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For the hardware side, the project builds on an Internet of Things (IoT) system of sensors that will monitor several targeted plant growth parameters in real-time. For our project’s specific case, we will be using sensors to monitor the soil and temperature, the soil moisture and the humidity of the microgreen crops. Each of these sensors will be wired and programmed to send data to a microcontroller that will transmit data to a computer. Using this architecture, the project will be scalable. Depending on the size of the farm, a new microcontroller can be added with its own set of sensors and communicate with the computer. This makes it so the project can be implemented potentially on all scales of farms. After the computer receives the data it will in turn share its acquired data with Cayenne; an IoT platform. By doing this, anybody with access to the Cayenne account, can access the sensor information. Making it easy for someone to login into Cayenne from a computer or a phone to access the information from any location. Making it very convenient for a user, instead of having to go to the farm and checking themselves.

For the website, the client wanted a website that they could use to market the company and display all of their products. To do this, we used two pages for the website, a home page that displays some background information about the company and what kind of products they can buy, and a products page that shows the individual items and their descriptions. To design the look of the webpages, we used a software called NicePage that allowed us to drag and drop html objects into a page and export them to html without the hassle of writing any complex css or javascript ourselves. Once we had the pages designed, we imported them to an IDE to edit the text and images on the page, and to add some final touches. We used a flask which is python based micro-framework, which is a basic MVC format for the website. Originally, we were planning on using AWS(Amazon Web Services) to host the website, but since the website doesn’t need much storage or much REST end-points, we decided to use Netlify instead. Netlify is a free service with a Git-based workflow and serverless platform to host sites.

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This project was a collaboration with Adobe to develop workshops for students using Adobe Spark. These workshops were designed to improve students’ digital literacy skills by training them how to create visually attractive personal portfolios of their academic and industry experience. Two professors worked with a team of eight students recruited from various design programs at the college – Graphic Design, Photography, Advertising, Journalism, and Human Centred Design.
In addition to live workshops, online ‘Playbook’ versions of the workshops were also developed, available on the Algonquin College Library website at https://algonquincollege.libguides.com/digital-portfolios.
This project reinforced the college’s strategic goal of preparing students for work in an increasingly digitized working environment. Adobe benefitted from the improved awareness and usage of their software at Algonquin College and other post-secondary institutions. The number of Adobe Creative Cloud license activations at Algonquin College increased by 100% from 5,000 to over 10,000 activations over duration of project.

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The project is designed to be a Sustainable Residence complying to the client’s requirements. Passive design strategies are implemented throughout the project to make use of the available site and climatic conditions, strategies that drove the design of the project. It follows a modular construction method which responds sensitively to the surrounding environment, as the site contains immense vegetation in and around it. This means that the design is tailor made for the site, hence making it unique to the client.
The passive design strategies adopted after research include-
• CLT construction with concrete topped flooring for harnessing the thermal mass available during winter.
• Hempcrete for prefabricated modules that have good thermal capacities along with being a carbon sink.
• Manually movable louver panels that can be adjusted according to the sun path for optimum adjustment of internal comfort.
• Shading system that is built with carefully selected angles to let in the sun during winters while blocking the harsh sun of the summers.
sun studies were conducted, along with daylighting and shading study to analyse the impact of the design strategies in reduction of energy use for artificial lighting, and to arrange the space layouts to harness maximum passive heat.

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This project is the concept of the snowblower robot. It follows a designated path and removes the snow along the way. This is shown as an automated means of conventional snow removal where human intervention is required. We have based this project on Arduino Uno R3. We have a pre-programmed path of the robot, it will follow the same path unless there is an obstacle. If there is an obstacle, it will avoid it and come back to its original path by the use of an ultrasonic sensor. All is driven by motor and done automatically.

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Machine Learning is an exciting field of computer science that begins to blur the line between man and machine. By giving a computer lots of real-world data along with the expected results, it can begin to form an accurate idea of how to interpret new data. This is the basis for our transcript-creating application, STELLA (Speech Transcript Extraction and Labelling Linguistics Application).

Our client, Todd Kelley, approached us with an idea for a mobile application that would record a meeting and produce an accurate transcript, including a written account of the spoken text with timestamps and speaker identification. The goal was to improve the efficiency of taking meeting minutes by automating the process.

Machine Learning was an obvious first step in creating our application. A trained algorithm could take recorded audio and translate it into text separated by the individual speakers. We leveraged existing algorithms created by the top minds in the field, such as Google, to form the basis of our application.

In order to increase our developmental efficiency, we used a development framework that allowed us to create both an iOS and Android application from a single codebase, rather than having to create two full separate applications. This allowed us to focus more on the core features instead of having to duplicate everything for the differences between the two platforms.

We integrated user accounts into our application in order to allow users to access their meetings across devices. Users can sign up right from the application to streamline the account creation process. All audio is encrypted in storage to protect the users’ privacy, and meetings can only be accessed by the user who recorded them.

Management of meetings was a big requirement for our application. As with humans, the speech-to-text algorithms do not hear the spoken words accurately 100% of the time, and so the ability to edit any mistakes is a must. Within a meeting, users can edit individual entries and set the names of the identified speakers.

When viewing a meeting, viewers can play back the original recorded audio. This allows a reference to the source in order to verify any potential discrepancies. While playing the audio, the current entry associated with that timestamp will be highlighted to easily follow along.

Users can also share a meeting using the mobile device’s native sharing features. This includes text messaging, email, or even just saving to a text file. Speakers, timestamps, and spoken text are included in the shared meetings.

We believe that this application has the potential to greatly increase the efficiency of creating written records of meetings. Along the way, our team learned a great deal about developing mobile applications and working with Machine Learning.

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FIRES is a system which uses Omron Infrared sensor to detect fire in a room. Whenever a fire initiates the source of fire is often small and these are called hot spots. Our Omron sensor will detect these source of fire (hotspots) and a nozzle which sprays water (or other fire extinguishing fluid) on these hotspots and extinguishing the fire even before it has time to spread.

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