Secure and Efficient Proxy Signature Scheme in the Standard Model

Existing proxy signature schemes are not proved to have complete security in the provable security model for proxy signature＇s whether the schemes are secure needs to be further studied. We show a generalized prov- able security model for proxy signature. Comparing with Boldyreva＇s security model, we introduce Schuldt＇s work to our security model. We propose a secure proxy signa- ture scheme, which is based on Waters＇ signature scheme in the standard model. Comparing with other proxy signa- ture schemes having a reduction to CDH assumption in the standard model, our scheme is more secure and efficient.     

A Power Control Scheme for Target Localization in WSN

In this paper, we focus on the target tracking problem in sensor networks and propose an Power-saving target localization scheme （PSTL） based on a conjec- ture model that reflects the moving pattern ofa target, and also a corresponding two-step communication protocol be- tween Base station （BS） and sensors. BS executes a query mechanism to determine which sensors should be used for detailed information according to a limited amount of data received from sensors. This scheme reduces both energy consumption and communication bandwidth requirement, prolongs the lifetime of the wireless sensor networks. Simulation results indicate that it can achieve a high accuracy while saving a large amount of energy.     

Action-Based Access Control Modelt

Access control is one of the powerful and generalized approaches for restricted resource access. The environmental state is introduced and the term “action” is defined based on roles, temporal states and environmental states. Actions can be used to capture security-relevant aspects of roles, environmental and temporal states in different information systems. Then, the action hierarchy, environmental hierarchy, temporal hierarchy and Action- based access control （ABAC） model are presented. ABAC is compared with the existing models and the result shows that the ABAC model can solve the problem of access control in information systems with mobile computation for its convenient and flexible designs. An application example of ABAC model is described at the end of the paper.     

A Part-of-speech Tagging Model Employing Word Clustering and Syntactic Parsing

Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classi- cal HMM, Markov family model （MFM）, a new statisti- cal model was introduced. Secondly, to solve the prob- lem of data sparseness, we propose a bottom-to-up hierar- chical word clustering algorithm. Then we combine syn- tactic parsing with part-of-speech tagging. The Part-of- Speech tagging experiments show that the improved Part- Of-Speech tagging model has higher performance than Hidden Markov models （HMMs） under the same test- ing conditions, the precision is enhanced from 94.642% to 97.235%.     

Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model

On the basis of the simple ice-sheet freeze- thaw physical model, a new algorithm of Antarctic ice- sheet freeze-thaw detection was proposed for the automatic threshold segmentation, which did not depend on the priori freeze-thaw distribution. That was the histogram statistics for the data of ice-sheet freeze-thaw physical model by the use of generalized Gaussian model to automatically get the optimal threshold of the dry snow and the wet snow, so as to get the Antarctic freeze-thaw areas. The algorithm improves the computational efficiency, usability and oper- ability of the ice-sheet freeze-thaw detection because the algorithm does not rely on the actual melt information and can automatically select many samples. To some extent, the algorithm also improves the accuracy of the ice-sheet freeze-thaw detection.     

PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection

The Pulse coupled neural network （PCNN） has been widely used in digital image processing, but the automatic parameters determination is still a difficult as- pect, which becomes the focus of PENN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neu- rons, and reveal the “mathematics firing” phenomenon of PCNN. Based on this, we propose a new method of auto- matic parameters determination based on both eliminating the “mathematics firing” and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Ex- perimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge de- tection of the test images.     

Virtual Network Mapping Through Locality-aware Topological Potential and Influence Node Ranking

Efficiently mapping multiple independent Virtual networks （VNs） over a common infrastructure sub- strate is a challenging problem on cloud computing plat- forms and large-scale future Internet testbeds. Inspired by the idea of data fields, we apply a topological poten- tial function to node ranking and propose an algorithm called Locality-aware node topological potential ranking （LNTPR）, which can precisely and efficiently reflect the relative importance of nodes. Using LNTPR and the con- cept of locality awareness, we develop the Locality-aware influence choosing node （LICN） algorithm based on a node influence model that considers the mutual influence be- tween a mapped node and its candidate mapping nodes. LNTPR and LICN improve the integration of node and link mapping. Simulation results demonstrate that the proposed algorithms exhibit good performance in deter- mining revenue, acceptance ratio, and revenue/cost ratio.     

A Novel Refined Track Initiation Algorithm for Group Targets Based on Group Model

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that, compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.     

Performance Analysis of Capture Effect in High Speed Wireless Networks

The influence of capture effect on the high speed wireless networks is investigated in this paper. A new garkov chain model considering capture effect for the binary exponential back-off scheme in the MAC layer has been proposed. A new throughput model used in the IEEE 802.11 networks is proposed Based on this new Markov chain model, and then we analyzed throughput impacted by the capture effect under different transmission speeds. The performance analysis shows the capture effect has more impact on the throughput of high speed wireless networks than that on the low speed wireless networks.     

An Improved Cochlear Implant Strategy Incorporating Frequency Modulation Information

Some currently popular strategies in Cochlear implants （CIs） fail to encode the temporal fine structure cues, which are crucial for speech perception in noise or melody appreciation of CI patients. We propose an improved strategy based on the CIS （Continuous interleaved sampling） model by introducing partial lowfrequency temporal fine structure cues or Frequency modulation （FM） information into the slowly varying temporal envelops. A psychoacoustic experiment was conducted to validate the improved strategy by measuring the Mandarin vowel, tone, consonant and sentence recognitions on normal hearing listeners. Experimental data show that the introduction of frequency modulation information can improve the CI performance greatly, especially for vowel and tone perception. Firstly even at the most severe noise condition the vowel perception can get nearly half intelligibility, and the tone recognition scores increased over 20% at various noise conditions. Secondly under moderate noise conditions or in quiet the fine structure cues also contribute significantly to the consonant and sentence recognitions. Finally, the proposed strategy has its own application values because it does not introduce too many high-frequency components into the model, which can not be perceived by deaf patients. .     

