Signal processing techniques for white light interferometric sensing systems for smart structures

2000 ◽  
Author(s):  
William B. Spillman, Jr.
Keyword(s):  
Signal Processing ◽  
White Light ◽  
Smart Structures ◽  
Sensing Systems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Improving the Robustness of Odor Sensing Systems by Multivariate Signal Processing

2012 ◽  
pp. 296-316 ◽  
Author(s):  
Marta Padilla ◽  
Jordi Fonollosa ◽  
Santiago Marco
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
New Technologies ◽  
Principal Component ◽  
Statistical Signal Processing ◽  
Electronic Noses ◽  
Sensing Systems ◽  
Data Arrangement ◽  
Processing Techniques ◽  
Signal Processing Techniques

Electronic noses or artificial olfaction systems based on chemical gas sensors present lack of robustness, a problem that is mainly technological and requires more research to improve fabrication processes and develop new technologies. However, statistical signal processing can help to mathematically reduce those unwanted effects on the sensors responses before the prediction step. In this chapter, the authors explore the concept of robustness in electronic nose instruments and the use of several multivariate signal processing techniques to deal with two specific problems related to such lack of robustness: time instability (drift) and the detection of a possible faulty sensor in the array. In particular, three different techniques that deal with drift problems are reviewed. These techniques address drift by correction of unwanted variance, by taking advantage of the characteristics of a three-way data arrangement, or by using a blind strategy to extract information with chemical meaning. Finally, a method based on principal component analysis is presented for fault detection, faulty sensor identification, and correction of a fault in a sensor array.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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