This paper describes a novel driving scheme, ACC Accurate Color Capture, for improving color accuracy of LCD's. ACC changes RGB gamma curves separately to reduce the color shift with respect to the gray level. ACC dramatically reduced correlated color temperature shift from 3300K to 75K through 255‐64 gray range with 2.4 gray gamma value. As experimental results, ACC is little affected by gamma value variation of 2.0–2.8.

This paper describes a novel method for image contrast enhancement by controlling gamma curve in AMLCD. The key idea is to automatically manipulate gamma voltage in accordance with the image data distribution. This method is applied to 17″ SXGA LCD monitor module. The contrast ratio and the brightness are enhanced by about 3 times and 1.7 times, respectively, by using the proposed method.

Reduced Voltage Differential Signaling (RVDS) is a new interface for TFT‐LCD panel with Chip‐On‐Glass (COG) structure which has point‐to‐point topology and voltage mode differential signaling scheme. The display source driver IC with RVDS interface performs higher data rate up to 500Mbps, lower current consumption of 2.2mA, and lower EMI compared with conventional current mode interface.

With the development of the Internet and communication technologies, the types of services provided by multitier Web systems are becoming more diverse and complex compared to those of the past. Ensuring a continuous availability of business services is crucial for multitier Web system providers, as service performance issues immediately affect customer experience and satisfaction. Large companies attempt to monitor the system performance indicator (SPI) that summarizes the status of multitier Web systems to detect performance anomalies at an early stage. However, the current anomaly detection methods are designed to monitor a single specific SPI. Moreover, the existing approaches consider performance anomaly detection and its root cause analysis separately, thereby aggravating the burden of resolving the performance anomaly. To support the system provider in diagnosing the performance anomaly, we propose an advanced causative metric analysis (ACMA) framework. First, we draw out 191 performance metrics (PMs) closely related to the target SPI. Among these PMs, the ACMA determines 62 vital PMs that have the most influence on the variance of the target SPI using several statistical methods. Then, we implement a performance anomaly detection model to identify the causative metrics (CMs) between the vital PMs using random forest regression. Even if the target SPI changes, our detection model does not require any change in its model structure and can derive closely related PMs of the target SPI. Based on our experiments, wherein we applied the ACMA to the business services in an enterprise system, we observed that the proposed ACMA could correctly detect various performance anomalies and their CMs.