Some applications of the interconnected embedded {1.s such as sensor networks rely on all nodes in the network to execute certain tasks simultaneously. To meet this demand for simultaneity, a multi-timer model base...
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ISBN:
(纸本)9781509035502
Some applications of the interconnected embedded {1.s such as sensor networks rely on all nodes in the network to execute certain tasks simultaneously. To meet this demand for simultaneity, a multi-timer model based fully distributed task synchronization algorithm is proposed in this paper. In this multi-timer model, each node containing an embedded {1. is characterized by a timer. The microcontrollers (MCUs) within the interconnected embedded {1.s are switched to the assigned tasks by timer interrupts. Each timer decides when to trigger interrupts by only using the information from its neighbors. Task synchronization is realized by using the proposed synchronization algorithm. Some simulation examples are presented in the end to verify the effectiveness of the proposed synchronization algorithm.
In this paper, the problem of rudder-roll damping control for a class of cruise keeping ships subject to both actuator fault and state saturation is investigated. A manoeuvring and rudder control model of marine vesse...
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ISBN:
(纸本)9781467380768
In this paper, the problem of rudder-roll damping control for a class of cruise keeping ships subject to both actuator fault and state saturation is investigated. A manoeuvring and rudder control model of marine vessels is firstly reviewed based on the existing literature. Actuator faults which including partial loss of rudder effectiveness and actuator-bias faults are considered in the model. Then a switching control strategy is developed to compensate for the actuator faults and to guarantee the stability of the rudder-roll damping control system. Moreover, some relationship regarding state saturation bounds, actuator fault limits and control parameters are analyzed in this paper. Finally, simulation results are given to illustrate the proposed procedures and their effectiveness.
Automatic image registration method is important because data amount acquired and processed is increasingly heavy. For SAR and optical sensor images, it is important to align the same object shown in different images ...
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ISBN:
(纸本)9781510818934
Automatic image registration method is important because data amount acquired and processed is increasingly heavy. For SAR and optical sensor images, it is important to align the same object shown in different images from multi sensors. Considering extracting and matching features in SAR and optical images, a template registration strategy is proposed in this paper. A particular similarity measurement which is robust in matching remote sensing images of general sceneries is hardly to construct. Being the invertible and essential tendency nowadays in the field of earth observation, high pixel-resolution images can offer much more information of the ground. Thus subpixel image registration methods have been researched, among which can successfully achieve 1.50 pixel level accuracy in matching homologous sensor image. This paper firstly investigated the performance of 2D Gaussian fitting for low correlated synthetic images and real satellite images acquired by Radat SAT-2, COSMO and TerraSAR-X satellites. Three algorithms for subpixel locating have been utilized with consideration of the low correlated coefficient peak and corrupted shape of neighbourhood around it. The results show 2D Gaussian fitting is the most efficient on noisy coefficient maps. Then a subpixel image registration method for SAR and optical images is proposed. Normalized gradient correlation (NGC) algorithm is used to solve the problem of different intensity property at first, and by combining 2D Gaussian fitting to relocate matched points at a subpixel level accuracy. Experiment results have shown that the proposed method has enough capabilities in handling differences between images from multi-sensors. The proposed approach has been found to be simple and efficient in implementation with a low computation load. And the most important performance is the results presented here have achieved matching accuracy within 1.pixel.
Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the...
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Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.
A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing...
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A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing the arrival times of static target echoes. To estimate the Doppler frequencies of moving targets, we divide the radar data into a large number of seg- ments, and reformat these segments into a detection matrix. Applying the cepstrum and the Fourier transform to the fast and slow time dimensions respectively, we can obtain the range {1. and Doppler {1. of the moving targets. Based on the CEPMTD outlined above, an improved CEPMTD algorithm is proposed to improve the detection performance. Theoretical analyses show that only the target＇s peak can be coherently added. The performance of the improved CEPMTD is initially vali- dated by simulations, and then by experiments. The simulation results show that the detection performance of the improved CEPMTD algorithm is 1..3 dB better than that of the CEPMTD algorithm and 6.4 dB better than that of the classical detection algorithm based on the radar cross ambiguity function （CAF）. The experiment results show that the detection performance of the improved CEPMTD algorithm is 1.63 dB better than that of the radar CAF.
