Models of behavior mining from logs in adaptive systems are one from many methods how to obtain useful information for personalized navigation. Target of this paper is to describe modified results of full scan algorit...
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Models of behavior mining from logs in adaptive systems are one from many methods how to obtain useful information for personalized navigation. Target of this paper is to describe modified results of full scan algorithm used to creation recommended links to concepts. For creation recommended links is used technique of traversal patterns. Established weights up to concepts and links along with the results of full scan algorithm created relevant recommended links within the scope of all subject matter and within the scope of topic in actual concept. The fundamental part is facilitating orientation for user in hyperspace.
Consider a multiuser communication system in a frequency selective environment whereby users share a common spectrum and can interfere with each other. Assuming Gaussian signaling and treating interference as noise, w...
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Consider a multiuser communication system in a frequency selective environment whereby users share a common spectrum and can interfere with each other. Assuming Gaussian signaling and treating interference as noise, we study optimal spectrum sharing strategies for the maximization of weighted sum-rate. In this work, we show that, if the normalized crosstalk gains are larger than a given threshold (roughly equal to 1/2), then the optimal spectrum sharing strategy is frequency division multiple access (FDMA). We also propose several simple distributed spectrum allocation algorithms that can approximately maximize weighted sum-rates. Numerical simulation of DSL applications shows that these algorithms are efficient and can achieve substantially larger weighted sumrates than those obtained by the existing iterative waterfilling algorithm.
Several widely used methods have been proposed for fusing high resolution panchromatic data and lower resolution multi-channel data. However, many of these methods fail to maintain the spectral consistency of the fuse...
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Several widely used methods have been proposed for fusing high resolution panchromatic data and lower resolution multi-channel data. However, many of these methods fail to maintain the spectral consistency of the fused high resolution image, which is of high importance to many of the applications based on satellite data. Additionally, most conventional methods are loosely connected to the image forming physics of the satellite image, giving these methods an ad hoc feel. Vesteinsson et al. [1] proposed a method of fusion of satellite images that is based on the properties of imaging physics in a statistically meaningful way and was called spectral consistent panshapening (SCP). In this paper we improve this framework for satellite image fusion by introducing a better image prior, via data-dependent image smoothing. The dependency is obtained via total variation edge detection method.
The aim of this paper is to present a simple simulation technique based in Laplace transform, which allows the analysis of both transient and steady state response of switch mode power supplies (SMPS). Further, an exp...
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The aim of this paper is to present a simple simulation technique based in Laplace transform, which allows the analysis of both transient and steady state response of switch mode power supplies (SMPS). Further, an experimental LCR meter is also presented in order to evaluate the capacitor equivalent circuit parameters at the converter operating frequency. Both techniques can be used at the design stage of SMPS, in order to improve the quality of the final prototype, since the effects of these parasitic elements present in electrolytic capacitors can be foreseen. Simulation, experimental and theoretical results are then presented in order to prove the applicability of the present simulation tool.
In this work, the idea of key frames extraction from single shots in video sequences is presented. The method is implemented by an efficient two-step algorithm, which is classified neither to clustering nor to tempora...
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ISBN:
(纸本)9781424412730
In this work, the idea of key frames extraction from single shots in video sequences is presented. The method is implemented by an efficient two-step algorithm, which is classified neither to clustering nor to temporal variations based techniques. In the first step, an MST (minimal spanning tree) graph is constructed, where each node is associated to a single frame of the shot. In the second step, extracts key frames based on the principle of their maximum spread, are extracted. The number of the selected key frames is controlled by an adaptively defined threshold, while the validity of the results is evaluated by the fidelity measure.
This paper addresses the problem of establishing stability of interconnected nonlinear systems consisting of individually input-to-state stable (ISS) subsystems. This paper aims to extend the capability of the ISS sma...
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ISBN:
(纸本)9781424414970;1424414970
This paper addresses the problem of establishing stability of interconnected nonlinear systems consisting of individually input-to-state stable (ISS) subsystems. This paper aims to extend the capability of the ISS small-gain technique with emphasis on Lyapunov functions and necessity. This paper derives an explicit formula for constructing Lyapunov functions characterizing ISS property of interconnected systems from dissipation inequalities of subsystems. This paper focuses on the case where nonlinear loop gain approaches unity asymptotically as magnitude of signals tends to zero and infinity. The new formula of Lyapunov functions deals with the non-uniform contraction of loop gain and covers one of authors' previous developments as a special case. The Lyapunov functions obtained here are differentiable. The formulation based on dissipation inequalities also allows us to prove the necessity of the small-gain condition without making a technical assumption of uniformity.
