Conditional random field with the multi-granular contextual information for pixel labeling

To make full use of the contextual information object recognition and scene understanding, a multi-granular context conditional random field (MGCCRF) model is presented to combine context information in a variety of scales. It is efficiently implemented through extending the pairwise clique to the multi-granular context windows. In the fine-granular context window, the label consistency of similar features can be obtained with the probability of the label transferring between two adjacent pixels. At the same time, the spatial relationships among different classes in the coarse-granular context window are explicated in details. To train the MGCCRF model, a piecewise training method with the bound optimization algorithm is designed to improve the performance. Experiments on two real-world image databases show that compared with other methods, the modified conditional random field model is more competitive and effective in terms of the quantitative and qualitative labeling performance.     

Visual topic discovering,tracking and summarization from social media streams

Multimedia Tools and Applications - Nowadays, microblogging has become popular, with hundreds of millions of short messages being posted and shared every minute on a variety of topics in social...     

Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform

Multimedia Tools and Applications - Most of the existing image encryption algorithms based on compressive sensing are too complex to operate. An image compression-encryption scheme with simple...     

A weight-based item recommendation approach for electronic commerce systems

The recommendation system is a useful tool that can be employed to identify potential relationships between items and users in electronic commerce systems. Consequently, it can remarkably improve the efficiency of a business. The topic of how to enhance the accuracy of a recommendation has attracted much attention by researchers over the past decade. As such, many methods to accomplish this task have been introduced. However, more complex calculations are normally necessary to achieve a higher accuracy, which is not suitable for a real-time system. Hence, in this paper, we propose a weight-based item recommendation approach to provide a balanced formula between the recommended accuracy and the computational complexity. The proposed methods employ a newly defined distance to describe the relationship between the users and the items, after which the recommendations and predictive algorithms are developed. A data analysis based on the MovieLens datasets indicates that the methods applied can obtain suitable prediction accuracy and maintain a relatively low computational complexity.     

A novel Minkowski-distance-based consensus clustering algorithm

Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. Finally, this consensus clustering algorithm is applied to a froth flotation process.     

Robust Stochastic Stability and Control for Uncertain Singular Markovian Jump Systems with Multiplicative Noise

This paper focuses on the problems of robust stability and stabilization and robust control for uncertain singular Markovian jump systems with (x,v)‐dependent noise. The parameter uncertainties appearing in state, input, disturbance as well as diffusion terms are assumed to be time‐varying but norm‐bounded. Based on the approach of generalized quadratic stability, the memoryless state feedback controller is designed for the robust stabilization problem, which ensures that the resulting closed‐loop system has an impulse‐free solution and is asymptotically stable in the mean square. Furthermore, the results of robust control problem are derived. The desired state feedback controller is presented, which not only meets the requirement of robust stabilization but also satisfies a prescribed performance level. The obtained results are formulated in terms of strict LMIs. What we have obtained can be viewed as corresponding extensions of existing results on uncertain singular systems. A numerical example is finally given to demonstrate the application of the proposed method.     

Online evolution reconstruction from a single measurement record with random time intervals for quantum communication

Online reconstruction of a time-variant quantum state from the encoding/decoding results of quantum communication is addressed by developing a method of evolution reconstruction from a single measurement record with random time intervals. A time-variant two-dimensional state is reconstructed on the basis of recovering its expectation value functions of three nonorthogonal projectors from a random single measurement record, which is composed from the discarded qubits of the six-state protocol. The simulated results prove that our method is robust to typical metro quantum channels. Our work extends the Fourier-based method of evolution reconstruction from the version for a regular single measurement record with equal time intervals to a unified one, which can be applied to arbitrary single measurement records. The proposed protocol of evolution reconstruction runs concurrently with the one of quantum communication, which can facilitate the online quantum tomography.     

Bifurcation control in a delayed two-neuron fractional network

The issue of bifurcation control for a delayed fractional network involving two neurons is concerned. Delay-dependent stability conditions and the bifurcation point are established by discussing the associated characteristic equation of the proposed network. Then, a delayed feedback controller is firstly designed to stabilize the Hopf bifurcation, and desirable dynamics is achieved. It is indicated that the designed controller is extremely effective which can postpone the onset of bifurcation by carefully selecting the feedback gain. Finally, simulation results are given to verify the efficiency of the theoretical results.     

Temporal and Spatial Distribution of Ulva.prolifera in the Yellow Sea and Influence of Typhoon “CHAN-HOM” in 2015

A recurrent floating green algae bloom was detected in the Yellow Sea since 2007.The Ulva.prolifera is non\|toxic,but the massive accumulations can result in significant environmental damage and cause economic loss to marine industries.In this study,the spatial and temporal patterns of Ulva.prolifera green tides were investigated in the Yellow Sea during 2015 using HJ\|1A/1B and MODIS satellite images by means of NDVI (normalized difference vegetation index)and artificial interpretation.The results showed：(1)A little Ulva.prolifera was discovered firstly in adjacent sea of Yancheng,Jiangsu province in early May with distribution area 0.831 km².Under the action of the southeast monsoon,Ulva.prolifera was gradually drifted to Shandong peninsula waters from south to north.The influential area and range reached a peak value with 1 752.756 km² in late June,and gradually subsided from July to August.And Ulva.prolifera about 38.791 km² was monitored in the South Bay of North Korea.In conclusion,Ulva.prolifera in the Yellow Sea in 2015 has experienced five major processes including “Occur\|Development\|Outbreak\|Recession\|Disappeared”.(2)Typhoon "CHAN\|HOM" certainly influenced the northward pathway of Ulva.prolifera and shifted towards the southwest,resulting in most of Ulva.prolifera moved to the east coast of Lianyungang,and speculated that minority Ulva.prolifera drifted to the South Bay of North Korea.(3)From the monitoring data,the spatial resolution between MODIS and ENVISAT (HJ\|1A / 1B)is difference significantly,250 m and 30 m respectively.A functional relation of the two data with monitoring area difference about 2.26 times was established to make up for the shortage of the environmental satellite (HJ\|1A/1B)images. 