6 resultados para thermocouple probes

em Boston University Digital Common


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ERRATA: We present corrections to Fact 3 and (as a consequence) to Lemma 1 of BUCS Technical Report BUCS-TR-2000-013 (also published in IEEE INCP'2000)[1]. These corrections result in slight changes to the formulae used for the identifications of shared losses, which we quantify.

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Current Internet transport protocols make end-to-end measurements and maintain per-connection state to regulate the use of shared network resources. When two or more such connections share a common endpoint, there is an opportunity to correlate the end-to-end measurements made by these protocols to better diagnose and control the use of shared resources. We develop packet probing techniques to determine whether a pair of connections experience shared congestion. Correct, efficient diagnoses could enable new techniques for aggregate congestion control, QoS admission control, connection scheduling and mirror site selection. Our extensive simulation results demonstrate that the conditional (Bayesian) probing approach we employ provides superior accuracy, converges faster, and tolerates a wider range of network conditions than recently proposed memoryless (Markovian) probing approaches for addressing this opportunity.

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The hybridization kinetics for a series of designed 25mer probe�target pairs having varying degrees of secondary structure have been measured by UV absorbance and surface plasmon resonance (SPR) spectroscopy in solution and on the surface, respectively. Kinetic rate constants derived from the resultant data decrease with increasing probe and target secondary structure similarly in both solution and surface environments. Specifically, addition of three intramolecular base pairs in the probe and target structure slow hybridization by a factor of two. For individual strands containing four or more intramolecular base pairs, hybridization cannot be described by a traditional two-state model in solution-phase nor on the surface. Surface hybridization rates are also 20- to 40-fold slower than solution-phase rates for identical sequences and conditions. These quantitative findings may have implications for the design of better biosensors, particularly those using probes with deliberate secondary structure.

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For high-intensity focused ultrasound (HIFU) to continue to gain acceptance for cancer treatment it is necessary to understand how the applied ultrasound interacts with gas trapped in the tissue. The presence of bubbles in the target location have been thought to be responsible for shielding the incoming pressure and increasing local heat deposition due to the bubble dynamics. We lack adequate tools for monitoring the cavitation process, due to both limited visualization methods and understanding of the underlying physics. The goal of this project was to elucidate the role of inertial cavitation in HIFU exposures in the hope of applying noise diagnostics to monitor cavitation activity and control HIFU-induced cavitation in a beneficial manner. A number of approaches were taken to understand the relationship between inertial cavitation signals, bubble heating, and bubble shielding in agar-graphite tissue phantoms. Passive cavitation detection (PCD) techniques were employed to detect inertial bubble collapses while the temperature was monitored with an embedded thermocouple. Results indicate that the broadband noise amplitude is correlated to bubble-enhanced heating. Monitoring inertial cavitation at multiple positions throughout the focal region demonstrated that bubble activity increased prefocally as it diminished near the focus. Lowering the HIFU duty cycle had the effect of maintaining a more or less constant cavitation signal, suggesting the shielding effect diminished when the bubbles had a chance to dissolve during the HIFU off-time. Modeling the effect of increasing the ambient temperature showed that bubbles do not collapse as violently at higher temperatures due to increased vapor pressure inside the bubble. Our conclusion is that inertial cavitation heating is less effective at higher temperatures and bubble shielding is involved in shifting energy deposition at the focus. The use of a diagnostic ultrasound imaging system as a PCD array was explored. Filtering out the scattered harmonics from the received RF signals resulted in a spatially- resolved inertial cavitation signal, while the amplitude of the harmonics showed a correlation with temperatures approaching the onset of boiling. The result is a new tool for detecting a broader spectrum of bubble activity and thus enhancing HIFU treatment visualization and feedback.

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Growing interest in inference and prediction of network characteristics is justified by its importance for a variety of network-aware applications. One widely adopted strategy to characterize network conditions relies on active, end-to-end probing of the network. Active end-to-end probing techniques differ in (1) the structural composition of the probes they use (e.g., number and size of packets, the destination of various packets, the protocols used, etc.), (2) the entity making the measurements (e.g. sender vs. receiver), and (3) the techniques used to combine measurements in order to infer specific metrics of interest. In this paper, we present Periscope: a Linux API that enables the definition of new probing structures and inference techniques from user space through a flexible interface. PeriScope requires no support from clients beyond the ability to respond to ICMP ECHO REQUESTs and is designed to minimize user/kernel crossings and to ensure various constraints (e.g., back-to-back packet transmissions, fine-grained timing measurements) We show how to use Periscope for two different probing purposes, namely the measurement of shared packet losses between pairs of endpoints and for the measurement of subpath bandwidth. Results from Internet experiments for both of these goals are also presented.

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A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.