New robust stability analysis for genetic regulatory networks with random discrete delays and distributed delays

This paper investigates the delay-probability-distribution-dependent stability problem of stochastic genetic regulatory networks (GRNs) with random discrete time delays and distributed time delays which exist in both translation process and feedback regulation process. The information of the probability distribution of the discrete time delays is considered and transformed into parameter matrices of the GRN models. By introducing a new Lyapunov functional which takes into account the ranges of delays and employing some free-weighting matrices, some new delay-probability-distribution-dependent stability criteria are established in the form of linear matrix inequalities (LMIs) to guarantee the GRNs to be asymptotically stable in the mean square. In addition, when estimating the upper bounds of the derivative of Lyapunov functionals, we carefully handle the additional useful terms about the distributed delays, which may lead to the less conservative results. The new criteria are applicable to both slow and fast time delays. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results and less conservativeness of the proposed method.     

myStone: A system for automatic kidney stone classification

Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.     

Translating relational schema into XML schema definition with data semantic preservation and XSD graph

Many legacy systems have been created by using relational database operating not for the Internet expression. Since the relational database is not an efficient way for data explosion, electronic transfer of data, and electronic business on the Web, we introduce a methodology in which a relational schema will be translated to an Extensible Markup Language (XML) schema definition for creating an XML database that is a simple and efficient format on the Web. We apply the Indirect Schema Translation Method that is a semantic-based methodology in this project. The mechanism is that the Relational Schema will be translated into the conceptual model, an Extended Entity Relationship (EER) Model using Reverse Engineering. Afterward, the EER model will be mapped to an XML Schema Definition Language (XSD) Graph as an XML conceptual schema using Semantic Transformation. Finally, the XSD Graph will be mapped into the XSD as an XML logical schema in the process of Forward Engineering, and the data semantics of participation, cardinality, generalization, aggregation, categorization, N-ary and U-ary relationship are preserved in the translated XML schema definition.     

Dual-demodulation large-scope high-sensitivity refractive index sensor based on twin-core PCF

In this paper, a refractive index (RI) sensor based on the twin-core photonic crystal fiber (TC-PCF) is presented. Introducing the rectangular array in the core area makes the PCF possible to obtain high birefringence and low confinement loss over the wavelength range from 0.6 μm to 1.7 μm. Therefore, the core region can enhance the interaction between the core mode and the filling material. We studied theoretically the evolution characteristics of the birefringence and operating wavelength corresponding to the strongest polarization point under the condition of filling the rectangular array with RI matching fluid range from 1.33 to 1.41. Simulation results reveal that the proposed TC-PCF has opposite evolutions of change rates between the B and wavelength, and the maximum RI sensing sensitivities of 1.809×10-2 B/RIU and 8 700 nm/RIU at low and high RI infill are obtained respectively, which means that the TC-PCF features of dual-parameter demodulation for the RI sensing can maintain a high refractive index sensing sensitivity within a large scope of RI ranging from 1.33 to 1.41. Compared with the results of single-parameter demodulation, it is an optimized method to improve the sensitivity of low refractive index sensors, which has great application potency in the field of biochemical sensing and detection.     

Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

A refractive index (RI) sensor based on elliptical core photonic crystal fiber (EC-PCF) has been proposed. The asymmetric elliptical core introduces the polarization-dependent characteristics of the fiber core modes. The performances of intermodal interference between the intrinsic polarization fiber core modes are investigated by contrast in two interferometers based on the Mach-Zehnder (M-Z) and Sagnac interference model. In addition, the RI sensing characteristics of the two interferometers are studied by successively filling the three layers air holes closest to the elliptical core in the cladding. The results show that the M-Z interference between LP01 and LP11 mode in the same polarized direction is featured with the incremental RI sensing sensitivity as the decreasing interference length, and the infilled scope around the elliptical core has a weak correlation with the RI sensing sensitivity. Due to the high birefringence of LP11 mode, the Sagnac interferometer has better RI sensing performance, the maximum RI sensing sensitivity of 12 000 nm/RIU is achieved under the innermost one layer air holes infilled with RI matching liquid of RI=1.39 at the pre-setting EC-PCF length of 12 cm, which is two orders of magnitude higher than the M-Z interferometer with the same fiber length. The series of theoretical optimized analysis would provide guidance for the applications in the field of biochemical sensing.     

