Earticle

Fingerprint authentication systems have been widely deployed in both civilian and government applications, however, whether fingerprint authentication systems is security or not has been an important issue under fraudulent attempts through artificial spoof fingerprints. In this paper, inspired by popular feature descriptors such as gray level co-occurrence matrix (GLCM) and Gradient (difference matrix (DM)), we propose a novel software-based fingerprint liveness detection algorithm called difference co-occurrence matrix (DCM). In doing so, quantization operation is firstly conducted on the images. DMs are constructed by calculating difference matrices of horizontal and vertical pixel values of images; difference co-occurrence arrays are constructed from the difference matrices between adjacent pixels. To reduce the influence of abnormal pixel values, truncation is used for DMs. Then, we compute four parameters (Angular Second Moment, Entropy, Inverse Differential Moment and Correlation) used as feature vectors of fingerprint images. For the first time in the fingerprint liveness detection, we construct eight difference co-occurrence matrices and extract texture features from processed DCMs. Finally, SVM classifier is used to predict classification accuracy. The experimental results reveal that our proposed method can achieve more accurate classification compared with the best algorithms of 2013 Fingerprint Liveness Detection Competition, while being able to recognize spoofed fingerprints with a better degree of accuracy.

In this paper, we propose a GRNN(: Generalized Regression Neural Network) algorithms for new eyes and face recognition identification system to solve the points that need corrective action in accordance with the existing problems of facial movements gaze upon it difficult to identify the user and . Using a Kalman filter structural information elements of a face feature to determine the authenticity of the face was estimated future location using the location information of the current head and the treatment time is relatively fast horizontal and vertical elements of the face using a histogram analysis the detected. And the light obtained by configuring the infrared illuminator pupil effects in real-time detection of the pupil, the pupil tracking was - to extract the text print vector. The abstract is to be in fully-justified italicized text as it is here, below the author information.

The sizes and changes in the retinal arteries branching angles are an important characteristic for diagnose of hypertension, and also used as a preliminary index of the future location of the branch. An algorithm that can automatically calculate the branching angle which is defined according to vascular blood flow dynamics is presented. The result shows that the theoretical prediction and manual measurement have good agreement.

The Hough Transform algorithm based on RPSO (Reduced Particle Swarm Optimization) is widely used in image detection. But it has several defects, such as being apt to plunge a local extremum and low target detection precision. In order to overcome these defects of the original algorithm, an improved algorithm, which is updated with the mechanism of SA (simulated annealing), is presented in this paper. In the improved algorithm, the output parameters of the Hough Transform was regarded as particle positions, and the opposite value of accumulation array in Hough Transform was employed as a fitness function of RPSO algorithm. Because the mechanism of the SA was involved, the velocities and positions of the particles are updated in real-time in the process of the crossover and Gaussian mutation. Consequently, the ability of converging to global optimum solution is obviously improved. Then, the comparison and analysis of the experiment results between the original algorithm and the improved one have been carried out in the application of the train wheel image detection. The experiment results demonstrate that the accuracy of image detection is evidently increased and the evolution speed is significantly boosted in the proposed algorithm, especially as the image has complex background or high level noise.

Recently, social media has been used as an independent marketing tool, and as a result, the needs for platforms or tools to manage the data collected by social networks are greatly increasing. To date, many marketing techniques that utilize social media have been proposed, but no research has been done on the problem of helping marketers systematically choose appropriate marketing methods that engage both online social networks and customer experience with offline events. To address this problem, we propose an O2O (Online to Offline) based social-media marketing approach, uLike, which collects customer-experience focused data from offline events to online media in real-time and helps advertise products or brands more easily. To evaluate our approach, we conduct an on and offline marketing-event experiment. Our results show that the approach can be more effective and efficient for increasing the exposure of online advertisements than the traditional social media marketing approaches.

