Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Performance of M-PSK in mobile satellite communication over combined ionospheric scintillation and flat fading channels with MRC diversity

Abstract: Combined scintillation and terrestrial fadings occur in mobile satellite communication channels as the signal passes through the ionosphere and the lower atmosphere. This results in a product fading channel, which negatively affects the performance of the system. The challenge is to evaluate the performance of the system, in terms of the average bit error probability (BEP). In this paper, through the use of the moment generating function, we derive expressions for the average BEP and an upper bound for M-ary phase-shift keying (M-PSK) modulation with maximal-ratio combining (MRC) diversity over the product RiciantimesRician channel. The results are expressed as double summations in terms of the generalized hypergeometric function, which can be computed using standard commercial software. For a large Rician factor, the expression is simplified to a single summation. Numerical results are obtained from the derived expressions and compared with simulation results. They show very good agreement for various Rician factors and the number of diversity branches. The upper bound is also evaluated and shown to be reasonably tight.

Multilayered mouse preosteoblast MC3T3-E1 sheets harvested from temperature-responsive poly(N-isopropylacrylamide-co-acrylamide) grafted culture surface for cell sheet engineering

Abstract: A novel temperature-responsive tissue culture surface was prepared using ultraviolet irradiation to graft poly(N-isopropylacrylamide-co-acrylamide) (PNIAM-co-AM) onto commercial tissue culture surfaces. The physical properties of the grafted surfaces were confirmed by Fourier Transform Infrared Spectroscopy, contact angle measurement, and Atomic Force Microscopy. The grafted surface was also tested and shown to be nontoxic using mouse preosteoblast MC3T3-E1 cells. The cells grew as successfully on the grafted surface as those on ungrafted surface. When the temperature was reduced from 37 to 10°C for 30 min, followed by 20°C for 60 min, the confluent cells could be detached as a continuous sheet. A histological examination showed that the harvested cell sheet preserved tight junctions and extracellular matrix proteins, allowing the sheet to adhere to other cell sheets as multilayers. The resulting multilayered sheets were in good condition, as indicated by the LIVE/DEAD stain. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

View-Independent Gait Recognition Using Joint Replacement Coordinates (JRCs) and Convolutional Neural Network

Abstract: Gait recognition has received increasing attention for security and authentication since it can be done unintrusively from afar and without a subject’s awareness. In this work, we propose a new model-based gait recognition technique called JRC-CNN gait recognition. We introduce three new concepts. (1) We create a new way to preprocess skeleton data by rotating skeleton data using two virtual axes. This process reduces the fluctuation in movements and resolves the multi-viewpoint issue. All postures in a walk are observed from the same angle. (2) We introduce new Joint Replacement Coordinates (JRCs), which represent the movements of the left and right joints in a group of three connected joints. These JRC gait features are designed to put more emphasis on local movements than the movements of non-connected joints. (3) We construct a new Convolution Neural Network (CNN) for the classification process, which consists of a convolutional layer on each JRC and two fully-connected layers. A convolutional layer is designed to discover relations within a group of three connected joints. Fully-connected layers also find the relations of all groups of three connected joints throughout an entire body (in a posture). Our JRC-CNN technique achieves above 98.4% accuracy and significantly outperforms other existing techniques for all free-direction walk datasets. It also performs well under the gallery-size test and the CMC curve test. This means that our proposed JRC-CNN gait recognition technique can be used in a real-world situation. Experimental results also suggest that a person can be identified by a unique posture (an entire body is observed as a whole) with the focus on the movements of connected joints.