This paper presents a new Synthetic Aperture Radar（SAR） Automatic Target Recognition（ATR） method based on slow feature analysis. Slow feature analysis（SFA） is a method for learning invariant or slowly varying fe...
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This paper presents a new Synthetic Aperture Radar（SAR） Automatic Target Recognition（ATR） method based on slow feature analysis. Slow feature analysis（SFA） is a method for learning invariant or slowly varying features from multi-dimensional input signal. The SFA-based SAR ATR system does not require any pre-{1., such as filtering or pose estimation of the image. The performance of the method is evaluated via three classification experiments on Moving and Stationary Target Acquisition and Recognition（MSTAR） database. The experiment results show the effectiveness of the proposed method on SAR ATR problem.
The Dynamic Adaptive Streaming over HTTP (DASH) enables bitrate adaptation through different representations of the same content. It is common to encode random access point (RAP) pictures at segment boundaries to supp...
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ISBN:
(纸本)9781479983407
The Dynamic Adaptive Streaming over HTTP (DASH) enables bitrate adaptation through different representations of the same content. It is common to encode random access point (RAP) pictures at segment boundaries to support representation switching. As an open group of pictures (GOP) results into a temporary discontinuity of the video playback due to the inability to decode some pictures when switching representations, closed GOP prediction structures are normally used in DASH. This paper proposes two similar methods for using the open GOP prediction structure in DASH representations while a full picture rate is maintained also during representation switching. The first method is enabled with straightforward changes in the decoding of the High Efficiency Video Coding (H.265/HEVC) standard, whereas the second method utilizes the adaptive resolution change feature of the scalable (SHVC) extension of H.265/HEVC. Experiments show that the proposed methods outperform the use of closed GOPs by 5.6% on average in terms of Bjontegaard delta bitrate (BD-rate).
With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image reta...
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ISBN:
(纸本)9781479989591
With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image retargeting techniques have been proposed. However, little effective objective quality assessment metric for image retargeting has been proposed. In this paper, we propose an objective image retargeting quality assessment method based on Hybrid Distortion Pooled Model (HDPM) considering image local similarity, content information loss and image structural distortion. The proposed HDPM method measures the retargeted image's local similarity based on matching the similar block by Scale-Invariant Features Transform (SIFT) features and computing the corresponding blocks' similarity by structural similarity (SSIM). Furthermore, the image content information loss in retargeted image, which is regarded as the SIFT feature loss, is taken into account. Besides, we also consider image's structural distortion in the proposed method, which is based on GLCM (Gray-level co-occurrence matrix). To evaluate the effectiveness of the proposed method, extensive experiments have been conducted, and the results show improved consistency between the proposed HDPM method and the corresponding subjective evaluations.
In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing appr...
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ISBN:
(纸本)9781479986897
In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing approach to image coding in the cloud, as it removes knowledge redundancy among images that share the same model. In this paper, we make an attempt to model-based image coding for landmark images, where our model consists of three-dimensional (3-D) point-cloud plus image patches to describe the geometry and surface color of the landmark respectively. The camera parameters of an input image are estimated based on the 3-D point-cloud and the patches in the model, and then prediction image is generated by selecting, warping, and stitching image patches as well as illuminance compensation, the residue between original and prediction images is compressed by P-frame coding in HEVC encoder. We perform experiments on an Internet photo collection to verify the effectiveness of the proposed scheme. Preliminary results display the superior performance of our scheme that achieves as high as 39.9% bits saving compared to HEVC intra on a single image. The proposed scheme indicates a promising approach to image coding in the cloud and is worthy of in-depth investigation.
In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its {1. identical ...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its {1. identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.
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