A series of experiments exploring the propagation of a solitary wave over a submerged step were performed using a flow-field visualization measurement system, an image-connection technique as well as model simulations. The experimental data were used to validate a one-layer finite-element non-hydrostatic model and a multi-layer finite-difference non-hydrostatic σ model for various submerged step configurations and wave conditions—combinations of step height ratios d/h, width ratios B/h and solitary wave height ratios H/h, where d denotes the step height, B the step width, H the solitary wave height, and h the still water depth. The main differences between the numerical results and the experimental data are highlighted. The effect of the height and width of the submerged step as well as the wave height of the solitary wave are quantified in terms of reflection (R), transmission (T), and energy dissipation (D). Through a series of numerical experiments, an optimal combination of the height ratio d/h, width ratio B/h, and solitary wave height ratio H/h for breakwater design for coastal protection is suggested.

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.