Neural Processing Letters - In this article, the finite time (FT) synchronization problem of fractional order quaternion valued neural networks with time delay is investigated. Without separating... 相似文献
The purpose is to study the applicability of digital and intelligent real-time Image Processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on Deep Learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%; the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.
Understanding the TCP congestion control mechanism from a global optimization point of view is not only important in its own right, but also crucial to the design of other transport layer traffic control protocols with provable properties. In this paper, we derive a global utility function and the corresponding optimal control law, known as TCP control law, which maximizes the global utility. The TCP control law captures the essential behaviors of TCP, including slow start, congestion avoidance, and the binary nature of congestion feedback in TCP. We find that the utility function of TCP is linear in the slow start phase and is proportional to the additive increase rate and approaches the well-known logarithm function as the data rate becomes large in the congestion avoidance phase. We also find that understanding the slow start phase with a fixed threshold is critical to the design of new transport layer control protocols to enable quality of service features. Finally, as an application, we design a Minimum Rate Guaranteed (MRG) traffic control law that shares the same utility function as the TCP control law. Our simulation study of the MRG control law indicates that it is indeed TCP friendly and can provide minimum rate guarantee as long as the percentage of network resource consumed by the MRG flows is moderately small. 相似文献
In this paper, we consider finite-time control problems for linear multi-agent systems subject to exogenous constant disturbances and impulses. Some sufficient conditions are obtained to ensure the finite-time boundedness of the multi-agent systems, which could be then reduced to a feasibility problem involving linear matrix inequalities. Numerical examples are given to illustrate the results. 相似文献
Multimedia Tools and Applications - The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions for single sample face recognition, but it performs... 相似文献
Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels for segmenting pathological lungs from thoracic CT images accurately. A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from superpixels are taken as inputs of a group of RF classifiers. With the fusion of classification results of RFs by a fractional-order gray correlation approach, we capture an initial segmentation of pathological lungs. We finally utilize a divide-and-conquer strategy to deal with segmentation refinement combining contour correction of left lungs and region repairing of right lungs. Our algorithm is tested on a group of thoracic CT images affected with interstitial lung diseases. Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing lung segmentation methods, our algorithm exhibits a robust performance on pathological lung segmentation. Our algorithm can be employed reliably for lung field segmentation of pathologic thoracic CT images with a high accuracy, which is helpful to assist radiologists to detect the presence of pulmonary diseases and quantify its shape and size in regular clinical practices.
We propose a new Geographic Information System (GIS) three-dimensional (3D) data model based on conformal geometric algebra (CGA). In this approach, geographic objects of different dimensions are mapped to the corresponding basic elements (blades) in Clifford algebra, and the expressions of multi-dimensional objects are unified without losing their geometric meaning. Geometric and topologic computations are also processed in a clear and coordinates-free way. Under the CGA framework, basic geometrics are con... 相似文献
