Peter Marchetto

Temperature compensation of a quartz tuning-fork clock crystal via post-processing

The dependence of a tuning-fork quartz crystal oscillators frequency f on temperature T is observed over the temperature range -5 to 20°C. From this, a parabolic f(T) function is fit to the crystals data, and used to compensate for sampling period drift in an Analog to Digital Converter (ADC) system based around this crystal at various temperatures. Resolution and uncertainty of this method are discussed.

A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

Hyperspectral imaging to identify salt-tolerant wheat lines

In order to address the worldwide growing demand for food, agriculture is facing certain challenges and limitations. One of the important threats limiting crop productivity is salinity. Identifying salt tolerate varieties is crucial to mitigate the negative effects of this abiotic stress in agricultural production systems. Traditional measurement methods of this stress, such as biomass retention, are labor intensive, environmentally influenced, and often poorly correlated to salinity stress alone. In this study, hyperspectral imaging, as a non-destructive and rapid method, was utilized to expedite the process of identifying relatively the most salt tolerant line among four wheat lines including Triticum aestivum var. Kharchia, T. aestivum var. Chinese Spring, (Ae. columnaris) T. aestivum var. Chinese Spring, and (Ae. speltoides) T. aestivum var. Chinese Spring. To examine the possibility of early detection of a salt tolerant line, image acquisition was started one day after stress induction and continued on three, seven, and 12 days after adding salt. Simplex volume maximization (SiVM) method was deployed to detect superior wheat lines in response to salt stress. The results of analyzing images taken as soon as one day after salt induction revealed that Kharchia and (columnaris)Chinese Spring are the most tolerant wheat lines, while (speltoides) Chinese Spring was a moderately susceptible, and Chinese Spring was a relatively susceptible line to salt stress. These results were confirmed with the measuring biomass performed several weeks later.

Improving acoustic time-of-arrival location estimates by correcting for temperature drift in time base oscillators

Using multiple acoustic sensors in an array for estimating sound source location relies on time synchrony among the devices. When independent time synchrony methods—such as GPS time stamps—are unavailable, the precision of the time base in individual sensors becomes one of the main sources of error in synchrony, and consequently increases the uncertainty of location estimates. Quartz crystal oscillators, on which many acoustic sensors base sampling rate timing, have a vibration frequency that varies with temperature f(T). Each oscillator exhibits a different frequency-temperature relationship, leading to sensor-dependent sample rate drift. Our Marine Autonomous Recording Units (MARUs) use such oscillators for their sample rate timing, and they experience variations in temperature of at least 20°C between preparation in air and deployment underwater, leading to sample rate drift over their deployments. By characterizing each MARU’s oscillator f(T) function, and measuring the temperature of the MARU during ...