KIBANGOU

Alain

Associate Professor HDR

Research:

My
research interests include distributed estimation, graph filtering, wireless sensor
networks, traffic prediction and estimation, filtering, identification,
and tensor analysis. My current
research activities invlove network systems and graph analysis, distributed estimation, tensor analysis, and transportation networks and smart mobility. Some other contributions on nonlinear
systems
identification, nonlinear filtering, and signal processing for
communications can be found in my publication list.

This is a research topic at the boundaries between graph theory and
dynamical systems theory. A first main line of research is to
study complex systems whose
interactions are modeled with graphs, and to unveil the effect of the
graph topology on system-theoretic properties such as observability,
controllability and input and state observability. Significant recent
results include: average observability of large scale networks,
structural and strongly structural characterization of input and state
observability for linear network systems being linear time invariant,
time-varying or topology time-varying, and characterization of
observability of consensus networks
over distance-regular graphs.

A second line of research concerns graph discovery, namely algorithms aiming at reconstructing some properties of the graph (graph robustness through Laplacian eigenvalues estimation and network topology reconstruction). In particular, distributed algorithms are devised for estimating Laplacian eigenvalues using transient measurements of consensus protocols and for reconstruction the network topology.

A second line of research concerns graph discovery, namely algorithms aiming at reconstructing some properties of the graph (graph robustness through Laplacian eigenvalues estimation and network topology reconstruction). In particular, distributed algorithms are devised for estimating Laplacian eigenvalues using transient measurements of consensus protocols and for reconstruction the network topology.

Selected papers:

- S. GRACY, F. GARIN, and A.Y. KIBANGOU, "Input and state observability of network systems with time-varying topology". IEEE Transactions on Control of Network Systems, In press. [DOI: 10.1109/TCNS.2018.2880304]
- T.-M.-D. TRAN and A.Y. KIBANGOU, "Distributed estimation of Laplacian eigenvalues via constrained consensus optimization problems", Systems and Control letters, 2015.[ DOI: 10.1016/j.sysconle.2015.04.001]
- A.Y. KIBANGOU and C. COMMAULT, "Observability in connected strongly regular graphs and distance regular graphs", IEEE Trans. on Control of Network Systems, Vol. 1, No 4, pp. 360-369, 2014. [DOI: 10.1109/TCNS.2014.2357532]
- F. MORBIDI and A.Y. KIBANGOU, "A distributed solution to the network reconstruction problem", Systems and Control Letters, No. 70, pp. 85-91, 2014. [DOI: 10.1016/j.sysconle.2014.05.008]

This research topic concerns distributed data combination from multiple
sources (sensors), and related information fusion, to achieve more
specific inference than that could be achieved by using a single source
(sensor). It plays an essential role in many networked applications,
such as networked control, monitoring, and surveillance.
One key ingredient for distributed algorithms is the well known
consensus issue. We have been mainly focused on synthesis of
finite-time average consensus protocols and also on the joint state and
unkown input estimation problem in a distributed setting.

Selected papers:

- A.Y. KIBANGOU, "Step-size sequence design for finite-time average consensus in secure wireless sensor networks", Systems and Control Letters, No. 67, pp. 19-23, 2014. [DOI: 10.1016/j.sysconle.2014.01.010]
- T.M.D. TRAN and A.Y. KIBANGOU “Distributed design of finite-time average consensus protocols”. 4th IFAC Workshop on Distributed Estimation and control (Necsys), Koblenz, Germany, Sep. 2013.
- A. ESNA ASHARI, A.Y. KIBANGOU, and F. GARIN, "Distributed input and state estimation for linear discrete-time systems", Proc. 51st IEEE Conf. on Decision and Control (CDC), Honolulu, Hawai, Usa, Dec. 2012.

This issue concerns the analysis and the processing of multidimensional
data, organized in multiway array. Precisely, we
devise distributed algorithms for tensor decompositions (PARAFAC) and
study decomposition of large tensors, a crucial issue in our big data
era. Applications concern distributed detection in complex networks and
distributed signal processing for collaborative networks and traffic data imputation. This topic is
developed in strong collaboration with Federal University of Ceara (Brazil) and I3S (Sophia Antipolis).

Selected papers:

# Transportation networks and smart mobility

This is currently the main application domain of the NeCS team. We are
working on robust traffic estimation and prediction issues. The
developped methods are assessed using real data from the Grenoble south
ring through the city lab GTL. Other results concern pedestrian navigation through attitude estimation.

Selected papers:

Selected papers:

- J.H.M. de GOULART, A.Y. KIBANGOU, and G. FAVIER, "Traffic data imputation via tensor completion based on soft thresholding of Tucker core". Transportation Research Part C: Emerging technologies, vol. 85, pp. 348-362, 2017.
- A.L.F. de ALMEIDA and A.Y. KIBANGOU “Distributed Large-Scale Tensor Decomposition ”. Proc. 39th Int. Conf. on Acoustics, Speech, and Signal Process. (ICASSP), Florence, Italy, May 2014.
- A.L.F. de ALMEIDA and A.Y. KIBANGOU “Distributed computation of tensor decompositions in collaborative networks”. Proc. 5th IEEE Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Saint-Martin, France, Dec. 2013.
- A.L.F. de ALMEIDA, A.Y. KIBANGOU, S. MIRON, and D. ARAUJO "Joint data and connection topology recovery in collaborative wireless sensor networks". Proc. 38th, IEEE Int. Conf. on Acoustics, Speech, and Signal Process. (ICASSP), Vancouver, Canada, May 2013.

Selected papers:

- A. LADINO, A.Y. KIBANGOU, C. CANUDAS DE WIT, and H. FOURATI, "A real-time forecasting tool for dynamic travel time from clustered time series". Transportation Research Part C: Emerging technologies, vol. 80, pp. 216-238, 2017.
- E. LOVISARI, C. CANUDAS DE WIT, and A.Y. KIBANGOU, "Density/Flow reconstruction via heterogeneous sources and Optimal Sensor Placement in road networks", Transportation Research Part C :Emerging Technologies, vol. 69, pp.451 - 476, 2016. [DOI: 10.1016/j.trc.2016.06.019]
- A. MAKNI, H. FOURATI, and A.Y. KIBANGOU, "Energy-aware Adaptive Attitude Estimation Under External Acceleration", IEEE/ASME Trans. on Mechatronics, vol. 21, No. 3, pp. 1366–1375, 2016. [DOI: 10.1109/TMECH.2015.2509783 ]
- C. CANUDAS DE WIT, F. MORBIDI, L. LEON OJEDA, A.Y. KIBANGOU, I. BELLICOT, and P. BELLEMAIN "Grenoble traffic lab: An experimental platform for advanced traffic monitoring and forecasting", IEEE Control Systems Magazine, vol. 35, No 3, pp. 23-39, 2015.

Grenoble Images Parole Signal Automatique laboratoire

UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal