Safe Compositional Network Sketches: NetSketch Tool Implementation

Numerous problems exist that can be modeled as traffic through a network in which constraints exist to regulate flow. Vehicular road travel, computer networks, and cloud based resource distribution, among others all have natural representations in this manner. As these networks grow in size and/or complexity, analysis and certification of the safety invariants becomes increasingly costly. The NetSketch formalism introduces a lightweight verification framework that allows for greater scalability than traditional analysis methods. The NetSketch tool was developed to provide the power of this formalism in an easy to use and intuitive user interface.

Exploiting Redundancy for Timeliness in TCP Boston

While ATM bandwidth-reservation techniques are able to offer the guarantees necessary for the delivery of real-time streams in many applications (e.g. live audio and video), they suffer from many disadvantages that make them inattractive (or impractical) for many others. These limitations coupled with the flexibility and popularity of TCP/IP as a best-effort transport protocol have prompted the network research community to propose and implement a number of techniques that adapt TCP/IP to the Available Bit Rate (ABR) and Unspecified Bit Rate (UBR) services in ATM network environments. This allows these environments to smoothly integrate (and make use of) currently available TCP-based applications and services without much (if any) modifications. However, recent studies have shown that TCP/IP, when implemented over ATM networks, is susceptible to serious performance limitations. In a recently completed study, we have unveiled a new transport protocol, TCP Boston, that turns ATM's 53-byte cell-oriented switching architecture into an advantage for TCP/IP. In this paper, we demonstrate the real-time features of TCP Boston that allow communication bandwidth to be traded off for timeliness. We start with an overview of the protocol. Next, we analytically characterize the dynamic redundancy control features of TCP Boston. Next, We present detailed simulation results that show the superiority of our protocol when compared to other adaptations of TCP/IP over ATMs. In particular, we show that TCP Boston improves TCP/IP's performance over ATMs for both network-centric metrics (e.g., effective throughput and percent of missed deadlines) and real-time application-centric metrics (e.g., response time and jitter).

PHOTOREFRACTIVE CRYSTAL-BASED ACOUSTO-OPTIC IMAGING IN THE NEAR-INFRARED AND ITS APPLICATIONS

Acousto-optic (AO) sensing and imaging (AOI) is a dual-wave modality that combines ultrasound with diffusive light to measure and/or image the optical properties of optically diffusive media, including biological tissues such as breast and brain. The light passing through a focused ultrasound beam undergoes a phase modulation at the ultrasound frequency that is detected using an adaptive interferometer scheme employing a GaAs photorefractive crystal (PRC). The PRC-based AO system operating at 1064 nm is described, along with the underlying theory, validating experiments, characterization, and optimization of this sensing and imaging apparatus. The spatial resolution of AO sensing, which is determined by spatial dimensions of the ultrasound beam or pulse, can be sub-millimeter for megahertz-frequency sound waves.A modified approach for quantifying the optical properties of diffuse media with AO sensing employs the ratio of AO signals generated at two different ultrasound focal pressures. The resulting “pressure contrast signal” (PCS), once calibrated for a particular set of pressure pulses, yields a direct measure of the spatially averaged optical transport attenuation coefficient within the interaction volume between light and sound. This is a significant improvement over current AO sensing methods since it produces a quantitative measure of the optical properties of optically diffuse media without a priori knowledge of the background illumination. It can also be used to generate images based on spatial variations in both optical scattering and absorption. Finally, the AO sensing system is modified to monitor the irreversible optical changes associated with the tissue heating from high intensity focused ultrasound (HIFU) therapy, providing a powerful method for noninvasively sensing the onset and growth of thermal lesions in soft tissues. A single HIFU transducer is used to simultaneously generate tissue damage and pump the AO interaction. Experimental results performed in excised chicken breast demonstrate that AO sensing can identify the onset and growth of lesion formation in real time and, when used as feedback to guide exposure parameters, results in more predictable lesion formation.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.