Hichem Snoussi | Université de Technologie de Troyes (UTT) (original) (raw)
Papers by Hichem Snoussi
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008
In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc net... more In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc networks. The proposed technique, based on interval analysis, is suited to the limited computational and memory resources of mobile nodes. The incertitude about the estimated position of each node is propagated in an interval form. The propagation is based on a state space model and formulated by a constraints satisfaction problem. Observations errors as well as anchor nodes imperfections are taken into account in a simple and computationalconsistent way. A simple Waltz algorithm is then applied in order to contract the solution, yielding a guaranteed and robust online estimation of the mobile node position. Simulation results on mobile node group trajectories corroborate the efficiency of the proposed technique and show that it compares favorably to particle filtering methods.
Image Processing algorithms implemented in hardware have emerged as the most viable solution for ... more Image Processing algorithms implemented in hardware have emerged as the most viable solution for improving the performance of image processing systems. The introduction of reconfigurable devices and high level hardware programming languages has further accelerated the design of image processing in FPGA.
International Joint Conference on Biomedical Engineering Systems and Technologies, 2009
We present a new source separation method which maximizes the likelihood of a model of noisy mixt... more We present a new source separation method which maximizes the likelihood of a model of noisy mixtures of stationary, possibly Gaussian, independent components. The method has been devised to address an astronomical imaging problem. It works in the spectral domain where, thanks to two simple approximations, the likelihood assumes a simple form which is easy to handle (low dimensional sufficient statistics) and to maximize (via the EM algorithm).
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt o... more ABSTRACT In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Over the last years, Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. But, this method only focuses on second orders statistics. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. So, we propose an improved KPCA method based on Mahalanobis kernel as a preprocessing step to extract relevant feature for classification and to prevent from the abnormal events. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel (MKPCA). Then the classification was done on real hyperspectral Intel Berkeley data from urban area. Results were positively compared to a version of a standard KPCA specially designed to be use with wireless sensor networks (WSNs).
2006 10th IEEE Singapore International Conference on Communication Systems, 2006
... learming approach avoiding a lossy message compression and even outperforming the particle fi... more ... learming approach avoiding a lossy message compression and even outperforming the particle filter based ... Substituting the filtering distribution (9) in (8) and taking into account the prior mean-scale ... between the node position and the target position, in a limited sensing range. ...
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008
Over the past few years, wireless sensor networks received tremendous attention for monitoring ph... more Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its computational complexity scales badly with the number of available sensors, which tends to be large. In order to circumvent this drawback, we propose in this paper a reduced-order model approach. To this end, we take advantage of recent developments in sparse representation literature, and show the natural link between reducing the model order and the topology of the deployed sensors. To learn this model, we derive a gradient descent scheme and show its efficiency for wireless sensor networks. We illustrate the proposed approach through simulations involving the estimation of a spatial temperature distribution.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking adv... more In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. We also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper presents a new method for analysis of center of pressure (COP) signals using Empirical... more This paper presents a new method for analysis of center of pressure (COP) signals using Empirical Mode Decomposi- tion (EMD). The EMD decomposes a COP signal into a finite set of band-limited signals termed as intrinsic mode functions (IMFs). Thereafter, a signal processing technique used in continuous chaotic modeling is used to investigate the differ- ence between experimental conditions on
TENCON 2008 - 2008 IEEE Region 10 Conference, 2008
Center of pressure (COP) measurements are often used to identify balance problems. A new method f... more Center of pressure (COP) measurements are often used to identify balance problems. A new method for analysis of COP signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion is proposed in this paper. The EMD decomposes a COP signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs), before FB expansion is applied on each IMF
Lecture Notes in Computer Science, 2012
Image Processing algorithms implemented in hardware have emerged as the most viable solution for ... more Image Processing algorithms implemented in hardware have emerged as the most viable solution for improving the performance of image processing systems. The introduction of reconfigurable devices and high level hardware programming languages has further accelerated the design of image processing in FPGA.
2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2012
Abstract During the sudden catastrophic events that have occurred in this last decade, social med... more Abstract During the sudden catastrophic events that have occurred in this last decade, social media have proven their importance in the creation and management of ad-hoc crisis communities. These platforms are increasingly used as complementary support tools for conventional crisis management teams. Recent disasters (eg Haiti, Australia, Japan, Mexico, etc.) have demonstrated their real potential in providing support to emergency operations for crisis management. However, several questions remain unanswered regarding the ...
2007 IEEE International Symposium on Signal Processing and Information Technology, 2007
An efficient, economical and robust strategy for target tracking in binary sensor network is prop... more An efficient, economical and robust strategy for target tracking in binary sensor network is proposed in this paper. By adopting the binary variational filtering algorithm, considerable tracking quality is ensured, while decreasing communication between sensors compared to a particle filtering algorithm. Based on the proactive clustering, the entire sensor network is subdivided into several clusters. Only cluster heads are configured with more available energy and high processing capability, reducing thus the hardware expenditure. Furthermore, precise prediction of the target position and the cluster activation protocol ensure that the most potential cluster is activated to perform target tracking, reducing consumed energy during the hand-off operation. Employing of the binary variational filtering algorithm and the exception handle scheme ensure robustness in coping with the case of highly non-linear and non-Gaussian environments.