In this paper, a robust digital watermarking scheme is proposed, in which the watermark is embedded several times in different places to improve robustness. First, an original image is transformed into a coefficients matrix through bandeletization. Then the watermark image is embedded into different places of the bandeletization coefficients matrix simultaneously guided by human visual system (HVS). Because the number of non-zero wavelet coefficients have been greatly decreased by bandletization and the most information has been concentrated on some regions, this scheme possessed a stronger robustness against the noise attacks. At last, the watermarked image is reconstructed through inverse bandelet transformation. In the process of extracting watermark image, the watermark image is extracted by using independent component analysis (ICA) and principle component analysis (PCA) and image fusion. Experimental results demonstrate that the proposed scheme is possess of a good performance in both imperceptibility and robustness.

The concept of future hyper-connected city will include ubiquitous, frequent connections among people, objects, and our environment. In this sense, crowd information is re-interpreted as the level of interactions in which people communicate with physical devices at rooms and stores, and even on streets in various ways. Therefore, it becomes one of the most meaningful and dynamic data that our future data analysis technology will consume. In order to measure and inform crowd information to people, we propose to use an emerging fog computing model, instead of a conventional cloud. A fog moves portions of computing capability of the cloud down to the network edge and form a local computing environment. By distributing computing and networking services closer to where user data is generated, the fog best meet the emerging demands. We develop a testbed of fog computing architecture and implement a crowd monitoring service on top of it. Then, we deploy all the testbed on a public street and run experiments in a real world environment.

To improve the limitations of information entropy in the power quality evaluation process, a fuzzy comprehensive evaluation method was proposed on Modified Entropy Weights and Catastrophe Verification. Through summing up the six indexes of power quality, and establishing the evaluation level boundary, the positive and negative entropy weights were reckoned for the power quality monitoring units; The fuzzy features and evaluation was improved to be depicted for power quality with Exponent method, and the five level membership function was set for the power quality evaluation; Then, the mechanism was set up for the fuzzy comprehensive evaluation operation. Finally, with the modified entropy information combination mode and the fuzzy catastrophe mode, the fuzzy comprehensive evaluation and comparative analysis was carried on for such power quality. Case showed, the power quality evaluation conclusion of Information catastrophe mode has the good consistency with the weight combination mode of the positive and negative entropy, which could realize the distinction and system evaluation to the different monitoring units.

With the rapidly increasing number of Web services, the consumers have to choose suitable service from a wealth of web services according to the comprehensive factors. QoS is an important criteria to assess whether the consumer’s requirements are met by web service. But the QoS attributes offered by many web service suppliers are uncertain, especially the non-functional QoS attributes. In this paper, in view of the uncertain QoS attributes in the process of selecting web services, presents the method of selecting web service on basis of QFD-IFS (Quality Function Deployment- Intuitionistic Fuzzy Sets). Describe the consumer’s requirements and QoS attributes with intuitionistic fuzzy sets. Construct the QFD model transforming requirements of consumer into QoS attributes, which gives comprehensive consideration to the relations between requirements of consumer and QoS attributes, and to the impact of the correlation between the QoS attributes on the weights of QoS attributes. After the modified intuitionistic fuzzy entropy is used to obtain the entropy weights of the QoS attributes and alternatives. In the end an example show that the proposed method is feasible and effective.

In this paper, the ML-LEACH routing protocol for the improvement of the performance of in wireless sensor networks is studied. ML-LEACH is derived from LEACH routing protocols. ML-LEACH is a Multi-Hop Layer. ML-LEACH is proposed to compensate for a phenomenon that the energy consumption of the protocols of the sharp drop in the disadvantage of a wide sensor field of LEACH routing protocol. ML-LEACH routing protocol is set upin the environment in accordance with the specific number of Layer and a node in a wireless sensor network and its performance is compared. The simulation MATLAB is used tolet derives the number of Layer and the number of cluster heads.

With the development of information technology, the video surveillance has been widely used in different fields. The real-time video monitoring and controlling system adopts embedded technology, video decoding technology, communication and network technology and some other technologies. Therefore, there is much significance to research it. In this paper the Cortex-A9 is used as core hardware platform to develop the embedded remote video monitoring system, and its key technology is also researched. Based on the research of domestic video monitoring system, and combining with the practical application the overall hardware and software design of the embedded remote video monitoring system is presented. The hardware part of this system includes the microprocessor iMX6DL based on the Cortex-A9 framework which produced by FeiLing company and the USB camera based on the UT_USB_CAMERA module. The software pate includes the transplantation of the embedded operating system Linux, the development of streaming media server, the constructing of website and the writing the payer widget to obtaining a system which has integral real-time monitoring function. The test manifests that the system design is reasonable and stable, it also can be extended to Smart Home, Smart City, Internet of Vehicles and other applications fields.

