Lots of noise photons limit the application of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in forest canopy height retrieval. Abundant noise photons lead to unstable filtering results, thus affecting the canopy top photons classification. Therefore, this study proposes a method that takes account of the background noise level in the photon cloud. First, we propose an improved Differential, Regressive, and Gaussian Adaptive Nearest Neighbor (DRAGANN) filtering approach based on the DRAGANN filtering method. To obtain a more stable filtering result, large-scale and small-scale search radiuses are combined to improve the DRAGANN filtering performance. Second, we retrieve sub-canopy terrain topography by the multi-scale window detection method from the filtered photon cloud. Finally, a robust photon acquisition criterion based on the elevation difference of the filtered photons and the uneven density of signal photons is proposed to extract the canopy-top surface, which aims to mitigate the influence of inconsistencies of residual noise photons along the track. In addition, considering the fluctuation of the canopy surface, we use a non-spline interpolation method to obtain a continuous canopy surface, which avoids the Runge phenomenon caused by the spline interpolation method. The ICESat-2 data acquired in the Harvard Forest Region (HARV) is selected to assess the proposed method. The filtering performance of the improved DRAGANN approach shows stable than that of the DRAGANN approach. The proposed method’s root means square error (RMSE) and coefficient of determination (R2) reach 3.85 m and 0.55, respectively. The results indicate that the proposed method can retrieve reliable forest canopy height from the ICESat-2 data and performs significantly better than the ATL08 canopy height product in the test site.

Nearly all existing image registration algorithms are based on the full information (e.g., intensity and/or features) carried by the images being registered. Such a full-image-based strategy can achieve high-precision registration (e.g., at the subpixel level) but at the expense of long computation times, especially for high spatial resolution remote-sensing (RS) images. This letter presents a novel subimage-based registration method that is expected to dramatically reduce the time consumption but still meet subpixel accuracy requirements. The core idea behind this method is that the transform pattern of the reference and sensed images under a global transformation is theoretically similar to that of subimages derived from the full image, and vice versa. Hence, this method first divides the original reference image into subimages with nearly equal sizes, and extracts the subimage with the maximum wavelet coefficient summation to serve as a new reference image using the wavelet transform technique. Then, the two-step methods based on the phase correlation and matrix-multiply discrete Fourier transform (DFT) algorithms are applied to register the sensed image to the reference subimage. Finally, the proposed method is tested with simulated data sets. The results indicate that this method can perform subpixel registration as achieved by full-image-based methods but with the benefit of much shorter computation times (e.g., approximately 89% compared with a widely used rapid registration algorithm named enhanced phase correlation).

In unit testing, mocking is popularly used to ease test effort, reduce test flakiness, and increase test coverage by replacing the actual dependencies with simple implementations. However, there are no clear criteria to determine which dependencies in a unit test should be mocked. Inappropriate mocking can have undesirable consequences: under-mocking could result in the inability to isolate the class under test (CUT) from its dependencies while over-mocking increases the developers' burden on maintaining the mocked objects and may lead to spurious test failures. According to existing work, various factors can determine whether a dependency should be mocked. As a result, mocking decisions are often difficult to make in practice. Studies on the evolution of mocked objects also showed that developers tend to change their mocking decisions: 17% of the studied mocked objects were introduced sometime after the test scripts were created and another 13% of the originally mocked objects eventually became unmocked. In this work, we are motivated to develop an automated technique to make mocking recommendations to facilitate unit testing. We studied 10,846 test scripts in four actively maintained open-source projects that use mocked objects, aiming to characterize the dependencies that are mocked in unit testing. Based on our observations on mocking practices, we designed and implemented a tool, MockSniffer, to identify and recommend mocks for unit tests. The tool is fully automated and requires only the CUT and its dependencies as input. It leverages machine learning techniques to make mocking recommendations by holistically considering multiple factors that can affect developers' mocking decisions. Our evaluation of MockSniffer on ten open-source projects showed that it outperformed three baseline approaches, and achieved good performance in two potential application scenarios.

This paper presents an ultra-low power wireless transceiver specialized for but not limited to medical implantable applications. It operates at the 402-405-MHz medical implant communication service band, and also supports the 420-450-MHz industrial, scientific, and medical band. Being IEEE 802.15.6 standard compliant with additional proprietary modes, this highly configurable transceiver achieves date rates from 11 kb/s to 4.5 Mb/s, which covers the requirements of conventional implantable applications. The phase-locked loop-based transmitter architecture is adopted to support various modulation schemes with limited power budget. The zero-IF receiver has programmable gain and bandwidth to accommodate different operation modes. Fabricated in 40-nm CMOS technology with 1-V supply, this transceiver only consumes 1.78 mW for transmission and 1.49 mW for reception. The ultra-low power consumption together with the 15.6-compliant performance in term of modulation accuracy, sensitivity, and interference robustness make this transceiver competent for various implantable applications.

A fully differential envelope detector (ED) operating at 2.4GHz is designed in 90nm CMOS technology. The new design uses the common-gate topology to deal with large common-mode input signals through first-order current cancellation. Thereby, a fully differential ultra-low power super-regenerative front-end is enabled. It has a measured output voltage swing of 2.8–127mV and achieves 19.6dB output SNR at sensitivity input level. The circuit consumes 3µW from a 1.2V power supply.