Denis Gingras - Academia.edu (original) (raw)

Papers by Denis Gingras

In this paper, we will review the problem of estimating in real-time the position of a vehicle fo... more In this paper, we will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, we will look at the mathematical framework and technologies involved to design positioning systems. We will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. We will then look at the case of robustness of the positioning system when one or some of the sensors are faulty. We will describe how the positioning system can be made more robust and adaptive in order to take account the occurrence of faulty sensors. Finally, we will go one step further and explore possible architectures for collaborative positioning systems whereas many vehicles are interacting and exchanging data to improve their own location estimate. We close the paper with some concluding remarks on the future evolution of ...

In this article, a new method that combines a discrete wavelet transform with the RBI method to p... more In this article, a new method that combines a discrete wavelet transform with the RBI method to process cat gait ENG signals is introduced. The method proved to be very efficient in segmentation of the signal into phases, especially in faulty cycles, and can reveal up to five gait phases within a cycle.

Polarization and Color Techniques in Industrial Inspection, 1999

An autonomous approach for learning the colors of specific objects assumed to have known body spe... more An autonomous approach for learning the colors of specific objects assumed to have known body spectral reflectances is developed for daylight illumination conditions. The main issue is to be able to find these objects autonomously in a set of training images captured under a wide variety of daylight illumination conditions, and to extract their colors to determine color space regions that are representative of the objects' colors and their variations. The work begins by modeling color formation under daylight using the color formation equations and the semi-empirical model of Judd, MacAdam and Wyszecki (CIE daylight model) for representing the typical spectral distributions of daylight. This results in color space regions that serve as prior information in the initial phase of learning which consists in detecting small reliable clusters of pixels having the appropriate colors. These clusters are then expanded by a region growing technique using broader color space regions than those predicted by the model. This is to detect objects in a way that is able to account for color variations which the model cannot due to its limitations. Validation on the detected objects is performed to filter out those that are not of interest and to eliminate unreliable pixel color values extracted from the remaining ones. Detection results using the color space regions determined from color values obtained by this procedure are discussed.

ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing

AR PLUS NOISE MODEL Asymptotic statistics for spectral density estimates of noise corrupted autor... more AR PLUS NOISE MODEL Asymptotic statistics for spectral density estimates of noise corrupted autoregressive (AR) series are evaluated. The "high-order" Yule-Walker equation estimates of the autoregressive parameters are used to form a spectral density estimate. The estimate is shown to be a consistent asymptotically normal (CAN) estimate. An expression for the variance of the limiting distribution in terms of the AR process parameters and the noise variance is provided.

ICASSP '82. IEEE International Conference on Acoustics, Speech, and Signal Processing

It has been shown that autoregressive spectral estimators can provide very fine spectral resoluti... more It has been shown that autoregressive spectral estimators can provide very fine spectral resolution estimates for time series which satisfy the all pole assumption. When the observed time series consists of the sum of an auto-regressive process plus white noise, the "all-pole" assumption is no longer valid. The appropriate model is the autoregressive-moving average representation. In this paper, it is

Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling

ABSTRACT

Journal of Control Science and Engineering

This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal ... more This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also developed.

Journal of Physics E: Scientific Instruments

In a previous article (Kunski and Vanier 1982, to be referred to as KV) the authors reported the ... more In a previous article (Kunski and Vanier 1982, to be referred to as KV) the authors reported the results of measurements of the magnetic shielding characteristics of concentric cylindrical enclosures made of Moly Permalloy, such as are used in hydrogen masers (Kleppner et al 1965, Vanier 1982). The arrangement studied was a system of five shields spaced by 25.4 mm

Advanced Algorithms and Architectures for Signal Processing III

ABSTRACT

2014 Ieee 12th International New Circuits and Systems Conference, Jun 1, 2014

International Journal of Vehicle Autonomous Systems, 2013

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

Proceedings of Spie the International Society For Optical Engineering, Mar 27, 1997

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based... more A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance

A technique for measuring the spectral sensitivity curves of color cameras is described. Details ... more A technique for measuring the spectral sensitivity curves of color cameras is described. Details on the difficulties that might be encountered in such measurements are discussed. These are mostly related to the non-ideal behavior of cameras, and the properties of the quasi-monochromatic light that must be presented to the camera. The usefulness of these curves is illustrated on a specific example: that of detecting colored objects of known spectral reflectances under daylight illumination conditions.

ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics, 1997

Conventional pixelwise classi cation techniques have two drawbacks. The rst is that they tend to ... more Conventional pixelwise classi cation techniques have two drawbacks. The rst is that they tend to occur isolated misclassi cations because they classify each site independently using spectral information only. The second is that sample data which represent each class are indispensable to estimate model parameters or to train classi cation Neural Networks. In this manuscript a new unsupervised contextual method is proposed for multispectral remote sensing image classi cation. Markov Random Field (MRF) models are used for modeling the observed and classi ed images in the proposed method. Both spectral and spatial information can be exploited by the MRF models so as to dissolve contextual inconsistency caused by the rst drawback. In addition the Vector Quantization (VQ) method is introduced to dissolve the second drawback. The VQ method classi es the observed data into several clusters without using any sample data. The image classi ed by the VQ method is so coarse that it includes misclassi cation and unknown-class sites, however it can be used as an initial classi ed image for the following MRF-based classi cation algorithm. The proposed method was applied to LANDSAT Thematic Mapper data to discriminate deciduous trees from evergreen trees. The accuracy of classi cation was 64.4% by the VQ method and it was improved up to 88.8% by the MRF-based method.

2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014

Cooperative approaches are becoming of great interest in automotive research. One of the most imp... more Cooperative approaches are becoming of great interest in automotive research. One of the most important ITS applications is cooperative localization. In this paper, the concept of a dynamic base station DGPS (DDGPS) and its application in the vehicular cooperative localization is introduced and discussed. The DDGPS is a decentralized cooperative method which aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated, based on the receiver's belief on its positioning and its uncertainty, and then broadcasted to other GPS receivers. A new method for fusing all the received corrections from different sources is proposed and the data dependency problem is also discussed.

2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014

In this paper a novel Vehicular cooperative map matching method is presented. This map matching m... more In this paper a novel Vehicular cooperative map matching method is presented. This map matching method uses the V2V communication in a VANET to exchange GPS information between vehicles. Then vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. The dependency of GPS measurements between different vehicles, which can lead to an over converged positioning result has been considered and circumvented in our method. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.

In this paper, we will review the problem of estimating in real-time the position of a vehicle fo... more In this paper, we will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, we will look at the mathematical framework and technologies involved to design positioning systems. We will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. We will then look at the case of robustness of the positioning system when one or some of the sensors are faulty. We will describe how the positioning system can be made more robust and adaptive in order to take account the occurrence of faulty sensors. Finally, we will go one step further and explore possible architectures for collaborative positioning systems whereas many vehicles are interacting and exchanging data to improve their own location estimate. We close the paper with some concluding remarks on the future evolution of ...

In this article, a new method that combines a discrete wavelet transform with the RBI method to p... more In this article, a new method that combines a discrete wavelet transform with the RBI method to process cat gait ENG signals is introduced. The method proved to be very efficient in segmentation of the signal into phases, especially in faulty cycles, and can reveal up to five gait phases within a cycle.

Polarization and Color Techniques in Industrial Inspection, 1999

An autonomous approach for learning the colors of specific objects assumed to have known body spe... more An autonomous approach for learning the colors of specific objects assumed to have known body spectral reflectances is developed for daylight illumination conditions. The main issue is to be able to find these objects autonomously in a set of training images captured under a wide variety of daylight illumination conditions, and to extract their colors to determine color space regions that are representative of the objects' colors and their variations. The work begins by modeling color formation under daylight using the color formation equations and the semi-empirical model of Judd, MacAdam and Wyszecki (CIE daylight model) for representing the typical spectral distributions of daylight. This results in color space regions that serve as prior information in the initial phase of learning which consists in detecting small reliable clusters of pixels having the appropriate colors. These clusters are then expanded by a region growing technique using broader color space regions than those predicted by the model. This is to detect objects in a way that is able to account for color variations which the model cannot due to its limitations. Validation on the detected objects is performed to filter out those that are not of interest and to eliminate unreliable pixel color values extracted from the remaining ones. Detection results using the color space regions determined from color values obtained by this procedure are discussed.

ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing

AR PLUS NOISE MODEL Asymptotic statistics for spectral density estimates of noise corrupted autor... more AR PLUS NOISE MODEL Asymptotic statistics for spectral density estimates of noise corrupted autoregressive (AR) series are evaluated. The "high-order" Yule-Walker equation estimates of the autoregressive parameters are used to form a spectral density estimate. The estimate is shown to be a consistent asymptotically normal (CAN) estimate. An expression for the variance of the limiting distribution in terms of the AR process parameters and the noise variance is provided.

ICASSP '82. IEEE International Conference on Acoustics, Speech, and Signal Processing

It has been shown that autoregressive spectral estimators can provide very fine spectral resoluti... more It has been shown that autoregressive spectral estimators can provide very fine spectral resolution estimates for time series which satisfy the all pole assumption. When the observed time series consists of the sum of an auto-regressive process plus white noise, the "all-pole" assumption is no longer valid. The appropriate model is the autoregressive-moving average representation. In this paper, it is

Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling

ABSTRACT

Journal of Control Science and Engineering

This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal ... more This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also developed.

Journal of Physics E: Scientific Instruments

In a previous article (Kunski and Vanier 1982, to be referred to as KV) the authors reported the ... more In a previous article (Kunski and Vanier 1982, to be referred to as KV) the authors reported the results of measurements of the magnetic shielding characteristics of concentric cylindrical enclosures made of Moly Permalloy, such as are used in hydrogen masers (Kleppner et al 1965, Vanier 1982). The arrangement studied was a system of five shields spaced by 25.4 mm

Advanced Algorithms and Architectures for Signal Processing III

ABSTRACT

2014 Ieee 12th International New Circuits and Systems Conference, Jun 1, 2014

International Journal of Vehicle Autonomous Systems, 2013

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

Proceedings of Spie the International Society For Optical Engineering, Mar 27, 1997

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based... more A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance

A technique for measuring the spectral sensitivity curves of color cameras is described. Details ... more A technique for measuring the spectral sensitivity curves of color cameras is described. Details on the difficulties that might be encountered in such measurements are discussed. These are mostly related to the non-ideal behavior of cameras, and the properties of the quasi-monochromatic light that must be presented to the camera. The usefulness of these curves is illustrated on a specific example: that of detecting colored objects of known spectral reflectances under daylight illumination conditions.

ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics, 1997

Conventional pixelwise classi cation techniques have two drawbacks. The rst is that they tend to ... more Conventional pixelwise classi cation techniques have two drawbacks. The rst is that they tend to occur isolated misclassi cations because they classify each site independently using spectral information only. The second is that sample data which represent each class are indispensable to estimate model parameters or to train classi cation Neural Networks. In this manuscript a new unsupervised contextual method is proposed for multispectral remote sensing image classi cation. Markov Random Field (MRF) models are used for modeling the observed and classi ed images in the proposed method. Both spectral and spatial information can be exploited by the MRF models so as to dissolve contextual inconsistency caused by the rst drawback. In addition the Vector Quantization (VQ) method is introduced to dissolve the second drawback. The VQ method classi es the observed data into several clusters without using any sample data. The image classi ed by the VQ method is so coarse that it includes misclassi cation and unknown-class sites, however it can be used as an initial classi ed image for the following MRF-based classi cation algorithm. The proposed method was applied to LANDSAT Thematic Mapper data to discriminate deciduous trees from evergreen trees. The accuracy of classi cation was 64.4% by the VQ method and it was improved up to 88.8% by the MRF-based method.

2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014

Cooperative approaches are becoming of great interest in automotive research. One of the most imp... more Cooperative approaches are becoming of great interest in automotive research. One of the most important ITS applications is cooperative localization. In this paper, the concept of a dynamic base station DGPS (DDGPS) and its application in the vehicular cooperative localization is introduced and discussed. The DDGPS is a decentralized cooperative method which aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated, based on the receiver's belief on its positioning and its uncertainty, and then broadcasted to other GPS receivers. A new method for fusing all the received corrections from different sources is proposed and the data dependency problem is also discussed.

2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014

In this paper a novel Vehicular cooperative map matching method is presented. This map matching m... more In this paper a novel Vehicular cooperative map matching method is presented. This map matching method uses the V2V communication in a VANET to exchange GPS information between vehicles. Then vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. The dependency of GPS measurements between different vehicles, which can lead to an over converged positioning result has been considered and circumvented in our method. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.