Ching-Tsorng Tsai

NCASH: NFC PHONE-ENABLED PERSONALIZED CONTEXT AWARENESS SMART-HOME ENVIRONMENT

Near Field Communication (NFC) is a two-way communication technology based on radio frequency identification (RFID), which in recent years has become one of the most popular devices in a great variety of applications. The NFC-equipped phone (NFC phone) that embeds NFC technology into a cellular phone makes it increasing attractive for business uses, such as for making payments or ticketing. This paper presents a novel architecture for NFC phone-driven, personalized, context-aware smart spaces. With this architecture, users can employ the phone, which carries predefined personal desires, to control devices, such as home appliances. The appliances are automatically controlled and driven in response to a request sent from the phone by using predefined ontology and rule-based reasoning. We also implement a prototype to demonstrate the feasibility of the architecture, evaluate its performance, and show its efficiency.

EVOLVING A TEAM IN A FIRST-PERSON SHOOTER GAME BY USING A GENETIC ALGORITHM

Evolving game agents in a first-person shooter game is important to game developers and players. Choosing a proper set of parameters in a multiplayer game is not a straightforward process because consideration must be given to a large number of parameters, and therefore requires effort and thorough knowledge of the game. Thus, numerous artificial intelligence (AI) techniques are applied in the designing of game characters’ behaviors. This study applied a genetic algorithm to evolve a team in the mode of One Flag CTF in Quake III Arena to behave intelligently. The source code of the team AI is modified, and the progress of the game is represented as a finite state machine. A fitness function is used to evaluate the effect of a teams tactics in certain circumstances during the game. The team as a whole evolves intelligently, and consequently, effective strategies are discovered and applied in various situations. The experimental results have demonstrated that the proposed evolution method is capable of evolving a teams behaviors and optimizing the commands in a shooter game. The evolution strategy enhances the original game AI and assists game designers in tuning the parameters more effectively. In addition, this adaptive capability increases the variety of a game and makes gameplay more interesting and challenging.

An Efficient Video De-interlacing with Scene Change Detection

An efficient video de-interlacing technique with scene change detection is proposed and its performances are examined. Scene changes happen quite often in film broadcasting and they tend to destabilize the quality of performance such as jagged effect, blurred effect, and artifacts effect, while de-interlacing technique is utilized. Therefore, the issue of scene change needs to be addressed with de-interlacing process. In the proposed method, de-interlacing begins with scene change detection, which is to ensure that the interfield information is used correctly. To improve the quality of de-interlacing, the factors of scene change are taken into account when de-interlacing techniques are applied. The simulation results show that the proposed algorithm exhibits better performances than other interpolation algorithms

An Annealed Chaotic Competitive Learning Network with Nonlinear Self-feedback and Its Application in Edge Detection

An unsupervised parallel approach called Annealed Chaotic Competitive Learning Network (ACCLN) for the optimization problem is proposed in this paper. The goal is to modify an unsupervised scheme based on the competitive neural network using the chaotic technique governed by an annealing strategy so that on-line learning and parallel implementation to find near-global solution for image edge detection is feasible. In the ACCLN, the edge detection is conceptually considered as a clustering problem. Here, it is a kind of competitive learning network model imposed by a 2-dimensional input layer and an output layer working toward minimizing an objective function defined as the contextual information. The interconnection strength, composed by an internal state and a transient state with a non-linear self-feedback manner, is connected between neurons in input and output layers. To harness the chaotic dynamic and convergence process, an annealing strategy is also embedded into the ACCLN. In addition to retain the characteristics of the conventional neural units, the ACCLN displays a rich range of behavior reminiscent of that observed in neurons. Unlike the conventional neural network, the ACCLN has rich range and flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimum results.

