Traffic congestion remains a serious problem in transportation networks. Widely used navigation systems can only react to the presence of traffic jams but not to prevent their creation. One of the possibilities to pre...
Traffic congestion remains a serious problem in transportation networks. Widely used navigation systems can only react to the presence of traffic jams but not to prevent their creation. One of the possibilities to prevent congestion is to manage road traffic within the urban area. This work considers a route reservation approach with possibility to reroute a vehicle during a journey. This approach decomposes road segments into time-spatial slots and for every vehicle it makes the slots reservation for the corresponding route. Since the travel time in real networks cannot be determined precisely and can be considered as stochastic, we propose to use a rerouting procedure to minimize the traveling time. The experiments are carried out in microscopic simulation of a real-world traffic environment in the transportation network of Samara, Russia, using multi-agent transport simulation MATSim.
Orthogonal Frequency Division Multiplexing (OFDM) which is widely used transmission technique for all 4G communication systems faces a major issue of Peak to average power ratio (PAPR). Partial Transmit Sequence (PTS)...
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ISBN:
(纸本)9781467391979
Orthogonal Frequency Division Multiplexing (OFDM) which is widely used transmission technique for all 4G communication systems faces a major issue of Peak to average power ratio (PAPR). Partial Transmit Sequence (PTS) is the most preferred technique for the reduction of PAPR. But it involves complex searching algorithms for finding the most optimal combinations of OFDM signals. Increased complexity with any increase in the number of sub-blocks is a major drawback of PTS. In this paper, Iterative-Grouping and image-PTS (IGI-PTS) technique is proposed which mainly focuses on reducing the computational complexity involved in search of optimal phase factors. It is combination of two basic grouping and imaging techniques and further using iterations to simplify the searching process when the numbers of sub-blocks are in significantly high.
Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in dev...
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ISBN:
(纸本)9781509054442
Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.
Super-resolution reconstruction for image sequences is a promising imageprocessing technology that using complementary information among a set of images to reconstruct a high-resolution *** super-resolution reconstru...
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ISBN:
(纸本)9781509009107
Super-resolution reconstruction for image sequences is a promising imageprocessing technology that using complementary information among a set of images to reconstruct a high-resolution *** super-resolution reconstruction algorithms have been studied in the literature to reconstruct a high-resolution *** this paper,first,after presenting a condensed introduction of image registration algorithms including Lucchese algorithm,Vandewalle algorithm and Keren algorithm,we experimentally compare the relative merits of these registration algorithms in terms of registration accuracy and noise ***,we experimentally compare four image reconstruction methods:projection onto convex sets method(POCS),iterative back-projection method(IBP),robust super resolution(Robust SR) and structure-adaptive normalized convolution(Structure-Adaptive NC),mainly in terms of Peak Signal to Noise Ratio(PSNR),in which salt and pepper noise is added in the low resolution *** is clearly demonstrated that the combination of Keren algorithm and Structure-Adaptive NC can achieve the best performance regarding the Lena image.
Digital imageprocessing is versatile study in this era. Many researchers implement different types of association like image restoration, enhancement and segmentation etc. Implementing image-processingsystems can en...
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In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (P...
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ISBN:
(纸本)9781509013531
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis (PA). Besides, Local Standard Deviation (LSD) which is a method for extracting meaningful sub-features is applied to whole image for successful segmentation. The effect of segmentation success rate is analyzed by selected features. Consequently, a novel algorithm is proposed for minimizing total computation time and error of dimension reduction significantly. It is seen that the results of the algorithm are approximately same as conventional segmentation algorithms.
In this paper, we describe a modification of the previously developed on-board imageprocessing method applied to hyperspectral images. algorithms on which the method is based were finalized and parametrically adjuste...
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In this paper, we describe a modification of the previously developed on-board imageprocessing method applied to hyperspectral images. algorithms on which the method is based were finalized and parametrically adjusted. Computational experiments consider formation and storage specifics for hyperspectral images. It has been shown that the proposed method based on HGI-compression can be recommended for implementation in on-board processingsystems and transmission over communication channels.
In this study, we have designed a GPGPU (General-Purpose Graphics processing Unit)-based algorithm for determining the minimum distance from the tip of a CUSA (Cavitron Ultrasonic Surgical Aspirator) scalpel to the cl...
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Artificial motion and warping of images taken at long range is one of the most significant and troublesome effects of atmospheric turbulence. It is important to understand and model this effect correctly in order to: ...
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ISBN:
(数字)9781510604094
ISBN:
(纸本)9781510604087;9781510604094
Artificial motion and warping of images taken at long range is one of the most significant and troublesome effects of atmospheric turbulence. It is important to understand and model this effect correctly in order to: 1) fully characterize turbulence between the target and the observer, 2) devise efficient post-processing strategies for artificial motion correction, and 3) exploit information about statistics of this atmospheric motion to distinguish between real and fake movement in a scene. This paper discusses two types of motion: G-tilt and Z-tilt, highlighting the differences between them. Optimal image block size for de-warping algorithms and bandwidth considerations are given special attention. Finally, strategies for turbulence characterization based on differential image motion are discussed.
The aim of this article is to present a method to detect visual objects from color digital images by volumetric segmentation. We will discuss algorithms for visual and multimedia computing. The problem of partitioning...
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