrial and Engineering Chemistry Research 51 (2012): 5469, doi: 10.1021/ie201998b. Copyright 2012 American Chemical Society.</p

International Technical Textiles Congress 14-16

ABSTRACT Dibutyrylchitin (DBC) is an ester derivative of a natural polysaccharide -chitin. DBC is obtained by reaction of chitin with butyric anhydride in the presence of a catalyst. The production me thods of DBC were elaborated and optimized. DBC is easily soluble in common organic solvents and has film-and fibre forming properties. Such characteristic allows obtaining classical fibres from the polymer solutions. DBC is also a raw material for manufacturing yarn and for a broad range of textile dressing materials. Fibres with good mechanical properties were obtained by an optimized spinning process from the DBC solutions. The excellent biomedical properties of the DBC were confirmed by different experimental results which proved that DBC is a biocompatible and biodegradable polymer and stimulates regeneration of damaged tissues. Tests of these DBC dressing materials under clinical conditions were done and proved the excellent results of DBC-based dressing materials for the ordered healing of tissues and wounds. The DBC dressing materials accelerate the healing of the wound and are biodegraded during the healing process. From the clinical tests, it was clearly observed that the DBC dressing materials were absorbed into the fresh tissue formed during the healing process of the wounds. DBC and DBC-based dressing materials are good bioactive textile materials for wound healing and for understanding the biological properties of chitin derivatives. The obtained results proved the importance of the O-substitution of the hydroxyl groups present in chitin, not only for the solubility of the derivatives and the mechanical properties of the produced fibres, but still more important for the biological properties of these ester derivatives of chitin containing butyric acid. This development creates a link between textile products, based on material properties, and human health, based on the biological properties of the basic material. The mechanical properties of DBC were further optimized by blending it with poly(ε -caprolactone). Good transparent and flexible products, such as films, with a high elongation to break were obtained by blending 10 to 20wt% of poly(ε-caprolactone) with DBC. This creates new possible bioactive applications for DBC or poly(ε-caprolactone)