A Multi-Person Selfie System via Augmented Reality

The limited length of a selfie stick always poses the problem of distortion in a selfie, in spite of the prevalence of selfie stick in recent years. We propose a technique, based on modifying existing augmented reality technology, to support the selfie of multiple persons, through properly aligning different photographing processes. It can be shown that our technique helps avoiding the common distortion drawback of using a selfie stick, and facilitates the composition process of a group photo. It can also be used to create some special effects, including creating an illusion of having multiple appearances of a person.     

Semantic clustering and affinity measure of subject-oriented language texts

The problem of semantic clustering of subject-oriented natural language texts is discussed. Based on the theory of Formal Concept Analysis, an approach is proposed to determine the measure of semantic affinity of texts.     

Re‐Compositable Panoramic Selfie with Robust Multi‐Frame Segmentation and Stitching

It is a challenging task for ordinary users to capture selfies with a good scene composition, given the limited freedom to position the camera. Creative hardware (e.g., selfie sticks) and software (e.g., panoramic selfie apps) solutions have been proposed to extend the background coverage of a selife, but to achieve a perfect composition on the spot when the selfie is captured remains to be difficult. In this paper, we propose a system that allows the user to shoot a selfie video by rotating the body first, then produce a final panoramic selfie image with user‐guided scene composition as postprocessing. Our key technical contribution is a fully Automatic, robust multi‐frame segmentation and stitching framework that is tailored towards the special characteristics of selfie images. We analyze the sparse feature points and employ a spatial‐temporal optimization for bilayer feature segmentation, which leads to more reliable background alignment than previous image stitching techniques. The sparse classification is then propagated to all pixels to create dense foreground masks for person‐background composition. Finally, based on a user‐selected foreground position, our system uses content‐preserving warping to produce a panoramic seflie with minimal distortion to the face region. Experimental results show that our approach can reliably generate high quality panoramic selfies, while a simple combination of previous image stitching and segmentation approaches often fails.     

TCPW-REAL：基于无线网络的TCPW改进协议

传统的TCP协议在有线网络中能够良好地工作,但用于无线网络时则性能有所下降.其原因在于,传统的TCP协议无法分辨网络丢包原因,如网络拥塞、链路断开、信道错误或者链路改变.为了提高TCP协议在无线网络中的性能,提出了TCPW-REAL协议,该方案参考了TCPW协议,通过改变其时应答包流的计算方式来时带宽进行更精确的估测.同时,给出了TCPW-REAL的算法并利用仿真对算法进行了验证.分析和仿真结果表明,该方案能够有效地改进TCPW在无线网络的性能.     

Microfluidic-based biosensor: signal enhancement by gold nanoparticle

Biosensor is an analytical device to detect the biomolecules assisted by the transducer and physicochemical detector. A good biosensor is expecting to be with low cost, easy to perform and identify the results without prior experience. In addition, a good biosensor has two main key characteristics such as sensitivity and specificity; these are mainly determined by the affinity of biomolecules with the assistance of sensing system. Microfluidic-based lab-on-chip is one of the fast growing technologies in the field of biosensor bring the positive characteristics with a fast delivery set-up. On the other hand, gold nanoparticle (GNP) is the powerful tool to enhance the biomolecular detection with higher sensitivity and it has been proved for the effective applications with different sensors. In this review, we discussed the applications of microfluidic-based delivery and GNP for biosensing with the new level of developments, which elevate a step ahead.     

Model enhancement: Improving theoretical optimization with simulation

Many optimization techniques are based on mathematical representations. In this case, important modeling simplifications need to be made. The solution thus provided, even if proven to be theoretically one of the best, might not be so good in practice. Simulation can be used to evaluate the actual performance of the solution. We propose here a coupling between optimization and simulation that tries to improve the solution provided by a mathematical model. This approach, named “model enhancement” here, still focuses on optimizing the theoretical objective function, contrary to the common optimization–simulation coupling that focuses on improving the objective function evaluated from simulation. We propose to illustrate this approach on a routing problem, and present numerical results on the quality of the solution and the efficiency of both coupling approaches.     

