Earticle

Recent research has been devoted and focused on detecting pedestrians and vehicles in intelligent vehicles except for vulnerable road users (VRUs). Histogram of Oriented Gradients (HOGs) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection. In this paper we present a method for two-wheelers detection based on the periodicity of the size of spatial regions; cells and blocks. Contrary to concatenating feature vectors using a dense grid of HOG, proposed algorithm storage extracted feature vectors according to the period in each step. Experimental results show that the new approach gives higher detection accuracy than that of the conventional method.

In this paper, analysis scenario, detection and risk and the negative risk classification and measurement industry through Analysis of data for types of topics, including through change according to the situation in Predictive Evaluation and Monitoring Forensic by designing ICANN-based frame based, focusing the arrangements. The proposed system is business through links to the existing legacy systems and operates in the insolvency of corporate assets and the Predictive Evaluation of operational, the firm's assets by managing, and Risk Management for efficient and reliable support for such assets, management and recovery for by business interests can be enhanced. Also Forensic data to utilize an analysis, monitoring the standardization of arrangements are based also, Min, Protos, or corporate disputes such as criminal proceedings for gathering evidence through the data .It can prevent accidents and civil affairs and others, we can provide. For corporations as well as the situation of the Enterprise during system operation and can be verified by analysis of data, by providing the framework that can provide cost, and Software reuse can do.

Words are basic structural units of language that combine with each other to form sentences. The learning strength of combinative relations between words is of key importance in sentence structure analysis. Inspired by the analogies between words and lymphocytes, a multi-word-agent autonomous learning model based on an artificial immune system is proposed to learn word combination strength. The model is constructed via Cellular Automation, and words are modeled as B cell word agents and as antigen word agents. The language network is then simulated as an immune network. Meanwhile, Spreading Activation is employed to simulate idiotypic interactions between B cells. This research provides a completely new perspective on language and words and introduces biologically inspired processes from immune systems into the proposed model. The most significant advantage of the model is the ability of continuous learning and the concise implementation method. According to the graph-based dependency parsing method, the syntax dependency tree of a sentence can be predicted based on word combination strength in a bottom-up paradigm, from pairs of smaller structures to larger structures. Therefore, the effectiveness of the model can be verified by sentence dependency parsing. The experimental results on the Penn Chinese Treebank 5.1 indicate that our model can effectively and continuously learn word combination strength.

This paper proposes a zero-video watermark scheme based on 2D-DWT and pseudo 3D-DCT, otherwise Singular Value Decomposition and log-polar transform are applied. The method makes no changes to original images while embedding the owner information of images so as to achieve high transparency. The log-polar transform ensures that the method is robust to rotation operations. In order to achieve the high robustness and security, we use chaotic logistic mapping and spread spectrum (CLMSS) to spread the watermark information. we also use the visual cryptography (VC) scheme to split the secret image into two shares. In the scheme, we use spread spectrum to encode the watermark to a Code Division Multiple Access watermark and use dual transform and log-polar to generate the feature values. Then the visual cryptography scheme is applied to generate the secret image from the feature values and the watermark. In the extraction scheme, we use the secret image which is registered to certification authority and the feature values extracted from the examined image with visual cryptography scheme to generate the CDMA watermark, and then decrypt it to get the watermark information. The experimental are conducted to verify the robustness through a series of experiments.

Under the era of new media, as user preference becomes acquainted with visual and spatial media, it has been changed to a form that audiences are participating in the art work through interactive design. Interaction characterized by interactivity has been researched in diversified fields as a strong tool by which audience is able to experience art work by attracting and concentrating user’s concern based on multi- sensory experience in a space. Therefore, in this study, a questionnaire for increasing user experience in experimental digital art field was performed. The objective of future research is to implement immersive art (IA) work by producing art work utilizing the result of questionnaire.

A modified second-order sliding mode (SOSM) control scheme is developed to synchronize two different fractional order hyperchaotic systems. On the basis of chattering free SOSM control scheme, a special fractional sliding mode surface is firstly proposed. Subsequently, a robust control law is designed to ensure the occurrence of sliding mode in a given time. This control scheme is rigorously proved by Lyapunov stability theory. Finally, a numerical example is given to illustrate the effectiveness of the proposed synchronization approach.

A robust twofold zero-watermarking scheme for secret QR-Code (Quick Response Code) sharing is proposed in order to increase the security of commercial activities on the internet and media. In this paper we will present a twofold scheme for zero-watermarking to be used for copyright protection, implemented in discrete wavelet transform (DWT) as the first fold and discrete cosine transform (DCT) as a second fold for color images in which the visual secret sharing is used to generate unexpanded master and secret shares for the same QR-Code watermark. The experimental results indicate that the proposed scheme is highly robust and the QR-Code can be decodable even after different types of attack being applied.

Hand Gesture recognition systems enable people to interact with digital systems naturally. Due to the spread of body motion capture device, depth information is available for getting more delicate and effective gesture recognition results. However, due to the limitation of devices such as Microsoft Kinect, it is still very difficult to obtain hand gesture information in rela-time. This paper proposes an integrated approach of real-time hand gesture recognition based on feature points. It explains our solutions for hand segmentation and feature points abstraction based on real-time motion captured images. Having been tested with a series of applications, our method is proved to be robust and effective, and suitable for further extension in real-time hand gesture recognition systems.