We perform numerical calculations for the diffraction efficiency of single-sided microprism designs with optical beams spread over several microprisms. For small angles (less than 1.5 degrees) the far-field diffractio...
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The finger vein image acquired with an acquisition system should be properly aligned to proceed with comparing algorithm. However it is not easy to find control the points since the images are naturally blurred with a...
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In Internet, IP multicast has been used successfully to provide an efficient, best-effort delivery service for group communication applications. However, applications such as multiparty private conference, distributio...
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ISBN:
(纸本)9780889868021
In Internet, IP multicast has been used successfully to provide an efficient, best-effort delivery service for group communication applications. However, applications such as multiparty private conference, distribution of stock market information, pay per view and other subscriber services require secure multicast to protect integrity and confidentiality of the group traffic, and validate group member's authenticity. Providing secure multicast for group communication is problematic without a robust group key management. In this paper, we propose an anonymous group key management with ID-based Signature and secret sharing technologies to distribute a new group key securely to each participant of a group with only one rekey message whenever the group membership changes. The key management guarantees that a legal user, without revealing his/her real identity, can join a group after finishing the mutual authentication with the group initiator, and enforces the forward secrecy and backward secrecy.
The goal of this research study was to develop a music therapy tool using a computer-generated harp which could provide users with visual, audio, and haptic feedback during interaction with the virtual instrument. Rea...
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High-frequency power Doppler imaging of angiogenesis can be challenging given the presence of small blood vessels and slow flow velocities. In the presence of substantial Doppler artifacts such as false-positive color...
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A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and...
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This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (in...
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This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (information granules and rule base) and rules antecedent and consequent parameters from data (inductive learning). Its main purpose is continuous learning, self-organization, and adaptation to unknown environments. The IBeM approach develops global model of a system using a fast, one-pass learning algorithm, and modest memory requirements. To illustrate the effectiveness of the approach, the paper considers actual time series forecasting applications concerning electricity load and stream flow forecasting.
The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The sugge...
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The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems.
Modelling and Simulation (M&S) is increasingly important in numerous disciplines. M&S also has gained acceptance as a discipline in its own right. There is growing demand for at least two different approaches ...
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Binocular indirect ophthalmoscope (BIO) provides a wider view of fundus with stereopsis contrary to the direct one. Proposed system is composed of portable BIO and 3D viewing unit. The illumination unit of BIO utilize...
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