The Telecommunications and Data Acquisition Report

Archival reports are given on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA), including space communications, radio navigation, radio science, ground-based radio and radar astronomy, and the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, supporting research and technology, implementation, and operations. Also included is TDA-funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA. In the search for extraterrestrial intelligence (SETI), implementation and operations for searching the microwave spectrum are reported. Use of the Goldstone Solar System Radar for scientific exploration of the planets, their rings and satellites, asteroids, and comets are discussed

A review of state-of-the-art speech modelling methods for the parameterisation of expressive synthetic speech

This document will review a sample of available voice modelling and transformation techniques, in view of an application in expressive unit-selection based speech synthesis in the framework of the PAVOQUE project. The underlying idea is to introduce some parametric modification capabilities at the level of the synthesis system, in order to compensate for the sparsity and rigidity, in terms of available emotional speaking styles, of the databases used to define speech synthesis voices. For this work, emotion-related parametric modifications will be restricted to the domains of voice quality and prosody, as suggested by several reviews addressing the vocal correlates of emotions (Schröder, 2001; Schröder, 2004; Roehling et al., 2006). The present report will start with a review of some techniques related to voice quality modelling and modification. First, it will explore the techniques related to glottal flow modelling. Then, it will review the domain of cross-speaker voice transformations, in view of a transposition to the domain of cross-emotion voice transformations. This topic will be exposed from the perspective of the parametric spectral modelling of speech and then from the perspective of available spectral transformation techniques. Then, the domain of prosodic parameterisation and modification will be reviewed

Extensions to the Latent Dirichlet Allocation Topic Model Using Flexible Priors

Intrinsically, topic models have always their likelihood functions fixed to multinomial distributions as they operate on count data instead of Gaussian data. As a result, their performances ultimately depend on the flexibility of the chosen prior distributions when following the Bayesian paradigm compared to classical approaches such as PLSA (probabilistic latent semantic analysis), unigrams and mixture of unigrams that do not use prior information. The standard LDA (latent Dirichlet allocation) topic model operates with symmetric Dirichlet distribution (as a conjugate prior) which has been found to carry some limitations due to its independent structure that tends to hinder performance for instance in topic correlation including positively correlated data processing. Compared to classical ML estimators, the use of priors ultimately presents another unique advantage of smoothing out the multinomials while enhancing predictive topic models. In this thesis, we propose a series of flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL) for our topic models within the collapsed representation, leading to much improved CVB (collapsed variational Bayes) update equations compared to ones from the standard LDA. This is because the flexibility of these priors improves significantly the lower bounds in the corresponding CVB algorithms. We also show the robustness of our proposed CVB inferences when using simultaneously the BL and GD in hybrid generative-discriminative models where the generative stage produces good and heterogeneous topic features that are used in the discriminative stage by powerful classifiers such as SVMs (support vector machines) as we propose efficient probabilistic kernels to facilitate processing (classification) of documents based on topic signatures. Doing so, we implicitly cast topic modeling which is an unsupervised learning method into a supervised learning technique. Furthermore, due to the complexity of the CVB algorithm (as it requires second order Taylor expansions) in general, despite its flexibility, we propose a much simpler and tractable update equation using a MAP (maximum a posteriori) framework with the standard EM (expectation-maximization) algorithm. As most Bayesian posteriors are not tractable for complex models, we ultimately propose the MAP-LBLA (latent BL allocation) where we characterize the contributions of asymmetric BL priors over the symmetric Dirichlet (Dir). The proposed MAP technique importantly offers a point estimate (mode) with a much tractable solution. In the MAP, we show that point estimate could be easy to implement than full Bayesian analysis that integrates over the entire parameter space. The MAP implicitly exhibits some equivalent relationship with the CVB especially the zero order approximations CVB0 and its stochastic version SCVB0. The proposed method enhances performances in information retrieval in text document analysis. We show that parametric topic models (as they are finite dimensional methods) have a much smaller hypothesis space and they generally suffer from model selection. We therefore propose a Bayesian nonparametric (BNP) technique that uses the Hierarchical Dirichlet process (HDP) as conjugate prior to the document multinomial distributions where the asymmetric BL serves as a diffuse (probability) base measure that provides the global atoms (topics) that are shared among documents. The heterogeneity in the topic structure helps in providing an alternative to model selection because the nonparametric topic model (which is infinite dimensional with a much bigger hypothesis space) could now prune out irrelevant topics based on the associated probability masses to only retain the most relevant ones. We also show that for large scale applications, stochastic optimizations using natural gradients of the objective functions have demonstrated significant performances when we learn rapidly both data and parameters in online fashion (streaming). We use both predictive likelihood and perplexity as evaluation methods to assess the robustness of our proposed topic models as we ultimately refer to probability as a way to quantify uncertainty in our Bayesian framework. We improve object categorization in terms of inferences through the flexibility of our prior distributions in the collapsed space. We also improve information retrieval technique with the MAP and the HDP-LBLA topic models while extending the standard LDA. These two applications present the ultimate capability of enhancing a search engine based on topic models

Securing Multi-Layer Communications: A Signal Processing Approach

Security is becoming a major concern in this information era. The development in wireless communications, networking technology, personal computing devices, and software engineering has led to numerous emerging applications whose security requirements are beyond the framework of conventional cryptography. The primary motivation of this dissertation research is to develop new approaches to the security problems in secure communication systems, without unduly increasing the complexity and cost of the entire system. Signal processing techniques have been widely applied in communication systems. In this dissertation, we investigate the potential, the mechanism, and the performance of incorporating signal processing techniques into various layers along the chain of secure information processing. For example, for application-layer data confidentiality, we have proposed atomic encryption operations for multimedia data that can preserve standard compliance and are friendly to communications and delegate processing. For multimedia authentication, we have discovered the potential key disclosure problem for popular image hashing schemes, and proposed mitigation solutions. In physical-layer wireless communications, we have discovered the threat of signal garbling attack from compromised relay nodes in the emerging cooperative communication paradigm, and proposed a countermeasure to trace and pinpoint the adversarial relay. For the design and deployment of secure sensor communications, we have proposed two sensor location adjustment algorithms for mobility-assisted sensor deployment that can jointly optimize sensing coverage and secure communication connectivity. Furthermore, for general scenarios of group key management, we have proposed a time-efficient key management scheme that can improve the scalability of contributory key management from O(log n) to O(log(log n)) using scheduling and optimization techniques. This dissertation demonstrates that signal processing techniques, along with optimization, scheduling, and beneficial techniques from other related fields of study, can be successfully integrated into security solutions in practical communication systems. The fusion of different technical disciplines can take place at every layer of a secure communication system to strengthen communication security and improve performance-security tradeoff

Unsupervised Selection and Estimation of Non-Gaussian Mixtures for High Dimensional Data Analysis

Lately, the enormous generation of databases in almost every aspect of life has created a great demand for new, powerful tools for turning data into useful information. Therefore, researchers were encouraged to explore and develop new machine learning ideas and methods. Mixture models are one of the machine learning techniques receiving considerable attention due to their ability to handle efficiently and effectively multidimensional data. Generally, four critical issues have to be addressed when adopting mixture models in high dimensional spaces: (1) choice of the probability density functions, (2) estimation of the mixture parameters, (3) automatic determination of the number of components M in the mixture, and (4) determination of what features best discriminate among the different components. The main goal of this thesis is to summarize all these challenging interrelated problems in one unified model. In most of the applications, the Gaussian density is used in mixture modeling of data. Although a Gaussian mixture may provide a reasonable approximation to many real-world distributions, it is certainly not always the best approximation especially in computer vision and image processing applications where we often deal with non-Gaussian data. Therefore, we propose to use three highly flexible distributions: the generalized Gaussian distribution (GGD), the asymmetric Gaussian distribution (AGD), and the asymmetric generalized Gaussian distribution (AGGD). We are motivated by the fact that these distributions are able to fit many distributional shapes and then can be considered as a useful class of flexible models to address several problems and applications involving measurements and features having well-known marked deviation from the Gaussian shape. Recently, researches have shown that model selection and parameter learning are highly dependent and should be performed simultaneously. For this purpose, many approaches have been suggested. The vast majority of these approaches can be classified, from a computational point of view, into two classes: deterministic and stochastic methods. Deterministic methods estimate the model parameters for a set of candidate models using the Expectation-Maximization (EM) framework, then choose the model that maximizes a model selection criterion. Stochastic methods such as Markov chain Monte Carlo (MCMC) can be used in order to sample from the full a posteriori distribution with M considered unknown. Hence, in this thesis, we propose three learning techniques capable of automatically determining model complexity while learning its parameters. First, we incorporate a Minimum Message Length (MML) penalty in the model learning step performed using the EM algorithm. Our second approach employs the Rival Penalized EM (RPEM) algorithm which is able to select an appropriate number of densities by fading out the redundant densities from a density mixture. Last but not least, we incorporate the nonparametric aspect of mixture models by assuming a countably infinite number of components and using Markov Chain Monte Carlo (MCMC) simulations for the estimation of the posterior distributions. Hence, the difficulty of choosing the appropriate number of clusters is sidestepped by assuming that there are an infinite number of mixture components. Another essential issue in the case of statistical modeling in general and finite mixtures in particular is feature selection (i.e. identification of the relevant or discriminative features describing the data) especially in the case of high-dimensional data. Indeed, feature selection has been shown to be a crucial step in several image processing, computer vision and pattern recognition applications not only because it speeds up learning but also because it improves model accuracy and generalization. Moreover, the learning of the mixture parameters ( i.e. both model selection and parameters estimation) is greatly affected by the quality of the features used. Hence, in this thesis, we are trying to solve the feature selection problem in unsupervised learning by casting it as an estimation problem, thus avoiding any combinatorial search. Finally, the effectiveness of our approaches is evaluated by applying them to different computer vision and image processing applications

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Proceedings of the Fifth International Mobile Satellite Conference 1997

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial communications services. While previous International Mobile Satellite Conferences have concentrated on technical advances and the increasing worldwide commercial activities, this conference focuses on the next generation of mobile satellite services. The approximately 80 papers included here cover sessions in the following areas: networking and protocols; code division multiple access technologies; demand, economics and technology issues; current and planned systems; propagation; terminal technology; modulation and coding advances; spacecraft technology; advanced systems; and applications and experiments

The Deep Space Network

Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations are reported