A demonstration of wireless communication between two PIC Training systems utilizing two MRF24J40 24GHz transceiver modules to transmit reduced data stored on the 25LC256 SPI bus serial EEPROM. The data is displayed on an LCD module on the receiving unit.
This project has developed a facial detection device that can detect human faces in a dynamic environment. This device consists of a single-board computer mounted on a 3-D printed robot head and neck mechanism which is controlled by three servo motors. This project has adopted an unsupervised machine learning algorithm for facial detection. The experiment results show the effectiveness of the device to detect human faces in certain dynamic environments.
This project uses Raspberry Pi 1 model B+ that has 700MHz Broadcom BCM2835 CPU and 512 MB of SDRAM along with a LM35 temperature sensor and a HIH-4000-002 humidity sensor on webhosting in a real-time displaying/reporting format.
Integrated the PIC training system with, joystick, wireless camera, ultra sound sensor, infrared sensor, and 2.4GHz transceiver to either remote or self-guide the vehicle in different controls and navigations.
Use the PIC Training System with 2.4GHz, MEF24J40AM transceiver control a Rhino Robot skeleton robot arm movement remotely.
Integrated the PIC training system with stepper motors and custom designed gimbal to control/navigate a leaser pointer where the system will remember and repeat the navigation.
Integrated the PIC training system with a used Rhino robot skeleton, approximate sensor, and vacuum pump to pick and place an egg.
The Z Tracking Light project uses Bosch BNO055 absolute orientation sensors, TI PCA9306 bidirectional level translators, 2.4GHz Xbee RF modules, and PIC16F1784 MCUs. The bidirectional level translator is utilized to support I2C comms. The MCU compares the heading of the user’s head relative to that of the vehicle and drives a servo motor, via pulse width modulation, based on the difference between the two heading angles. The MCU is programmed and debugged utilizing the Microchip Pickit3 in conjunction with ODU’s PIC Trainer Board Rev2. Software is written in assembly language utilizing MPLAB X.
Integrated the PIC training system with a real time clock: DS1305, EEPROM: 25LC256, sensors of temperature: LM34, pressure: MS5801-01BA Altimeter module, and humidity: HM1500LF relative humidity module, and Microchip, 2.4GHz, MRF24J40MA transceiver in a master and two slaves in SPI communication protocols on a remote sensing project.