An integrated ambient light sensor (ALS) system in low-temperature polycrystalline silicon (LTPS) thin-film-transistor liquid-crystal-displays (TFT-LCDs) is proposed and prototyped in this study. It is designed as a 4-bit (16-step-grayscale) ALS and includes a noise subtraction circuit, a comparator as an analog-to-digital converter (ADC), 4-bit counters, and a parallel-to-serial converter. LTPS lateral p-i-n diodes with a long i-region are employed as photodetectors in the system. An LSI source driver is mounted on the LCD panel with a sensor control block which provides programmable clocks and reference voltages to the ALS circuit on the glass substrate for sensitivity tuning. The reliability tests were conducted for 300 hours with 30000 lux illumination at 70 °C and at -20 °C. The observed deviations of the ALS values for dark, 1000 lux, and 10000 lux were within ±1.

Recent studies suggest that learning “how to learn” is important because learners must be self-regulated to take more responsibility for their own learning processes, meta-cognitive control, and other generative learning thoughts and behaviors. The mechanism that enables a learner to self-regulate his/her learning strategies has been actively studied in classroom settings, but has seldom been studied in the area of real-world learning in out-of-school settings (e.g., environmental learning in nature). A feature of real-world learning is that a learner's cognition of the world is updated by his/her behavior to investigate the world, and vice versa. This paper models the mechanism of real-world learning for executing and self-regulating a learner's cognitive and behavioral strategies to self-organize his/her internal knowledge space. Furthermore, this paper proposes multimodal analytics to integrate heterogeneous data resources of the cognitive and behavioral features of real-world learning, to structure and archive the time series of strategies occurring through learner-environment interactions, and to assess how learning should be self-regulated for better understanding of the world. Our analysis showed that (1) intellectual achievements are built by self-regulating learning to chain the execution of cognitive and behavioral strategies, and (2) a clue to predict learning outcomes in the world is analyzing the quantity and frequency of strategies that a learner uses and self-regulates. Assessment based on these findings can encourage a learner to reflect and improve his/her way of learning in the world.

In this study, we use a vision-based driving monitoring sensor to track drivers’ visual scanning behavior, a key factor for preventing traffic accidents. Our system evaluates driver’s behaviors by referencing the safety knowledge of professional driving instructors, and provides real-time voice-guided safety advice to encourage safer driving. Our system’s evaluation of safe driving behaviors matched the instructor’s evaluation with accuracy over 80%.