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A monocular vision based rear vehicle detection and tracking system is presented for Lane Change Assist (LCA), which does not need road boundary and lane information. Our algorithm extracts regions of interest (ROI) using the shadow underneath a vehicle, and accurately localizes vehicle regions in ROI by vehicle features such as symmetry, edge and shadow underneath vehicles. The algorithm realizes vehicle verification by combining knowledge-based and learning-based methods. During vehicle tracking, templates are dynamically created on-line, tracking window is adaptively adjusted with motion estimation, and confidence is determined for tracked vehicle. The algorithm was tested under various traffic scenes at different daytime, the result illustrated good performance.

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Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations. mangrove forests is essential for the accurate formulation of future studies and management of mangrove ecosystems [8][9][10].Remote sensing has served as a sustainable tool in the mapping and monitoring of mangrove forests for decades, primarily because of the logistical and practical difficulties involved in field surveys of the muddy environments [11,12]. Before the launch of the high resolution satellite sensors, it was impossible to accurately discriminate mangrove species with traditional medium resolution satellite data [13]. Recently, with the development of commercial sensors (high spatial resolution, hyperspectral, and active remote sensor), many studies have employed single or combined airborne or satellite imagery to map mangrove species [2,3,8]. However, so far, the mapping of mangrove species with freely accessible imagery still remains a challenge, as mangrove species often exhibit similar spectral signatures and spatial textures [10].The aforementioned issues indicate the need to explore more substantial features to improve the detection of mangrove species from remote sensing data. A phenological trajectory of plants can be acquired from the time series of remote sensing images through delineating the temporal variation in spectrum during the growing period [14]. To date, remotely sensed plant phenology has been widely used to conduct vegetation discrimination, but most of the studied vegetation has been crops or inland forests [15,16]. Although mangroves are evergreen plants, different mangrove species have different phenophase peaks [17]. However, to date, no studies have dealt with mangrove phenological trajectories in remote sensing-based ...

The fabrication of discrete nanostructures with both plasmonic circular dichroism (PCD) and chiral features is still a challenge. Here, gold nanoarrows (GNAs) with both chiroptical responses and chiral morphologies are achieved by using L‐selenocystine (L‐SeCys2) as a chiral inducer. While L‐SeCys2 generates GNAs with a weak PCD signal, the irradiated L‐SeCys2 (irr‐L‐SeCys2) leads to GNAs with featured helical grooves (HeliGNAs) accompanying with a strong PCD signal. It is revealed that when L‐SeCys2 is photo‐irradiated, the emergence of selenyl radicals plays an important role in the formation of HeliGNAs and enhancement of the chiroptical signal. In comparison with L‐SeCys2 and the other kinds of sulfur‐containing amino acids, the formation mechanism of helical grooves on the surface of GNAs is proposed. Both HeliGNAs and GNAs are used to discriminate amino acids by utilizing surface enhanced Raman scattering (SERS) effect. In the presence of either GNAs or HeliGNAs as the substrate, Fmoc‐L‐Phe shows more significant SERS than Fmoc‐D‐Phe. This study may advance the design of discrete plasmonic nanomaterials with both chiral morphology and potential applications in discrimination of chiral molecules.

Location data is one of the most widely used context data types in context-aware and ubiquitous computing applications. To support locating applications in indoor environments, numerous systems with different deployment costs and positioning accuracies have been developed over the past decade. One useful method, based on received signal strength (RSS), provides a set of signal transmission access points. However, compiling a remeasurement RSS database involves a high cost, which is impractical in dynamically changing environments, particularly in highly crowded areas. In this study, we propose a dynamic estimation resampling method for certain locations chosen from a set of remeasurement fingerprinting databases. Our proposed method adaptively applies different, newly updated and offline fingerprinting points according to the temporal and spatial strength of the location. To achieve accuracy within a simulated area, the proposed method requires approximately 3% of the feedback to attain a double correctness probability comparable to similar methods; in a real environment, our proposed method can obtain excellent 1 m accuracy errors in the positioning system.

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