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4 results on '"Tong KW"'

1. Aluminum-doped n-type ZnSTe alloy grown by molecular beam epitaxy

Author

Sou, IK, Yang, Z., Mao, J., Ma, ZH, Tong, KW, Yu, P., Wong, GKL, Sou, IK, Yang, Z., Mao, J., Ma, ZH, Tong, KW, Yu, P., and Wong, GKL

Abstract

Successful n-type doping of ZnSTe alloy using elemental aluminum source has been carried out by molecular beam epitaxy. Hall effect measurement (300-77 K) was performed on as-grown ZnS0.977Te0.023 epilayers with various dopant concentrations. Electron carrier concentration as high as 1.3X10(19) cm(-3) has been achieved. For carrier concentration higher than 5X10(18) cm(-3), the carrier concentration is independent of temperature, possibly indicating formation of a very shallow donor level. A group of ZnS1-xTex epilayers with different x values was doped using a constant aluminum beam flux for studying the dependence of the dopant activation on Te composition. Good activation of Al dopant was obtained for x value from 0 to a few percent, but it became poor for larger x value and finally Al became inactive for x values higher than 10\%. Room temperature photoluminescence measurements on doped and undoped ZnS and ZnS1-xTex layers indicate that Al dopants from deep-level radiative centers in addition to a shallow donor level. The characteristics of these deep levels as a function of Te composition have also been studied. (C) 1996 American Institute of Physics.

Published
1996

2. Efficient image processing applications on the MasPar massively parallel computers

Author

Hamdi, Mounir, Pan, Yi, Tong, KW, Hamdi, Mounir, Pan, Yi, and Tong, KW

Abstract

Image processing applications are suitable candidates for parallelism and have at least in part motivated the design and development of some of the pioneering massively parallel processing systems including the CLIP family, the DAP, the MPP and the GAPP. In this paper, we describe the implementation of various image processing algorithms on the MasPar massively parallel computer system. The suitability of the MasPar for solving image processing algorithms is demonstrated either by parallelizing the algorithms using successful known techniques and/or developing new techniques suitable for the MasPar architecture. We quantitatively evaluate the performance of MasPar in solving these problems. Then, we compare its performance to various related massively parallel architectures. It is shown that the MasPar system compares favorably to these architectures, and is able to execute many fundamental image processing applications in a time amenable to real-time processing. Thus, the MasPar seems to be a promising architecture for massively parallel real-time image processing applications.

Published
1995

4. Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer.

Author

Chan LW, Ding T, Shao H, Huang M, Hui WF, Cho WC, Wong SC, Tong KW, Chiu KW, Huang L, and Zhou H

Abstract

Background: Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited., Methods: Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People's Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO)., Results: The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar's test p = 0.0003)., Conclusions: A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chan, Ding, Shao, Huang, Hui, Cho, Wong, Tong, Chiu, Huang and Zhou.)

Published
2022
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