2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007
Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limi... more Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and inter-node information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decision-making. Its binary extension further reduces the resource consumption by locally exchanging only few bits.
IEEE Globecom 2006, 2006
In this contribution, we propose an efficient collaborative strategy for online change detection,... more In this contribution, we propose an efficient collaborative strategy for online change detection, in a distributed sensor network. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communication bandwith. The observed systems are assumed to have each a finite set of states, including the abrupt change behavior. For each discrete state, an observed system is assumed to evolve according to a linear state-space model. An efficient Rao-Blackwellized collaborative particle filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellization procedure combines a sequential Monte Carlo filter with a bank of distributed Kalman filters. Only sufficient statistics are communicated between smart nodes. The spatio-temporal selection of the leader node and its collaborators is based on a trade-off between error propagation, communication constraints and information content complementarity of distributed data.
Wireless Networks, 2011
This paper addresses target tracking in wireless sensor networks (WSN) where the observed system ... more This paper addresses target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by optimally quantizing the data collected by the sensors. Recently, VF has been proved to be suitable to the communication constraints of WSN. Its
Molecular Ecology, 2006
The wild grapevine, Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi, considered as the ancestor o... more The wild grapevine, Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi, considered as the ancestor of the cultivated grapevine, is native from Eurasia. In Spain, natural populations of V. vinifera ssp. sylvestris can still be found along river banks. In this work, we have performed a wide search of wild grapevine populations in Spain and characterized the amount and distribution of their genetic diversity using 25 nuclear SSR loci. We have also analysed the possible coexistence in the natural habitat of wild grapevines with naturalized grapevine cultivars and rootstocks. In this way, phenotypic and genetic analyses identified 19% of the collected samples as derived from cultivated genotypes, being either naturalized cultivars or hybrid genotypes derived from spontaneous crosses between wild and cultivated grapevines. The genetic diversity of wild grapevine populations was similar than that observed in the cultivated group. The molecular analysis showed that cultivated germplasm and wild germplasm are genetically divergent with low level of introgression. Using a model-based approach implemented in the software STRUCTURE, we identified four genetic groups, with two of them fundamentally represented among cultivated genotypes and two among wild accessions. The analyses of genetic relationships between wild and cultivated grapevines could suggest a genetic contribution of wild accessions from Spain to current Western cultivars.
The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2000
Abstract. This paper considers the problem of source separation in the case of noisy instantaneou... more Abstract. This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as iid hidden ...
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008
In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc net... more In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc networks. The proposed technique, based on interval analysis, is suited to the limited computational and memory resources of mobile nodes. The incertitude about the estimated position of each node is propagated in an interval form. The propagation is based on a state space model and formulated by a constraints satisfaction problem. Observations errors as well as anchor nodes imperfections are taken into account in a simple and computationalconsistent way. A simple Waltz algorithm is then applied in order to contract the solution, yielding a guaranteed and robust online estimation of the mobile node position. Simulation results on mobile node group trajectories corroborate the efficiency of the proposed technique and show that it compares favorably to particle filtering methods.
Image Processing algorithms implemented in hardware have emerged as the most viable solution for ... more Image Processing algorithms implemented in hardware have emerged as the most viable solution for improving the performance of image processing systems. The introduction of reconfigurable devices and high level hardware programming languages has further accelerated the design of image processing in FPGA.
International Joint Conference on Biomedical Engineering Systems and Technologies, 2009
We present a new source separation method which maximizes the likelihood of a model of noisy mixt... more We present a new source separation method which maximizes the likelihood of a model of noisy mixtures of stationary, possibly Gaussian, independent components. The method has been devised to address an astronomical imaging problem. It works in the spectral domain where, thanks to two simple approximations, the likelihood assumes a simple form which is easy to handle (low dimensional sufficient statistics) and to maximize (via the EM algorithm).
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt o... more ABSTRACT In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Over the last years, Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. But, this method only focuses on second orders statistics. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. So, we propose an improved KPCA method based on Mahalanobis kernel as a preprocessing step to extract relevant feature for classification and to prevent from the abnormal events. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel (MKPCA). Then the classification was done on real hyperspectral Intel Berkeley data from urban area. Results were positively compared to a version of a standard KPCA specially designed to be use with wireless sensor networks (WSNs).
2006 10th IEEE Singapore International Conference on Communication Systems, 2006
... learming approach avoiding a lossy message compression and even outperforming the particle fi... more ... learming approach avoiding a lossy message compression and even outperforming the particle filter based ... Substituting the filtering distribution (9) in (8) and taking into account the prior mean-scale ... between the node position and the target position, in a limited sensing range. ...
