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Further Improvement on Phase-Compensating-System Design |
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Tian-Bo Deng(Toho University, Japan) |
| In this paper, we improve the existing non-iterative optimization strategy for designing an all-pass digital phase-compensating system (PCS). The goal of the digital PCS design is to best approximate a given ideal phase with the maximum absolute error minimized. Although this is a nonlinear-programming problem, it can be approximately formulated as a non-iterative quadratic-cone (Qcone) programming (Qcone programming) problem and then solved by using a Qcone programming solver. This paper shows that the existing non-iterative can be further enhanced to get better performance. |
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Automatic Segmentation of Cell Candidate Regions in Microscopy Images Based on an Optimization Algorithm |
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Kouki Tsuji, Hyoungseop Kim, Joo Kooi Tan(Kyushu Institute of Technology, Japan), Kazue Yoneda(University of Occupational and Environmental Health, Japan) |
| Circulating tumor cells (CTCs) is an informative biomarker which assists pathologists in early diagnosis and evaluating therapeutic effects of patients with malignant tumors. The blood from a cancer patient is analyzed by a microscope and a large number of pictures including many cells are generated for each case. Thus, analyzing them is time-consuming work for pathologists, and misdiagnosis may happen since the diagnosis of CTCs tends to depend on the individual skill of pathologist. In this paper, we propose a method which detects cell candidate regions in microscopy images automatically to |
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Extraction of GGO candidate regions from the LIDC database using deep learning |
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Kazuki Hirayama, Hyoungseop Kim, Joo Kooi Tan(Kyushu Institute of Technology, Japan) |
| In recent years, development of the computer-aided diagnosis (CAD) systems for the purpose of reducing the false positive on visual screening and improving accuracy of lesion detection has been advanced. Lung cancer is the leading cause of cancer death in the world. Among them, GGO (Ground Glass Opacity) that exhibited early in the before cancer lesion and carcinoma in situ shows a pale concentration, have been concerned about the possibility of undetected on the screening. In this paper, we propose an automatic extraction method of GGO candidate regions from the chest CT image. Our proposed |
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Pilot Hand Detecting and Tracking in the cockpit based on Depth Image Using the Intel RealSense Camera |
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Tao Wang(Shanghai Jiao Tong University, China) |
| The identification of the pilot’s hand movement in the cockpit is very important for the calculation of pilot’s workload. Here, the paper proposed a tracking technique for the identification of the pilot’s hand movement based on the depth image obtained from an Intel RealSense camera. By using the technique, it is able to segment the hand using the region growing method and locate hand center in the area of the hand. In addition,an approach to activate the algorithm has also been demonstrated, and the results of experiments show that the technique can be applied to identify pilot’s actions |
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A Combination of Independent Component Analysis, Relative Wavelet Energy, and Support Vector Machine for Mental State Classification |
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Hoang-Anh T. Nguyen, Huy-Hoang Tran, Thang T. Vu(Institute of Information Technology-Vietnam Academy of Science and Technology, Viet Nam), Quyen T.T. Bui(Institute of Information Technology, Vietnam Academy of Science and Technology, Viet Nam) |
| Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and... |
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