A 128128 pixel functional image sensor was implemented. The sensor was able to capture images at 1,000 frame/s and extract the sizes and positions of 10 objects/frame when clocked at 8 MHz. The size of each pixel was 18 µm18 µm and the fill factor was 28%. The chip, 3.24 mm3.48 mm in size, was implemented with a 0.35 µm CMOS sensor process; the power consumption was 29.7 mW at 8 MHz.

Processing structures required in sensing are designed to convert real-world information into useful information, and there are various restrictions and performance goals depending on physical restrictions and the target applications. On the other hand, network technologies are mainly designed for data exchange in the information world, as is seen in packet communications, and do not go well with sensing structures from the viewpoints of real-time properties, spatial continuity, etc. This indicates the need for understanding the architectures and restrictions of sensor technologies and network technologies when aiming to fuse these technologies. This paper clarifies the differences between these processing structures, proposes some issues to be addressed in order to achieve real fusion of them, and presents future directions toward real fusion of sensor technologies and network technologies.

Vocabulary acquisition based on the traditional pen-and-paper approach is outdated, and has been superseded by the multimedia-supported approach. In a multimedia-supported foreign language learning environment, a learning material comprised of a still-image, a text, and the corresponding sound data is considered to be the most effective way to memorize a noun. However, extraction of an appropriate still image for a noun has always been a challenging and time-consuming process for learners. Learners' burden would be reduced if a system could extract an appropriate image for representing a noun. Therefore, the present study purposed to extract an appropriate image for each noun in order to assist foreign language learners in acquisition of foreign vocabulary. This study presumed that, a learning material created with the help of an appropriate image would be more effective in recalling memory compared to the one created with an inappropriate image. As the first step to finding appropriate images for nouns, concrete nouns have been considered as the subject of investigation. Therefore, this study, at first proposed a definition of an appropriate image for a concrete noun. After that, an image re-ranking algorithm has been designed and implemented that is able to extract an appropriate image from a finite set of corresponding images for each concrete noun. Finally, immediate-after, short- and long-term learning effects of those images with regard to learners' memory retention rates have been examined by conducting immediate-after, delayed and extended delayed posttests. The experimental result revealed that participants in the experimental group significantly outperformed the control group in their long-term memory retention, while no significant differences have been observed in immediate-after and in short-term memory retention. This result indicates that our algorithm could extract images that have a higher learning effect. Furthermore, this paper briefly discusses an on-demand learning system that has been developed to assist foreign language learners in creation of vocabulary learning materials.