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008
Over the past few years, wireless sensor networks received tremendous attention for monitoring ph... more Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its computational complexity scales badly with the number of available sensors, which tends to be large. In order to circumvent this drawback, we propose in this paper a reduced-order model approach. To this end, we take advantage of recent developments in sparse representation literature, and show the natural link between reducing the model order and the topology of the deployed sensors. To learn this model, we derive a gradient descent scheme and show its efficiency for wireless sensor networks. We illustrate the proposed approach through simulations involving the estimation of a spatial temperature distribution.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking adv... more In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. We also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper presents a new method for analysis of center of pressure (COP) signals using Empirical... more This paper presents a new method for analysis of center of pressure (COP) signals using Empirical Mode Decomposi- tion (EMD). The EMD decomposes a COP signal into a finite set of band-limited signals termed as intrinsic mode functions (IMFs). Thereafter, a signal processing technique used in continuous chaotic modeling is used to investigate the differ- ence between experimental conditions on
TENCON 2008 - 2008 IEEE Region 10 Conference, 2008
Center of pressure (COP) measurements are often used to identify balance problems. A new method f... more Center of pressure (COP) measurements are often used to identify balance problems. A new method for analysis of COP signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion is proposed in this paper. The EMD decomposes a COP signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs), before FB expansion is applied on each IMF
Lecture Notes in Computer Science, 2012
Image Processing algorithms implemented in hardware have emerged as the most viable solution for ... more Image Processing algorithms implemented in hardware have emerged as the most viable solution for improving the performance of image processing systems. The introduction of reconfigurable devices and high level hardware programming languages has further accelerated the design of image processing in FPGA.
2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2012
Abstract During the sudden catastrophic events that have occurred in this last decade, social med... more Abstract During the sudden catastrophic events that have occurred in this last decade, social media have proven their importance in the creation and management of ad-hoc crisis communities. These platforms are increasingly used as complementary support tools for conventional crisis management teams. Recent disasters (eg Haiti, Australia, Japan, Mexico, etc.) have demonstrated their real potential in providing support to emergency operations for crisis management. However, several questions remain unanswered regarding the ...
2007 IEEE International Symposium on Signal Processing and Information Technology, 2007
An efficient, economical and robust strategy for target tracking in binary sensor network is prop... more An efficient, economical and robust strategy for target tracking in binary sensor network is proposed in this paper. By adopting the binary variational filtering algorithm, considerable tracking quality is ensured, while decreasing communication between sensors compared to a particle filtering algorithm. Based on the proactive clustering, the entire sensor network is subdivided into several clusters. Only cluster heads are configured with more available energy and high processing capability, reducing thus the hardware expenditure. Furthermore, precise prediction of the target position and the cluster activation protocol ensure that the most potential cluster is activated to perform target tracking, reducing consumed energy during the hand-off operation. Employing of the binary variational filtering algorithm and the exception handle scheme ensure robustness in coping with the case of highly non-linear and non-Gaussian environments.
2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007
Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limi... more Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and inter-node information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decision-making. Its binary extension further reduces the resource consumption by locally exchanging only few bits.
IEEE Globecom 2006, 2006
In this contribution, we propose an efficient collaborative strategy for online change detection,... more In this contribution, we propose an efficient collaborative strategy for online change detection, in a distributed sensor network. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communication bandwith. The observed systems are assumed to have each a finite set of states, including the abrupt change behavior. For each discrete state, an observed system is assumed to evolve according to a linear state-space model. An efficient Rao-Blackwellized collaborative particle filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellization procedure combines a sequential Monte Carlo filter with a bank of distributed Kalman filters. Only sufficient statistics are communicated between smart nodes. The spatio-temporal selection of the leader node and its collaborators is based on a trade-off between error propagation, communication constraints and information content complementarity of distributed data.
Wireless Networks, 2011
This paper addresses target tracking in wireless sensor networks (WSN) where the observed system ... more This paper addresses target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by optimally quantizing the data collected by the sensors. Recently, VF has been proved to be suitable to the communication constraints of WSN. Its
Molecular Ecology, 2006
The wild grapevine, Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi, considered as the ancestor o... more The wild grapevine, Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi, considered as the ancestor of the cultivated grapevine, is native from Eurasia. In Spain, natural populations of V. vinifera ssp. sylvestris can still be found along river banks. In this work, we have performed a wide search of wild grapevine populations in Spain and characterized the amount and distribution of their genetic diversity using 25 nuclear SSR loci. We have also analysed the possible coexistence in the natural habitat of wild grapevines with naturalized grapevine cultivars and rootstocks. In this way, phenotypic and genetic analyses identified 19% of the collected samples as derived from cultivated genotypes, being either naturalized cultivars or hybrid genotypes derived from spontaneous crosses between wild and cultivated grapevines. The genetic diversity of wild grapevine populations was similar than that observed in the cultivated group. The molecular analysis showed that cultivated germplasm and wild germplasm are genetically divergent with low level of introgression. Using a model-based approach implemented in the software STRUCTURE, we identified four genetic groups, with two of them fundamentally represented among cultivated genotypes and two among wild accessions. The analyses of genetic relationships between wild and cultivated grapevines could suggest a genetic contribution of wild accessions from Spain to current Western cultivars.
The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2000
Abstract. This paper considers the problem of source separation in the case of noisy instantaneou... more Abstract. This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as iid hidden ...