P.O Box 631 Station A
Toronto, ON, M1K 5E9
"Robotica" is designed as a mobile pick and place robot. Her robotic arm has six degrees of freedom (DOF). The design uses a Raspberry Pi 3 and Python platform. She is designed with an ultrasonic sensor module, computer vision, and a web server, supporting the four-wheel assembly, which is controlled by an H-bridge motor controller protoboard. The wheel assembly gives Robotica DOF, namely front, back, left, and right. The ultrasonic sensor module can protect her from unforeseen collisions.
The design of robotic arm includes three articulated joints such as base-joint, arm-joint, wrist-joint, and a claw gripper. The base-joint is controlled by 9gram rated micro servo with two DOF namely left and right. The arm-joint is connected to the base-joint that is controlled by 9 gram rated micro servo. The arm-joint is responsible for the arm’s front and back movement. The wrist-joint controls the arm’s up and down movement. The claw gripper manipulates objects by picking and placing. Although Robotica has collision avoidance module, she needs the computer vision for manipulating objects. The computer vision is designed using edge detection algorithm and OpenCV module. The computer vision allows for non-line of sight (N-LOS) situations. Robotica’s web server will be designed using an Apache webserver. The web server is planned for future work.
Robotica envisioned by 10-year-old, fellow Dark Star Intern, the Quantum Qid, Aaron Wang. Aaron’s plan for Robotica to feed the world, has help from the U.S. Government, as an approved project with Dark Star and the U.S. Department of Defense. Aaron’s UN 17 Sustainable Development Goals includes SDG#2 “Zero Hunger”. Aaron inspires us. "
"I programmed a Python script in a Raspberry Pi device to communicate with AWS IoT Core via MQTT protocol, using AWS IoT Device SDK module for Python. The device controls its GPIO pins and also generates PWM signals to control 3 servomotors, based on the received MQTT messages while subscribing to a MQTT topic.
"Our user friendly app, enables its users to perform the following:
- Access their health information and records in real time (also known as Store & Forward)
- Schedule and attend virtual and in-person appointments
- Enable push notification reminders
- Utilize their QR code as another mechanism of validating your OHIP ID
- A faster and more secure way to acquire prescriptions, confirm in-person appointments and a method of tracking patients who enter and exit the hospital and/or clinic.
Unlike many other Telehealth Apps, our application prides itself on its innovative QR code functionality containing their OHIP ID; given that this makes it faster for a patient to identify themselves and pick up a prescription and/or confirm their in-person appointment, as well as accommodate a more secure firewall to the patients’ identities owing to the fact that patients’ data will be encrypted and virtually inaccessible to hackers."
Our project is a research-based project focused on detecting the onset of muscle fatigue in people during their exercise time. The stages in our project include acquisition phase, feature extraction phase and comparison phase. In the acquisition phase, myoware sensor will be used to acquire the raw EMG signal during muscular activity from the biceps (brachial muscle) where the surface electrodes are placed. The sensor data is transferred to the PC using the Arduino UNO as interface. In the feature extraction phase, here the acquired raw EMG signal will be preprocessed to extract the useful signal that can be used for classification. As the muscle is contracting during the fatigue stage, there is change in EMG amplitude and a shift of the frequency spectrum to different values. This can be extracted and used in the comparison phase; the muscle state should be classified as fatigue and non-fatigue to identify the difference.
"The endotracheal tube used as an artificial airway to provide invasive mechanical ventilation has a small cuff that seals the airway to allow positive pressure ventilation. The endotracheal tube cuff (ETT-cuff) must be sustained inflated within the recommended pressure range (20–30 cmH2O) to minimize risks of clinical complications, such as tracheal injury when the pressure is too high, or impaired ventilation and ventilator-associated pneumonia when the pressure is too low.
The “Automatic Cuff Pressure Controller” has the objective to avoid overinflating and under-inflating during the mechanical ventilation period. This device has an electronic closed-loop system controlled by a single microcontroller (PIC24HJ128GP502). The device’s pressure sensor operates connected to the ETT-cuff pressure line, then it sends an analog signal to the microcontroller that converts it to digital, interprets, and provides outputs. The pressure data is the parameter to trigger the pump to inflate the ETT-cuff when the pressure is below the lower limit, triggering the valve to release air from the ETT-cuff when pressure overcomes the upper limit, and also can trigger the alarm (LED and buzzer). The LED will blink when the pressure is out of range, and the buzzer will sound in case of failing to adjust the pressure after 30 seconds.
The user interfaces are the LCD screen and four push-buttons for command/setup: menu, increase, decrease, and silence alarm. The menu has three setup options: upper limit pressure, lower limit pressure and release-delay. The release-delay is a function that provides a delay to open the release valve when the cuff pressure overcomes the high limit, which is a strategy to prevent the release of undesired air in case of sudden pressure changes, such as neck movement or cough. This function is an innovative component of this project compared to other devices."
"This project is to design and build an electronic device that utilizes a red laser diode (640 nanometre wavelength) in accordance with a photoresistor to measure the concentration of glucose in a test tube via visible light spectrophotometry, a form of spectroscopy. Upon activation, via either a serial or bluetooth command from a laptop, the device will activate into its calibration stage, activating the laser diode, shining the beam through an empty test tube, and striking the photoresistor, which will convert the light intensity into a numerical value via the microcontroller’s analog to digital converter (ADC). This is the initial intensity I0 . The device then enters its measurement stage, replacing the empty test tube with one filled with a selected concentration of glucose solution, and following the same steps as the calibration stage, with the acquired ADC value being the transmitted intensity I. The absorbance A is then calculated on the microcontroller using the equation A = log10(I0/I).
This is then used to calculate the concentration using the Beer Lambert Law A = ecl, with:
- e = molecular attenuation coefficient.
- l = length of light path.
- c = concentration.
Further plans for this device involved the utilization of several select wavelengths, corresponding with the spectrometric absorption peaks of glucose, to acquire several glucose concentration readings, with the intention of noninvasive blood glucose monitoring."