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Movement similarity is a hot topic and the foundation of a plethora of methodologies in movement analysis. In this thesis, movement similarity is classified into two categories. The first category, explicit movement similarity, is defined as the closeness of trajectories in space and time. It is directly quantified along the path of individuals using a wide variety of trajectory similarity measures, such as Fréchet distance and dynamic time warping. On the other hand, the second category, implicit movement similarity, is defined as the consistency in movement and behavioral patterns of an individual or a group of moving entities. It can serve as a high-level representation of movement patterns of an individual or a group of moving entities and can be applied to solve many types of movement-related problems, such as trajectory prediction and interpolation. Surrounding the topic of movement similarity, this thesis investigates two methodologies based on the explicit and implicit movement similarity, respectively, to demonstrate their applications in movement analysis. Explicit movement similarity in this thesis is utilized to unravel the associations between migration paths and underlying environmental correlates influencing movement choices of migratory turkey vultures (Cathartes aura) in North America. Multiple commonly used trajectory-similarity measures including Fréchet distance, dynamic time warping (DTW), Hausdorff distance, longest common subsequence (LCSS), and edit distance are integrated into a hierarchical clustering approach to identify variations in turkey vultures’ migration path choices over multiple seasons. At each hierarchy, the optimal clustering setting, i.e., a distance metric together with the number of clusters, is selected automatically based on the silhouette coefficient. Using 15 years of tracking data of turkey vultures during their fall and spring migration seasons, seasonal clusters are identified and then annotated with environmental variables for Kolmogorov-Smirnov (KS) test and Jensen-Shannon distance (JSD) calculation to examine the variation between clusters and the background and variation between clusters.In terms of the application of implicit movement similarity, this thesis proposes a trajectory interpolation model with an encoder-decoder architecture based on gated recurrent units (GRUs) to interpolate trajectory gaps (missing values). The proposed model is able to read a trajectory containing a gap in both chronological and reverse chronological orders. The information obtained from these two directions is fused to learn the implicit movement similarity contained in that trajectory, which is later used to reconstruct the complete trajectory with the original gap filled in. The proposed interpolation method is validated using turkey vulture migration trajectories. Interpolation results demonstrate that the proposed method is capable of capturing implicit movement similarity from trajectories for interpolation purposes, since without which, some gaps would be difficult to interpolate accurately using traditional interpolation methods. Movement similarity is a promising field in computational movement analysis and is often used as a foundation of other machine learning and modeling methodologies such as trajectory classification, behavioral model detection, and movement prediction. On the foundation of explicit and implicit movement similarity, researchers can build multifarious models for diverse moving entities, such as animals, humans, vessels, and vehicles.

Single-molecule measurements provide statistical distributions of molecular properties, in addition to the ensemble averages. Evanescent detection approaches have been widely used for single-molecule detection because the evanescent field can significantly enhance the light-analyte interaction and reduce the background noise. However, current evanescent single-molecule detection systems mostly require specially designed sensing components. Here, we show that single proteins can be imaged on a plain cover glass surface by detecting the evanescent waves scattered by the target molecules. This allows us to quantify the protein-antibody interactions at the single-molecule level. This protocol describes a label-free single-molecule imaging approach with conventional consumables and may pave the road for detecting single molecules with commercial optical microscopy.

Crowd sourcing vehicle trajectory data imply the latest road network information. Therefore, studies on the extraction of road features from trajectory data provide the opportunity for efficient construction and renewal of road datasets. Since a road network is composed of road intersections and road segments, the extraction of road intersections plays an important role in road network generation. Due to the lack of accurate mechanisms for intersection extraction, problems such as omission and distortion of road intersections occur frequently. A method is proposed to identify and extract road intersections from vehicle trajectory data. Firstly, it is analyzed that the differences in shape and kinetic features between trajectories from intersection areas and non-intersection areas. Secondly, the decision tree method is employed to construct a trajectory segment classification model, which enables the extraction of lane-changing segments in intersection areas with the support of trajectory division model using a sliding window strategy. Thirdly, a method that based on Hausdorff distance is designed to cluster trajectory segments in intersection areas, and intersection structures are obtained by extracting the central lines of the trajectory segment clusters. Experiments on real-life trajectory datasets were implemented and results showed the effectiveness of the proposed method.