We investigate ambient sensing techniques that recognize writer's psychological states by measuring vibrations of handwriting on a desk panel using a piezoelectric contact sensor attached to its underside. In particular, we describe a technique for estimating the subjective difficulty of a question for a student as the ratio of the time duration of thinking to the total amount of time spent on the question. Through experiments, we confirm that our technique correctly recognizes whether or not a person writes something down on paper by measured vibration data at the accuracy of over 80 %, and that the order of computed subjective difficulties of three questions is coincident with that reported by the subject in 60 % of experiments. We also propose a technique to estimate a writer's psychological stress by using the standard deviation of the spectrum of the measured vibration. Results of a proof-of-concept experiment show that the proposed technique correctly estimates whether or not the subject feels stress at least 90 % of the time.
A self-powered urinary incontinence sensor system consisting of a urine-activated coin battery and a wireless transmitter has been developed as an application for wireless biosensor networks. The urine-activated battery makes possible both the sensing of urine leakage and self-powered operation. An intermittent power-supply circuit that uses an electric double-layer capacitor (EDLC) with a small internal resistance suppresses the supply voltage drop due to the large internal resistance of the battery. This circuit and a 1-V surface acoustic wave (SAW) oscillator reduce the power dissipation of a wireless transmitter. The SAW oscillator quickly responds to the on-off control of the power supply, which is suitable for intermittent operation. To verify the effectiveness of the circuit scheme, the authors fabricated a prototype sensor system. When the volume of urine is 0.2 ml, the battery outputs a voltage of over 1.3 V; and the sensor system can transmit signals over a distance of 5 m.
This paper describes a Web-based image viewer which was developed to monitor high-definition agricultural images. In the cultivation of crops, physiological data and environmental data are important to increase crop yields. However, it is a burden for farmers to collect such data. Against this backdrop, the authors developed a monitoring system to automatically collect high-definition crop images, which can be viewed on a specialized Web-based image viewer. Users can easily observe detailed crop images over the Internet and easily find differences among the images. The authors experimentally installed the monitoring system in an apple orchard and observed the apples growing there. The system has been operating since August 11, 2009. In this paper, we confirm the ability of the monitoring system to perform detailed observations, including tracing the progress of a disease that affects the growth of an apple.
To measure the quantitative surface color information of agricultural products with the ambient information during cultivation, a color calibration method for digital camera images and a remote monitoring system of color imaging using the Web were developed. Single-lens reflex and web digital cameras were used for the image acquisitions. The tomato images through the post-ripening process were taken by the digital camera in both the standard image acquisition system and in the field conditions from the morning to evening. Several kinds of images were acquired with the standard RGB color chart set up just behind the tomato fruit on a black matte, and a color calibration was carried out. The influence of the sunlight could be experimentally eliminated, and the calibrated color information consistently agreed with the standard ones acquired in the system through the post-ripening process. Furthermore, the surface color change of the tomato on the tree in a greenhouse was remotely monitored during maturation using the digital cameras equipped with the Field Server. The acquired digital color images were sent from the Farm Station to the BIFE Laboratory of Mie University via VPN. The time behavior of the tomato surface color change during the maturing process could be measured using the color parameter calculated based on the obtained and calibrated color images along with the ambient atmospheric record. This study is a very important step in developing the surface color analysis for both the simple and rapid evaluation of the crop vigor in the field and to construct an ambient and networked remote monitoring system for food security, precision agriculture, and agricultural research.
The paper shows a compact hard real-time operating system for wireless sensor nodes called PAVENET OS. PAVENET OS provides hybrid multithreading: preemptive multithreading and cooperative multithreading. Both of the multithreading are optimized for two kinds of tasks on wireless sensor networks, and those are real-time tasks and best-effort ones. PAVENET OS can efficiently perform hard real-time tasks that cannot be performed by TinyOS. The paper demonstrates the hybrid multithreading realizes compactness and low overheads, which are comparable to those of TinyOS, through quantitative evaluation. The evaluation results show PAVENET OS performs 100 Hz sensor sampling with 0.01% jitter while performing wireless communication tasks, whereas optimized TinyOS has 0.62% jitter. In addition, PAVENET OS has a small footprint and low overheads (minimum RAM size: 29 bytes, minimum ROM size: 490 bytes, minimum task switch time: 23 cycles).
This paper describes a mobile phone-based data logging system for monitoring the growing status of Satsuma mandarin, a type of citrus fruit, in the field. The system can provide various feedback to the farm producers with collected data, such as visualization of related data as a timeline chart or advice on the necessity of watering crops. It is important to collect information on environment conditions, plant status and product quality, to analyze it and to provide it as feedback to the farm producers to aid their operations. This paper proposes a novel framework of field monitoring and feedback for open-field farming. For field monitoring, it combines a low-cost plant status monitoring method using a simple apparatus and a Field Server for environment condition monitoring. Each field worker has a simple apparatus to measure fruit firmness and records data with a mobile phone. The logged data are stored in the database of the system on the server. The system analyzes stored data for each field and is able to show the necessity of watering to the user in five levels. The system is also able to show various stored data in timeline chart form. The user and coach can compare or analyze these data via a web interface. A test site was built at a Satsuma mandarin field at Kumano in Mie Prefecture, Japan using the framework, and farm workers monitor in the area used and evaluated the system.
The authors developed a slope disaster monitoring system using distributed sensors. The sensor node has wireless communication and tilt detection capabilities. Each sensor node communicates with others and sends the corrected data to a base station that can send an alert message to administrators in case of emergency. Wireless communication abilities and the accuracy of an acceleration sensor that will be used as a tilt detection sensor of the developed sensor nodes were checked through a small field test in the Osaka University campus. The developed slope disaster monitoring system was installed on a slope along the Chugoku Expressway, and a performance evaluation experiment has been performed over 8 months. Throughout the field experiment study, the authors found that the developed sensor network system is effective to detect a slope disaster. They also found that, depending on geometric arrangements of the nodes, for establishing a steady network connection at the starting up stage of the system, relatively long time was required. Efficient sensor arrangements may require further studies.
For bridge diagnosis, the authors developed a wireless sensor network (WSN) to measure and gather the vibration data of bridges. In previous bridge diagnosis experiments, node failure and data packet loss occurred in the WSN, which caused some corruption in the collected data and hence the WSN could not be used to analyze the health status of the bridge. Furthermore, it was always difficult to determine the location of the nodes in order to ensure the link quality, when all the nodes of the WSN deployed for the first time. In this paper, a self-health monitoring system called distributed localized decision monitoring system (DLDMS) is presented to monitor the health of the WSN. Key features of the system include high detection accuracy, high responsiveness, and low energy consumption. Experimental data is given based on experiments at Kitakyushu in Japan.