This project will address the production test needs of a silicon-based high-speed photonic transceiver solution for metro-reach terabit optical modems. For this project, the partnership will be between Ciena, DA Integrated (test development contractor) and Prof. Gordon W. Roberts from McGill University (academic expert in DFT and mixed-signal design and test).
Facial expression is a universal language to convey emotions and significantly affects social interactions. While psychologists have investigated facial expressions for decades, they have recently found their way into human-computer interactions and the gaming industry. A lot of research has been published on automatic detection of human emotions given either a single image or a series of images. In this project, we propose a new method for facial expression interpretation over a time series of images.
On time discovery of problems and constant monitoring of construction sites have great economical benefit. It requires the capability of highly efficient and accurate object detection and segmentation algorithms that can work with coarsely labelled training samples. The project is aimed to develop new learning-based object detection and segmentation algorithms for problem detection and mapping of construction sites with high accuracy and efficiency. This project will improve operation efficiency for construction related projects.
Injury is a leading cause of death and disability world-wide, however rates are especially high for low- and middle-income countries (LMICs). Trauma registriesâ databases that document information on the injured patient related to the injury event, demographics, process of care, and outcomeâ are commonly used in high-income countries and have proven extremely effective in reducing rates of death and disability through informing injury prevention and quality improvement programs.
SOTI MoblControl is an enterprise mobllity management solution that secures and manages, mobile devices and the mobile data across all endpomts. To ensure end-to-end security, MobiControl encrypts all
communication between the MobiControl Manager and the Deployment Server uslng Secure Socket layer (SSL). The SSL Is a cryptographic protocol that is use widely for secure communication. The process of the encryption and the decryption In the SSL require considerable computation power. Specially in a situation of handling a large number of devices.
The main objective of this project is to develop a retail cluster detection algorithm and improve the accuracy of the identification. The final deliverable will be an algorithm that runs through Google dataflow that will be able to ingest a month of usersâ location breadcrumbs and output user-location clusters. The output of the algorithm will be a unique cluster identifier that will be used in the visit algorithm for visit identification. Besides latitude and longitude data, the project will have access to altitude information.
In this project, we propose to expand support of SOTI's MobiControl (MC) to Tizen Operating System. SOTI MobiControl has assisted numerous enterprises to overcome the management issues due to lack of security and improve business performance by monitoring the health and safety of employees, and increasing productivity, with the introduction of wearables and other IoT (Internet of Things) devices.
Based on the userâs geo-location, timestamp and other attributes (eg. time of day, past visit history and app behavior categories, etc.), a machine learning algorithm can be developed to find which cluster the users belong to. Overall, the data of geo-location and timestamp are used to roughly locate the potential clusters. This project will involve some techniques and algorithms like cloud computing i.e Google Cloud Dataproc, sliding windows, histogram and machine learning algorithms. The challenge of first phase would be coming up with a good way of estimating the number of clusters.
Progress monitoring is an essential task in all construction projects. A proper progress monitoring can minimize the level of project overruns, which is very common among construction projects but incurring significant loss to public and private funds annually. Manual methods of progress tracking are too infrequent to represent continuous and live insights about the workplace. Recent advancements in the area of computer vision and machine learning inspired researchers to use these techniques for development of new automated progress monitoring systems.
Analytical applications in large organizations across even intermediate time ranges are often made complex, costly or even impractical due to temporal inconsistencies in the available data. The ever-changing nature of organizations causes categorical labels in data to change over time. This is particularly true for HR data, as the organization adjusts to changes in skillsets, market and operations. This project aims at establishing automated methods of defining consistent employee group labelling across time.