A Novel Coupled Bi-directional Diffused Image Inpainting Model with Morphological Invariant Property

In this paper, a novel coupled inpainting model which bi-directionally diffuses image information is proposed. It is morphological invariant which restores the target region based on geometric property. The image information is diffused along both the direction normal to edges and along isophotes. Total variation （TV） model is used to diffuse along the direction normal to edges which reconnects the broken lines in the target region directly. The along isophotes diffused part is the inviscid Helmholtz vorticity equation in fluid dynamics. The Helmholtz equation diffuses a smooth measure of image along isophotes, and it is morphological invariant. The novel model is comprised by 2nd order Partial differential equations （PDEs）, so the numerical scheme of it is simple and the processing time is limited. Experimental results demonstrate that the novel model smoothly restores the target region by diffusing along two orthogonal directions and preserves the linear structure which leads to its better performance than conventional inpainting models.     

Design and Implementation of a Driver＇s Eye State Recognition Algorithm Based on PERCLOS

In order to improve the general detection accuracy of eye state, this paper puts forward an innovative method for judging human eye state based on PERCLOS. After pretreatment of the eye image, Hough transformation is used for ellipse detection and pupil position. The gray projection variance threshold analysis is then used to help make the final detection. Freeman chain and the Snake model algorithm are used for the corner detection and pre- cise calculation of the height of an open eye. Thus the PER- CLOS value and the eye state can be figured out. The performance of our eye state recognition algorithm is validated by more than 1000 images within product database. The statistics result shows that the fatigue detection accuracy rate can meet the need of usage in complex environment.     

A Novel Method for Analyzing the Noise Characteristic of Solar Cells with En-In Model

In this paper, a novel method for analyzing the noise characteristic of solar cells with En-In model was studied. The En-In noise model in two-port network was introduced to study the low-frequency noise characteristic of solar cells. According to the relationship between the output noise power spectrum and the two noise parameters in En-In noise model of the solar cell, known as En and In, an accurate method for extracting the two noise parame- ters was proposed. At the same time, the measurement method for the both parameters from 1Hz to 10kHz was studied. After 1/f noise curve fitting and characteristic frequency of G-R noise extraction on the noise spectrums of a large amount of solar ceils, the analyzing results of spectrum compositions proved the validity and significance of the En-In noise model for noise analysis of solar cells. It also provided the essential theoretical and experimental basis for the further research on noise characteristic and reliability estimation of solar cells and PV modules.     

Unified Maneuvering Model for Target Tracking by Using Range-rate Measurement

A novel Unified maneuvering model （UMM） for tracking the target performed the complex curvilinear motion in two dimensions is presented, where the model is constructed by time-varying parameters and driven by the along-track and cross-track acceleration in- put, and the range rate plays a key role to update vari- able parameters online. In Cartesian coordinates, by incorporating the cross-track acceleration component which is estimated online by using the range-rate, and the along- track acceleration component which is determined under the assumption of zero-mean first order Markovian process, the proposed UMM exhibits highly self-adjustment capability to compensate the mismatch between the actual motion and the mathematic model adaptively, espe-cially, shows a well capability to approximate the standard Interacting multiple model （IMM） under the situation of complex curvilinear motion and a higher level of the measurement noise. Simulation results validate our theory and show that UMM-based filtering is superior to that using the Current statistic model （CSM）, and has a well approx- imation to IMM, meanwhile, has a low computational load substantially.     

New Latency Model for Dynamic Frequency Scaling on Network-on-Chip

Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.     

Short Texts Classification Through Reference Document Expansion

With the rapid development of information technology, short texts arising from socialized human inter- action are gradually predominant in network information streams. Accelerating demands are requiring the industry to provide more effective classification of the brief texts. However, faced with short text documents, each of which contains only a few words, traditional document classifi- cation models run into difficulty. Aggressive documents expansion works remarkably well for many cases but suf- fers from the assumption of independent, identically dis- tributed observations. We formalize a view of classification using Bayesian decision theory, treat each short text as ob- servations from a probabilistic model, called a statistical language model, and encode classification preferences with a loss function defined by the language models and the ex- ternal reference document. According to Vapnik＇s meth- ods of Structural risk minimization （SRM）, the optimal classification action is the one that minimizes the struc- tural risk, which provides a result that allows one to trade off errors on the training sample against improved gener- alization performance. We conduct experiments by using several corpora of microblog-like data, and analyze the ex- perimental results. With respect to established baselines, results of these experiments show that applying our pro- posed document expansion method produces better chance to achieve the improved classification performance.     

＂Smart Cafe＂： A Mobile Local Computing System Based On Indoor Virtual Cloud

With network developing and virtualization rising, more and more indoor environment （POIs） such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components： 1）As to application, we create a parser to generate the ＂method call and cost tree＂ and analyze it to identify resource- intensive methods. 2） As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3） As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.     

An Improved Gate Charge Model of HEMTs by Direct Formulating the Branch Charges

We present an improved gate charge model for High electron mobility transistors （HEMTs） that is direct formulated from the branch charges. Different charge modeling procedures based on the charge conservation principle have been discussed and the proposed model is more accurate, easier to be implemented into commercial simulators. An improved modeling method on channel length modulation parameter to account for the drain-current kink effect of HEMTs has also been introduced. The nonlinear model is verified by comparing the measurements and simulations using 0.25μm gate-length GaAs HEMT devices.