Layer-based coding, smoothing, and scheduling of low-bit-rate video for teleconferencing over tactical ATM networks

This work investigates issues related to distribution of low bit rate video within the context of a teleconferencing application deployed over a tactical ATM network. The main objective is to develop mechanisms that support transmission of low bit rate video streams as a series of scalable layers that progressively improve quality. The hierarchical nature of the layered video stream is actively exploited along the transmission path from the sender to the recipients to facilitate transmission. A new layered coder design tailored to video teleconferencing in the tactical environment is proposed. Macroblocks selected due to scene motion are layered via subband decomposition using the fast Haar transform. A generalized layering scheme groups the subbands to form an arbitrary number of layers. As a layering scheme suitable for low motion video is unsuitable for static slides, the coder adapts the layering scheme to the video content. A suboptimal rate control mechanism that reduces the kappa dimensional rate distortion problem resulting from the use of multiple quantizers tailored to each layer to a 1 dimensional problem by creating a single rate distortion curve for the coder in terms of a suboptimal set of kappa dimensional quantizer vectors is investigated. Rate control is thus simplified into a table lookup of a codebook containing the suboptimal quantizer vectors. The rate controller is ideal for real time video and limits fluctuations in the bit stream with no corresponding visible fluctuations in perceptual quality. A traffic smoother prior to network entry is developed to increase queuing and scheduler efficiency. Three levels of smoothing are studied: frame, layer, and cell interarrival. Frame level smoothing occurs via rate control at the application. Interleaving and cell interarrival smoothing are accomplished using a leaky bucket mechanism inserted prior to the adaptation layer or within the adaptation layerhttp://www.archive.org/details/layerbasedcoding00parkLieutenant Commander, United States NavyApproved for public release; distribution is unlimited

Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.Comment: 33 pages, 14 figures, submitted to ApJ

An Integrated Method for Optimizing Bridge Maintenance Plans

Bridges are one of the vital civil infrastructure assets, essential for economic developments and public welfare. Their large numbers, deteriorating condition, public demands for safe and efficient transportation networks and limited maintenance and intervention budgets pose a challenge, particularly when coupled with the need to respect environmental constraints. This state of affairs creates a wide gap between critical needs for intervention actions, and tight maintenance and rehabilitation funds. In an effort to meet this challenge, a newly developed integrated method for optimized maintenance and intervention plans for reinforced concrete bridge decks is introduced. The method encompasses development of five models: surface defects evaluation, corrosion severities evaluation, deterioration modeling, integrated condition assessment, and optimized maintenance plans. These models were automated in a set of standalone computer applications, coded using C#.net in Matlab environment. These computer applications were subsequently combined to form an integrated method for optimized maintenance and intervention plans. Four bridges and a dataset of bridge images were used in testing and validating the developed optimization method and its five models. The developed models have unique features and demonstrated noticeable performance and accuracy over methods used in practice and those reported in the literature. For example, the accuracy of the surface defects detection and evaluation model outperforms those of widely-recognized machine leaning and deep learning models; reducing detection, recognition and evaluation of surface defects error by 56.08%, 20.2% and 64.23%, respectively. The corrosion evaluation model comprises design of a standardized amplitude rating system that circumvents limitations of numerical amplitude-based corrosion maps. In the integrated condition, it was inferred that the developed model accomplished consistent improvement over the visual inspection procedures in-use by the Ministry of Transportation in Quebec. Similarly, the deterioration model displayed average enhancement in the prediction accuracies by 60% when compared against the most commonly-utilized weibull distribution. The performance of the developed multi-objective optimization model yielded 49% and 25% improvement over that of genetic algorithm in a five-year study period and a twenty five-year study period, respectively. At the level of thirty five-year study period, unlike the developed model, classical meta-heuristics failed to find feasible solutions within the assigned constraints. The developed integrated platform is expected to provide an efficient tool that enables decision makers to formulate sustainable maintenance plans that optimize budget allocations and ensure efficient utilization of resources

Compression et transmission d'images avec énergie minimale application aux capteurs sans fil