Concealing information in image mosaics based on tile image features

This article presents a steganographic scheme that conceals secret information in image mosaics based on tile image features. An image mosaic, which is composed of many tiles, resembles a source image that is divided into small grids of identical size. The feature values of each grid and tile image are extracted by a wavelet transform. The similarity between a grid and a candidate tile is taken into account and secret data is inserted into a tile image according to its feature value. There is no modification of tile images and the stego-mosaic as a whole resembles its source image. The hidden data can be extracted by finding the feature values of all tile images of the mosaic. The stego-mosaic looks like its source image to avoid any suspicion; the hidden data are secure since no codebook is required to extract the concealed data and they are not easy to extract without knowing the algorithm. In addition, the concealment capacity is easy to expand by taking more bits of the coefficient of each tile. The experimental results have demonstrated that the embedded image mosaics created from the proposed method have good image qualities and large hidden capacity.

AN EVOLUTIONARY STRATEGY FOR A COMPUTER TEAM GAME

Computer team games have attracted many players in recent years. Most of them are rule‐based systems because they are simple and easy to implement. However, they usually cause a game agent to be inflexible, and it may repeat a failure. Some studies investigated the learning of a single game agent, and its learning capability has been improved. However, each agent in a team is independent and it does not cooperate with others in a multiplayer game. This article explores an evolution strategy for a computer team game based on Quake III Arena. The Particle Swarm Optimization (PSO) algorithm will be applied to evolve a non‐player character (NPC) team in Quake III to be more efficient and intelligent. The evolution of a single NPC, which accommodates to its team and, moreover, the team has learning and cooperating abilities, will be discussed. An efficient team is composed of various members with their own specialties, and the leader is capable of evaluating the performance of a member and assigning it a proper job. Furthermore, the leader of an intelligent team will adapt a strategy appropriate for various circumstances and obtain the teams best performance. Instead of considering the tactic of an individual bot, this article takes the strategy of a team into account.

A piecewise linear convolution interpolation with third-order approximation for real-time image processing

This paper presents a high-performance architecture of a piecewise linear convolution interpolation for digital image. The kernel of the proposed method is built up of piecewise linear polynomial and approximates the ideal sinc-function in interval [−2, 2]. The proposed architecture reduces the computational complexity of generating weighting coefficients and provides a simple hardware architecture design, low computation cost and is easy to meet real-time requirement. The architecture is implemented on the Virtex-II FPGA, and the VLSI architecture has been successfully designed and implemented with TSMC 0.13µm standard cell library. The simulation results indicate that the interpolation quality of the proposed architecture is better than cubic convolution interpolations mostly, which is able to process various-ratio image scaling for HDTV in real-time.

Creating image mosaics with a surrounding matching scheme

The usual methods of generating image mosaics suffer from abrupt color change where the color varies little in the source image, and from discontinuity of texture or shape where they are continuous in the source image. Our proposed method is based on wavelet transforms. It not only considers the similarity between a single tile and the corresponding original grid image, but also takes into account the match of surrounding tiles. The results of our experiments show that color changes are gentler in areas with similar colors originally, shapes are smoother, and textures are more refined in our image mosaics.

Image watermarking through attack simulation

One of the most significant requirements for a useful watermarking system is strong resistance to a variety of common attacks. Accordingly, the watermark embedding is designed from the attack perspective in this paper. We attempt to develop a new just-noticeable distortion (JND) in the discrete cosine transform (DCT) domain, based on an attack simulation scheme that reversely derives the maximum modification amount embedded in the DCT domain under a condition of imperceptibility. A new location selection approach to watermark embedding is also addressed. We evaluate the fitness of the embedding location by considering the embedding capacity, i.e., the JND of the considered frequency component, in addition to the frequency and the spectral strength, which are usually adopted by the existing methods. Experimental results show satisfactory ability of our method to resist various common attacks such as JPEG compression, smoothing, sharpening, cropping, and noise attacks.

Image watermarking through attack simulation based on block classification

A block-based image classification-based technique is developed in this article to improve our previous simulated attack-based watermarking system. By taking advantage of characteristics of the human visual system, the domain coefficients in block image classification are determined by obtaining the maximum hidden information of embedded watermarks so that the difference in image quality between block-based watermarks and simulated attack watermarks is hardly to be detected and a better robustness of the embedded watermarks can be achieved. The experimental results demonstrate that the proposed system can resist various common attacks such as JPEG compression, smoothing, sharpening, cropping, and noise at the expense of a small amount of loss in the quality of embedded watermarks due to simulated attacks.