Thumrongrat Amornraksa

Bio: Thumrongrat Amornraksa is an academic researcher from King Mongkut's University of Technology Thonburi. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 11, co-authored 105 publications receiving 484 citations. Previous affiliations of Thumrongrat Amornraksa include University of Surrey.

Fingerprint recognition using DCT features

Abstract: A method of fingerprint recognition based on the DCT features of a discrete image is proposed. Its performance is evaluated by the k-nearest neighbour (k-NN) classifier. Experimental results show the achievement of the proposed method in terms of the recognition rate and the low computational effort.

Scrambling and key distribution scheme for digital television

Abstract: The scrambling scheme is a part of the conditional access system (CAS) that is used to prevent unauthorized access to Pay-TV systems. We propose a new scrambling scheme and key distribution scheme. The scrambling scheme is used in conjunction with ordinary encryption techniques, for protecting the digital video streams from unauthorized viewing. A hash function and a pseudo-random number generator are used to prepare the video stream before being encrypted. The proposed scheme helps reduce computational time and complexity while providing the same level of security as encrypting the entire video stream. In addition, the proposed secure key distribution scheme can be used with any, scrambling scheme e.g. with our scrambling scheme. By using the Chinese remainder theorem (CRT) for distributing parameters in the scrambling process, the security of the scheme can be increased. Moreover, our scheme can prevent two common problems, namely, smart card cloning and McCormac Hack.

Gaussian pixel weighting marks in amplitude modulation of color image watermarking

Abstract: A watermark embedding method, using an amplitude modulation technique, based on Gaussian pixel-weighting marks is proposed. The method utilizes a technique of weighting a mark by averaging the luminance of its own pixel and its neighboring pixels, and uses it as an additional scaling factor in existing watermark embedding methods. The watermark retrieval process is borrowed from the technique of image averaging from the cross-shaped neighboring pixels of the watermarked image, and hence has no need of the original image. The results show that improved performance is obtained when applying the Gaussian pixel-weighting marks technique, compared to other techniques, namely the equal gain pixel-weighting and the no pixel-weighting marks techniques.

Applying FFT Features for Fingerprint Matching

Abstract: In this paper, a feature extraction method based on the fast Fourier transform (FFT) is proposed for the use of fingerprint matching. The performance of our approach is evaluated by the amount of time required in the features extracting and feature matching processes. The recognition rate obtained from the proposed method is also evaluated by the k-NN classifier. The experimental results show that, by using our approach, the processing time in the features extraction and searching & matching processes is reduced by up to 34.95 times, and decreased by up to 0.39 milliseconds, respectively, compared to the method proposed in [6]. Furthermore, with our proposed method, the fingerprint recognition rate can be improved by 1.1% to 13.19%.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

美聯邦航空廳(Federal Aviation Administration)의 航空機 製作檢査 制度의 現況

Abstract: The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004. FAA is required to license or permit commercial space launches, but to allow the space tourism industry to develop, the act prohibited FAA from regulating crew and spaceflight participant safety before 2012—a moratorium that was later extended but will now expire on September 30, 2015. Since October 2014, there have been three mishaps involving FAA licensed or permitted launches.

An Overview of Digital Video Watermarking

Abstract: The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of the digital video watermarking techniques in which their applications, challenges, and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering, and multi-view video watermarking. We discuss each technique, and then present a survey of the literature. Finally, we provide a summary of this paper and propose some future research directions.