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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 2 shifted relative to Si(111)-CH 3 samples by +0.27 V for n-Si and by up to +0.10 V for p-Si.Residual surface recombination limited the E oc of p-Si samples at high θ TFPA despite the favorable shift in the band-edge positions induced by the surface modification process.

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.Plain Language Summary Seismic stations record not only earthquake signals but also a wide variety of nuisance signals. Some of these nuisance signals are impulsive and can initially look very similar to real earthquake signals. This is a problem for earthquake early warning (EEW) algorithms, which sometimes misinterpret such signals as being real earthquake signals, and which may then send out false alerts. For each registered impulsive signal, EEW systems need to decide (or, classify) in real-time whether or not the signal stems from an actual earthquake. State-of-the-art machine learning (ML) classifiers have been shown to strongly outperform more standard linear classifiers in a wide range of classification problems. Here we analyze the performance of a variety of different ML classifiers to identify which type of classifier leads to the most reliable signal/noise discrimination in an EEW context. We find that we can successfully train complex deep learning classifiers that can discriminate between nuisance and earthquake signals very reliably (accuracy of 99.5%). Less complex ML classifiers also outperform a linear classifier, but with significantly higher error rates. The deep ML classifiers may allow EEW systems to almost entirely avoid false and missed signal detections...

the technology to automate the recommendations for big data analytic models that define data characteristics and problems).

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.

In recent days, unmanned aerial vehicles (UAVs) becomes more familiar because of its versatility, automation abilities, and low cost. Dynamic scene classification gained significant interest among the UAV-based surveillance systems, e.g., high-voltage power line and forest fire monitoring, which facilitate the object detection, tracking process and drastically enhances the outcome of visual surveillance. This paper proposes a new optimal deep learning-based scene classification model captured by UAVs. The proposed model involves a residual network-based features extraction (RNBFE) which extracts features from the diverse convolution layers of a deep residual network. In addition, the several parameters in RNBFE lead to many configuration errors due to manual parameter tuning. So, self-adaptive global best harmony search (SGHS) algorithm is employed for tuning the parameters of the RNBFE. The resultant feature vectors undergo classification by the use of latent variable support vector machine (LVSVM) model. The presented optimal RNBFE (ORNBFE) model has been tested using two open access datasets namely UC Merced (UCM) Land Use Dataset and WHU-RS Dataset. The presented technique attains maximum scene classification accuracy over the other recently proposed methods.