Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

TapChain: A Rule Chain Recognition Model Based on Multiple Features

Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

Research on Machine Learning-Based Error Correction Algorithm for Spoken French

In order to overcome the problems of low error capture accuracy and long response time of traditional spoken French error correction algorithms, this study designed a French spoken error correction algorithm based on machine learning. Based on the construction of the French spoken pronunciation signal model, the algorithm analyzes the spectral features of French spoken pronunciation and then selects and classifies the features and captures the abnormal pronunciation signals. Based on this, the machine learning network architecture and the training process of the machine learning network are designed, and the operation structure of the algorithm, the algorithm program, the algorithm development environment, and the identification of oral errors are designed to complete the correction of oral French errors. Experimental results show that the proposed algorithm has high error capture accuracy and short response time, which prove its high efficiency and timeliness.

Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint

Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve both the accuracy and speed of a forensic automotive paint examination. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. Applying this correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, the large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, each of the twenty-six cross-sectioned automotive paint samples was correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library.

Hyper-reoriented walking in minimal space

We present a new reorientation technique, “hyper-reoriented walking,” which greatly reduces the amount of physical space required in virtual reality (VR) applications asking participants to walk along a grid-like path (such as the most common layout in department stores). In hyper-reoriented walking, users walk along the gridlines with a virtual speed of twice the speed of real walking and perform turns at cross-points on the grid with half the speed of the rotation speed in the physical space. The impact of the technique on participants’ sense of orientation and increase in simulator sickness was investigated experimentally involving 19 participants walking in a labyrinth of infinite size that included straight corridors and 90° T-junctions at the end of the corridors. Walking accuracy was assessed by tracking the position of the head mounted display, and cyber-sickness was recorded with the simulator sickness questionnaire and with open questions. Walking straight forward was found to closely match the ideal path, which is the grid line, but slight errors occasionally occurred when participants turned at the T-junctions. A correction algorithm was therefore necessary to bring users back to the gridline. For VR experiments in a grid-like labyrinth with paths of 5 m in length, the technique reduces required size of the tracked physical walking area to 3 m × 2 m.

An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman Signature Spectra

Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.