Advances in Automotive Radar: A framework on computationally efficient high-resolution frequency estimation

2017 ◽  
Vol 34 (2) ◽  
pp. 36-46 ◽  
Author(s):  
Florian Engels ◽  
Philipp Heidenreich ◽  
Abdelhak M. Zoubir ◽  
Friedrich K Jondral ◽  
Markus Wintermantel
Keyword(s):  
High Resolution ◽  
Frequency Estimation ◽  
Computationally Efficient ◽  
Automotive Radar

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images

Micro-Doppler Extraction of Pedestrian Limbs for High Resolution Automotive Radar

2019 ◽  
Author(s):  
Dagmar Steinhauser ◽  
Patrick HeId ◽  
Alexander Kamann ◽  
Andreas Koch ◽  
Thomas Brandmeier
Keyword(s):  
High Resolution ◽  
Automotive Radar

A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Lei Ma ◽  
Shreyes N. Melkote ◽  
James B. Castle
Keyword(s):  
Cutting Force ◽  
Efficient Method ◽  
Frequency Estimation ◽  
Chip Thickness ◽  
Computationally Efficient ◽  
Model Based ◽  
Chatter Suppression ◽  
Force Signal ◽  
Regeneration Effect ◽  
Milling Chatter

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages and for chatter frequency estimation by monitoring the cutting force signals. Based on a complex exponentials model for the dynamic chip thickness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the method is shown to be capable of distinguishing between chatter and the dynamic transients in the cutting forces arising from sudden changes in workpiece geometry and tool entry/exit. To facilitate chatter suppression once the onset of chatter is detected, a time domain algorithm is proposed so that the dominant chatter frequency can be accurately determined without using computationally expensive frequency domain transforms such as the Fourier transform. The proposed method is experimentally validated.

scholarly journals Real-Time Moving Object Detection in High-Resolution Video Sensing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3591 ◽  
Author(s):  
Haidi Zhu ◽  
Haoran Wei ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Nasser Kehtarnavaz
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Time Processing ◽  
Video Frames ◽  
High Resolution Images

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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