Sökning: "stepping response"
Visar resultat 1 - 5 av 20 avhandlingar innehållade orden stepping response.
1. Development of functional asymmetries in young infants : A sensory-motor approach
Sammanfattning : Human functional laterality, typically involving a right-sided preference in most sensory-motor activities, is still a poorly understood issue. This is perhaps particularly true in terms of what underlying mechanisms that may govern lateral biases, as well as the developmental origins and course of events. LÄS MER
2. Stepping, placing and headturning biases in newborn infants : A neurodevelopmental perspective
Sammanfattning : In the present thesis the stepping, placing and head turning responses in healthy humanfullterm newborns are investigated. The main focus is put on a study of these newbornresponses in relation to functional asymmetries, while at the same time exploring anddiscussing different factors that possibly can affect the outcome of such studies. LÄS MER
3. Energy-momentum conserving time-stepping algorithms for nonlinear dynamics of planar and spatial Euler-Bernoulli/Timoshenko beams
Sammanfattning : Large deformations of flexible beams can be described using either the co-rotational approach or the total Lagrangian formalism. The co-rotational method is an attractive approach to derive highly nonlinear beam elements because it combines accuracy with numerical efficiency. LÄS MER
4. Analysis of basic motor behaviors in quadrupeds
Sammanfattning : Ability to perform locomotion in different directions and maintain upright body posture is crucial for normal life. At present, mice, which allows employing genetic approaches, are widely used in studying the locomotor system. In these investigations different experimental setups are used to evoke locomotion. LÄS MER
5. Deep learning applied to system identification : A probabilistic approach
Sammanfattning : Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. LÄS MER