Hidenori Sakanashi

Evolvable hardware chips and their applications

The paper describes Evolvable Hardware (EHW) chips and their industrial applications. EHW refers to hardware devices that can adjust their circuit structure to adapt to varying environments. Unlike traditional hardware, EHW is capable of autonomously changing its functionality whilst operating in a real environment, and thus represents a major new approach to hardware design and development. A number of the applications of EHW currently under development at the Electrotechnical Laboratory are introduced, and in particular, a data compression EHW chip for electrophotographic printers, which has achieved compression ratios twice those of the international standard methods, is described in some detail.

A Lossless Compression Method for Halftone Images Using Evolvable Hardware

This paper proposes a lossless (reversible) data compression method for bi-level images, particularly printing images. In this method, called Dispersed Reference Compression (DRC), the coding scheme is changed according to the characteristics of the images to be compressed by Evolvable Hardware. Computational simulatioils demonstrate that DRC provides compression ratios that are up to 30% better than the current international standard for bi-level image compression. This paper also reports on the progress in discussions to incorporate a DRC-based compression method as an improvement to the international standard.

Anomaly detection for capsule endoscopy images using higher-order Local Auto Correlation features

Capsule endoscopy is a painless way more and more utilized in gastrointestinal examination. Nevertheless, there is an issue comes out that the efficiency and accuracy of capsule endoscopy diagnosis is now restricted by the large quantity of images. In this paper, an anomaly detection method for capsule endoscopy images captured within the range of small intestine is described. Aiming to realize the anomaly detection, this paper takes the advantage of Higher-order Local Auto Correlation features and subspace method using PCA (Principal Component Analysis). The proposed method is validated over capsule endoscopy image sets and its effectiveness is demonstrated by experimental results.

An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images

In histopathological diagnosis, a clinical pathologist discriminates between normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens on meeting the demands for such diagnoses, and this is becoming a serious social problem. Currently, it is necessary to develop new medical technologies to help reduce their burdens. Therefore, as a diagnostic support technology, this paper describes an extended method of HLAC feature extraction for classification of histopathological images into normal and anomaly. The proposed method can automatically classify cancerous images as anomaly by using an extended geometric invariant HLAC features with rotation- and reflection-invariant properties from three-level histopathological images, which are segmented into nucleus, cytoplasm and background. In conducted experiments, we demonstrate a reduction in the rate of not only false-negative errors but also of false-positive errors, where a normal image is falsely classified as an image with an anomaly that is suspected as being cancerous.

Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features

This paper proposes a technology for histopathological diagnostic support that utilizes the correlation-based features of histopathological tissues. In histopathological diagnosis, a clinical pathologist conducts a diagnosis of normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens to meet the demands for such diagnoses, and this is causing serious social problems. In order to overcome this problem, we propose a technology of histopathological diagnostic support that uses higher-order local autocorrelation (HLAC) features. The proposed method can automatically screen tissue that is believed to be normal tissue to detect cancerous tissue as well as tissue that is suspected of being cancerous to detect abnormalities. Consequently, we can reduce the burden on clinical pathologists, allowing them to concentrate on diagnosing cancer.

Adaptive Optical Proximity Correction Using an Optimization Method

This paper proposes a new approach to the optical proximity correction (OPC) method which reduces OPC calculation loads by employing an optimization method. OPC is a method of correcting for a mask pattern to improve the fidelity of an image pattern on a silicon wafer. However, conventional OPC calculations have become increasingly complex as the size of semiconductor devices becomes even smaller. In order to overcome this problem, we propose an adaptive OPC technology using an optimization method to realize OPC for the full-chip area at fast operational speeds. The effectiveness of this approach in terms of reduced OPC calculation times and highly-accurate correction is demonstrated through computational experiments.

Hotspot prevention and detection method using an image-recognition technique based on higher-order local autocorrelation

Abstract. Although a number of factors relating to lithography and material stacking have been investigated to realize hotspot-free wafer images, hotspots are often still found on wafers. For the 22-nm technology node and beyond, the detection and repair of hotspots with lithography simulation models is extremely time-consuming. Thus, hotspots represent a critical problem that not only causes delays to process development but also represents lost business opportunities. In order to solve the time-consumption problem of hotspots, this paper proposes a new method of hotspot prevention and detection using an image recognition technique based on higher-order local autocorrelation, which is adopted to extract geometrical features from a layout pattern. To prevent hotspots, our method can generate proper verification patterns to cover the pattern variations within a chip layout to optimize the lithography conditions. Moreover, our method can realize fast hotspot detection without lithography simulation models. Obtained experimental results for hotspot prevention indicated excellent performance in terms of the similarity between generated proposed patterns and the original chip layout patterns, both geometrically and optically. Moreover, the proposed hotspot detection method could achieve turn-around time reductions of >95% for just one CPU, compared to the conventional simulation-based approach, without accuracy losses.

An objective evaluation method of ulcerative colitis with optical colonoscopy images based on higher order local auto-correlation features

This study aims to establish a new method of objective evaluation for optical colonoscopy that can quantify the severity of colonic mucosa for ulcerative colitis (UC). UC is an intractable disease and has been the subject of survey research for long time. However, because there are enormous variations in the patterns of symptoms associated with UC, universal diagnostic standards have yet to be established. Accordingly, diagnostic accuracy is highly dependent on the experience and knowledge of the medical doctor. In order to overcome this problem, this paper describes a method of objective evaluations for UC based on image recognition techniques and multi-discriminant analysis. The proposed method extracts geometrical features using higher order local auto-correlations from the saturation element of the HSV color space for the colonoscopy images, and makes classifications according to the UC severity based on the subspace method. This study provides an index for UC severity to support colonoscopy diagnosis.

Adaptive waveform control in a data transceiver for multi-speed IEEE1394 and USB communication

This paper proposes an adaptive waveform control in a data transceiver and demonstrates an adaptive transceiver LSI with a waveform controller. The LSI optimizes on-site transmission performance, with adjustments based on measurements for the whole transmission system, including cable properties. Utilizing genetic algorithm (GA), our adjustment method has achieved a transmission speed that is four times faster (1.6GHz) than current standards (400MHz) for IEEE1394.

EHW Applied to Image Data Compression

In this chapter, EHW is applied to the lossless image compression, and it is implemented in a chip. The current international standard for bi-level image coding, JBIG2-AMD2, is modified by the proposed method to achieve high compression ratios, where compression parameters are optimized by the enhanced genetic algorithm (GA). The results of computer simulations show a 171% improvement in compression ratios with the proposed method compared to JBIG2 without optimization. The experiment shows that when the method is implemented by hardware with an evolvable hardware chip, the processing speed is dramatically faster than execution with software. This chapter also de-scribes activities concerning ISO standardization to adopt part of the technology used in this method to the JBIG2 standard.