Annotated Reconstruction of 3D Spaces Using Drones

As the fields of robotics and drone technologies are continually advancing, the challenge of teaching these agents to learn and maneuver in the real world becomes increasingly important. A critical component of this is the ability for a robot to map and understand its surrounding unknown environment, both in terms of physical structure and object classification. In this project we tackle the challenge of mapping a 3D space with annotations using only 2D images acquired from a Parrot Drone. In order to make such a system operate efficiently in close to real time, we address a number challenges including (1) creating a optimized version of Faster RCNN that can operate on drone hardware while still being accurate, (2) developing a method to reconstruct 3D spaces from 2D images annotated with bounding boxes, and (3) using generated 3D annotations to complete drone motion planning for unknown space exploration

Control of the Band-Edge Positions of Crystalline Si(111) by Surface Functionalization with 3,4,5-Trifluorophenylacetylenyl Moieties

Functionalization of semiconductor surfaces with organic moieties can change the charge distribution, surface dipole, and electric field at the interface. The modified electric field will shift the semiconductor band-edge positions relative to those of a contacting phase. Achieving chemical control over the energetics at semiconductor surfaces promises to provide a means of tuning the band-edge energetics to form optimized junctions with a desired material. Si(111) surfaces functionalized with 3,4,5-trifluorophenylacetylenyl (TFPA) groups were characterized by transmission infrared spectroscopy (TIRS), X-ray photoelectron spectroscopy (XPS), and surface recombination velocity (S) measurements. Mixed methyl/TFPA-terminated (MMTFPA) n- and p-type Si(111) surfaces were synthesized and characterized by electrochemical methods. Current density versus voltage and Mott-Schottky measurements of Si(111)–MMTFPA electrodes in contact with Hg indicated that the barrier height, Φb, was a function of the fractional monolayer coverage of TFPA (θTFPA) in the alkyl monolayer. Relative to Si(111)–CH3 surfaces, Si(111)–MMTFPA samples with high θTFPA produced shifts in Φb of ≥0.6 V for n-Si/Hg contacts and ≥0.5 V for p-Si/Hg contacts. Consistently, the open-circuit potential (Eoc) of Si(111)–MMTFPA samples in contact with CH3CN solutions that contained the 1-electron redox couples decamethylferrocenium/decamethylferrocene (Cp*2Fe+/0) or methyl viologen (MV2+/+●) shifted relative to Si(111)–CH3 samples by +0.27 V for n-Si and by up to +0.10 V for p-Si. Residual surface recombination limited the Eoc of p-Si samples at high θTFPA despite the favorable shift in the band-edge positions induced by the surface modification process

Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning

In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental - and difficult - tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors, and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real-time signal/noise discrimination. We develop and compare a series of non-linear classifiers with variable architecture depths, including fully connected, convolutional (CNN) and recurrent neural networks, and a model that combines a generative adversarial network with a random forest (GAN+RF). We train all classifiers on the same data set, which includes 374k local earthquake records (M3.0-9.1) and 946k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers, and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3s long waveform snippets, the CNN and the GAN+RF classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts

Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning

In earthquake early warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental—and difficult—tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real‐time signal/noise discrimination. We develop and compare a series of nonlinear classifiers with variable architecture depths, including fully connected, convolutional and recurrent neural networks, and a model that combines a generative adversarial network with a random forest. We train all classifiers on the same data set, which includes 374 k local earthquake records (M3.0–9.1) and 946 k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3‐s‐long waveform snippets, the convolutional neural network and the generative adversarial network with a random forest classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts

Band edge control of crystalline silicon by chemical functionalization of the surface

Methyl-termination of the silicon (111) crystal plane has been shown to yield nearly complete termination of the silicon atop sites with Me groups to yield exceptional stability to oxidn. and low elec. defect densities at the surface. However, Me groups impart a -0.4 eV surface dipole that shifts the semiconductor band-edge positions of p-type silicon unfavorably for the prodn. of fuels, namely hydrogen, from sunlight. Incorporation of electroneg. elements, such as fluorine, into alkyl monolayers can effectively reverse the unfavorable shift on the band-edge positions and maximize the efficiency of solar-fuels devices. Thus, a mixed methyl/4- fluorobenzyl monolayer has been developed herein to shift the band-edge positions on a sliding scale while maintaining low elec. defect densities at the surface. The band-edge positions were detd. using electrochem. measurements and photoelectron spectroscopy to develop a relationship between the band-edge positions and the monolayer compn. Samples with favorable band-edge positions for the prodn. of hydrogen were tested electrochem. to demonstrate the improved efficiency of devices fabricated using mixed methyl/4-fluorobenzyl monolayers compared with homogeneous Me monolayers. This work holds promise to motivate the development of a new class of solar-fuels devices based on chem. functionalization of semiconductor surfaces