In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.

This paper proposes a Java online plug-in mechanism that can be used to modify any part in a system file coded in Java, even while the part is running, without service interruption. The Java-related plug-in capabilities are devised by using the plug-in functional elements offered by the existing C++ online plug-in that we proposed. In particular, measures on how to deal with the use of Just In Time compilation and inline expansion are considered. New linkage and file-back up techniques are proposed for this purpose. Case studies reveal its wide applicability and the degree of memory area saving effects. Evaluation proves this mechanism does not affect the performance of ordinary service processing. It is expected to be used in practice for Java-based service processing such as VoIP and Instant Messaging.

The sizes of recent Java-based server-side applications, like J2EE containers, have been increasing continuously. Past techniques for improving the performance of Java applications have targeted relatively small applications. Moreover, when the methods of these small target applications are invoked, they are not usually distributed over the entire memory space. As a result, these techniques cannot be applied efficiently to improve the performance of current large applications. We propose a dynamic code repositioning approach to improve the hit rates of instruction caches and translation look-aside buffers. Profiles of method invocations are collected when the application performs with its heaviest processor load, and the code is repositioned based on these profiles. We also discuss a method-splitting technique to significantly reduce the sizes of methods. Our evaluation of a prototype implementing these techniques indicated 5% improvement in the throughput of the application.

This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.

In this paper, we present a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a CAD system for colorectal endoscopic images with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification on the embedded DSP core. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image.