Synchronization recovery and state model reduction for soft decoding of variable length codes

Variable length codes exhibit de-synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted are known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This paper presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity as well as the probability that the VLC decoder does not re-synchronize in a strict sense, are not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size

Shapelet-based remaining useful life estimation.

International audienceIn the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition and processing, fusion, diagnostics, prognostivs and decision are involved in this domain. This paper presents an approach for estimating the Remaining Useful Life (RUL) of equipments based on shapelet extraction and characterization. This approach makes use in a first step of an history of run-to-failure data to extract discriminative rul-shapelets, i.e. shapelets that are correlated with the RUL of the considered equipment. A library of rul-shapelets is extracted from this step. Then, in an online step, these rul-shapelets are compared to different test units and the ones that match these units are used to estimate their RULs. This approach is hence different from classical similarity-based approaches that matches the test units with training ones. Here, discriminative patterns from the training set are first extracted and then matched to test units. The performance of our approach is assessed on a data set coming from a previous PHM Challenge. We show that this approach is efficient to estimate the RUL compared to other approaches

Codes joints source-canal pour transmission robuste sur canaux mobiles

This thesis concerns the design of joint source-channel codes for robust transmission over wireless and mobile channels.The Shannon theorem states that source and channel coding can be performed separately without loss of optimality. However, this is the case under particular assumptions. We have studied joint source-channel coding schemes with arithmetic and quasi-arithmetic codes.L’étude menée dans cette thèse s’inscrit dans le contexte du codage conjoint source-canal. L’émergence de ce domaine depuis quelques années est dû aux limites, notamment au niveau applicatif, du théorème de séparation de Shannon. Ce théorème stipule que les opérations de codage de source et de codage de canal peuvent être réalisées séparément sans perte d’optimalité. Depuis quelques années, de nombreuses études visant à réaliser conjointement ces deux opérations ont vu le jour. Les codes de source, comme les codes à longueur variables ou les codes quasi-arithmétique ont beaucoup été étudiés.Nous avons travaillé sur ces deux types de codes dans le contexte de codage conjoint source-canal dans ce document. Un modèle d’état pour le décodage souple des codes à longueur variable et des codes quasi-arithmétique est proposé. Ce modèle est paramétré par un entier T qui permet de doser un compromis entre performance et complexité du décodage. Les performances des codes sur ce modèle sont ensuite analysées en étudiant leurs propriétés de resynchronisation. Les méthodes d’étude de ces propriétés ont dû être adaptées aux codes QA et au canal à bruit additif blanc gaussien pour les besoins de cette analyse. Les performances des codes sur le modèle agrégé peuvent ainsi être prévues en fonction de la valeur du paramètre T et du code considéré. Un schéma de décodage robuste avec information adjacente est ensuite proposé. La redondance ajoutée se présente sous la forme de contraintes partielles appliquées pendantle décodage. Cette redondance peut être ajoutée de manière très flexible et ne nécessite pas d’être introduite à l’intérieur du train binaire. Enfin, deux schémas de codage de source distribué utilisant des codes quasi arithmétiques sont proposés. Le premier schéma réalise la compression en poinçonnant la sortie du code, tandis que le deuxième utilise un nouveau type de code : les codes quasi-arithmétiques avec recouvrement d’intervalle. Les résultats présentés par ces deux schémas sont compétitifs comparativement aux schémas classiques utilisant des codes de canal

Unsupervised Kernel Regression Modeling Approach for RUL Prediction.

International audienceRecently, Prognostics and Health Management (PHM) has gained attention from the industrial world since it aims at increasing safety and reliability while reducing the maintenance cost by providing a useful prediction about the RemainingUseful Life (RUL) of critical components/system.In this paper, an Instance-Based Learning (IBL) approach is proposed for RUL prediction. Instances correspond to trajectories representing run-to-failure data of a component. These trajectories are modeled using Unsupervised Kernel Regression (UKR). A historical database is used to learn a UKR model for each training unit. These models fuse the run-to-failure data into a single feature that evolves over time and hence allow the construction of a library of instances. When unseen sensory data arrive, the learned UKR models are used to construct the test degradation trajectories. RUL is deduced by comparing the test degradation trajectory to the library of instance. Only the most similar train instances are kept for RUL prediction. The proposed approach was tested and compared to approaches that apply linear regression and PCA to model the library of instances. Results highlight the benefit of using UK compared to other approaches

RUL prediction based on a new similarity-instance based approach.

International audiencePrognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety,reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step.The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enablesus to construct a library of health indicators (HI) from run-to-failure data. These HI’s are then used online to estimate the RUL of components at an early stage of life, by comparing their HI’s to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches

CLIP-CLOP: CLIP-Guided Collage and Photomontage

The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art. Increasingly more sophisticated generators enhanced the artworks' realism and visual appearance, and creative prompt engineering enabled stylistic expression. Guided by an artist-in-the-loop ideal, we design a gradient-based generator to produce collages. It requires the human artist to curate libraries of image patches and to describe (with prompts) the whole image composition, with the option to manually adjust the patches' positions during generation, thereby allowing humans to reclaim some control of the process and achieve greater creative freedom. We explore the aesthetic potentials of high-resolution collages, and provide an open-source Google Colab as an artistic tool.Comment: 5 pages, 7 figures, published at the International Conference on Computational Creativity (ICCC) 2022 as Short Paper: Dem

Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian–Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.Peer ReviewedPostprint (published version

Optically monitored nuclear spin dynamics in individual GaAs quantum dots grown by droplet epitaxy

We report optical orientation experiments in individual, strain free GaAs quantum dots in AlGaAs grown by droplet epitaxy. Circularly polarized optical excitation yields strong circular polarization of the resulting photoluminescence at 4K. Optical injection of spin polarized electrons into a dot gives rise to dynamical nuclear polarization that considerably changes the exciton Zeeman splitting (Overhauser shift). We show that the created nuclear polarization is bistable and present a direct measurement of the build-up time of the nuclear polarization in a single GaAs dot in the order of one second.Comment: 7 pages, 3 figure

Prédiction du niveau de nappes phréatiques : comparaison d'approches locale, globale et hybride

International audienceCet article présente l'exploration d'une méthode autorégressive de prévision d'une série temporelle pour répondre au défi de la prédiction du niveau de nappes phréatiques. Une méthode autorégressive estime une valeur future d'une série temporelle par régression à partir des valeurs historiques de la série. Plusieurs méthodes de régression peuvent alors être employées. Dans cet article, on présente des expérimentations visant à identifier la meilleure configuration pour prédire de manière précise le niveau de nappes phréatiques. On compare pour cela différents prédicteurs, l'apprentissage de modèle par série ou par groupe de séries, et l'utilisation de données exogènes. Des expérimentations intensives ont été menées et nous permettent de conclure sur le choix de la méthode que nous utiliserons pour répondre au défi