In a wireless network composed of multiple access points, a long delay during roaming from one access point to another may cause a disruption for streaming traffic. Roaming in wireless LAN is generally composed of two...
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In a wireless network composed of multiple access points, a long delay during roaming from one access point to another may cause a disruption for streaming traffic. Roaming in wireless LAN is generally composed of two parts, 1) searching for a new access point and 2) performing authentication at the new access point. To reduce the second part delay, we propose an innovative lightweight authentication scheme called SFRIC (secure fast /foaming using ID-based cryptography). SFRIC employs ID-based cryptography to simplify the authentication process. It performs mutual authentication for the mobile client and AP with a 3-way handshake, then generates a PTK (pairwise transient key) directly without pre-distributing PMK (pairwise master key). It does not require contacting an authentication server or exchanging certificates. SFRIC is composed of two phases. In the first phase (the preparation phase), each mobile client obtains a temporary private key from the PKG (private key generator). In the second phase (the roaming authentication phase), mutual authentication and key distribution are performed. Our preliminary analysis indicates that SFRIC can complete the roaming authentication within a period much less than the critical 20 ms threshold, required for maintaining streaming traffic, when the cryptographic operations are performed in hardware.
This paper treats the output feedback H 2 control with H ∞ norm constraint (mixed H 2 /H ∞ control problem). In general, this problem is a BMI problem which is difficult to be solved theoretically. The purpose of...
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This paper treats the output feedback H 2 control with H ∞ norm constraint (mixed H 2 /H ∞ control problem). In general, this problem is a BMI problem which is difficult to be solved theoretically. The purpose of this paper is to propose an iterative method for obtaining a sub-optimal solution of the mixed H 2 /H ∞ control problem. The key idea of this paper is to employ a full-order observer-based controller. The proposed method produces a sequence of controllers which approaches to the unconstrained globally optimal H 2 controller while it satisfies the H ∞ norm constraint. A numerical example shows the effectiveness of our method.
In this paper, a learning approach to semantic image analysis and classification is proposed that combines global and local information, with explicitly defined knowledge in the form of an ontology. The ontology speci...
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In this paper, a learning approach to semantic image analysis and classification is proposed that combines global and local information, with explicitly defined knowledge in the form of an ontology. The ontology specifies the selected domain, its sub-domains, the concepts related to each sub-domain as well as contextual information. Support Vector Machines (SVMs) are employed in order to provide image classification to one of the defined sub-domains based on global image descriptions and, after a segmentation algorithm is applied, to perform an initial mapping between region low-level visual features and the concepts in the ontology. Then, a decision function, that receives as input the region to concepts associations together with contextual information, realizes image classification based on local-level information. The contextual information used is in the form of frequency of appearance of each concept in every particular sub-domain. A fusion mechanism combines the intermediate classification results, provided by the local-and global-level information processing, and decides on the final classification. A Genetic Algorithm (GA) is employed for optimizing the fusion process. Experiments with images from the personal collection domain demonstrate the performance of the proposed approach.
Computational approaches for protein classification have been proposed over the last years in order to speed up the analysis of the biological mechanics in living organisms. Most of the approaches tend to focus in geo...
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Computational approaches for protein classification have been proposed over the last years in order to speed up the analysis of the biological mechanics in living organisms. Most of the approaches tend to focus in geometrical comparison of the 3D molecules to reach their goals. In this paper a method suitable for partial (sub)graph matching of 3D proteinic models, in order to achieve fast and accurate classification, is proposed. The 3D objects are firstly segmented to their molecular structure. Then, descriptors are extracted for each segment using spherical harmonics algorithms, and graphs are constructed for the molecules. Next, a sub-graph matching procedure is utilized and the results are refined using biochemical properties to get biological meaningful classification. The experimental results proved that the proposed method achieves accurate classification of the proteinic data.
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