User security enhancement: A worldwide security project

The Society for Worldwide Interbank Financial Telecommunication (S.W.I.F.T.) provides financial processing and communication services of the highest quality and integrity. Its network and value-added services enable customers to reduce costs, raise productivity and control risks. Security is one of the key benefits that S.W.I.F.T. offers to customers, and the User Security Enhancement (USE) programme is a major part of the company's security strategy.     

Mars microrover navigation: Performance evaluation and enhancement

In 1996, NASA will launch the Mars Pathfinder spacecraft, which will carry an 11 kg rover to explore the immediate vicinity of the lander. To assess the capabilities of the rover, as well as to set priorities for future rover research, it is essential to evaluate the performance of its autonomous navigation system as a function of terrain characteristics. Unfortunately, very little of this kind of evaluation has been done, for either planetary rovers or terrestrial applications. To fill this gap, we have constructed a new microrover testbed consisting of the Rocky 3.2 vehicle and an indoor test arena with overhead cameras for automatic, real-time tracking of the true rover position and heading. We create Mars analog terrains in this arena by randomly distributing rocks according to an exponential model of Mars rock size frequency created from Viking lander imagery. To date, we have recorded detailed logs from over 85 navigation trials in this testbed. In this paper, we outline current plans for Mars exploration over the next decade, summarize the design of the lander and rover for the 1996 Pathfinder mission, and introduce a decomposition of rover navigation into four major functions: goal designation, rover localization, hazard detection, and path selection. We then describe the Pathfinder approach to each function, present results to date of evaluating the performance of each function, and outline our approach to enhancing performance for future missions. The results show key limitations in the quality of rover localization, the speed of hazard detection, and the ability of behavior control algorithms for path selection to negotiate the rock frequencies likely to be encountered on Mars. We believe that the facilities, methodologies, and to some extent the specific performance results presented here will provide valuable examples for efforts to evaluate robotic vehicle performance in other applications.     

Fingerprint image enhancement: algorithm and performance evaluation

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy     

Vesselness enhancement diffusion

We propose a new anisotropic diffusion filter to enhance the local coherence of multiscale tubular structures on 2D images. The proposed filter uses a diffusion tensor with diffusion direction and strength determined by the local structure, and chooses automatically for each pixel the diffusion tensor scale. In this paper, we show how this filter enhances X-ray coronary angiographic images to facilitate vessel segmentation. To this aim, we present experimental results of the performance of the filter on synthetic and real images.     

Micro-differential evolution: Diversity enhancement and a comparative study

Differential evolution (DE) algorithm suffers from high computational time due to slow nature of evaluation. Micro-DE (MDE) algorithms utilize a very small population size, which can converge faster to a reasonable solution. Such algorithms are vulnerable to premature convergence and high risk of stagnation. This paper proposes a MDE algorithm with vectorized random mutation factor (MDEVM), which utilizes the small size population benefit while empowers the exploration ability of mutation factor through randomizing it in the decision variable level. The idea is supported by analyzing mutation factor using Monte-Carlo based simulations. To facilitate the usage of MDE algorithms with very-small population sizes, a new mutation scheme for population sizes less than four is also proposed. Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i.e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and pre-mature convergence. The MDEVM is implemented using a population-based parallel model and studies are conducted on 28 benchmark functions provided for the IEEE CEC-2013 competition. Experimental results demonstrate high performance in convergence speed of the proposed MDEVM algorithm.     

Weak signal detection: Condition for noise induced enhancement

For the detection of a weak known signal in additive white noise, a generalized correlation detector is considered. In the case of a large number of measurements, an asymptotic efficacy is analytically computed as a general measure of detection performance. The derivative of the efficacy with respect to the noise level is also analytically computed. Positivity of this derivative is the condition for enhancement of the detection performance by increasing the level of noise. The behavior of this derivative is analyzed in various important situations, especially showing when noise-enhanced detection is feasible and when it is not.     

Digital redesign of observer-based output-feedback tracking controllers: enhancement

Digital redesign for observer-based output-feedback tracking controller with time-varying references is developed. The improvements have been done by more precisely approximating the reference and considering the state matching of the estimation error in a plant dynamics for a large sampling period, and by using the delta-operated discrete-time modelling for an extremely small sampling period.     

