Design and simulations of a new biaxial silicon resonant micro-accelerometer

This paper presents a new biaxial silicon resonant micro-accelerometer. The device basically consists of a single proof mass, four pairs of decoupled beams, four lever mechanisms and two pairs of resonators, which provides 2-D in-plane acceleration measurement with the decoupled two pairs of resonators. Structure optimization is implemented by taking advantage of the finite element analyses. From the simulation results we can see that the effective frequencies of two acceleration sensitive modes are 1010.18 and 1010.13 Hz respectively, and the undesired modes and effective modes are isolated apparently. Additionally, high linear relationship between the input acceleration and the resonant frequency shifts of resonators are demonstrated by the input–output characteristic simulation. Moreover, simulation results reveal the scale factor for the x-axis is 180.03 Hz/g, and the scale factor for y-axis is 180.75 Hz/g, while the cross-axis sensitivity for x-axis is 0.046 Hz/g, and the cross-axis sensitivity for y-axis is 0.027 Hz/g. The high sensitivity and low cross-axis sensitivity are thus adequately confirmed. By the way, the simulation of temperature dependent characteristics demonstrate that the differential scheme can effectively suppress the influence of temperature variation, and the thermal analysis shows that the device can bear the thermal stress induced by temperature change. All these simulations above can verify the feasibility of the structure design.     

Crystal dislocation in SEM with image processing

Image processing algorithm is implemented to detect the grain boundary of the crystal using (SEM) Scanning Electron Microscopy. This paper presents a method for edge-detection in color image based on Sobel, Canny operator’s algorithm and discrete wavelet transform. The performance of these methods is effective and faster. Filtering is another approach to clear the noise of an image. Scanning Electron Microscopy (SEM) used to inspect semiconductor materials and devices for several decades, continues to increase in importance. Removal of noise is an important step in the image restoration process, but de-noising of the image has remained a challenging problem in recent research associated with image process. De-noising is used to remove the noise from corrupted images, while retaining the edges and other detailed features too are an essential part of de-noising.     

Online learning of dynamic multi-view gallery for person Re-identification

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.     

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.