5967501
Real-Time Adaptive Hand Motion Recognition Using a Sparse Bayesian Classifier
An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10 gestures by a sparse Bayesian classifier. The classifier is designed in a way that it supports online incremental learning and it can be thus re-trained to increase its adaptability to an input captured under a new condition. Experiments show that the accuracy of the classifier can be boosted from less than 40% to over 80% by re-training it using 5 newly captured samples from each gesture class. Apart from having a better adaptability, the system can work reliably in real-time and give a probabilistic output that is useful in complex motion analysis.
2761285
Continuous Gesture Recognition using a Sparse Bayesian Classifier
An approach to recognise and segment 9 elementary gestures from a video input is proposed and it can be applied to continuous sign recognition. An isolated gesture is recognised by first converting a portion of video into a motion gradient orientation image and then classifying it into one of the 9 gestures by a sparse Bayesian classifier. The portion of video used is decided by using a sampling technique based on condensation framework. By doing so, gestures can be segmented from the video in a probabilistic manner. Experiments show that the proposed method can achieve accuracy around 90% in both isolated and continuous gesture recognition without using special equipment such as glove devices and the system can run in real-time
12209522
Workflow-enabled distributed component-based information architecture for digital medical imaging enterprises
Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.
18516994
Fast analytical approach to finding steady-state waveforms for power electronics circuits using orthogonal polynomial basis functions
This paper describes a general analytical procedure for steady-state waveform analysis of power electronics circuits using orthogonal polynomials. The proposed method exploits the time-domain piecewise property of power electronics circuits as well as the orthogonality of certain polynomials in order to maximize the accuracy and computational efficiency. This new analytical approach can provide a fast means to obtain improved and more robust solutions of steady-state waveforms of power electronics circuits
46438966
76-Space analysis of grey matter diffusivity: Methods and applications
Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are widely used in the study and diagnosis of neurological diseases involving the White Matter (WM). However, many neurological and neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) are generally considered to involve the Grey Matter (GM). Investigation of GM diffusivity of normal aging and pathological brains has both scientific significance and clinical applications. Most of previous research reports on quantification of GM diffusivity were based on the manually labeled Region of Interests (ROI) analysis of specific neuroanatomic regions. The well-known drawbacks of ROI analysis include inter-rater variations, irreproducible results, tediousness, and requirement of a priori definition of interested regions. In this paper, we present a new framework of automated 76-space analysis of GM diffusivity using DWI/DTI. The framework will be evaluated using clinical data, and applied for study of normal brain, Creutzfeldt-Jakob disease and Schizophrenia.
8528863
Stability Analysis of RED Gateway with Multiple TCP Reno Connections
It has been observed that a bottleneck random early detection (RED) gateway becomes oscillatory when regulating a flow in multiple TCP connections. The stability boundary of the TCP-RED system depends on various network parameters, making the adjustment of the RED gateway a difficult task. Based on a fluid-flow model, analytical conditions were formulated that describe the stable boundary of the RED gateway depending on the number of TCP Reno connections. The proposed model accurately generates a stability boundary surface in a four dimensional space, which facilitates the adjustment of parameters for stable operation of the RED gateway. The accuracy of the analytical results has been verified using the ns-2 network simulations.
14916260
Spurious modulation on current-mode controlled DC/DC converters: an explanation for intermittent chaotic operation
We explain the mechanism that causes "intermittent" instability and chaos in a current-mode controlled switching converter. The circuit model used to study the phenomenon incorporates a coupling process through which a spurious signal is coupled to the current sensing and ramp compensation circuitry, resulting in a modulation of the compensation slope which causes the system to become unstable intermittently. We describe a way to find the parameter boundaries where intermittent chaotic operation emerges.