The application of autonomous robotics to close-contact healthcare tasks, such as the nasopharyngeal (NP) swab test, has clear potential for reducing infection risks to staff and improving efficiency. We propose a control system that performs the NP swab test with a collaborative manipulator arm, guided by an instrumented end-effector that measures force and visual information. We assume a scenario where the patient is unrestrained, with hardware general enough for other types of close contact tasks. The system employs visual servo control to align the swab with the nostrils. A compliant joint velocity controller inserts the swab into the nasal cavity, following a planned trajectory adjusted with force-feedback. Fuzzy logic systems are designed to detect when the swab reaches the nasopharynx and enforce safety criteria. We validate the system using a second robotic arm that holds a nasal cavity phantom and simulates natural head motions. Extensive experiments identify controller configurations capable of effectively performing the NP swab test even with significant head motion.

Sentinel-1 is a synthetic aperture radar platform that provides free and open-source images of the Earth. A product type of Sentinel-1 is ground range detected (GRD), which records intensity while discarding phase information from the radar backscatter. Especially in cross-polarized GRD images, there are noticeable intensity changes throughout the image that are caused by amplifying the noise floor of the signal, which varies due to the nonuniform radiation pattern of the satellite’s antenna. While Sentinel-1 has instrument processing facility (IPF) software to estimate the noise floor, even in the newer versions (3.1 or above) of the IPF software there are still instances where the estimates provided do not fit the actual noise floor in the image, which is particularly noticeable in transitions between adjacent subswaths. In this work, we propose a method that reduces the impact of the varying noise-floor throughout the image. The method models the intensity of the noise floor to be a power function of the radiation pattern power. The method divides the swath into several sections depending on the location of the local minimum and maximum of the radiation pattern power with respect to the range. The parameter estimation is portrayed as a geometric programming problem that is transformed into a linear programming problem by logarithmic transformation. Affine offsets are computed for each subswath by a weighted least squares approach. Vast improvement is found on extra-wide (EW) and interferometric wide (IW) Sentinel-1 modes over cross-polarized images. Code implementation is available athttps://github.com/PeterQLee/sentinel1_denoise_rs.

Synthetic aperture radar images from the Sentinel-1 program are obtained by interpreting signals from a non-uniform radiation pattern. Cross-polarized images in extra-wide mode show significant additive noise patterns that take the form of varying intensity and are independent of the ground targets. While Sentinel-1 provides a method for removing these noise patterns via noise-calibration files, there still remain significant issues in the transformed products, particularly from discontinuous changes among adjacent subswaths. In this work, we consider recalibration by assuming the noise-gain to be a power function of the platform's radiation pattern. We propose a method that estimates the scaling and exponent parameters of the power-function by applying linear programming to the log transform of the data and the radiation pattern intensity, alone with affine rescaling with least-squares estimation. Our method is able to rescale Sentinel-1 scenes to have a more consistent intensity profile among the subswaths of the image.