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Adobe Digital Portfolio Project

Adobe Digital Portfolio Project

Client Adobe
Professor(s) Melanie Farquhar, Gerry Paquette
Program Graphic Design, Photography, Advertising, Journalism, Human Centred Design, Project Management
Students Adam Starkey (Advertising), Camilla Sola (Journalism) , Kim MIredin (Human Centred Design), Genevieve Lepine (Graphic Design), Christine Volden (Graphic Design), Shreeja Bais Project Management), Sarah Aldousary (Human Centred Design), and Victoria Crawley (Photography)

Project Description:

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.

Short Description:

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.

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Adobe Digital Portfolio Project milestones Adobe Digital Portfolio Project team.
Adobe Digital Portfolio Project image of participant. Adobe Digital Portfolio Project program icons

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OCI logo

sustainable residence at Carp Road, Ottawa

sustainable residence at Carp Road, Ottawa

Client  
Professor(s) Marjan Ghannad,
Program Eco Design
Students Melva Joseph
Sweedel Dcunha
Ernest Haddad

Project Description:

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.

Short Description:

The project is designed to be a Sustainable Residence complying to the client’s requirements and responding to the site.

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sustainable residence at Carp Road, Ottawa sustainable residence at Carp Road, Ottawa
sustainable residence at Carp Road, Ottawa sustainable residence at Carp Road, Ottawa

 

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Sort and Ordering

Sort and Ordering banner image.

Client  
Professor(s) Gino Rinaldi,
Program Electro Mechanical Engineering Technology
Students Benjamin Cue (Team Lead)
Ricky Chen
Elizabeth Ramsay

 

Project Description:

 

 

Short Description:

We created a device that would sort object by physical characteristics such as color, and have the ability to request them using hand gestures in order to limit the amount of contact between people.

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Sort and Ordering prototype.

 

 

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Autonomous snow Blower

Autonomous snow Blower banner image.

Client  
Professor(s) Gino Rinaldi,
Program Electro-Mechanical Engineering Technician
Students Vinay Narola
Brijesh Chaudhary
Wentao Cheng

Project Description:

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.

Short Description:

Without worrying about any obstacles the automated snowblower robot follows a designated path set by the user to complete its operation in a given time.

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Prototype side view. Prototype front view.
Prototype perspective view. Prototype top view.

 

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UNIVERSAL GRIPPER

UNIVERSAL GRIPPER
Client  
Professor(s) GINO RINALDI,
Program ELECTROMECHANICAL ENGINEERING TECHNICIAN
Students SUMIT SEHGAL
BALPREET KAUR
PRATHYUSH PERALA

 

Project Description:

 

 

Short Description:

The Universal gripper is a solution for the everyday gripper use which provides a cheap and much more affordable option for gripping objects manually or even by the use of the robotic arm.

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STELLA: A Machine-Learning Speech-to-Text Mobile App

STELLA: A Machine-Learning Speech-to-Text Mobile App
Client Todd Kelley
Professor(s) Jason Mombourquette,
Program Computer Engineering Technology – Computing Science
Students Johnathan Gonzalez
Liang Chu
Robin Saini
Mayank Khera
Nick Sturgeon
Justin Dennison

 

Project Description:

 

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.

 

Short Description:

STELLA (Speech Transcript Extraction and Labelling Linguistics Application) is a mobile app developed to ease taking meeting minutes by utilizing machine learning to identify and decipher spoken audio. It allows management and sharing of meetings.

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STELLA: A Machine-Learning Speech-to-Text Mobile App STELLA: A Machine-Learning Speech-to-Text Mobile App
STELLA: A Machine-Learning Speech-to-Text Mobile App STELLA: A Machine-Learning Speech-to-Text Mobile App
STELLA: A Machine-Learning Speech-to-Text Mobile App STELLA: A Machine-Learning Speech-to-Text Mobile App

 

 

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Focused Infrared fire extinguishing system

Focused Infrared fire extinguishing system
Client
Professor(s) Prof. Gino Rinaldi,
Program Electro-Mechanical Engineering Technician
Students Mohit Patel
Harmanpreet Singh
Isaiah Bookhout

 

Project Description:

 

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.

 

Short Description:

Our project is called the FIRES
It stands for Focused InfraRed fire Extinguishing System

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POPTikR

POPTikR banner image.
Client Kamal Dhanoa
Professor(s) John Kozodoj,
Program Interactive Media Design
Students – Mardig Bandek
– Oliver Rojas
– Marwa Younes
– Kunal Puri
– Smiledeep Sandhu
– Rohankumar Ahir
– Parwinder Singh
– Megane Simo

 

Project Description:

 

 

Short Description:

POPTikR is a retail tech and business marketing platform with over 15+ years of experience looking for promotional videos to use on the launch of their app.

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POPTikR ad 1. POPTikR ad 2.
   
   

 

 

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TRD Armor And Plow

TRD Armor And Plow
Client PIYUSH_BADRESHIYA
Professor(s) GINO_RINALDI,
Program EMET
Students TAREK MOHAMMED
PIYUSH BADRESHIYA
JAGJOT SINGH

 

Project Description:

 

 

Short Description:

 

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Project Orange Lens

Project Orange Lens

Client Deanna Cormier
Professor(s) Reginald Dyer,
Program Computer Programming
Students May Aboalrejal​

Svetlana Dukkardt​

Khushboo Khushboo​

Tejus Revi​

Badriah Watt​

Project Description:

Deanna Cormier has been using a Facebook page for booking and updates, as well as Instagram for portfolio and gallery updates. However, in addition to having a social media presence she would like a professional website that will merge all functions into a single location. Our task was to make a website that included functionalities such as a calendar that displayed her current commitments and potential bookings, allows customers to book through the site, have her portfolio displayed through an online gallery, an “About Me” section, as well as social media links. We also added testimonials and photo highlights. This website now encompasses all the requirements from the client as well as all the features needed to stand out as a professional photography website.

Short Description:

The creation of a professional website that merged booking, portfolio, and customer experiences into one location.

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Project Orange Lens Project Orange Lens
Project Orange Lens Project Orange Lens

 

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