Un réseau de capteurs d'images sans fil (RCISF) est un réseau ad hoc formé d'un ensemble de noeuds autonomes dotés chacun d'une petite caméra, communiquant entre eux sans liaison filaire et sans l'utilisation d'une infrastructure établie, ni d'une gestion de réseau centralisée. Leur utilité semble majeure dans plusieurs domaines, notamment en médecine et en environnement. La conception d'une chaîne de compression et de transmission sans fil pour un RCISF pose de véritables défis. L'origine de ces derniers est liée principalement à la limitation des ressources des capteurs (batterie faible , capacité de traitement et mémoire limitées). L'objectif de cette thèse consiste à explorer des stratégies permettant d'améliorer l'efficacité énergétique des RCISF, notamment lors de la compression et de la transmission des images. Inéluctablement, l'application des normes usuelles telles que JPEG ou JPEG2000 est éner- givore, et limite ainsi la longévité des RCISF. Cela nécessite leur adaptation aux contraintes imposées par les RCISF. Pour cela, nous avons analysé en premier lieu, la faisabilité d'adapter JPEG au contexte où les ressources énergétiques sont très limitées. Les travaux menés sur cet aspect nous permettent de proposer trois solutions. La première solution est basée sur la propriété de compactage de l'énergie de la Transformée en Cosinus Discrète (TCD). Cette propriété permet d'éliminer la redondance dans une image sans trop altérer sa qualité, tout en gagnant en énergie. La réduction de l'énergie par l'utilisation des régions d'intérêts représente la deuxième solution explorée dans cette thèse. Finalement, nous avons proposé un schéma basé sur la compression et la transmission progressive, permettant ainsi d'avoir une idée générale sur l'image cible sans envoyer son contenu entier. En outre, pour une transmission non énergivore, nous avons opté pour la solution suivante. N'envoyer fiablement que les basses fréquences et les régions d'intérêt d'une image. Les hautes fréquences et les régions de moindre intérêt sont envoyées""infiablement"", car leur pertes n'altèrent que légèrement la qualité de l'image. Pour cela, des modèles de priorisation ont été comparés puis adaptés à nos besoins. En second lieu, nous avons étudié l'approche par ondelettes (wavelets ). Plus précisément, nous avons analysé plusieurs filtres d'ondelettes et déterminé les ondelettes les plus adéquates pour assurer une faible consommation en énergie, tout en gardant une bonne qualité de l'image reconstruite à la station de base. Pour estimer l'énergie consommée par un capteur durant chaque étape de la 'compression, un modèle mathématique est développé pour chaque transformée (TCD ou ondelette). Ces modèles, qui ne tiennent pas compte de la complexité de l'implémentation, sont basés sur le nombre d'opérations de base exécutées à chaque étape de la compression

Scalable video compression with optimized visual performance and random accessibility

This thesis is concerned with maximizing the coding efficiency, random accessibility and visual performance of scalable compressed video. The unifying theme behind this work is the use of finely embedded localized coding structures, which govern the extent to which these goals may be jointly achieved. The first part focuses on scalable volumetric image compression. We investigate 3D transform and coding techniques which exploit inter-slice statistical redundancies without compromising slice accessibility. Our study shows that the motion-compensated temporal discrete wavelet transform (MC-TDWT) practically achieves an upper bound to the compression efficiency of slice transforms. From a video coding perspective, we find that most of the coding gain is attributed to offsetting the learning penalty in adaptive arithmetic coding through 3D code-block extension, rather than inter-frame context modelling. The second aspect of this thesis examines random accessibility. Accessibility refers to the ease with which a region of interest is accessed (subband samples needed for reconstruction are retrieved) from a compressed video bitstream, subject to spatiotemporal code-block constraints. We investigate the fundamental implications of motion compensation for random access efficiency and the compression performance of scalable interactive video. We demonstrate that inclusion of motion compensation operators within the lifting steps of a temporal subband transform incurs a random access penalty which depends on the characteristics of the motion field. The final aspect of this thesis aims to minimize the perceptual impact of visible distortion in scalable reconstructed video. We present a visual optimization strategy based on distortion scaling which raises the distortion-length slope of perceptually significant samples. This alters the codestream embedding order during post-compression rate-distortion optimization, thus allowing visually sensitive sites to be encoded with higher fidelity at a given bit-rate. For visual sensitivity analysis, we propose a contrast perception model that incorporates an adaptive masking slope. This versatile feature provides a context which models perceptual significance. It enables scene structures that otherwise suffer significant degradation to be preserved at lower bit-rates. The novelty in our approach derives from a set of "perceptual mappings" which account for quantization noise shaping effects induced by motion-compensated temporal synthesis. The proposed technique reduces wavelet compression artefacts and improves the perceptual quality of video

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems