While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low. Although replacing SIFT with fully affine invariant features ASIFT (Affine-SIFT) can increase the number of feature points, it results in matching inefficiency and a non-uniform distribution of matched feature point pairs. To address these problems, this paper proposes the novel matching method ICA-ASIFT, which matches HR remote sensing urban images captured at different phases by using an Independent Component Analysis algorithm (ICA) and ASIFT features jointly. First, all possible affine deformations are modeled for the image transform, extracting ASIFT features of remote sensing images captured at different times. The ICA algorithm reduces the dimensionality of ASIFT features and improves matching efficiency of subsequent ASIFT feature point pairs. Next, coarse matching is performed on ASIFT feature point pairs through the algorithms of Nearest Vector Angle Ratio (NVAR), Direction Difference Analysis (DDA) and RANdom SAmple Consensus (RANSAC), eliminating apparent mismatches. Then, fine matching is performed on rough matched point pairs using a Neighborhoodbased Feature Graph Matching algorithm (NFGM) to obtain final ASIFT matching point pairs of remote sensing images. Finally, final matching point pairs are used to compute the affine transform matrix. Matching HR remote sensing images captured at different phases is achieved through affine transform. Experiments are used to compare the performance of ICA-ASFIT and three other algorithms (i.e., Harris- SIFT, PCA-SIFT, TD-ASIFT) on HR remote sensing images captured at different times in different regions. Experimental results show that the proposed ICA-ASFIT algorithm effectively matches HR remote sensing urban images and outperforms other algorithms in terms of matching accuracy and efficiency.
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Ryan Wails, Yixin Sun, Aaron Johnson, Mung Chiang and Prateek Mittal
Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; pathselection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client’s ISP to a malicious host or ISP. The adversary behind each attack is relatively weak – generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.
Halpin and Alexandre Monnin. Volume 43, Issue 4, pp. 480–498. Reprinted in: Harry Halpin and Alexandre Monnin (Eds) Philosophical Engineering: Toward a Philosophy of the Web . Wiley-Blackwell, 2014, pp. 149-167.
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Agnieszka Leszczyńska, Aleksandra Kowalska, Krzysztof Grudzień, Andrzej Romanowski and Dominik Sankowski
The control of dense phase pneumatic convening process is not a trivial task. The control of bulk solids flow phenomena requires detailed knowledge about the temporal and spatial changes of material concentration during flow. This paper describes application of electrical capacitance tomography for the characterization of the pneumatic conveying of solids. The level of concentration changes obtained with the aid of tomography imaging and calculated flow velocity allows monitoring flow behaviour during the granular material transport process. The cross-correlation technique applied for image processing as well as the calculated pixels-based flow velocity profile together with the proposed visualisation method of tomography images sequence provide information about flow conditions. The study of flow characterization, based on ECT data, was conducted for measurements gathered with different flow conditions, including flow blockage.
Hristo Chervenkov, Vladimir Ivanov, Georgi Gadzhev and Kostadin Ganev
The oncoming climate changes will exert influence on the ecosystems, on all branches of the international economy, and on the quality of life. Global Circulation Models (GCMs) are the most widespread and successful tools employed for both numerical weather forecast and climate research since the 1980s. However, growing demands on accurate and reliable information on regional and sub-regional scale are not directly met by relatively coarse resolution global models, mainly due to the excessive costs affiliated with the use of the model in very high resolution. Regional Climate Models (RCMs) are important instruments used for downscaling climate simulations from GCMs. Main aim of the numerical experiment Tuning an Validation of Regional Climate Model (RegCM-TVRegCM) is to quantify the impact of some tunable factors in the RegCM set-up on the model outputs. Thus, on the first stage of the study, the skill of 20 different model configurations in representing the basic spatial and temporal patterns of the Southeast European (SE) climate for the period 1999-2009, is evaluated. Based on these outcomes, the present work is dedicated on more detailed inspection of the model set-ups with recognizable better performance. The Pearson’s correlation coefficient between the time series of the temperature and precipitation of the 6 most promising model set-ups and the E-OBS on monthly basis are calculated. The main conclusion is that this test does not reveal single one model set-up that definitely over performs the other considered ones.
Ivan Bosankic, Lejla Banjanovic-Mehmedovic and Fahrudin Mehmedovic
Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.
The share of video streaming services in the global network traffic is increasing every year. For this reason the problem of optimal use of the available bandwidth, while ensuring an acceptable level of quality of the transmitted video is becoming an important issue. A combination of two techniques, DASH streaming and H.264/AVC video coding can be an important step towards solving this problem. The article presents the discussion the choice of the proper gradation of SVC encoding parameters inside representation of video content in DASH systems. We propose the Temporal Preservation (T-P) method of selection of the SVC stream structure to optimize the grading changes of video quality. This method allows for the implementation of DASH adaptation to changes in available bandwidth on the transmission link, which minimizes the impact of this process on the quality of the received video. According to results of conducted tests, the T-P method offers a better final video quality in DASH systems than similar solutions based on a standard adaptation method. The comparison with the DASH system that uses H.264/AVC encoding also showed superiority of the T-P method, especially in situations of high network congestion