Sort by
Refine Your Search
-
Listed
-
Field
-
cycle or non-award courses of Higher Education Institutions. Preference factors: • Prior work with radar, video processing, or sensor fusion technologies.; • Familiarity with data acquisition frameworks
-
of algorithms for analyzing physiological and biomechanical signals acquired by devices with integrated sensors, which will perform electrocardiography, electromyography and movement measurements, among others
-
sensors, which will perform measurements, for example, of electromyography and inertial data Objective and efficient feature identification and recognition of personalized patterns for integration
-
of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
-
of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
-
neural networks, enabling us to estimate the reliability of a single decision of this algorithm. Regarding generalisation, recent self-supervised learning paradigms have strong synergies with the multi
-
. Functional testing of developed PCBs, including experimental validation of electronic circuits and S-parameter characterization using a vector network analyzer (VNA).; 4. Experimental characterization