In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approache...
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In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.
With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes ...
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With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes developed for these videos mostly encode the background as the long-term reference (LTR) to further improve the coding efficiency. However, since the bit allocation of the long-term background reference is not intensively studied, the coding efficiency is still unsatisfactory. Based on the stability analysis of the video content, an efficient background picture coding algorithm for videos obtained from static cameras, which is embedded with the basic unit level bit allocation, is proposed in this paper. Experimental results reveal that on top of the default mode in HEVC, our method offers the performance with 10.8% BD-rate reduction on average. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences with negligible increases of computational complexity in both encoder and decoder.
This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynami...
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This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM). However, Kleinberg's model is originally proposed for the burst of keywords, the frequency counts it models are not proper to describe the burst states of topics, leading to some unwanted results. A more reasonable way is to model the influence burst states put on each document's topic distribution. Considering this, we propose a unified statistical model that takes the burst states as markov latent variables that influence the topic allocation of documents. We derive a Gibbs sampling algorithm for the proposal. Experiment results confirm our model's advantages both qualitatively and quantitatively.
A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy. The variational model considers local tra...
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Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing fea...
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In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the predic...
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In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the prediction accuracy more robust to the topic number K;(2), improving the recommendation quality for users with few existed ratings. We achieve our goals by employing the Logistic Normal distribution to capture the correlation between latent topics following the Correlated Topic Model, as well as modifying the generative process to meet the requirement of rating-based collaborative filtering. We derive a parameter estimation algorithm based on variational inference for the proposal. Experiment results on the Movielens data set demonstrate our model's advantages on both referred aspects.
When browsing through photographs taken during a trip, it can be a distressing discovery to find many other bystanders captured within the frame. A visually compelling snapshot preserves the desired subject in the for...
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The High Efficiency Video Coding (HEVC) with the transform bypass mode is simple but inefficient for lossless coding. For this reason, we propose a novel transform to further eliminate the redundancy between residues ...
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Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur re...
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ISBN:
(纸本)9781479947249
Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur request staleness issue, the loss of Interest and Data packets and communication delay. Through analysis, we consider how to forward the buffered data from old access point(AP) to new one and how to keep routing consistency during handoff stage are two key points to address mobility issue in NDN. To minimize the loss of Interest and Data,handoff delay during moving, we design a mobility support architecture(MobiNDN), which is a centralized system architecture and consists of initialization and three stages:registration,deletion and updating. We design three mobility scenarios and schemes and evaluate the performance of MobiNDN by comparing it against exiting NDN using extensive ndnSIM simulation. Our simulation results clearly show that MobiNDN architecture effectively decreases handoff delay and receives data packets during/after handoffs comparing with the exiting NDN.
Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty...
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Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.
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