Near Field Communication (NFC) is an emerging short-range wireless communication technology that offers great and varied promise in services such as payment, ticketing, gaming, crowd sourcing, voting, navigation, and many others. NFC technology enables the integration of services from a wide range of applications into one single smartphone. NFC technology has emerged recently, and consequently not much academic data are available yet, although the number of academic research studies carried out in the past two years has already surpassed the total number of the prior works combined. This paper presents the concept of NFC technology in a holistic approach from different perspectives, including hardware improvement and optimization, communication essentials and standards, applications, secure elements, privacy and security, usability analysis, and ecosystem and business issues. Further research opportunities in terms of the academic and business points of view are also explored and discussed at the end of each section. This comprehensive survey will be a valuable guide for researchers and academicians, as well as for business in the NFC technology and ecosystem.

The Survey on Near Field Communication

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

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UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

Image registration is an important problem in image processing, especially in medical imaging. It is a hard optimization problem with objective function that is not appropriate for deterministic mathematical optimization. In this paper we propose a bi-resolution algorithm for rigid image registration based on recent fireworks algorithm. We tested our approach on retinal images from standard benchmark datasets and compared the results with other approaches from literature. Our proposed algorithm succeeded in registering images in all cases, and was more accurate than other compared methods. Studies in Informatics and Control, Vol. 26, No. 1, March 2017 http://www.sic.ici.ro 33 optimize the control points collected by directional finding algorithm to retrieve the binary registered result. In [31], particle swarm optimization algorithm was adjusted for optimization of transformation parameters with hybrid mutual information as a similarity measurement. Revised quantum-behaved particle swarm optimization and diversity-controlled quantum PSO were applied to medical image registration in [41]. In [36] a PSO algorithm was combined with sequential quadratic programming for rigid image registration. In [30] the artificial bee colony, more recent stochastic optimization algorithm, was used for rigid image registration. Another relatively new swarm intelligence algorithm, firefly algorithm, was used for searching maximal similarity measurement in [37] and [35]. Paper [5] deals with the time complexity issue by using multithreading parallel processing technique. In this paper we propose to use one of the latest swarm intelligence algorithms, the fireworks algorithm (FWA), for finding optimal parameters of rigid transformation for retinal image registration of 2D images. The resolution of retinal images from the used dataset is 565x584 pixels. This resolution is too large for finding the optimal parameters for wide search range so we proposed one resolution reduction in order to narrow the search range. In this paper the optimal reduction factor for retinal images as well as optimal parameters for the fireworks algorithm were empirically determined. The rest of the paper is organized as follows. A mathematical model for image registration is presented in Section 2. In Section 3 bi-resolution image technique is explained. Section 4 provides details about fireworks algorithm. Our proposed algorithm and para-meter settings are presented in Section 5. Experimental results and comparative analysis are given in Section 6. At the end, in Section 7 conclusions and future work are presented. 2. Mathematical Model for Image Registration Algorithms for image registration start with two images, one labelled as reference and one as floating image. Floating image should be transformed to align with the reference image. Next, it is necessary to choose criterion function that determines the similarity between two images. The last step is to optimize the transformation so that the stopping criteria are satisfied. Stopping criteria can be the number of iterations for optimization algorithm or desired similarity between images. Image registration refers to the mapping that is the best match between two or more images of the same scene taken at different times or taken by different sensors. Assuming that B is a floating image that should to be aligned with the reference image A, mathematically mapping can be defined as: )) , ( ( ( ) , ( y x T B y x A α ζ = (1) where ζ is the intensity calibration function and Tα is transformation function that maps spatial coordinates x and y to new spatial coordinates x’ and y’ with the transformation parameters α: ) , ( ) ' , ' ( y x T y x α = . (2) Image registration determines spatial transformation which maximizes similarity of the reference and floating images. Previous description of image registration can be represented by the following equation: )), ( , ( max arg * B T A O T α = (3) where Tα is one possible transformation and O represents the objective function which is actually the similarity measurement. In this case, represents transformation with parameters α which maximizes the value of the objective function over all possible transformations. For different purposes different transformations are used. One type of transformations T are linear transformations that include translation, rotation, scaling and other affine transformations. Common name for these transformations is rigid transformations. On the other hand, there are non-linear transformations, also called elastic or non-rigid transformations, which include radial basis functions, physical continuum models and large deformation models. Elastic transformations map curve lines to other curve lines. In this paper we used rigid transformations that include translation and rotation. These transformations are defined by two translation parameters tx and ty and angle of rotation θ . Transformation formula can be written in the matrix form: 1 0 0 ) cos( ) sin( ) sin( ) cos(

