Upconversion Spectral Rulers for Transcutaneous Displacement Measurements

We describe a method to measure micron to millimeter displacement through tissue using an upconversion spectral ruler. Measuring stiffness (displacement under load) in muscles, bones, ligaments, and tendons is important for studying and monitoring healing of injuries. Optical displacement measurements are useful because they are sensitive and noninvasive. Optical measurements through tissue must use spectral rather than imaging approaches because optical scattering in the tissue blurs the image with a point spread function typically around the depth of the tissue. Additionally, the optical measurement should have low background and minimal intensity dependence. Previously, we demonstrated a spectral encoder using either X-ray luminescence or fluorescence, but the X-ray luminescence required an expensive X-ray source and used ionizing radiation, while the fluorescence sensor suffered from interference from autofluorescence. Here, we used upconversion, which can be provided with a simple fiber-coupled spectrometer with essentially autofluorescence-free signals. The upconversion phosphors provide a low background signal, and the use of closely spaced spectral peaks minimizes spectral distortion from the tissue. The small displacement noise level (precision) through tissue was 2 µm when using a microscope-coupled spectrometer to collect light. We also showed proof of principle for measuring strain on a tendon mimic. The approach provides a simple method to study biomechanics using implantable sensors.

The initial run-in and long-term drift of the adhesive force between polycrystalline silicon MEMS sidewalls

In this paper we measure the evolution of adhesion between two polycrystalline silicon sidewalls of a microelectromechanical adhesion sensor during three million contact cycles. We execute a series of AFM-like contact force measurements with comparable force resolution, but using real MEMS multi-asperity sidewall contacts mimicking conditions in real devices. Adhesion forces are measured with a very high sub-nanonewton resolution using a recently developed optical displacement measurement method. Measurements are performed under well-defined, but different, low relative humidity conditions. We found three regimes in the evolution of the adhesion force. (I) Initial run-in with a large of cycle-to-cycle variability, (II) Stability with low variability, and (III) device-dependent long term drift. The results obtained demonstrate that although a short run-in measurement shows stabilization, this is no guarantee for long-term stable behavior. Devices performing similarly in region II, can drift very differently afterwards. The adhesion force drift during millions of cycles is comparable in magnitude to the adhesion force drift during initial run-in. The boundaries of the drifting adhesion forces are reasonably well described by an empirical model based on random walk statistics. This is useful knowledge when designing polycrystalline silicon MEMS with contacting surfaces.

Spatiotemporal ICA/PCA decomposition of optical displacement field stacks: perspective for landslide time series inversion

<p>Monitoring ground surface motion is a key information to locate active landslides and possibly detect failure onsets but also to better understand their mechanical behavior in relation with environmental forcing. In-situ and remote technologies are available to provide measures of the ground displacement with different advantages and limitations (in terms of spatial coverage, sampling frequency, etc.). Image matching techniques have been commonly used to detect and measure landslide acceleration but this is often limited to a small amount of images. In the recent years, the number of optical satellite constellations have significantly increased providing global coverage with a frequent revisit time at medium to high spatial resolution and an open access policy (e.g. Sentinel 2, Landsat 7/8). These datasets present new perspectives for the monitoring of slow (cm/day) to moderate (m/month) landslide motion and poses challenges to discriminate between the different spatio-temporal sources (e.g. rainfall correlated signal, noise, seasonal signal, etc.) present in the time -series. </p><p>We investigate the use of spatiotemporal ICA/PCA decomposition on optical displacement stacks of landslide areas. The main goal aims at testing 1) the capability of ICA/PCA analysis to detect relevant deformation deformation sources in the case of landslide monitoring and 2) the possibility to improve the time-series inversion of landslide motion by removing spatiotemporal sources that can result from seasonal sun exposition or geometric inaccuracies. We use the MPIC-OPT-Slide service of the GeoHazards Exploitation Platform (GEP) to compute several correlograms and displacement fields (>500 per site) from Sentinel-2 acquisitions on the slow-moving La Valette landslide (Alpes-de-Haute-Provence, France) and the moderately-moving Aiguilles-Pas de l’Ours landslide (Hautes-Alpes, France). We show that in case of steady-state deformation, the noise can be significantly removed around the active parts of the slope. In the case of more complex deformation evolution, pertinent sources can be manually isolated but the choice of the number of sources and their automatic selection remain challenging. </p>

CALIBRATION OF OPTICAL DISPLACEMENT SENSOR SYSTEM

The optical displacement sensor was selected for accurate length measurement. The aim of this article is to determine the mathematical measurement model for displacement measurement using an assembled measuring chain by calibration. The uncertainty balance for the assembled measuring chain is determined in the next part of this article.