A Layout Structure for Matching Many Integrated Resistors

Planning of road networks is fundamental for public transportation. The impact of road network density on public transportation has been extensively studied, but few studies in this regard involved evaluation indicators for connectivity and layout of road networks. With 29 cities in China as the study cases, this paper quantifies the layout structure of the road network based on the network’s betweenness centralization and establishes a multivariate linear regression model to perform regression of the logarithm of the frequency of per capita public transportation on betweenness centralization. It is found in the present work that there is a significant correlation between the layout structure of an urban road network and the residents’ utilization degree of public transportation. A greater betweenness centralization of the urban road network, namely a more centralized road network, means a higher frequency of per capita public transportation of urban residents and a higher degree of the residents’ utilization of public transportation. In the development of public transportation, centralized and axial-shaped layout structures of road networks can be promoted to improve the utilization of public transportation.

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Compact Ultra-Broadband Power Dividers with Integrated Resistors

2018 48th European Microwave Conference (EuMC) ◽

10.23919/eumc.2018.8541600 ◽

2018 ◽

Cited By ~ 2

Author(s):

Bjorn Deutschmann ◽

Arne F. Jacob

Keyword(s):

Power Dividers ◽

Integrated Resistors

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A Robust Structured Tracker Using Local Deep Features

Electronics ◽

10.3390/electronics9050846 ◽

2020 ◽

Vol 9 (5) ◽

pp. 846 ◽

Cited By ~ 2

Author(s):

Mohammadreza Javanmardi ◽

Amir Hossein Farzaneh ◽

Xiaojun Qi

Keyword(s):

Optimization Model ◽

Spatial Information ◽

State Of The Art ◽

Semantic Representation ◽

Spatial Layout ◽

Image Sequences ◽

Superior Performance ◽

Group Sparsity ◽

Layout Structure ◽

Close Form

Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers.

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