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Adjusted Fireworks Algorithm Applied to Retinal Image Registration

In this study, we introduced the artificial bee colony (ABC) algorithm for the sake of improving two dimensional protein folding simulations. ABC is a novel intelligent problem solving technique under the inspiration of collective behavior on honey bees. It has better performance in function optimization problem compared with genetic algorithm, differential evolution, and particle swarm optimization. In this experiment, we compared ABC with the standard genetic algorithm and immune genetic algorithm on 4 benchmark problems with different chain lengths as 20, 36, 48, and 64. The results show that the ABC algorithm can be applied successfully to the protein folding problems based on the hydrophobic-hydrophilic lattice model, and ABC has higher success rate compared to standard genetic algorithm and immune genetic algorithm.

Artificial Bee Colony for Two Dimensional Protein Folding

We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.

Deep Learning for Joint Source-Channel Coding of Text

Keyword: rigid image registration, mutual information, bacteria chemotaxis, bacteria multiple colony chemotaxis Abstract A novel approach for image registration based on mutual information is presented in this paper. Firstly, normalized mutual information is introduced as the fitness function to supersede the traditional mutual information. Meanwhile, the extrema in fitness function resulted from interpolation is analyzed and the solution is presented as adding a weighted parameter into the variables set. Secondly, search model is improved. The 2 nd generation wavelet is utilized to decompose the original image into multi-resolution sub-images to search optimal solution in different precision layer, which harmonizes the contradiction between search precision and convergence rate. Finally, we introduce a novel optimization means—bacterial multiple colony chemotaxis. Experiments demonstrate the legitimacy and efficiency of our betterment.

Multi-resolution rigid image registration using bacterial multiple colony chemotaxis

In this paper, Hough transform and phase correlation are combined to solve the problem of image registration. Based on 1D phase correlation, a method is proposed to obtain the angle of rotation by searching a angle ridge. After de-rotation, the traditional 2D phase correlation is imported to get the translation. The method can take full advantage of the high efficiency of FFT. Experiments show that the proposed method can detect the rotation and translation between images efficiently and exactly.

Image registration based on Hough transform and phase correlation

To improve protein folding simulations, bacterial chemotaxis optimization (BCO) was investigated on a 2D lattice model. BCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared BCO with standard genetic algorithm (SGA), Immune Genetic Algorithm (IGA), and Artificial Immune System (AIS) for various chain lengths. It shows that BCO has the highest successful rate.

Bacterial Chemotaxis Optimization for Protein Folding Model

The paper studies the theoretical error lower bound of the mobile tracking problem in mixed Line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A new method is presented to compute the posterior Cramer-Rao lower bound (CRLB): the mobile state is first estimated by decentralized EKF method, then sigma point set and unscented transformation are applied to calculate Fisher information matrix (FIM). Numerical results show that the error performance of three algorithms is in a good agreement with the theoretical bounds.

Posterior Cramer-Rao lower bound for mobile tracking in mixed LOS/NLOS conditions

A new JSCCM scheme based on multi-layer feed-forward network is proposed. The coding and modulation of traditional system is replaced by a neural network. At the receive end, a network maps the deteriorated modulation signals to reconstructed signals, the demodulation and decoding of the traditional system is replaced. Simulation results show that the performance of the neural networks is superior to the traditional system. The result is a useful basis for applying neural networks to transmitter and receiver of communication system.

Wu Lenan

Papers

Multi-resolution rigid image registration using bacterial multiple colony chemotaxis

Image registration based on Hough transform and phase correlation

Bacterial Chemotaxis Optimization for Protein Folding Model

Posterior Cramer-Rao lower bound for mobile tracking in mixed LOS/NLOS conditions

Joint source/channel coding modulation based on BP neural networks