The possibility of the flexible configuration of dispersion-managed optical links combined with optical phase conjugation by adopting an artificial distribution of the lengths of single mode fiber (SMF) and residual dispersion per span (RDPS) of the fiber spans has been numerically investigated. We confirmed that ‘DA-DA’ pattern – i.e., the lengths of SMF and RDPS around the front and the rear of OPC are smaller, but the lengths of SMF and RDPS closer to the transmitter and the receiver are larger – is the best artificial pattern among the considered 16 artificial distribution patterns. Furthermore, the system performance in the optical link configured with this pattern is more improved than with the conventional uniform distribution of the SMF lengths and RDPS.

In this paper, we combine two moments of Hu and Zernike to try improve the recognition accuracy. And we apply PCA on the Zernike moments to speedup of recognition without degrading the recognition accuracy. We compare the recognition performance between Hu and Zernike moments. Also we show the recognition accuracy of new methods with two modified features. We evaluate the recognition accuracy with two datasets of hands and digits. For hand recognition, we localize hands from hand candidates with improved method against our preliminary study [2].

The Identifier/Locator Separation network is a new network architecture, and the mapping update theory in the model is an indispensable part in the process of communication. Besides, falsification attack, replay attack and network eavesdropping attack may be encountered in the process of mapping update. In order to resist these attacks, to ensure the secure transmission of update messages to the target node, this paper puts forward a intra-domain mapping update authentication method, this method can not only provide authentication services, but also can resist replay attack, DoS attack, and can ensure data confidentiality.

Despite the vast research amount on the analysis and retrieval sematic images, there are still significant challenges worthy of address. This paper proposes an ontology framework to analyze and retrieve text and image based semantic search. This framework can be described in three main processes. Firstly, the Query Engine process constructs the input image query in SPARQL language. Secondly, the process of matching module is to retrieve the most affined images based on the compliance with input query. This process extracts the shape features of image's objects via chord-length features. Furthermore, the ontology manger process inserts the new relevant object's features in ontology knowledge base. Finally, the ranking module process is to classify the images which displayed in descending ordered based on matching values. Our experiment on a trained benchmark mammals shows that the proposed framework is more vigorous and yields favorable results when applying auspicious with large number of tested images without sacrificing real-time performance.

In this paper, a machine-learning approach called Sparse Representation Classification(SRC) Viterbi Algorithm is proposed for automatic chord recognition in music. We extracted Pitch Class Profile(PCP) features or Log PCP from raw audio and achieved sparse representation of classes via -norm minimization on feature space to recognize 24 major and minor triads. This recognition model is evaluated MIREX'09 dataset including the Beatles corpus. Our method is also compared with various methods that entered the Music Information Retrieval Evaluation exchange (MIREX) in 2013 and 2014. Experimental results demonstrate that our method has good accuracy rate in recognizing signal chord and has fewer train data.

The families of patients who are socially disadvantaged are always anxious about their relatives being able to return home every time they leave the house. Although a variety of location management systems have been proposed to deal with such issues, these systems have a limited service range or even cause inconvenience for the socially disadvantaged by making them always carry a device, together with the possibility of getting lost. To resolve this issue, this study designed and implemented a location management system for the socially disadvantaged by attaching a GPS receiver and a CDMA modem on their shoes in order to register the location of a socially disadvantaged person to a server in real-time, which allows a guardian to identify his/her family member’s location from any place, at any time. In addition, the developed system has been verified by a core function test.

Depending on its simulcast capability for a transmit packet in ad hoc networks, each route is likely to have a different routing cost. Assigning a higher traffic rate on a route that has a larger simulcast capability may have an opposite effect between the larger amount of transmission due to its simulcast capability and the queue delay increase due to its heavy traffic load on a route. In this paper, the properties of a multiple routing scheme in ad hoc networks, based on a simulcast transmission that allocates randomly unequal transmission rates on multiple links according to the simulcast capabilities, are investigated.

