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This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine. HIGHLIGHTS The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring The obtained results show that the possibility of using engine with different failure mode and fault considerations The represented control structure is simple, efficient and provides the required air-to-fuel ratio

This paper presents the act of face recognition using neural network techniques and binary pattern methods. This is aimed at devising a technique for robotic control via facial expression. The Matlab environment was used in this work to provide accurate processing of image and facial expression categorization using a neural network. By using the microcontroller board of basic stamp 2 (BS2), the wireless transmitter circuit was built in this study. Also, the receiver part was designed with a decoder and BS2 microcontroller and interfaced with MATLAB by serial port communication. The major function of BS2 is to remain for convinced typescript from MATLAB and process it to confirm the command required to be wirelessly transmitted. Through serial port to BS2, the four characters were sent by MATLAB, with one quality for every emotion detected. The gestures and facial expressions provide intuitional cues for interpersonal communication. Local Binary Pattern (LBP) features were introduced for texture analysis and recently have been introduced to represent faces in facial image analysis. The most important properties of LBP features are their computational simplicity and their tolerance to illumination changes. LBP features can be derived very fast in a single scan through the raw image and are within the low-dimensional feature space for texture analysis; they are applied as a local feature extraction method in facial expression recognition. This work introduced facial expression examination algorithms for motionless images and efficient extension to the sequence of images. The results showed that the classification accuracy achieved was 41% for static expression images and 86.31% for series of images; this is significantly higher than the accuracy of other standard image processing and recognition techniques.

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