Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.     

Design,fabrication and characterization of miniature RF MEMS switched capacitor based phase shifter

This paper aims to present in detail the design, fabrication and the various characterization techniques adopted in realizing a novel miniature-size switched capacitor based phase shifter. The work strives to provide an all-round development of a DMTL (Distributed MEMS Transmission Line) based phase shifting unit yielding an overall phase shift of 15° at a frequency of 15 GHz. The RF MEMS (Radio Frequency Micro Electro Mechanical Systems) Switched Capacitor based phase shifter has highly miniaturized dimensions, and is capable of solving many limitations to which the conventional Switched Capacitors are mostly susceptible. This miniature RF MEMS switched capacitor actuates at a low actuation voltage of 12 V, exhibits a fundamental frequency of vibration as high as 1.468 MHz and a switching time of 1.4 µs which is an improvement over the other reported designs. Various characterization results seem to validate the simulations.     

A multi-object image segmentation C–V model based on region division and gradient guide

The Chan–Vese (C–V) model is an ineffective method for processing images in which the intensity is inhomogeneous. This is especially true for multi-object segmentation, in which the target may be missed or excessively segmented. In addition, for images with rich texture information, the processing speed of the C–V is slow. To overcome these problems, this paper proposes an effective multi-object C–V segmentation model based on region division and gradient guide. First, a rapid initial contour search is conducted using Otsu’s method. This contour line becomes the initial contour for our multi-object segmentation C–V model based on a gradient guide. To achieve the multi-object segmentation the image is then converted to a single level set whose evolution is controlled using an adaptive gradient. The feasibility of the proposed model is analyzed theoretically, and a number of simulation experiments are conducted to validate its effectiveness.     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Construction of cultural industry development factor model based on factor analysis,artificial intelligence and big data

The rapid development of the level of social informatization has allowed the content related to computer science to advance with the times and have derived two fields of big data and artificial intelligence. Specifically, the theory and methods of artificial intelligence themselves provide key support for big data. On the other hand, big data also provides auxiliary means for the research and analysis of artificial intelligence. Artificial intelligence will become the mainstream development trend of the future society, and the application of big data technology under the background of artificial intelligence will also be systematic in many fields. This paper analyzes the internal and external factors in the development of cultural industry by means of econometric research methods and big data and artificial intelligence, principal component analysis, factor analysis and multiple regression analysis, establishes the model of cultural industry development factors, breaks through the traditional research methods, analyzes the impact of the core, foundation, environment, kinetic energy, efficiency, potential and other factors on the comprehensive index of the development of cultural industry, and analyzes the comparative advantages and development trend of cultural industry in all provinces and municipalities of China. In this paper, the "three elements" model is comprehensively demonstrated by quantitative and qualitative methods, which solves the problem of lack of qualitative and quantitative research methods in cultural industry.     

Simulation of quantum key distribution in a secure star topology optimization in quantum channel

The field of quantum cryptography is mostly theoretical therefore in this paper we represent its implementation by means of virtual scenarios. The central issue in cryptography is the secure transmission of the key between nodes. Thus, in this paper we establish a secure channel using Quantum Key Distribution (QKD) for the transfer of the key material between the nodes and help to identify an eavesdropper in the channel. A graphical representation of the quantum channel traffic at the ideal state and also during network disruption has been established. Due to the complex nature of quantum networks and high cost of establishment, a physical implementation of the same is not feasible. Hence a simulation has been implemented via the use of NS-3 (Network Simulator Version 3) which has QKDNetSim module built into it. Finally, our simulation indicates the presence of an intruder by virtue of various network implementations within the quantum channel.     

Optimum design of a wideband planar N-way fork power divider with arbitrary power division and input-to-output impedance matching

An effective design procedure based on method of least squares is proposed for multi-section and multi-output fork power divider/combiner with arbitrary power division ratios among its outputs in a specified frequency bandwidth together with impedance matching among its arbitrary source/load impedances. Transmission and scattering matrices are obtained for its equivalent circuit. An error function is constructed based on design specifications on its output power division ratios, isolations among output ports, return losses at its input and output ports and source/load impedances in a desired frequency bandwidth. The design procedure is fully developed, which determines the widths and lengths of microstrip line sections and resistor values. A design example is described for unequal power division ratio and unequal input/output impedances. A 3-section and 3-output fork power divider is designed for equal power division and load/source impedances for 2–12 GHz band. It is fabricated and measured. Variations of its transmission coefficients are less than 0.5 dB, isolations at its outputs are better than −15 dB and reflection coefficients at its ports are better than −10 dB. Excellent agreement is observed among the results of the proposed design procedure, full wave computer software and measurement data.     

An efficient algorithm for community mining with overlap in social networks

Detecting communities in social networks represents a significant task in understanding the structures and functions of networks. Several methods are developed to detect disjoint partitions. However, in real graphs vertices are often shared between communities, hence the notion of overlap. The study of this case has attracted, recently, an increasing attention and many algorithms have been designed to solve it. In this paper, we propose an overlapping communities detecting algorithm called DOCNet (Detecting overlapping communities in Networks). The main strategy of this algorithm is to find an initial core and add suitable nodes to expand it until a stopping criterion is met. Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that DOCNet is efficient and highly reliable for detecting overlapping groups, compared with four newly known proposals.     

Medical image segmentation based on non-parametric mixture models with spatial information

Because of too much dependence on prior assumptions, parametric estimation methods using finite mixture models are sensitive to noise in image segmentation. In this study, we developed a new medical image segmentation method based on non-parametric mixture models with spatial information. First, we designed the non-parametric image mixture models based on the cosine orthogonal sequence and defined the spatial information functions to obtain the spatial neighborhood information. Second, we calculated the orthogonal polynomial coefficients and the mixing ratio of the models using expectation-maximization (EM) algorithm, to classify the images by Bayesian Principle. This method can effectively overcome the problem of model mismatch, restrain noise, and keep the edge property well. In comparison with other methods, our method appears to have a better performance in the segmentation of simulated brain images and computed tomography (CT) images.