D. Siva
D. Siva has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- B. P. Santosh Kumar
Department of ECE, Y. S. R. Engineering College of Yogi Vemana University, Proddatur, India
2 shared publications - Sathishkumar Karupusamy
Gobi Arts & Science College (Autonomous), Gobichettipalayam, India
2 shared publications - A. Pullareddy Reddy
Department of ECE, CREC, Tirupati, India
1 shared publication - A. Sai Pranathi1 shared publication
- B. Suresh1 shared publication
- C. Sunitha1 shared publication
Latest Publications
- 4 April 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (04) , 1-9
Real-Time Landslide Monitoring and Alarm System
Landslides pose a significant risk to communities worldwide, often resulting in substantial damage and loss of life. This project presents the design and implementation of a low-cost, efficient Landslide Monitoring System based on an ESP32 microcontroller, integrating both a MEMS accelerometer [1] (ADXL345) and a vibration sensor to detect early warning signs of landslides. The system is designed to differentiate between a mere vibration event, which indicates a preliminary instability, and a more severe tilt event that suggests an imminent landslide. Upon detection, the system triggers an audible alert via a buzzer and displays status information on an LCD, while simultaneously transmitting critical data to a remote web server for real-time monitoring and further analysis. This paper leverages the robust connectivity features of the [2] ESP32 to ensure that alerts and sensor data are rapidly communicated over Wi-Fi, allowing for timely intervention by local authorities or disaster management teams. Key software components include sensor data acquisition using the I2C protocol, sensor calibration, threshold-based decision making, and HTTP-based data upload routines. KEYWORDS: ESP32 Microcontroller, ADXL345 Accelerometer, Vibration Senor, Buzzer, Wi-Fi Module.
- 4 April 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (04) , 1-9
Real-Time Landslide Monitoring and Alarm System
Landslides pose a significant risk to communities worldwide, often resulting in substantial damage and loss of life. This project presents the design and implementation of a low-cost, efficient Landslide Monitoring System based on an ESP32 microcontroller, integrating both a MEMS accelerometer [1] (ADXL345) and a vibration sensor to detect early warning signs of landslides. The system is designed to differentiate between a mere vibration event, which indicates a preliminary instability, and a more severe tilt event that suggests an imminent landslide. Upon detection, the system triggers an audible alert via a buzzer and displays status information on an LCD, while simultaneously transmitting critical data to a remote web server for real-time monitoring and further analysis. This paper leverages the robust connectivity features of the [2] ESP32 to ensure that alerts and sensor data are rapidly communicated over Wi-Fi, allowing for timely intervention by local authorities or disaster management teams. Key software components include sensor data acquisition using the I2C protocol, sensor calibration, threshold-based decision making, and HTTP-based data upload routines. KEYWORDS: ESP32 Microcontroller, ADXL345 Accelerometer, Vibration Senor, Buzzer, Wi-Fi Module.
- 4 April 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (04) , 1-9
Smart RFID IOT Enabled EV Charging System
Smart RFID IOT Enabled EV Charging System with the increasing adoption of electric vehicles (EVs), an efficient, secure, and automated charging infrastructure is essential for seamless operation. This project proposes an RFID-Based Smart Electric Vehicle Charging Station, which enhances the existing system by integrating IoT-based monitoring, multiple authentication methods, and adaptive charging capabilities. The system utilizes Arduino Uno as the primary microcontroller, interfacing with an RFID reader for secure user authentication, a voltage sensor to monitor the charging process, and a relay module for controlled power delivery. An OLED display provides real-time charging status, and a buzzer alerts users upon charge completion. KEYWORDS- 12v Battery, RFID Tag, Arduino UNO, Voltage sensor, Relay, Relay Drive, Display, Buzzer.
- 4 April 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (04) , 1-9
Smart RFID IOT Enabled EV Charging System
Smart RFID IOT Enabled EV Charging System with the increasing adoption of electric vehicles (EVs), an efficient, secure, and automated charging infrastructure is essential for seamless operation. This project proposes an RFID-Based Smart Electric Vehicle Charging Station, which enhances the existing system by integrating IoT-based monitoring, multiple authentication methods, and adaptive charging capabilities. The system utilizes Arduino Uno as the primary microcontroller, interfacing with an RFID reader for secure user authentication, a voltage sensor to monitor the charging process, and a relay module for controlled power delivery. An OLED display provides real-time charging status, and a buzzer alerts users upon charge completion. KEYWORDS- 12v Battery, RFID Tag, Arduino UNO, Voltage sensor, Relay, Relay Drive, Display, Buzzer.
- 26 July 2022
- Published by Springer Nature in Applied Nanoscience
- Vol. 12 (9) , 2533-2550
A computer aided diagnosis framework for detection and classification of interstitial lung diseases using computed tomography (CT) images
- 26 July 2022
- Published by Springer Nature in Applied Nanoscience
- Vol. 12 (9) , 2533-2550
A computer aided diagnosis framework for detection and classification of interstitial lung diseases using computed tomography (CT) images
- 6 June 2022
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 78 (16) , 18318-18335
Fine-tuned convolutional neural network for different cardiac view classification
- 6 June 2022
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 78 (16) , 18318-18335
Fine-tuned convolutional neural network for different cardiac view classification