Current RFID tags process reveals that it is impossible to set the cost per tag to less than 5 cents. Similarly, area is a precious quantity. Much of the area in a tag is used by digital logic and capacitors. Merely adding more area is not a sustainable solution. This project will help the community to decrease the cost per tag to less than 5 cents as expected with much more compactness as compared to available tags.
The partner will benefit from the research by collaborating with one of the top research labs in the field.
Data has been recognized as one of the most valuable assets of modern business. The capacity to gather, store, analyze and interpret data in great quantities can determine to a large degree the ability of a company to achieve goals and adapt to largely volatile environments. This is especially true for financial institutions where data is directly connected to profitability.
We present a method for automatically estimating the lighting conditions from a single image. As opposed to most previous works which proposed methods that estimate only global lighting or use a limited illumination representation (low frequency SSH, parametric model), the proposed method attempt to use a new spatially-varying light representation with realist texture to render shiny objects. Our method will estimate a coarse (cuboid) geometry of an indoor scene from a single image and use this geometric information to hallucinate a realistic room texture used for illumination.
Structural health monitoring (SHM) of airplanes requires very compact and low-power stain sensors. Therefore, IPR wants to investigate how a commercial micro-fabrication process can be used to implement its MEMS sensor design, particularly using the electro-conductive properties of doped silicon vs. metal-coated crystalline silicon or polysilicon. The project will consist of a conceptual study of the current design provided by IPR and the design and evaluation through simulations of that design implemented in different technology.
This project aims at developing an automated system for water sampling along the coast of Nova Scotia using a swarm of flying robots. Currently, water is sampled by hand by teams of 2-5 people each on a number of boats, that must regularly travel to different sites, collect samples, and bring them to land for analysis. This task is slow and costly and exposes people to the inevitable dangers of the ocean.
With the development of advance telecommunication systems, new opportunities for real-time public transport monitoring has been created. Traffic congestion in the vehicular ad-hoc network can be typically caused by an accident, construction zones, special events, and adverse weather. This research presents a cognitive framework to address real-time routing problem and and arrival time prediction for bus system using a machine learning method.
With the increasing demand for data rates in modern high-speed links come new requirements for the simulation environments that are used for their design. With optical modulator now achieving beyond-100-GHz large-signal modulation bandwidth in hybrid silicon photonics, the main challenges that such systems are currently facing is the lack of efficient interconnects to interface with the outside world. These interconnects are designed and optimized using full-wave simulations.
The project aims at developing a new type of Reinforcement Learning algorithm that would allow to retain more control over the artificial agent once its training is completed. This framework would combine modern unsupervised modeling techniques to capture the variability of a set of demonstrations and user-defined programmatic functions that can characterise particularly important factors of variations. Ultimately, the user would be able to specify to the learned agent what type of behavior should be executed at test-time.
New generation of civil transport aircraft presents interaction between flight mechanics and structural dynamics. Innovative CLaws have been developed to address this issue. They need to be verified thoroughly by high-fidelity simulations. For a research and development project, the traditional industrial verification process is too demanding and would be too time consuming. Indeed, each high-fidelity simulation is very slow.
The computing solutions of tomorrow must be more energy efficient than those of today, which requires combined efforts to be conducted on multiple research areas: from new transistor technologies to innovative software algorithms by way of original processor architectures. This project enters into this last research area by revisiting the vector processing model, which provides a highly efficient way of exploiting data parallelism in scientific computations, sensor processing, and machine learning algorithms.