# M. Kamel - IRIT

## Contact Details

NameM. Kamel |
||

AffiliationIRIT |
||

Location |
||

## Pubs By Year |
||

## External Links |
||

## Pub CategoriesComputer Science - Learning (7) Computer Science - Robotics (5) Computer Science - Computer Vision and Pattern Recognition (4) Computer Science - Data Structures and Algorithms (2) Nuclear Experiment (2) Statistics - Machine Learning (1) Computer Science - Information Retrieval (1) Physics - Instrumentation and Detectors (1) Mathematics - Number Theory (1) High Energy Physics - Experiment (1) |

## Publications Authored By M. Kamel

Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Read More

This paper describes dynamic system identification, and full control of a cost-effective vertical take-off and landing (VTOL) multi-rotor micro-aerial vehicle (MAV) --- DJI Matrice 100. The dynamics of the vehicle and autopilot controllers are identified using only a built-in IMU and utilized to design a subsequent model predictive controller (MPC). Experimental results for the control performance are evaluated using a motion capture system while performing hover, step responses, and trajectory following tasks in the present of external wind disturbances. Read More

**Authors:**GlueX Collaboration, H. Al Ghoul, E. G. Anassontzis, A. Austregesilo, F. Barbosa, A. Barnes, T. D. Beattie, D. W. Bennett, V. V. Berdnikov, T. Black, W. Boeglin, W. J. Briscoe, W. K. Brooks, B. E. Cannon, O. Chernyshov, E. Chudakov, V. Crede, M. M. Dalton, A. Deur, S. Dobbs, A. Dolgolenko, M. Dugger, R. Dzhygadlo, H. Egiyan, P. Eugenio, C. Fanelli, A. M. Foda, J. Frye, S. Furletov, L. Gan, A. Gasparian, A. Gerasimov, N. Gevorgyan, K. Goetzen, V. S. Goryachev, L. Guo, H. Hakobyan, J. Hardin, A. Henderson, G. M. Huber, D. G. Ireland, M. M. Ito, N. S. Jarvis, R. T. Jones, V. Kakoyan, M. Kamel, F. J. Klein, R. Kliemt, C. Kourkoumeli, S. Kuleshov, I. Kuznetsov, M. Lara, I. Larin, D. Lawrence, W. I. Levine, K. Livingston, G. J. Lolos, V. Lyubovitskij, D. Mack, P. T. Mattione, V. Matveev, M. McCaughan, M. McCracken, W. McGinley, J. McIntyre, R. Mendez, C. A. Meyer, R. Miskimen, R. E. Mitchell, F. Mokaya, K. Moriya, F. Nerling, G. Nigmatkulov, N. Ochoa, A. I. Ostrovidov, Z. Papandreou, M. Patsyuk, R. Pedroni, M. R. Pennington, L. Pentchev, K. J. Peters, E. Pooser, B. Pratt, Y. Qiang, J. Reinhold, B. G. Ritchie, L. Robison, D. Romanov, C. Salgado, R. A. Schumacher, C. Schwarz, J. Schwiening, A. Yu. Semenov, I. A. Semenova, K. K. Seth, M. R. Shepherd, E. S. Smith, D. I. Sober, A. Somov, S. Somov, O. Soto, N. Sparks, M. J. Staib, J. R. Stevens, I. I. Strakovsky, A. Subedi, V. Tarasov, S. Taylor, A. Teymurazyan, I. Tolstukhin, A. Tomaradze, A. Toro, A. Tsaris, G. Vasileiadis, I. Vega, N. K. Walford, D. Werthmuller, T. Whitlatch, M. Williams, E. Wolin, T. Xiao, J. Zarling, Z. Zhang, B. Zihlmann, V. Mathieu, J. Nys

**Category:**Nuclear Experiment

We report measurements of the photon beam asymmetry $\Sigma$ for the reactions $\vec{\gamma}p\to p\pi^0$ and $\vec{\gamma}p\to p\eta $ from the GlueX experiment using a 9 GeV linearly-polarized, tagged photon beam incident on a liquid hydrogen target in Jefferson Lab's Hall D. The asymmetries, measured as a function of the proton momentum transfer, possess greater precision than previous $\pi^0$ measurements and are the first $\eta$ measurements in this energy regime. The results are compared with theoretical predictions based on $t$-channel, quasi-particle exchange and constrain the axial-vector component of the neutral meson production mechanism in these models. Read More

This paper shows a strategy based on passive force control for collaborative object transportation using Micro Aerial Vehicles (MAVs), focusing on the transportation of a bulky object by two hexacopters. The goal is to develop a robust approach which does not rely on: (a) communication links between the MAVs, (b) the knowledge of the payload shape and (c) the position of grasping point. The proposed approach is based on the master-slave paradigm, in which the slave agent guarantees compliance to the external force applied by the master to the payload via an admittance controller. Read More

Autonomous delivery of goods using a MAV is a difficult problem, as it poses high demand on the MAV's control, perception and manipulation capabilities. This problem is especially challenging if the exact shape, location and configuration of the objects are unknown. In this paper, we report our findings during the development and evaluation of a fully integrated system that is energy efficient and enables MAVs to pick up and deliver objects with partly ferrous surface of varying shapes and weights. Read More