In order to present the virtual scene of underground coal mining, a dynamic and data-driven method for the virtual scene generation was proposed and its experimental software was developed. The data model of the software system is classified as two types that are the primary model and the display model. The system structure is designed as four layers from bottom to top, namely storage layer, display layer, combination layer and operation layer. Storage layer, the lowest layer and the data center of the system to store all the primary models, is a database containing some data tables, views, constrains, rules and storage processes. Display layer as the second lower layer is to implement an essential data-driven algorithm to transform the selected primary models into display models through scaling, stretching, rotating or assembly operations. Combination layer focuses on the whole scene building business based on the operation of related display models such as visible setting, connecting, handling the shading and presenting special effects. Operation layer as the top layer is to respond to user’s instructions such as free roaming and screen pickup. Finally, the software is tested in Qinan colliery of China. Results show that the proposed method is practicable and efficient to implement virtual coal mining scene.

The chronic degenerative diseases and physical and mental living difficulties of the aging generation result in low quality of life and the financial burden of medical costs for the government. Better dieting habits affect the aging generation’s longevity and health. 54.5% of the aging generation enjoy increased well-being through diet control and diet services. It is necessary to solve the increasing social problem of the aging generation’s health. We propose a cloud computing technology-based platform and mobile technology-based services for the aging generation’s health management and communication. Elderly people using the mobile service should receive not only diet advice from nutritionists, but also suggestions from doctors and communication with colleagues. A cloud-based mobile diet management service will not only reduce government medical costs but also contribute to public health.

The user experience in APP interactive designdraws more and more attention in the APP market where the user population expands continuously and the competition becomes increasingly intense, butcurrent studies mostly focus on the user website experience. This paper is intended to classify and study user needs in APP interactive design based on Kano model and Better-Worse coefficient, and gain Kano two-dimensional attribute classification of user needsin the design. In addition, it carries out analysis on these needs based on the better-worse coefficient.

The color image enhancement is an important topic for last few decades and still many researchers are working and presenting their research findings in literature. There are two issues in digital camera, firstly color filtering and secondly low spatial resolution. To solve both issues, this paper presents a new super resolution approach. Although Bayer pattern CFA is widely used in camera industry, there are few alternatives such as X-Trans CFA pattern. As color restoration method for X-Trans CFA pattern is not many, this paper introduce filter based demosaicking method. We firstly design filters then apply it to X-Trans CFA pattern. Then, full color images are obtained. Experimental results indicate that the presented method provides satisfactory results.

Collaboration of each division and BIM data continuity have been discussed continuously along with the introduction of the BIM design method in the construction sector and there is an increasing demand for design data analysis and simulation rather than simple design modeling. However, difficulties regarding data exchange using different software from different divisions in BIM design arose early in the implementation stage of BIM. Thus, designs have been conducted based only on the exchange between shape information and partial attributes. As a result of suggesting the data exchange method for design collaboration of BIM design in the civil engineering sector and simulating the analysis process with application of Low Impact Development (LID) in BIM design, smooth collaboration was maintained along with continuity of data between BIM design software and BIM-based analysis was subsequently carried out.

With the rapid growth of the high image quality CCTV monitoring market, research on facial recognition from long as well as short distances is needed. We proposed a non-environment-sensitive hybrid facial recognition system using fixed-format and pan-tilt-zoom cameras. The proposed system enables efficient system load distribution and facial recognition in the transferred images from multiple sites and channels. Furthermore, this system enables the multiple face detection and image processing of multiple CCTV channels. This system can be used for security monitoring in the public areas, and can create a new intellectual video monitoring market.

Whereas game addiction has attracted scholarly and practical attention, there are ongoing controversies over the causes and consequences of addictive game playing especially among adolescents. In order to investigate how computer games overall and game addiction in particular are contextualized in the media, this study conducted a comparative content analysis of the United States of American (U.S.) and South Korean game coverage between 2011 and 2013. The findings show the press in both countries most often depicted the issue of computer games in terms of business and industry, while dealing with the possible costs and risks associated with excessive game playing to a much lesser degree. This tendency was more prominent in the Korean press. The Korean media contextualized computer games in a more positive light than their U.S. counterparts. These results indicate that the current game coverage has a pro-industry tendency. The media need to focus more on consumer-related issues and perspectives particularly for the welfare of young audiences.