3D holoscopic image coding scheme using HEVC with Gaussian process regression

3D holoscopic image, also known as integral imaging, light field imaging and plenoptic imaging, can provide a natural and fatigue-free 3D visualization. However, a large amount of data is required to represent the 3D holoscopic content. Therefore, efficient coding schemes for such particular type of image are needed. In this paper, we propose a Gaussian process regression based prediction scheme to compress the 3D holoscopic image. In the proposed scheme, the coding block and its prediction supports are modeled as a Gaussian process (GP) and Gaussian process regression (GPR) is used to obtain a better prediction of the coding block. Limited searching windows in horizontal and vertical directions are used to obtain the prediction supports, and a filtration method is designed to judge the reliability of the obtained prediction supports. Moreover, in order to alleviate the high complexity caused by GPR, a sparsification method is also put forward. Experimental results demonstrate the advantage of the proposed scheme for 3D holoscopic image coding in terms of different quality metrics as well as the visual quality of the views rendered from decompressed 3D holoscopic content, compared to the HEVC intra-prediction method and several other prediction methods in this field.     

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

It is novel to apply three-dimensional(3D) light field imaging technology to recognize two-dimensional(2D) fake pedestrians. In this paper, we propose a parallel support vector machine(SVM) method based on 3D light field imaging(light field camera) and machine learning techniques. A light field(LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient(HOG) feature extraction algorithm and SVM classification method a...     

Critical mass and willingness to pay for social networks

Disagreement surrounds a formal definition of ‘critical mass’ and of the economic willingness to pay for membership in a social network. Our paper adapts work from percolation theory to analyze the structure of social networks, and draws an analogy for critical mass in social networks to the concept of phase changes in materials. We show how network growth can be actively managed, and define how to manage the willingness to pay for membership. We show, if achieving a critical mass of members in a social network is our objective, that prior to achieving critical mass, (1) the probability of accepting an invitation must vary inversely with individuals’ breadth of contacts; and (2) the number of special interest groups of any size will decrease following a power law until immediately below critical mass. Targeted invitations enabled through sophisticated programs such as AdWords and IndexTools can help to actively maximize the probability of forming an acquaintance link. Our model defines a willingness to pay for network membership that is nearly zero below critical mass, and is an involved function above critical mass whose shape appears to be close to a logarithmic function. Our robust measure of the connectedness of members of a particular social network yields values that are consistent with the independently developed metrics of Odlyzko and Tilly [Odlyzko, A., and Tilly, B. A refutation of Metcalfe’s Law and a better estimate for the value of networks and network interconnections, 2005 (downloaded from http://www.dtc.umn.edu/~odlyzko July 3, 2008)], and differ from eponymous ‘laws’ of Sarnoff, Metcalfe and Reed. There also appears to be plausible evidence in support of the market actually pricing networks at values close to Odlyzko and Tilly’s estimates.     

Automatic diagnosis of mobile communication networks under imprecise parameters

In the last years, self-organization of cellular networks is becoming a crucial aspect of network management due to the increasing complexity of the networks. Automatic fault identification, i.e. diagnosis, is the most difficult task in self-healing. In this paper, a model based on discrete bayesian networks (BNs) is proposed for diagnosis of radio access networks of cellular systems. Normally, inaccuracies are unavoidable in the parameters of the model (interval limits for discretized symptoms and probabilities in the BN). In order to enhance the performance of BNs, a methodology to model the “continuity” in the human reasoning is presented, named smooth bayesian networks (SBNs). SBNs are intended to decrease the sensitivity of diagnosis accuracy to imprecision in the definition of the model parameters. An empirical research campaign has been carried out in a live GSM/GPRS network in order to assess the performance of the proposed techniques. Results have shown that SBNs outperform traditional BNs when there is inaccuracy in the model parameters.