The paper presents the project team's advanced sensor-computer sphere technology for real-time and continuous
monitoring of wastewater runoff at the sewer discharge outfalls along the receiving water. This research significantly
enhances and extends the previously proposed novel sensor-computer technology. This advanced technology offers new
computation models for an innovative use of the sensor-computer sphere comprising accelerometer, programmable in-situ
computer, solar power, and wireless communication for real-time and online monitoring of runoff quantity. This
innovation can enable more effective planning and decision-making in civil infrastructure, natural environment
protection, and water pollution related emergencies. The paper presents the following: (i) the sensor-computer sphere
technology; (ii) a significant enhancement to the previously proposed discrete runoff quantity model of this technology;
(iii) a new continuous runoff quantity model. Our comparative study on the two distinct models is presented. Based on
this study, the paper further investigates the following: (1) energy-, memory-, and communication-efficient use of the
technology for runoff monitoring; (2) possible sensor extensions for runoff quality monitoring.
Urban stormwater runoff has been a critical and chronic problem in the quantity and quality of receiving waters,
resulting in a major environmental concern. To address this problem engineers and professionals have developed a
number of solutions which include various monitoring and modeling techniques. The most fundamental issue in these
solutions is accurate monitoring of the quantity and quality of the runoff from both combined and separated sewer
systems. This study proposes a new water quantity monitoring system, based on recent developments in sensor
technology. Rather than using a single independent sensor, we harness an intelligent sensor platform that integrates
various sensors, a wireless communication module, data storage, a battery, and processing power such that more
comprehensive, efficient, and scalable data acquisition becomes possible. Our experimental results show the feasibility
and applicability of such a sensor platform in the laboratory test setting.
In this paper, we propose our image indexing technique and visual query processing technique. Our mental images are different from the actual retinal images and many things, such as personal interests, personal experiences, perceptual context, the characteristics of spatial objects, and so on, affect our spatial perception. These private differences are propagated into our mental images and so our visual queries become different from the real images that we want to find. This is a hard problem and few people have tried to work on it. In this paper, we survey the human mental imagery system, the human spatial perception, and discuss several kinds of visual queries. Also, we propose our own approach to visual query interpretation and processing.
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