Single-sensor based nonlinear density estimation for traffic networks with multiple routes and sections

2003 ◽  
Author(s):  
Z.Z. Ye ◽  
J. Amirzzodi ◽  
M. Alotaibi ◽  
I.M. Tshiojwe ◽  
M. Al-Harthi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Traffic Networks ◽  
Multiple Routes ◽  
Single Sensor

Single-sensor, cue-counting density estimation of highly broadband marine mammal calls

2014 ◽  
Vol 135 (4) ◽  
pp. 2334-2335
Author(s):  
Elizabeth T. Küsel ◽  
Martin Siderius ◽  
David K. Mellinger
Keyword(s):  
Density Estimation ◽  
Marine Mammal ◽  
Single Sensor

Single-sensor density estimation of highly broadband marine mammal calls

2015 ◽  
Vol 138 (3) ◽  
pp. 1761-1761
Author(s):  
Elizabeth T. Küsel ◽  
Martin Siderius ◽  
David K. Mellinger
Keyword(s):  
Density Estimation ◽  
Marine Mammal ◽  
Single Sensor ◽  
Sensor Density

scholarly journals A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 358 ◽  
Author(s):  
Song Wang ◽  
Xu Xie ◽  
Rusheng Ju
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Density Estimation ◽  
Particle Filters ◽  
Traffic Simulation ◽  
Measurement Data ◽  
Urban Traffic ◽  
Traffic Networks ◽  
Vehicle Density ◽  
Urban Traffic Networks

Traffic conditions can be more accurately estimated using data assimilation techniques since these methods incorporate an imperfect traffic simulation model with the (partial) noisy measurement data. In this paper, we propose a data assimilation framework for vehicle density estimation on urban traffic networks. To compromise between computational efficiency and estimation accuracy, a mesoscopic traffic simulation model (we choose the platoon based model) is employed in this framework. Vehicle passages from loop detectors are considered as the measurement data which contain errors, such as missed and false detections. Due to the nonlinear and non-Gaussian nature of the problem, particle filters are adopted to carry out the state estimation, since this method does not have any restrictions on the model dynamics and error assumptions. Simulation experiments are carried out to test the proposed data assimilation framework, and the results show that the proposed framework can provide good vehicle density estimation on relatively large urban traffic networks under moderate sensor quality. The sensitivity analysis proves that the proposed framework is robust to errors both in the model and in the measurements.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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