With the development of electronic commerce, agricultural products marketing also have a new development platform. In this paper, the author analyzes E-commerce consumer factors of agricultural products in agritourism based on data mining technology. The agricultural product marketing in the perspective of agritourism mainly takes the agritourism as an opportunity to carry out the sales, and E-commerce platform has greatly promoted the sales of agricultural products. Through the empirical research, we find out the influence factors of agricultural products sale as execution time, cost, credibility, reputation and other performance indicators, and credibility is one of the most important indicators. On this basis, we put forward relevant recommendations.

Baduk was produced by Emperors Yao and Shun in ancient China to instruct their sons, and it is the most intricate game that has ever invented and demands a high level of both intuition and insight in order for the players of the game to check their overall strategic position and consider their moves accordingly. Baduk has a symbolic significance as it represents the highest limit of human intellectual ability, and has remained so until this day. The game is also the holy grail of artificial intelligence (AI). For this research study, we analyzed Baduk matches that took place between AlphaGo, a supercomputer, and Lee Sedol, one of the top Baduk players in the world, as well as examining archival research in order to discuss learning capabilities and the appropriate learning techniques for students in the age of AI. In March 2016, AlphaGo played against Lee and beat him, and he was arguably the world’s best Baduk player (professional nine-dan level) in Google’s DeepMind Challenge Match. During the match, AlphaGo introduced a new model in Baduk as its play deviated significantly from the common plays of professional Baduk players. The age in which AI surpasses human intelligence is considered to be a point of “singularity” in the history of human civilization. A singularity is a term denoting an unknown territory in which we cannot make easy predictions. In order to prepare for the new age of AI, we may have to go back to the beginning of human civilization to learn from the wisdom of the educational system of the ancients. To be specific, to prepare for this new age, we can learn from Baduk education of ancient China and the Paideia education of ancient Greece, from which we can deduce the strengths kept within human nature.

This paper focuses on the disappearing Chinese Daur traditional craftwork on the graphical scale to reveal the great value of Daur culture from the culture conservation perspective. Specifically, typical Daur craftwork samples are surveyed and selected as the objective for analysis, based on which, color features and interest points feature are respectively extracted employing our color card quantization method and Scale-Invariant Feature Transform technique. Moreover, in order to apply the two extracted features into practice, principle component analysis scheme is utilized to lower the complexity and improve the accuracy of the feature information. Experimental results are accomplilshed on the collected Daur craftwork dataset, which also proves the applicability and efficiency of our approach.

Launch Vehicle software has played an important role in Launch Vehicle system. However, the reliability assessment of Launch Vehicle software is still a hard problem due to the complexity of Launch Vehicle software. Failure prediction can be an effective approach of Launch Vehicle reliability evaluation, whereas failure prediction of software has not yet been fully explored. In this paper, a Markov Bayesian Network model for Launch Vehicle software failure prediction (MBNG) is proposed. In MBNG, unique features of Launch Vehicle software is considered as an important part in the modeling process, which improves the effectiveness of this novel model. Experiments are conducted to verify the effectiveness of MBNG model and compare its performance with classic models.

In open decentralized networks, it is important to make certain data available to only a selected group of users. For example, in a secure e-mail system, a user may send an e-mail to multiple receivers at once. Recently, Chen proposed a secure multicast key protocol for e-mail system based on Chinese Remainder Theorem. They claimed that their protocol provide perfect forward secrecy and ensure confidentiality and authentication. But, in this paper, we show that Chen’s protocol suffers from the sender and the e-mail server impersonation attacks and mail content confidentiality attack. Moreover, we give an improvement to Chen’s protocol. To contribute a secure multireceiver e-mail protocol, we propose a novel protocol by adopting Lagrange polynomial interpolation. We also discuss the security of the novel multireceiver e-mail protocol. Our protocol provides the perfect forward secrecy and resists unknown key-share attack, replay attack, sender impersonation attack, e-mail server impersonation attack and mail content confidentiality attack.