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Real-time Compressive Tracking (CT) uses the compression sensing theory to provide a new research direction for the target tracking field. The algorithm is simple, efficient and real-time. But there are still shortcomings: tracking results prone to drift phenomenon, cannot adapt to tracking the target scale changes. In order to solve these problems, this paper proposes to use the Kalman filter to...
Clustering has widely been applied in various domains such as artificial intelligence and big data processing. The main task of clustering is to partition a data set into different classes or clusters according to a certain standard (such as distance criterion), which makes the similarity of the data objects in the same cluster as large as possible, while the difference between different clusters...
The new video coding standard, HEVC, achieves better compression performance relative to H.264 at the expense of a significant increase in computational complexity. It adopts coding tree structure, and implements partition and mode decision for each coding unit (CU) and prediction unit (PU). For intra coding, five levels of PUs from 64×64 to 4×4 should be searched and each PU selects one best mode...
Association rules have been used in data mining applications to capture relationships present among attributes in large data sets. It can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. In order to mine the frequency patterns of texture, each image can be considered as one transaction. If image...
In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, the energy consumption is a major concern due to the large scale of such kind of systems. There are a few existing efforts that try to lower the energy consumption of GPU-CPU architectures, but they address...
Human action recognition in videos draws strong research interest in computer vision because of its promising applications for video surveillance, video annotation, interactive gaming, etc. However, the amount of video data containing human actions is increasing exponentially, which makes the management of these resources a challenging task. Given a database with huge volumes of unlabeled videos,...
Traditional three-dimensional variational (3DVAR) schemes are inefficient in capturing the multi-scale information resolved by observations. This study first indicates that the root cause lies in the spatial incoherence of the gradient of cost function caused by the irregular distribution of observations. Then an improved 3DVAR scheme based on diffusion filter is proposed to solve this problem. By...
Automatic modulation recognition is extremely important for the receiver design in the broadband multimedia communication system. Based on the intrinsic time-scale decomposition algorithm for feature extraction on which the instantaneous amplitude, phase and frequency is precisely defined, this paper proposed a feature selection algorithm to find the features with high distinguishing capacity and...
An algorithm which can detect weak signals on the condition of strong correlated interference is designed in the paper. The algorithm based on conventional beam forming (CBF) can put nulls on given directions. And the weights only relating to interference's direction is given in the paper. Comparing to adaptive methods, the algorithm is robust and the calculation account is smaller. Simulation results...
Composite service selection is a research difficulties in service-oriented computing domain. Most selection approaches are based on service QoS information which is difficult to acquire accurately. In this paper, a distributed Web service usage pattern mining infrastructure based on services execution information is proposed. In order to meet the cross-organizational associated business requirement,...
For the purpose of enhance the security of poor working in coal mine, it's important to study on the problem of location and tracking for keeping mining personnel and down hole equipments safe. We put forward wireless sensor network underground location algorithm which is based on the superposition of RSSI and DV-Hop. This algorithm makes up for the shortage of RSSI algorithm and DV-Hop algorithm...
In order to enhance the security of poor working underground environment, it's important to study on the problem of location and tracking for keeping mining personnel and down hole equipments safe. We put forward wireless sensor network underground location algorithm which is based on the superposition of RSSI and DV-Hop. This algorithm makes up for the shortage of RSSI algorithm and DV-Hop algorithm...
The problem of network intrusion detection is an active research issue. Based on the techniques of sequence data mining, we propose a completely new approach based on intrinsic subsequence to detect intrusions in the network connection data. An intrinsic subsequence means that all items in it are always present together as a whole in the sequence. The total number of an intrinsic subsequence appeared...
Enlightened by the analysis method of modern anthropology for sociogroup, the ethnic group evolution algorithm (EGEA)*, whose population is formed of several ethnic groups, is proposed. The ethnic group is a kind of clustering form for individuals, and represents the possible evolution trends, which are implicit in population. In EGEA, ethnic groups are used as the competition and generation unit...
The research into intrusion detection system (IDS) inspired by immunology has been moderately successful, but was still tarnished by high false positive and poor adaptation which were caused by the lack of the environmental awareness. A novel hypothesis in immunology, the danger theory (DT), is emerging to meet these challenges. DT highlights the importance of the tissue and states that antigen presentation...
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