Real-time Multistream Routing over Integrated Services Networks

This paper presents an analytical framework for evaluating and optimizing routing and path establishment strategies for multimediacommunication in an integrated services network. Based on a given routing strategy, we show how to determine the probability of success for the establishment phase by using a stochastic knapsack approximation. Our method can be used to optimize the assignment routing paths in a manner which maximizes the probability of the network being able to support multimedia connection requests. This is done through the use of a new cost function called the marginal blocking cost of a link. 1 Introduction Integrated Service Networks (ISNs) capable of supporting real-time performance requirements are expected to form the basis of support for multimedia communication. Within an ISN model, one standard scenario for communication setup is as follows: clients make requests for the establishment of a multimedia communication session, or call. The network then executes a rout..

Specialized Video and Physiological Data Coding System for Remote Monitoring

Abstract —Patient monitoring via video and physiological data recording can now be performed outside hospitals. This procedure, usually performed in a prolonged manner, generates a considerable amount of data, which calls for efficient ways for archiving and transmission. In this work, we present a specialized system to code the video and the physiological data recorded from a patient, aiming at a reduced bandwidth requirement compared to the conventional methods. We’ve developed an object-based approach to coding the monitoring video. By applying two change detection methods, we decompose a video frame into three video object planes (VOPs) representing the background, the stationary foreground and the moving foreground. These VOPs are coded at different frame rates, leading to a reduced overall bit rate. For coding the physiological data (using electroencephalogram, i.e. EEG, as an example), we present an effective solution by using a combination of the lifting scheme and the SPIHT algorithm. This approach is featured with a wavelet-quantization algorithm that enables a scalable transmission. The feasibility of this proposed system is demonstrated by our experimental results. 1

Displaying raw MEG measurements with FreeSurfer

Magnetoencephalography (MEG) is a non-invasive technique of functional imaging which measures weak magnetic fields in the brain due to the currents generated from neural synapses. MEG systems contain a couple of hundred channels, making it difficult to visualize the raw measurements directly. As an alternative to rendering epileptic data, we demonstrate how MEG measurements can be mapped to a cortical surface by using a software package called FreeSurfer. We fuse MEG data with Magnetic Resonance Image (MRI) by plotting the MEG amplitude on top of the MRI images of gray matter surface. In addition to the surface, we render the MEG intensity in the convoluted regions, e.g. sulci, by computationally "inflating" the brain. These techniques are utilized for experimental study currently, and can be extended for diagnostic purposes in the future.</p

An Application of MAP-MRF to Change Detection in Image Sequence Based on Mean Field Theory

Change detection is one of the most important problems in video segmentation. In conventional methods, predetermined thresholds are utilized to test the variation between frames. Although certain reasonings about the thresholds are provided, appropriate determination of these parameters is still problematic. We present a new approach to change detection from an optimization point of view. We model the video frames and the change detection map (CDM) as Markov random fields (MRFs), and formulate change detection into a problem of seeking the optimal configuration of the CDM. Under the MRF assumption, the optimal solution, in the sense of maximum a posteriori (MAP), is obtained by minimizing the energy function associated with the MRF which is designed by utilizing the prior knowledge of noise and contextual constraints on the video frames. An algorithm that computes the potentials and optimizes the solution is constructed by applying the mean field theory (MFT). The experimental results show that the new method detects changes accurately and is robust to noise.</p

Laboratory for Computational Neuroscience

A new approach is presented for time-series modeling and prediction using recurrent neural networks(RNNs) and a discrete wavelet transform(DWT). A specific DWT, based on the cubic spline wavelet, produces a set of wavelet coefficients from coarse to fine scale levels. The RNN has its current output fed back to its input nodes, forming a nonlinear autoregressive model for predicting future wavelet coefficients. A predicted trend signal is obtained by constructing the interpolation function from the predicted wavelet coefficients at the coarsest scale level, V 0 . This method has been applied to intracranial pressure data collected from head trauma patients in the intensive care unit. The method has been shown to be more efficient than one which uses raw data to train the RNN. 1. INTRODUCTION To observe and predict physiological conditions for patients in the intensive care unit(ICU), we have developed an automatic ICU monitoring system[1]. This system acquires, displays, and analyzes ..

HIGHER ORDER STATISTICS AND SPECTRA ANALYSIS OF SLEEP SPINDLES

In this paper we analyze sleep spindles, observed in EEG recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second and third order correlations to reveal information on the stationarity of periodic spindle rhythms, detecting transitions between multiple activities. The frequency domain method, based on the normalized bispectrum, describes the frequency interactions associated with the second order nonlinearities occuring in the observed EEG. Results for real data are presented. 1

Finite element analysis of transcranial electrical stimulation for intraoperative monitoring

Transcranial Electrical Stimulation (TES) is an important intraoperative monitoring procedure. When neurosurgery is performed at certain difficult locations within the central nervous system (CNS), TES evaluates CNS functions during surgical manipulations to prevent post-operative complications. However, the parameters currently utilized in TES are usually experimentally determined which may not be optimal. We have constructed a finite elements model to analyze TES based on a 2D volume conductor model. A realistically shaped simulation domain is constructed by taking the inhomogeneous electrical conductivity values of different tissues into account. Laplace's equation is numerically solved with a set of appropriately selected boundary conditions using the finite element method. Our simulation has yielded clear maps of potential and current distributions within the intracranial space, providing valuable information to optimize the TES procedure. ??2005 IEEE