Compression vidéo basée sur l'exploitation d'un décodeur intelligent

This Ph.D. thesis studies the novel concept of Smart Decoder (SDec) where the decoder is given the ability to simulate the encoder and is able to conduct the R-D competition similarly as in the encoder. The proposed technique aims to reduce the signaling of competing coding modes and parameters. The general SDec coding scheme and several practical applications are proposed, followed by a long-term approach exploiting machine learning concept in video coding. The SDec coding scheme exploits a complex decoder able to reproduce the choice of the encoder based on causal references, eliminating thus the need to signal coding modes and associated parameters. Several practical applications of the general outline of the SDec scheme are tested, using different coding modes during the competition on the reference blocs. Despite the choice for the SDec reference block being still simple and limited, interesting gains are observed. The long-term research presents an innovative method that further makes use of the processing capacity of the decoder. Machine learning techniques are exploited in video coding with the purpose of reducing the signaling overhead. Practical applications are given, using a classifier based on support vector machine to predict coding modes of a block. The block classification uses causal descriptors which consist of different types of histograms. Significant bit rate savings are obtained, which confirms the potential of the approach.Cette thèse de doctorat étudie le nouveau concept de décodeur intelligent (SDec) dans lequel le décodeur est doté de la possibilité de simuler l’encodeur et est capable de mener la compétition R-D de la même manière qu’au niveau de l’encodeur. Cette technique vise à réduire la signalisation des modes et des paramètres de codage en compétition. Le schéma général de codage SDec ainsi que plusieurs applications pratiques sont proposées, suivis d’une approche en amont qui exploite l’apprentissage automatique pour le codage vidéo. Le schéma de codage SDec exploite un décodeur complexe capable de reproduire le choix de l’encodeur calculé sur des blocs de référence causaux, éliminant ainsi la nécessité de signaler les modes de codage et les paramètres associés. Plusieurs applications pratiques du schéma SDec sont testées, en utilisant différents modes de codage lors de la compétition sur les blocs de référence. Malgré un choix encore simple et limité des blocs de référence, les gains intéressants sont observés. La recherche en amont présente une méthode innovante qui permet d’exploiter davantage la capacité de traitement d’un décodeur. Les techniques d’apprentissage automatique sont exploitées pour but de réduire la signalisation. Les applications pratiques sont données, utilisant un classificateur basé sur les machines à vecteurs de support pour prédire les modes de codage d’un bloc. La classification des blocs utilise des descripteurs causaux qui sont formés à partir de différents types d’histogrammes. Des gains significatifs en débit sont obtenus, confirmant ainsi le potentiel de l’approche

Bibliography of Lewis Research Center technical publications announced in 1984

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1984. All the publications were announced in the 1984 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

Goddard Visiting Scientist Program for the Space and Earth Sciences Directorate

A visiting scientist program was conducted in the space and earth sciences at GSFC. Research was performed in the following areas: astronomical observations; broadband x-ray spectral variability; ground-based spectroscopic and photometric studies; Seyfert galaxies; active galactic nuclei (AGN); massive stellar black holes; the differential microwave radiometer (DMR) onboard the cosmic background explorer (COBE); atmospheric models; and airborne and ground based radar observations. The specific research efforts are detailed by tasks

The development of novel methods for the targeting and manipulation of neural circuits in vivo

Neural networks are at the core of the brain’s ability to compute complex responses to our external environment. Clinically, network dysfunction is emerging as a key component of several psychiatric and neurodegenerative disorders such as Alzheimer’s disease or schizophrenia. However, our ability to precisely and safely manipulate neural networks for research and deliver network-specific therapy remains limited. To address this problem, our lab recently developed a monosynaptically restricted Self-Inactivating Rabies virus (SiR) which enables the targeting of neural circuits without cytotoxicity. To expand the scope of SiR we further developed the technology in two directions: A) By incorporating the CRISPR/CAS9 gene-editing machinery into the SiR genome to successfully edit endogenous loci in vitro and in vivo. B) By designing an improved second generation SiR virus (SiR 2.0) which applies the same SiR technology to a challenge rabies strain (CVS-N2C). SiR 2.0 demonstrates increased neurotropism, increased trans-synaptic transfer efficiency and markedly decreased immunogenicity compared to the SiR 1.0 vector. These advancements expand the scope of SiR viruses to be used in the genome-editing of circuits in vivo. A combined SiR 2.0 CAS9 virus, in physiology, allows us to investigate the roles of genes within circuits in the brain function of live animals. For therapy, it paves the way for the rabies virus’ potential use to edit disease-related genes in dysfunctional circuits. Despite the circuit-basis of many neurological disorders, existing gene therapy vectors are not circuit specific. In addition, the practical difficulties of delivering therapeutic agents at high doses into the central nervous system exacerbates our inability to achieve high therapeutic loads into affected circuits. In contrast, a SiR 2.0 CAS9 virus would, following injection into peripheral organs, trans-synaptically spread into desired circuits of the central nervous system that are affected in neurological disease (e.g. networks demonstrating pathological protein propagation in neurodegenerative disorders) and edit disease-related genes. Lastly, our interest in network-level pathological protein propagation also led us to investigate the biology behind this observation. Due to additional evidence that a significant number of other proteins in physiology also show interneuronal movement, we hypothesised that perhaps this is an overlooked phenomena in neurobiology which could have key implication