Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.     

A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

Rate-smoothed schedule with tolerable data dropping for video coding stream

Most algorithms of smoothing schedule compute the required bit rate of video transmission to satisfy all the transmitted data. In this paper, our proposed tolerable data dropping algorithm can adjust transmitting data to fit available bit rate. MPEG-4 with fine grained scalability (FGS) can support partial data dropping to adapt to available bandwidth network. The algorithm is based on the minimum variance bandwidth allocation (MVBA) algorithm proposed by Salehi et al. to compute the bit rate such that still ensuring that the buffer never underflows and overflows for MPEG-4 FGS streams under the limited bandwidth resource. We prove that our proposed algorithm, named MVBADP, is smoother than the MVBA algorithm. The experimental results show the peak rate, the number of rate changes, and the ratio of total dropping data, and the PSNR for four test sequences with different content characteristics. They are varied by buffer sizes and tolerable dropping ratios. We found that the MVBADP algorithm can reduce the peak rate and the number of changes when the transmitted data are dropped by tolerable dropping ratio, especially on the video sequences with the high motion and complex texture characteristic and larger size change of the consecutive frame.     

Types and associated type families for hardware simulation and synthesis

In this article we overview the design and implementation of the second generation of Kansas Lava. Driven by the needs and experiences of implementing telemetry decoders and other circuits, we have made a number of improvements to both the external API and the internal representations used. We have retained our dual shallow/deep representation of signals in general, but now have a number of externally visible abstractions for combinatorial and sequential circuits, and enabled signals. We introduce these abstractions, as well as our abstractions for reading and writing memory. Internally, we found the need to represent unknown values inside our circuits, so we made aggressive use of associated type families to lift our values to allow unknowns, in a principled and regular way. We discuss this design decision, how it unfortunately complicates the internals of Kansas Lava, and how we mitigate this complexity. Finally, when connecting Kansas Lava to the real world, the standardized idiom of using named input and output ports is provided by Kansas Lava using a new monad, called Fabric. We present the design of this Fabric monad, and illustrate its use in a small but complete example.     

Extension of the DEMATEL method in an uncertain linguistic environment

The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations. Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to illustrate the procedure of the proposed method.     

Mutual learning of multi-consciousness agents,including an ego for an autonomous vehicle

In this paper, we propose a multi-agent learning system for the control of an intelligent robot, based on a model of the human consciousnesses, including the ego. We pay attention to the intelligent learning processes of human beings. We try to give a robot a high learning ability by modeling the roles of the human consciousnesses, including the ego. In most ordinary methods, the instructions for learning are given from outside the system only. In the proposed method, the instructions are given not only from outside, but also from inside (from other agents in the system). Therefore, the robot can learn efficiently because it has more instructions than usual. The learning is also more flexible, since an agent learns by instructions from other agents while the learning agent and one of the instructing agents exchange roles according to changes in the environment. We experimentally verified that the proposed method is efficient by using an actual robot.     

Path planning for an autonomous mobile robot considering a region with a velocity constraint in a real environment

Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated in the Real World Robot Challenge 2010. The experimental results are given.