応用水文 = Applied hydrology

Hydrology and the Management of Watersheds

A practical introduction to signal processing in scientific measurement, for scientists, engineers, researchers, instructors, and students working in academia, industry, environmental, medical, engineering, earth science, space, military, financial, and agriculture. Includes such topics as smoothing, differentiation, peak detection, integration and peak area measurements, harmonic analysis, convolution and deconvolution, Fourier filtering, least-squares curve fitting, multi-component spectroscopy, nonlinear iterative least-squares peak fitting. Provides many demonstrations and real data examples, plus download links to many free Matlab/Octave scripts and functions plus spreadsheet templates which has been widely used and have been cited in over 160 published papers, theses, and patents. Includes an appendix containing case studies, examples, and simulations, plus an alphabetical index.

A Pragmatic Introduction to Signal Processing: with applications in scientific measurement

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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A Systematic Review of IoT Solutions for Smart Farming

In the last few years, the Internet of Things (IoT) has proved to be an interesting and promising paradigm that aims to contribute to countless applications by connecting more physical “things” to the Internet. Although it emerged as a major enabler for many next-generation applications, it also introduced new challenges to already saturated networks. The IoT is already coming to life especially in healthcare and smart environment applications adding a large number of low-powered sensors and actuators to improve lifestyle and introduce new services to the community. The Internet Engineering Task Force (IETF) developed RPL as the routing protocol for low-power and lossy networks (LLNs) and standardized it in RFC6550 in 2012. RPL quickly gained interest, and many research papers were introduced to evaluate and improve its performance in different applications. In this paper, we present a discussion of the main aspects of RPL and the advantages and disadvantages of using it in different IoT applications. We also review the available research related to RPL in a systematic manner based on the enhancement area and the service type. In addition to that, we compare related RPL-based protocols in terms of energy efficiency, reliability, flexibility, robustness, and security. Finally, we present our conclusions and discuss the possible future directions of RPL and its applicability in the Internet of the future.

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RPL-Based Routing Protocols in IoT Applications: A Review

Biometric is an automated recognition process that measures the unique human characteristics, such as iris recognition. Of late, different kinds of iris recognition techniques are available that can be used in different platforms. Daugman algorithm is one of the iris recognition techniques that provide high percentage of accuracy. This study aims to develop a device that performs Iris Recognition using Daugman algorithm on Raspberry Pi. Through the implementation of Daugman algorithm on Raspberry Pi, more applications may come up for iris recognition. Also to make the system adapt with the limitations of Raspberry Pi and the existing application, this work created an image processing function that preprocesses the image before it is passed to the application. Based on the tests implemented, the False Acceptance Rate (FAR) and False Rejection Rate (FRR), the system is mostly to reject registered actors than to accept impostors due to the hardware limitations.

Iris Recognition using Daugman algorithm on Raspberry Pi

Wireless sensor network is widely used in agricultural domain for efficient farming management. The application of the network system for agricultural environment monitoring was successfully developed in this research. Different sensors were integrated for acquiring data of three essential environment parameters inside the Lettuce (Lactuca Sativa) growth chambers; the light, temperature and humidity. The used of Raspberry Pi as the main component in designing the sensor nodes gives a perfect platform for reliable but low-cost wireless sensor network monitoring system.

Wireless sensor network for agricultural environment using raspberry pi based sensor nodes

In this paper, the development of wireless sensor network applied in greenhouse monitoring is presented. The greenhouse monitoring system was developed by integrating environmental sensor such as temperature, humidity and soil moisture in a microcomputer Linux board which is the Raspberry Pi 3. Sensor calibration is also presented to ensure the data accuracy for a controlled environment. Utilizing the WiFi capability of the Raspberry Pi 3 established the wireless network between the nodes and the data aggregator. An environmental monitoring platform is created for the deployment of the sensor nodes and evaluation of the Greenhouse Monitoring System in an actual controlled chamber.

Environmental wireless sensor network using raspberry Pi 3 for greenhouse monitoring system

Structural health monitoring (SHM) is an advancing area in the field of structural engineering The main objective of SHM is the verification of structural health and status in order to ensure proper performance and cost savings for maintenance using nondestructive tests It is not limited to diagnosis but is extended to prognosis of what are the standards to implement in order to mitigate such destruction of civil infrastructures However, there are still implementation issues such as improper calibration of sensors resulting to poor data integrity This study is concerned in reducing the inconsistency of characteristics of the ADXL345 accelerometer and DS18B20 temperature sensors in laboratory and working conditions Experimental design was employed in this study Combined inclination sensing and subwoofer-setup calibration technique was employed to six accelerometers, which yielded 95796% accuracy Two-point calibration technique was employed to twelve temperature sensors, which yielded 90960% accuracy Less than 10 %, which is considered highly accurate measurement, were yielded from measuring structural vibration and temperature, and environment temperature data from a reinforced concrete bridge test platform Thus, the proposed experimental study addressed successful enhancement of sensing characteristics of sensors

Triaxial MEMS digital accelerometer and temperature sensor calibration techniques for structural health monitoring of reinforced concrete bridge laboratory test platform

Every person has different perspective on whether a food is spoiled, and this may lead to wrong assumptions on the food's condition Misinterpretation on food condition could lead to food-borne illness The need for electronic nose system for classifying if a food is spoiled is still in demand due to this prediction confusion This study aims to detect food spoilage of tomato-based Filipino cuisines using an electronic nose Specifically, this research aims to develop a device with an array of sensors to detect the gases emitted by spoiled tomato-based Filipino cuisines and implement Artificial Neural Network as an algorithm for the classification of the data reading of the sensor The hardware part of the e-nose system makes use of Gizduino 1281, Raspberry Pi 3 model B, 20×4 LCD Screen, 7 MQ gas sensors, and 1 temperature/humidity sensor Stochastic Gradient Descent together with Back propagation algorithm is used for training the Artificial Neural Network data The tomato-based Filipino cuisine is placed below the sensor chamber for easy recognition of the gas sensors emitted by the cuisine itself The study could be useful in classifying whether food is spoiled This electronic nose system could determine spoilage level of a specific tomato-based Filipino cuisine The researchers assigned level 0 to 12 as the spoilage level These levels correspond to the time when the food is observed every four hours starting 7:00 AM of Day 1 As the experiment continued, it was also that the food often spoiled between levels 5 and 6 Based on the confusion matrix, the error rate of the electronic nose system is 385%

Febus Reidj G. Cruz

Papers

Iris Recognition using Daugman algorithm on Raspberry Pi

Wireless sensor network for agricultural environment using raspberry pi based sensor nodes

Environmental wireless sensor network using raspberry Pi 3 for greenhouse monitoring system

Triaxial MEMS digital accelerometer and temperature sensor calibration techniques for structural health monitoring of reinforced concrete bridge laboratory test platform

Determining spoilage level against time and temperature of tomato-based Filipino cuisines using electronic nose