scholarly journals Continuous Frames and the Kadison-Singer Problem

2018 ◽  
pp. 63-88
Author(s):  
Marcin Bownik
Keyword(s):  
Continuous Frames

Closure to “Elasti-Plastic Analysis of Continuous Frames and Beams”

1959 ◽  
Vol 85 (9) ◽  
pp. 83-86
Author(s):  
Lawrence P. Johnson ◽  
Herbert A. Sawyer
Keyword(s):  
Plastic Analysis ◽  
Continuous Frames

Closure to “Seismic and Cyclonic Response of Continuous Frames”

1975 ◽  
Vol 101 (7) ◽  
pp. 1606-1608
Author(s):  
Gerald M. Smith ◽  
George C. Ernst ◽  
Mahendra Maheshwari
Keyword(s):  
Continuous Frames

Minimum Weight Plastic Design of Continuous Frames

1964 ◽  
Vol 90 (3) ◽  
pp. 39-52
Author(s):  
Donald A. Sawyer ◽  
Linton E. Grinter
Keyword(s):  
Minimum Weight ◽  
Plastic Design ◽  
Continuous Frames

scholarly journals Survey Analysis of Robust and Real-Time Multi-Lane and Single Lane Detection in Indian Highway Scenarios

2021 ◽  
Vol 309 ◽  
pp. 01117
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar
Keyword(s):  
Neural Network ◽  
System Integration ◽  
Large Scale ◽  
Feature Learning ◽  
Semantic Segmentation ◽  
Lane Detection ◽  
Learning Approaches ◽  
Survey Analysis ◽  
Lane Recognition ◽  
Continuous Frames

Studies on lane detection Lane identification methods, integration, and evaluation strategies square measure all examined. The system integration approaches for building a lot of strong detection systems are then evaluated and analyzed, taking into account the inherent limits of camera-based lane detecting systems. Present deep learning approaches to lane detection are inherently CNN's semantic segmentation network the results of the segmentation of the roadways and the segmentation of the lane markers are fused using a fusion method. By manipulating a huge number of frames from a continuous driving environment, we examine lane detection, and we propose a hybrid deep architecture that combines the convolution neural network (CNN) and the continuous neural network (CNN) (RNN). Because of the extensive information background and the high cost of camera equipment, a substantial number of existing results concentrate on vision-based lane recognition systems. Extensive tests on two large-scale datasets show that the planned technique outperforms rivals' lane detection strategies, particularly in challenging settings. A CNN block in particular isolates information from each frame before sending the CNN choices of several continuous frames with time-series qualities to the RNN block for feature learning and lane prediction.

Discussion of “Minimum Weight Plastic Design of Continuous Frames”

1964 ◽  
Vol 90 (5) ◽  
pp. 459-461
Author(s):  
Jacques Heyman ◽  
William Prager
Keyword(s):  
Minimum Weight ◽  
Plastic Design ◽  
Continuous Frames

Discussion of “Minimum Weight Plastic Design of Continuous Frames”

1965 ◽  
Vol 91 (1) ◽  
pp. 167-169
Author(s):  
R.H. Wood
Keyword(s):  
Minimum Weight ◽  
Plastic Design ◽  
Continuous Frames

Discussion of “Seismic and Cyclonic Response of Continuous Frames”

1975 ◽  
Vol 101 (1) ◽  
pp. 331-335
Author(s):  
Robert Park
Keyword(s):  
Continuous Frames

Steady-State Forced Vibration of Continuous Frames

1953 ◽  
Vol 118 (1) ◽  
pp. 794-814
Author(s):  
C. T. G. Looney
Keyword(s):  
Steady State ◽  
Forced Vibration ◽  
Continuous Frames

Automatic design of continuous frames in steel and reinforced concrete

1940 ◽  
Vol 229 (4) ◽  
pp. 540-541
Author(s):  
R.H. Oppermann
Keyword(s):  
Reinforced Concrete ◽  
Automatic Design ◽  
Continuous Frames

Moving Object Detection Based on Edge Difference and Contour Matching

2012 ◽  
Vol 182-183 ◽  
pp. 1863-1867
Author(s):  
Wei Liu ◽  
Xue Jun Xu ◽  
Bi Tao Fu ◽  
Xi Zhu
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Improved Method ◽  
Contour Matching ◽  
Continuous Frames ◽  
Good Detection

This paper presents an improved method to detect moving object and obtain the relative accurate location. First we detect the edge difference of continuous frames. Then we utilize the contour matching to find the edge pairs in order to reach a good detection of the moving object and location. The extensive experiments show that our method is robust and efficient to the moving object detection.

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