Spatial digital image analysis plays an important role in the information decision support systems, especially for regions frequently being affected by hurricanes and tropical storms. For the aerial and satellite imag...
详细信息
Spatial digital image analysis plays an important role in the information decision support systems, especially for regions frequently being affected by hurricanes and tropical storms. For the aerial and satellite imaging based patternrecognition, it is unavoidable that these images are affected by various uncertainties, like the atmosphere medium dispersing. Image denoising is thus necessary to remove noises and retain important signatures of digital images. The linear denoising approach is suitable for slowly varying noise cases. However, the spatial object recognition problem is essentially nonlinear. Being a nonlinear wavelet based technique, wavelet decomposition is effective to denoise blurring spatial images. The digital image can be split into four subbands, representing approximation (low frequency feature) and three details (high frequency features) in horizontal, vertical and diagonal directions. The proposed soft thresholding wavelet decomposition is simple and efficient for noise reduction. To further identify the individual targets, nonlinear K-means clustering based segmentation approach is proposed for image object recognition. The selected spatial images are taken across hurricane affected Louisiana areas. In addition to evaluate this integration approach via qualitative observation, quantitative measures are proposed on a basis of the information theory, where the discrete entropy, discrete energy and mutual information, are applied for the accurate decision support.
While the dynamic voltage scaling (DVS) techniques are efficient in reducing the dynamic energy consumption for the processor, varying voltage alone becomes less effective for the overall energy reduction as the stati...
详细信息
ISBN:
(纸本)9780769541556
While the dynamic voltage scaling (DVS) techniques are efficient in reducing the dynamic energy consumption for the processor, varying voltage alone becomes less effective for the overall energy reduction as the static power is growing rapidly. On the other hand, Quality of Service (QoS) is also a primary concern in the development of today's pervasive computing systems. In this paper, we propose a dynamic approach to minimize the overall energy consumption for soft real-time systems while ensuring the QoS-guarantee. The QoS requirements are deterministically quantified with the window-constraints, which require that at least m out of each non-overlapped window of k consecutive jobs of a task meet their deadlines. Necessary and sufficient conditions for checking the feasibility of task sets with arbitrary service times and periods are developed to ensure that the window-constraints can be guaranteed in the worst case. And efficient scheduling techniques based on pattern variation and dynamic slack reclaiming extensions are proposed to combine the task procrastination and dynamic slowdown to minimize the energy consumption. In contrast to the previous leakage-aware slack reclaiming work which never scales the job speed below the critical speed, we will show that it can be more energy efficient to reclaim the slack with speed lower than the critical speed when necessary. Through extensive simulations, our experiment results demonstrate that the proposed techniques significantly outperformed the previous research in both overall and idle energy reduction.
The transforming formula definition from the single valued data to vague valued data was presented, and two transforming formulas from the single valued data to vague valued data were presented, and a similarity measu...
详细信息
We present a method for 3D shape reconstruction of inextensible deformable surfaces from monocular image sequences. The key of our approach is to represent the surface as 3D triangulated mesh and formulate the reconst...
详细信息
The proceedings contain 89 papers. The topics discussed include: efficient extraction of news articles based on RSS crawling;WSS-NFP: tool for web service selection based on non-functional properties using soft comput...
ISBN:
(纸本)9781424486113
The proceedings contain 89 papers. The topics discussed include: efficient extraction of news articles based on RSS crawling;WSS-NFP: tool for web service selection based on non-functional properties using softcomputing;WeSPaS - Web specification pattern system;CxQWS: context-aware quality semantic web service;building a neural network-based english-to-arabic transfer module from an unrestricted domain;a comparative study of neural networks architectures on arabic text categorization using feature extraction;neural-linguistic classifier combination for large arabic word vocabulary recognition;a texture based approach for arabic writer identification and verification;Gaussian modeling and discrete cosine transform for efficient and automatic palmprint identification;towards multicriteria analysis: a new clustering approach;and activity regulation for the participative creation of data on-line.
A new star identification algorithm based on fuzzy line pattern matching is proposed for satellite attitude determination. In this algorithm, the star point pattern convert to line pattern by "Delaunay triangulat...
详细信息
In this study we propose a deformable patternrecognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps...
详细信息
pattern selection is an important part in the research fields of data mining and patternrecognition, especially for the high-dimensional data in the Bioinformatics. In this paper, a new pattern selection algorithm wa...
详细信息
This paper proposes a method for computing a quasi-dense set of matching points between three views of a scene. The method takes a sparse set of seed matches between pairs of views as input and then propagates the see...
详细信息
Multi-resident activity recognition is among a key enabler in many context-aware applications in a smart home. However, most of prior researches ignore the potential interactions among residents in order to simplify p...
详细信息
ISBN:
(纸本)9781424466757
Multi-resident activity recognition is among a key enabler in many context-aware applications in a smart home. However, most of prior researches ignore the potential interactions among residents in order to simplify problem complexity. On the other hand, multiple-resident activities are usually recognized using cameras or wearable sensors. However, due to human-centric concerns, it is more preferable to avoid using obtrusive sensors. In this paper, we propose dynamic Bayesian networks which extend coupled hidden Markov models (CHMMs) by adding some vertices to model both individual and cooperative activities. In order to improve performance of the model, we categorize sensor observations based on data association and some domain knowledge to model multiple-resident activity patterns. We then validate the performance using a multi-resident dataset from WSU (Washington State University), which only includes non-obtrusive sensors. The experimental result shows that our model performs better than other baseline classifiers.
暂无评论