We have developed a wide frequency range affordable optical chopper system from 10-550 Hz. It is a self-contained prototype built using 3D design and printing and arduino uno. The system consists of six chopper wheels...
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Automation the process of monitoring the garden can transform garden irrigation process from being manual and static to smart and dynamic one. This leads to higher comfortability, water using efficiency and less human...
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ISBN:
(纸本)9781510881679
Automation the process of monitoring the garden can transform garden irrigation process from being manual and static to smart and dynamic one. This leads to higher comfortability, water using efficiency and less human supervision effort. This paper proposes a cloud based Internet of Things (IoT) smart garden monitoring and irrigation system using arduino uno. The watering requirement for a plant can be adjusted by monitoring the soil moisture. Measuring the soil moisture of the plant gives information if the plant is ideally watered, over watered or under watered. The proposed system monitors and maintains two quantities of the garden, the garden soil moisture content and light intensity. This is done using soil moisture sensors and light intensity sensor. The monitored data is sent continuously to ThinkSpeak IoT cloud. In the cloud the data gathered from the system is analyzed and when a target threshold of soil moister is reached, an action is sent accordingly from the cloud to the garden automatic watering system to irrigate the garden. arduino uno microcontroller is used to implement the system control unit. IoT is used to keep the garden owner updated about the status of the sprinklers. Information from the sensors is regularly updated on a ThinSpeack IoT cloud and the user can check the water sprinklers status at any time. In addition, the sensor readings are transmitted to a ThingSpeak channel to generate graphs for analysis.
The key objective of this paper is to monitor the Fuel consumption, route deviations, driving habits, breakdown details, vehicle maintenance conditions, and other vital performance details of a Heavy vehicle especiall...
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The key objective of this paper is to monitor the Fuel consumption, route deviations, driving habits, breakdown details, vehicle maintenance conditions, and other vital performance details of a Heavy vehicle especially a college bus using a pre-fitted fuel sensor needed for an extensive fleet management system as operational costs and maintenance are escalating day by day. In particular, vehicle owners are confronting fuel break-ins, spare parts theft and illegal parking or route diversions in transport vehicles. In addition to this, vehicle proprietors don't ascertain operational details daily if they are illiterate. Various models are available which Standalone, Onboard diagnostics-based, GPS GPS-based fuel tracking Systems. This research project leverages IoT technology, GPS-based sensors, and a GSM tracking system to provide vehicle owners with real-time information on fuel consumption. This cloud-based and mobile application tracks the vehicle in real time. Most of the models are web-based and custom-built only. When the driver initiates fuel filling, the Fuel sensor in the tank automatically activates and senses the amount of fuel in the diesel tank by transmitting fuel input data to a connected cloud web server for storage and analysis along with other data. The gathered values are cross-verified with the database, and an alert message is promptly sent to the vehicle owner based on consumption patterns. This mobile app-based Vehicle Tracking System (VTS) application, using SMS and Vehicle tracking features offers numerous advantages to the vehicle owner, primarily focusing on preventing fuel theft.
IoT is essential in today's surveillance environment for maintaining safety and providing the best fire detection performance. Cameras are being installed in a lot of places because fire can seriously damage both ...
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IoT is essential in today's surveillance environment for maintaining safety and providing the best fire detection performance. Cameras are being installed in a lot of places because fire can seriously damage both residential and commercial sectors. However, the installed video monitoring device may generate data or perspectives that are odd or skewed. In view of these shortcomings, a convolutional neural network-based approach is proposed. The data set for testing the model is provided by an integrated sensor system that was built using an ESP-32 CAM image sensor, a number of complementing sensors, and an arduino uno microcontroller. Using drop out techniques, the model also solves the issue of overfitting in the paper. The performance of our approach was compared to that of other transfer learning models, such as MobileNet, Resnet50 and VGG19 that uses well-known state-of-the-art architectures. The main goal of this paper is to build a model which gives high accuracy while consuming low computational power during inference. The proposed model accuracy relative to latency is better than the transfer learning models. Furthermore, the approach is to build a well-generalised model for unknown data, resulting in efficient generalisation and fewer false predictions.
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