Tree height measurement based on image processing embedded in smart mobile phone

Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

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Study and Analysis a Powerful Debug Method for Smart Mobile Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.4270 ◽

2011 ◽

Vol 130-134 ◽

pp. 4270-4273

Author(s):

Jian Guo Yuan ◽

Sheng Gu

Keyword(s):

Complex Systems ◽

Mobile Phone ◽

Software Package ◽

Random Access ◽

Random Access Memory ◽

Exception Handling ◽

Mobile Systems ◽

Access Memory ◽

Smart Mobile Phone ◽

Smart Mobile

When dealing with such complex systems such as the software package of a smart mobile phone, it is necessary to apply powerful methods to detect and report errors when they occur. This paper probes and analyzes a powerful debug method called trap and exception handling, which is supported by the ARM platforms. The method can easily capture some valuable debug information. When a fatal error occurs during runtime or system detects an abnormal, a trap or an exception shall be logged and stored in the non-volatile random access memory (NVRAM). The information must be enough and easy for engineer to analyze the software error.

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Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys

Forests ◽

10.3390/f10080643 ◽

2019 ◽

Vol 10 (8) ◽

pp. 643 ◽

Cited By ~ 2

Author(s):

Guangpeng Fan ◽

Feixiang Chen ◽

Yan Li ◽

Binbin Liu ◽

Xu Fan

Keyword(s):

Root Mean Square Error ◽

Mobile Phone ◽

Mean Square Error ◽

Tree Species ◽

Tree Height ◽

Measurement Tool ◽

Mean Square ◽

Height Measurement ◽

Test Dataset ◽

Ground Measurement

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys.

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