Adaptation of Zerotrees Using Signed Binary Digit Representations for 3D Image Coding

Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one

Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties

A Branch and Price Algorithm for the k-splittable Maximum Flow Problem

The Maximum Flow Problem with flow width constraints is a NP-hard problem. Two models are proposed: the first model is a compact node-arc model using two flow conservation blocks per path. For each path, one block de?nes the path while the other one send the right amount of flow on it. The second model is an extended arc-path model. It is obtained from the first model after a Dantzig-Wolfe reformulation. It is an extended model as it relies on the set of all the paths between the source and the sink nodes. Some symmetry breaking constraints are used to improve the model. A branch and price algorithm is proposed to solve the problem. The column generation reduces to the computation of a shortest path whose cost depends on weights on the arcs and on the path capacity. A polynomial time algorithm is proposed to solve this subproblem. Computational results are shown on a set of medium-sized instances to show the effectiveness of our approach

A GRASPxELS with Depth First Search Split Procedure for the HVRP

Split procedures have been proved to be efficient within global framework optimization for routing problems by splitting giant tour into trips. This is done by generating optimal shortest path within an auxiliary graph built from the giant tour. An efficient application has been introduced for the first time by Lacomme et al. (2001) within a metaheuristic approach to solve the Capacitated Arc Routing Problem (CARP) and second for the Vehicle Routing Problem (VRP) by Prins (2004). In a further step, the Split procedure embedded in metaheuristics has been extended to address more complex routing problems thanks to a heuristic splitting of the giant tour using the generation of labels on the nodes of the auxiliary graph linked to resource management. Lately, Duhamel et al. (2010) defined a new Split family based on a depth first search approach during labels generation in graph. The efficiency of the new split method has been first evaluated in location routing problem with a GRASP metaheuristic. Duhamel et al. (2010) provided full numerical experiments on this topic

Multicommodity formulations for the prize collecting vehicle routing problem in the petrol industry

The Mobile Oil Recovery (MOR) unit is a truck designed to pump marginal oil wells in a petrol field. The MOR optimization Problem (MORP) consists in optimizing both the oil extraction and the travel costs. In this article, we describe several formulations for the MORP using a single vehicle and we propose two formulations to the case where several vehicles are used. We strengthen the proposed formulations by taking advantage of the MORP characteristics, by improving the number of subtour elimination constraints and by using cuts. Computational results are presented for instances close to the reality and optimality is proved for instances with up to 200 nodes

Décomposition d'un Problème de Lot-Sizing Multi-site en Problèmes de Localisation et de Multi-flots

2pNational audienceNous présentons une heuristique de résolution d'un problème difficile de Lot-Sizing à base de relaxation lagrangienne. Les modèles de Lot-Sizing concernent la planification de la production qui exploite les effets de regroupements de tâches en lots. Nous considérons ici, un ensemble de catégories de produits, un ensemble de sites, et un ensemble de périodes. Un site peut être simultanément producteur et demandeur et servir aussi de site de stockage ou encore de transporteur. Pour chaque période, chaque produit et chaque site, nous connaissons la demande. Il s'agit de définir les quantités à stocker et à transférer pour l'ensemble des sites et des demandes sur l'horizon de planification tout en minimisant les coûts de stockage, de transfert et de production et en respectant des contraintes de capacité. Les variables de décision sont les quantités à produire, à stocker, à transporter. Ces quantités sont soumises à des contraintes de capacité

High-performance computing for data analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques

Regulation of the mTOR signaling pathway: from laboratory bench to bedside and back again

Recent publications have moved us significantly closer to a complete understanding of the mammalian target of rapamycin (mTOR) signaling pathway, which plays a central role in the control of growth and metabolism and is dysregulated in a broad spectrum of human diseases, including cancer, tuberous sclerosis, diabetes, and cardiovascular and neurodegenerative diseases. Rapamycin-related mTOR inhibitors have shown clinical efficacy in several of these diseases, and novel inhibitors currently in development will be valuable tools for further dissections of the mTOR signaling network in human health and disease