Ignacio Bosch

A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing

The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we present a wireless sensor network where each sensor node takes images from the field and internally uses image processing techniques to detect any unusual status in the leaves. This symptom could be caused by a deficiency, pest, disease or other harmful agent. When it is detected, the sensor node sends a message to a sink node through the wireless sensor network in order to notify the problem to the farmer. The wireless sensor uses the IEEE 802.11 a/b/g/n standard, which allows connections from large distances in open air. This paper describes the wireless sensor network design, the wireless sensor deployment, how the node processes the images in order to monitor the vineyard, and the sensor network traffic obtained from a test bed performed in a flat vineyard in Spain. Although the system is not able to distinguish between deficiency, pest, disease or other harmful agents, a symptoms image database and a neuronal network could be added in order learn from the experience and provide an accurate problem diagnosis.

Optimum detection of ultrasonic echoes applied to the analysis of the first layer of a restored dome

Optimum detection is applied to ultrasonic signals corrupted with significant levels of grain noise. The aim is to enhance the echoes produced by the interface between the first and second layers of a dome to obtain interface traces in echo pulse B-scan mode. This is useful information for the restorer before restoration of the dome paintings. Three optimum detectors are considered: matched filter, signal gating, and prewhitened signal gating. Assumed models and practical limitations of the three optimum detectors are considered. The results obtained in the dome analysis show that prewhitened signal gating outperforms the other two optimum detectors.

Measurement of cement porosity by centroid frequency profiles of ultrasonic grain noise

In this paper, we propose a technique for material characterization by using centroid frequency profiles (CFP) of ultrasound echo signals. These echo signals are composed by grain noise due to the superposition of many small echoes from the inner microstructure plus observation noise. A CFP indicates the centroid frequency dependence on depth, corresponding to power spectrum density assessments at different depths. We show in the paper the relation between the mean and variance of the CFP and the grain-to-observation-noise-ratio (GOR) at every depth. The GOR depends on the material ultrasound attenuation, so that CFP may be used for material characterization. Although we consider here the estimation of cement paste porosity, the proposed technique may have general applicability. Cement paste is the main component of mortar and concrete. Therefore, cement porosity is an important problem because the vulnerability (and thence the durability) of these construction materials to external agents depends heavily on it. Experiments have been made to show the correlation between cement paste porosity and a penetration parameter obtained from the CFP.

Infrared image processing and its application to forest fire surveillance

This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.

Object Discrimination by Infrared Image Processing

Signal processing applied to pixel by pixel infrared image processing has been frequently used as a tool for fire detection in different scenarios. However, when processing the images pixel by pixel, the geometrical or spatial characteristics of the objects under test are not considered, thus increasing the probability of false alarms. In this paper we use classical techniques of image processing in the characterization of objects in infrared images. While applying image processing to thermal images it is possible to detect groups of hotspots representing possible objects of interest and extract the most suitable features to distinguish between them. Several parameters to characterize objects geometrically, such as fires, cars or people, have been considered and it has been shown their utility to reduce the probability of false alarms of the pixel by pixel signal processing techniques.

An extended energy detector for non-Gaussian and non-independent noise

Energy detectors are optimum to detect uncorrelated Gaussian signals or generalized likelihood ratio tests to detect completely unknown signals; in both cases, background noise must be uncorrelated Gaussian. However, energy detectors degrade when background noise is non-independent and non-Gaussian. An extension is presented in this paper to deal with this situation. Independence is achieved by means of a matrix linear transformation derived from independent component analysis. Non-Gaussianity is avoided by applying a scalar non-linear function to every element of the linearly transformed observation vector. Practical procedures for estimating the linear and nonlinear transformations are given in the paper. A SNR enhancement factor has been defined for the weak signal case, which is indicative of the expected improvement of the proposed extension. Some simulations illustrate the achieved improvements.

Development of a Conductivity Sensor for Monitoring Groundwater Resources to Optimize Water Management in Smart City Environments.

The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore the use of sensors can be a good solution. Groundwater is very important as a water resource. Just in the USA, aquifers represent the water source for 50% of the population. However, aquifers are endangered due to the contamination. One of the most important parameters to monitor in groundwater is the salinity, as high salinity levels indicate groundwater salinization. In this paper, we present a specific sensor for monitoring groundwater salinization. The sensor is able to measure the electric conductivity of water, which is directly related to the water salinization. The sensor, which is composed of two copper coils, measures the magnetic field alterations due to the presence of electric charges in the water. Different salinities of the water generate different alterations. Our sensor has undergone several tests in order to obtain a conductivity sensor with enough accuracy. First, several prototypes are tested and are compared with the purpose of choosing the best combination of coils. After the best prototype was selected, it was calibrated using up to 30 different samples. Our conductivity sensor presents an operational range from 0.585 mS/cm to 73.8 mS/cm, which is wide enough to cover the typical range of water salinities. With this work, we have demonstrated that it is feasible to measure water conductivity using solenoid coils and that this is a low cost application for groundwater monitoring.

Multisensor Network System for Wildfire Detection Using Infrared Image Processing

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.

Automatic Forest Surveillance Based on Infrared Sensors

Sensor networks are of great interest to complement human capabilities when monitoring wide-forest areas. This paper describes a remote system scheme for automatic forest surveillance based on infrared sensors. A complete system for forest fire detection is firstly presented although we focus on processing the images from infrared sensors. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ratio (SNR) is also evaluated.

A ground system for early forest fire detection based on infrared signal processing

This article presents a ground remote automatic system for forest surveillance based on infrared signal processing applied to early fire detection. Advanced techniques, which are based on infrared signal processing, are used in order to process the captured images. With the aim of determining the presence or absence of fire, the system performs the fusion of different detectors that exploit different expected characteristics of a real fire, such as persistence and increase. Theoretical simulations and practical results are presented to corroborate the control of the probability of false alarm. Results in a real environment are also presented to authenticate the accuracy of the operation of the proposed system. In particular, some experiments have been done to evaluate the delay of the system (tens of seconds on average) in detecting a controlled ground fire in a range of 1–10 km. Moreover, temporary evolution of false alarms and true detections are presented to evaluate the long-term performance of the system in a real environment. We have reached a detection probability of 100% at a false alarm rate of around 1 × 10−9.