A study of stone fragmentation in shock wave lithotripsy by customizing the acoustic field and waveform shape

Shock wave lithotripsy is the preferred treatment modality for kidney stones in the United States. Despite clinical use for over twenty-five years, the mechanisms of stone fragmentation are still under debate. A piezoelectric array was employed to examine the effect of waveform shape and pressure distribution on stone fragmentation in lithotripsy. The array consisted of 170 elements placed on the inner surface of a 15 cm-radius spherical cap. Each element was driven independently using a 170 individual pulsers, each capable of generating 1.2 kV. The acoustic field was characterized using a fiber optic probe hydrophone with a bandwidth of 30 MHz and a spatial resolution of 100 μm. When all elements were driven simultaneously, the focal waveform was a shock wave with peak pressures p+ =65±3MPa and p−=−16±2MPa and the −6 dB focal region was 13 mm long and 2 mm wide. The delay for each element was the only control parameter for customizing the acoustic field and waveform shape, which was done with the aim of investigating the hypothesized mechanisms of stone fragmentation such as spallation, shear, squeezing, and cavitation. The acoustic field customization was achieved by employing the angular spectrum approach for modeling the forward wave propagation and regression of least square errors to determine the optimal set of delays. Results from the acoustic field customization routine and its implications on stone fragmentation will be discussed.

On the Convergence of Statistical Techniques for Inferring Network Traffic Demands

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study strategies to improve the convergence of a powerful statistical technique based on an Expectation-Maximization iterative algorithm. First we analyze modeling approaches to generating starting points. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we study the convergence characteristics of our EM algorithm and compare it against a recently proposed Weighted Least Squares approach.

A Two-step Statistical Approach for Inferring Network Traffic Demands (Revises Technical Report BUCS-2003-003)

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.

Scalability of Multicast Delivery for Non-sequential Streaming Access

To serve asynchronous requests using multicast, two categories of techniques, stream merging and periodic broadcasting have been proposed. For sequential streaming access where requests are uninterrupted from the beginning to the end of an object, these techniques are highly scalable: the required server bandwidth for stream merging grows logarithmically as request arrival rate, and the required server bandwidth for periodic broadcasting varies logarithmically as the inverse of start-up delay. However, sequential access is inappropriate to model partial requests and client interactivity observed in various streaming access workloads. This paper analytically and experimentally studies the scalability of multicast delivery under a non-sequential access model where requests start at random points in the object. We show that the required server bandwidth for any protocols providing immediate service grows at least as the square root of request arrival rate, and the required server bandwidth for any protocols providing delayed service grows linearly with the inverse of start-up delay. We also investigate the impact of limited client receiving bandwidth on scalability. We optimize practical protocols which provide immediate service to non-sequential requests. The protocols utilize limited client receiving bandwidth, and they are near-optimal in that the required server bandwidth is very close to its lower bound.