Ultrasonic Sonar Sensor Research Articles

Low-cost approach plan and development of GSM-based smart home automation system

Modern residential and office spaces provide important elements like home security systems. Homes are protected by this structure from harmful gases, fire, and gatecrashers. It ought to be sensible, trustworthy, and practical an implementation of a GSM-based home security framework has been proposed and carried out in this article. A GSM module, an MQ-2 alarm system, an ultrasonic sonar sensor, an LM35 temperature sensor, an Arduino Uno, and a transfer module make up the framework. In order to detect harmful gases like LPG, CH4, CO, alcohol, smoke, or propane, the MQ-2 warning device is employed. If any of the hazardous gases listed above are found, the alarm will sound and an advisory message will appear on the LCD display to alert the owner. Additionally, the owner will receive an SMS from it. Additionally, our system has the ability to assess mild, and if the temperature rises beyond a certain range, it will prepare for it by implementing cooling through the hand-off module. The owner will also receive an SMS from the framework. With the use of an Ultrasonic sensor, it can also identify intruders and warn us by vibrating, sending an SMS, and displaying a message on the LCD. Because it is incredibly responsive, practical, and built using our own innovations, our framework is strong.

Classification of Sonar Targets in Air: A Neural Network Approach.

Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be exploited to identify and classify sound-reflecting objects (targets), which may then be used as landmarks for navigation. In the presented work, sonar targets of different geometric shapes and sizes are classified with custom-engineered features. Artificial neural networks (ANNs) with multiple hidden layers are applied as classifiers and different features are tested as well as compared. We concentrate on features that are related to target strength estimates derived from pulse-compressed echoes. In doing so, one is able to distinguish different target geometries with a high rate of success and to perform tests with ANNs regarding their capabilities for size discrimination of targets with the same geometric shape. A comparison of achievable classifier performance with wideband and narrowband chirp excitation signals was conducted as well. The research indicates that our engineered features and excitation signals are suitable for the target classification task.