Georoy: A location-aware enhancement to Viceroy peer-to-peer algorithm

The success of experiences such as Seattle and Houston Wireless has attracted the attention on the so called wireless mesh community networks. These are wireless multihop networks spontaneously deployed by users willing to share communication resources. Due to the community spirit characterizing such networks, it is likely that users will be willing to share other resources besides communication resources, such as data, images, music, movies, disk quotas for distributed backup, and so on. To support resource exchange in these wireless mesh community networks, algorithms for efficient retrieval of information are required. In this paper we introduce Georoy, an algorithm for the efficient retrieval of the information on resource location based on the Viceroy peer-to-peer algorithm. Differently from Viceroy, Georoy exploits the capability of setting and managing a direct mapping between the resource ID and the node which maintains information about its location so as to speed up the search process. Simulation results show that Georoy enables efficient and scalable search of resources and can be successfully used in wireless mesh community networks.     

Fingerprint image enhancement and recognition algorithms: a survey

Fingerprint systems have received a great deal of research and attracted many researchers’ effort since they provide a powerful tool for access control and security and for practical applications. A literature review of the techniques used to extract the features of fingerprint as well as recognition techniques is given in this paper. Some of the reviewed research articles have used traditional methods such as recognition techniques, whereas the other articles have used neural networks methods. In addition, fingerprint techniques of enhancement are introduced.     

Data warehouse enhancement: A semantic cube model approach

Many data warehouse systems have been developed recently, yet data warehouse practice is not sufficiently sophisticated for practical usage. Most data warehouse systems have some limitations in terms of flexibility, efficiency, and scalability. In particular, the sizes of these data warehouses are forever growing and becoming overloaded with data, a scenario that leads to difficulties in data maintenance and data analysis. This research focuses on data-information integration between data cubes. This research might contribute to the resolution of two concerns: the problem of redundancy and the problem of data cubes’ independent information. This work presents a semantic cube model, which extends object-oriented technology to data warehouses and which enables users to design the generalization relationship between different cubes. In this regard, this work’s objectives are to improve the performance of query integrity and to reduce data duplication in data warehouse. To deal with the handling of increasing data volume in data warehouses, we discovered important inter-relationships that hold among data cubes, that facilitate information integration, and that prevent the loss of data semantics.     

Double enhancement learning for explicit internal representations: unifying self-enhancement and information enhancement to incorporate information on input variables

In this paper, we propose a new type of information-theoretic method called “double enhancement learning,” in which two types of enhancement, namely, self-enhancement and information enhancement, are unified. Self-enhancement learning has been developed to create targets spontaneously within a network, and its performance has proven to be comparable with that of conventional competitive learning and self-organizing maps. To improve the performance of the self-enhancement learning, we try to include information on input variables in the framework of self-enhancement learning. The information on input variables is computed by information enhancement in which a specific input variable is used to enhance competitive unit outputs. This information is again used to train a network with the self-enhancement learning. We applied the method to three problems, namely, an artificial data, a student survey and the voting attitude problem. In all three problems, quantization errors were significantly decreased with the double enhancement learning. The topographic errors were relatively higher, but the smallest number of topographic errors was also obtained by the double enhancement learning. In addition, we saw that U-matrices for all problems showed explicit boundaries reflecting the importance of input variables.     

An image enhancement tool: Pattern Recognition Image Augmented Resolution

PRIAR (Pattern Recognition Image Augmented Resolution) is an innovative approach to singleframe super-resolution that combines common single-frame super-resolution with pattern-recognition algorithms. PRIAR uses the information gained through pattern-recognition to enhance resolution for low quality images, and to allow the end user to explore, recognize and super-resolve low-resolution images. In this paper, we present the basic functionality of the PRIAR algorithm that we have implemented. The program is modular and each module is easily combined. In addition, such modularity permits us to work on images where single modules can be changed in order to resolve different classes of problems. In this paper, we firstly present the features of the PRIAR program processing images reproducing animal cells recorded with a scanning probe microscope.