Automobiles have become essential parts of our everyday lives. It can correlate many factors that may affect a vehicle primarily those which may inconvenient or in some cases harm lives or properties. Thus, focusing on detecting an automatic transmission vehicle engine, body and other parts that cause vibration and sound may help prevent car problems using MATLAB. By using sound, vibration, and temperature sensors to detect the defects of the car and with the help of the transmitter and receiver to gather data wirelessly, it is easy to install on to the vehicle. A technique utilized from Toyota Balintawak Philippines that every car is treated as panels(a, b, c, d, and e) 'a' being from the hood until the front wheel of the car and 'e' the rear shield to the back of the car, this was applied on how to properly place the sensors so that precise data could be gathered. Data gathered would be compared to the normal graph taken from the normal status or performance of a vehicle, data that would surpass 50% of the normal graph would be considered that a problem has occurred. The system is designed to prevent car accidents by determining the current status or performance of the vehicle, also keeping people away from harm.
This study is about the determination of moisture content of milled rice using image processing technique and perceptron neural network algorithm. The algorithm involves several inputs that produces an output which is the moisture content of the milled rice. Several types of milled rice are used in this study, namely: Jasmine, Kokuyu, 5-Star, Ifugao, Malagkit, and NFA rice. The captured images are processed using MATLAB R2013a software. There is a USB dongle connected to the router which provided internet connection for online web access. The GizDuino IOT-644 is used for handling the temperature and humidity sensor, and for sending and receiving of data from computer to the cloud storage. The result is compared to the actual moisture content range using a moisture tester for milled rice. Based on results, this study provided accurate data in determining the moisture content of the milled rice.
Disasters such as typhoons, tornadoes, and earthquakes are inevitable. Aftermaths of these disasters include the missing people. Using robots with human detection capabilities to locate the missing people, can dramatically reduce the harm and risk to those who work in such circumstances. This study aims to: design and build a tele-operated robot; implement in MATLAB an algorithm for the detection of humans; and create a database of human identification based on various positions, angles, light intensity, as well as distances from which humans will be identified. Different light intensities were made by using Photoshop to simulate smoke, dust and water drops conditions. After processing the image, the system can indicate either a human is detected or not detected. Testing with bodies covered was also conducted to test the algorithm’s robustness. Based on the results, the algorithm can detect humans with full body shown. For upright and lying positions, detection can happen from 8 feet to 20 feet. For sitting position, detection can happen from 2 feet to 20 feet with slight variances in results because of different lighting conditions. The distances greater than 20 feet, no humans can be processed or false negatives can occur. For bodies covered, the algorithm can detect humans in cases made under given circumstances. On three positions, humans can be detected from 0 degrees to 180 degrees under normal, with smoke, with dust, and with water droplet conditions. This study was able to design and build a tele-operated robot with MATLAB algorithm that can detect humans with an overall precision of 88.30%, from which a database was created for human identification based on various conditions, where humans will be identified.
This paper presents an electronic tongue system with blind source separation (BSS) and wireless sensor network (WSN)
for remote multi-ion sensing applications. Electrochemical sensors, such as ion-sensitive field-effect transistor (ISFET)
and extended-gate field-effect transistor (EGFET), only provide the combined concentrations of all ions in aqueous
solutions. Mixed hydrogen and sodium ions in chemical solutions are observed by means of H+ ISFET and H+ EGFET
sensor array. The BSS extracts the concentration of individual ions using independent component analysis (ICA). The
parameters of ISFET and EGFET sensors serve as a priori knowledge that helps solve the BSS problem. Using wireless
transceivers, the ISFET/EGFET modules are realized as wireless sensor nodes. The integration of WSN technology into
our electronic tongue system with BSS capability makes distant multi-ion measurement viable for environment and
water quality monitoring.
In this paper, we present the design of electronic tongue system for multi-ion sensing applications. The ion-sensitive
field-effect transistor (ISFET) detects the concentration of a particular ion in aqueous solution. However, when the given
chemical solution contains two or more ions, the ISFET sensor can only provide the combined concentration of ions. In
this end, our electronic tongue included a blind source separation (BSS) method of independent component analysis (ICA) to process the ISFET signals and to extract the concentrations of individual ions in the solution. The results of ISFET modeling based on fixed interference method (FIM) serve as a priori knowledge to help solve this blind source problem. Experiments are conducted on this electronic tongue system using aqueous solution containing hydrogen and sodium ions flowing through the array of dual H+ ISFET devices. The results of ICA processing successfully determined the concentration of hydrogen ions amidst the presence of sodium ions. This capability of ion separation allows us to move towards the development of smart electronic tongue systems for environmental and water quality monitoring.
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