Precise trajectory tracking is a crucial property for \acp{MAV} to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for \ac{MAV} trajectory tracking. A classical \ac{LMPC} is presented and compared against a more advanced \ac{NMPC} that considers the full system model. Read More

Let $C$ be an elliptic curve defined over $\mathbb Q$ by the equation $y^2=x^3+Ax+B$ where $A,B\in\mathbb Q$. A sequence of rational points $(x_i,y_i)\in C(\mathbb Q),\,i=1,2,\ldots,$ is said to form a sequence of consecutive squares on $C$ if the sequence of $x$-coordinates, $x_i,i=1,2,\ldots$, consists of consecutive squares. We produce an infinite family of elliptic curves $C$ with a $5$-term sequence of consecutive squares. Read More

**Authors:**The GlueX Collaboration, H. Al Ghoul, E. G. Anassontzis, F. Barbosa, A. Barnes, T. D. Beattie, D. W. Bennett, V. V. Berdnikov, T. Black, W. Boeglin, W. K. Brooks, B. Cannon, O. Chernyshov, E. Chudakov, V. Crede, M. M. Dalton, A. Deur, S. Dobbs, A. Dolgolenko, M. Dugger, H. Egiyan, P. Eugenio, A. M. Foda, J. Frye, S. Furletov, L. Gan, A. Gasparian, A. Gerasimov, N. Gevorgyan, V. S. Goryachev, B. Guegan, L. Guo, H. Hakobyan, H. Hakobyan2, J. Hardin, G. M. Huber, D. Ireland, M. M. Ito, N. S. Jarvis, R. T. Jones, V. Kakoyan, M. Kamel, F. J. Klein, C. Kourkoumeli, S. Kuleshov, M. Lara, I. Larin, D. Lawrence, J. Leckey, W. I. Levine, K. Livingston, G. J. Lolos, D. Mack, P. T. Mattione, V. Matveev, M. McCaughan, W. McGinley, J. McIntyre, R. Mendez, C. A. Meyer, R. Miskimen, R. E. Mitchell, F. Mokaya, K. Moriya, G. Nigmatkulov, N. Ochoa, A. I. Ostrovidov, Z. Papandreou, R. Pedroni, M. Pennington, L. Pentchev, A. Ponosov, E. Pooser, B. Pratt, Y. Qiang, J. Reinhold, B. G. Ritchie, L. Robison, D. Romanov, C. Salgado, R. A. Schumacher, A. Yu. Semenov, I. A. Semenova, I. Senderovich, K. K. Seth, M. R. Shepherd, E. S. Smith, D. I. Sober, A. Somov, S. Somov, O. Soto, N. Sparks, M. J. Staib, J. R. Stevens, A. Subedi, V. Tarasov, S. Taylor, I. Tolstukhin, A. Tomaradze, A. Toro, A. Tsaris, G. Vasileiadis, I. Vega, G. Voulgaris, N. K. Walford, T. Whitlatch, M. Williams, E. Wolin, T. Xiao, J. Zarling, B. Zihlmann

The GlueX experiment at Jefferson Lab ran with its first commissioning beam in late 2014 and the spring of 2015. Data were collected on both plastic and liquid hydrogen targets, and much of the detector has been commissioned. All of the detector systems are now performing at or near design specifications and events are being fully reconstructed, including exclusive production of $\pi^{0}$, $\eta$ and $\omega$ mesons. Read More

Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solving problems such as denoising, inpainting, and coding, it may not lead to optimal solution in classification tasks, where the ultimate goal is to make the learned dictionary and corresponding sparse representation as discriminative as possible. Read More

This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that can be efficiently solved using the proposed algorithm. Read More

In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively-distributed data, which is formally known as the Column Subset Selection (CSS) problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. Read More

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques that apply to both regression and classification problems. Read More

The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures. The kernel $k$-means algorithm is however computationally very complex as it requires the complete data matrix to be calculated and stored. Further, the kernelized nature of the kernel $k$-means algorithm hinders the parallelization of its computations on modern infrastructures for distributed computing. Read More

Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are studied to combine features on different scales for texture classification of small image patches. Read More

Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)similarity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. Read More

In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, the recently introduced Hilbert Schmidt independence criterion (HSIC) is used. Read More

**Authors:**Eric Kergosien

^{1}, Mouna Kamel

^{2}, Christian Sallaberry

^{3}, Marie-Noëlle Bessagnet

^{4}, Nathalie Aussenac- Gilles

^{5}, Mauro Gaio

^{6}

**Affiliations:**

^{1}LIUPPA,

^{2}IRIT,

^{3}LIUPPA,

^{4}LIUPPA,

^{5}IRIT,

^{6}LIUPPA

**Category:**Computer Science - Information Retrieval

Automatic construction of ontologies from text is generally based on retrieving text content. For a much more rich ontology we extend these approaches by taking into account the document structure and some external resources (like thesaurus of indexing terms of near domain). In this paper we describe how these external resources are at first analyzed and then exploited. Read More