Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Mitacs brings innovation to more people in more places across Canada and around the world.
Learn MoreWe work closely with businesses, researchers, and governments to create new pathways to innovation.
Learn MoreNo matter the size of your budget or scope of your research, Mitacs can help you turn ideas into impact.
Learn MoreThe Mitacs Entrepreneur Awards and the Mitacs Awards celebrate inspiring entrepreneurs and innovators who are galvanizing cutting-edge research across Canada.
Learn MoreDiscover the people, the ideas, the projects, and the partnerships that are making news, and creating meaningful impact across the Canadian innovation ecosystem.
Learn MoreThe advantages of digital data (signal functions) processing have made this technique a standard for processing real world analog data in many areas of life, from music industry to seismology. Stored digitally, the data also become less sensitive to physical limitations than their analog counterparts. Other advantages include straightforward low frequency filtering procedures (e.g. as required in seismology, oceanography and other environmental monitoring), as well as frequency bounding (e.g. in telecommunications, sending large bandwidth signals over a narrow bandwidth). To transform analog functions to their digital counterpart, a discretization is necessary to obtain their corresponding lattice based versions. In many problems, it is sufficient to use discrete Fourier expansions of data sampled at equidistant points of a line (in 1D), or on a square lattice formed by two orthogonal 1D lattices (in 2D). We propose to develop and evaluate the methodology which will make use of the special symmetry properties of data that will be reflected in more efficient expansions, e.g. data sampled in 2D on a triangular lattice which will significantly improve multidimensional signal processing in terms of computational speed, memory and hardware requirements. The proposed methodology will be developed using semi-simple Lie group theory.
Dr. Jiri Patera
Marzena Szajewska, Lenka Motlochova & Gayane Malkhasyan
M-Health Solutions
Mathematics
Information and communications technologies
Université de Montréal
Accelerate
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find ProjectsThe strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.