Paired-angle-rotation scanning optical coherence tomography forward-imaging probe

We report a novel forward-imaging optical coherence tomography (OCT), needle-probe paired-angle-rotation scanning OCT (PARS-OCT) probe. The probe uses two rotating angled gradient-index lenses to scan the output OCT probe beam over a wide angular arc (∼19° half-angle) of the region forward of the probe. Among other advantages, this probe design is readily amenable to miniaturization and is capable of a variety of scan modes, including volumetric scans. To demonstrate the advantages of the probe design, we have constructed a prototype probe with an outer diameter of 1.65 mm and employed it to acquire four OCT images, with a 45° angle between adjacent images, of the gill structure of a Xenopus laevis tadpole. The system sensitivity was measured to be 93 dB by using the prototype probe with an illumination power of 450 μW on the sample. Moreover, the axial and the lateral resolutions of the probe are 9.3 and 10.3-12.5 μm, respectively

Management Effects on the Vegetation of Rangeland in the Middle of Southern Slope of Tianshan Mountains

Rangeland degradation is a widespread problem and its restoration remains a major challenge. In recent years, many scientists have discussed the primary causes of over-grazing and approaches to restoration of China’s grasslands (e.g. Harris 2010; Wang and Han 2005; Lu et al. 2005). The major evidence of grassland degradation is lower plant productivity, reduced biodiversity and increase in poisonous weeds (Zhao et al. 2010), increased frequency of rodent and grasshopper infestations, and large scale dust storms (Lu et al. 2005). Restoration of these impacted ecosystems is an important and challenging task, especially in Xinjiang Province, China, where the natural grassland is rapidly degrading year by year (Yuan et al. 2011). Many strategies have been used to restore condition to these degrading grasslands, but since not all have proved successful, efforts are continuing to find methods that promote vigorous growth low soil disturbance and minimal vegetation destruction. In this study we investigated the response of grassland species and soils to strategic rest and shallow cultivation relative to current overgrazed grassland in the Tianshan Mountains of the Xinjiang Uyghur Autonomous Region, China

ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection

Object detection is the key technique to a number of Computer Vision applications, but it often requires large amounts of annotated data to achieve decent results. Moreover, for pedestrian detection specifically, the collected data might contain some personally identifiable information (PII), which is highly restricted in many countries. This label intensive and privacy concerning task has recently led to an increasing interest in training the detection models using synthetically generated pedestrian datasets collected with a photo-realistic video game engine. The engine is able to generate unlimited amounts of data with precise and consistent annotations, which gives potential for significant gains in the real-world applications. However, the use of synthetic data for training introduces a synthetic-to-real domain shift aggravating the final performance. To close the gap between the real and synthetic data, we propose to use a Generative Adversarial Network (GAN), which performsparameterized unpaired image-to-image translation to generate more realistic images. The key benefit of using the GAN is its intrinsic preference of low-level changes to geometric ones, which means annotations of a given synthetic image remain accurate even after domain translation is performed thus eliminating the need for labeling real data. We extensively experimented with the proposed method using MOTSynth dataset to train and MOT17 and MOT20 detection datasets to test, with experimental results demonstrating the effectiveness of this method. Our approach not only produces visually plausible samples but also does not require any labels of the real domain thus making it applicable to the variety of downstream tasks

A Study on the Social Adjustment of Persons with Adventitious Visual Impairment in Taiwan: Focusing on the Character of Daily Living and Effects of Rehabilitation Training

筑波大学 (University of Tsukuba)201

Network of Flexible Capacitive Strain Gauges for Reconstruction of Surface Strain

Monitoring of surface strain on mesosurfaces is a difficult task, often impeded by the lack of scalability of conventional sensing systems. A solution is to deploy large networks of flexible strain gauges, a type of large area electronics. The authors have recently proposed a soft elastomeric capacitor (SEC) as an economical skin-type solution for large-scale deployment onto mesosurfaces. The sensing principle is based on a measurable change in the sensor\u27s capacitance upon strain. In this paper, we study the performance of the sensor at reconstructing surface strain map and deflection shapes. A particular feature of the sensor is that it measures surface strain additively, because it is not utilized within a Wheatstone bridge configuration. An algorithm is proposed to decompose the additive in-plane strain measurements from the SEC into principal components. The algorithm consists of assuming a polynomial shape function, and deriving the strain based on Kirchhoff plate theory. A least-squares estimator (LSE) is used to minimize the error between the assumed model and the SEC signals after the enforcement of boundary conditions. Numerical simulations are conducted on a symmetric rectangular cantilever thin plate under symmetric and asymmetric static loads to demonstrate the accuracy and real-time applicability of the algorithm. The performance of the algorithm is further examined on an asymmetric cantilever laminated thin plate constituted with orthotropic materials mimicking a wind turbine blade, and subjected to a non-stationary wind load. Results from simulations show good performance of the algorithm at reconstructing the surface strain maps for both in-plane principal strain components, and that it can be applied in real time. However, its performance can be improved by strengthening assumptions on boundary conditions. The algorithm exhibits robustness in performance with respect to load and noise in signals, except when most of the sensors\u27 signals are close to zero due to over-fitting form the LSE