The hippocampus plays a key role in the encoding and retrieval of information related to novel environments during spatial navigation. However, the neural basis for these processes in the human hippocampus remains unknown because it is difficult to directly measure neural signals in the human hippocampus. This study investigated hippocampal neural oscillations involved in encoding novel environments during spatial navigation in a virtual environment. Seven epileptic patients with implanted intracranial hippocampal depth electrodes performed three sessions of virtual environment navigation. Each session consisted of a navigation task and a location-recall task. The navigation task consisted of eight blocks, and in each block, the participant navigated to the location of four different objects and was instructed to remember the location of the objects. After the eight blocks were completed, a location-recall task was performed for each of the four objects. Intracranial electroencephalography data were monitored during the navigation tasks. Theta (5-8 Hz) and delta (1-4 Hz) oscillations were lower in the first block (novel environment) than in the eighth block (familiar environment) of the navigation task, and significantly increased from block one to block eight. By contrast, low-gamma (31-50 Hz) oscillations were higher in the first block than in the eighth block of the navigation task, and significantly decreased from block one to block eight. Comparison of sessions with high recall performance (low error between identified and actual object location) and low recall performance revealed that high-gamma (51-100 Hz) oscillations significantly decreased from block one to block eight only in sessions with high recall performance. These findings suggest that delta, theta, and low-gamma oscillations were associated with encoding of environmental novelty and high-gamma oscillations were important for the successful encoding of environmental novelty.
Since they are believed to provide more reliable and accurate tire contact parameters, intelligent tires have been widely studied for the purpose of the performance enhancement of the vehicle control systems such as anti-lock breaking system and the electronic stability program. Moreover, it is also expected that intelligent tires can be utilized to analyze tire dynamic response, taking into consideration that the measurements from the sensors inside the tire would contain considerable information on tire behavior in real driving scenarios. In this work, the tire physical characteristics related to in-plane dynamics of the tire, such as stiffness of the belt and sidewall and contact pressure distribution, were identified based on the combination of strain measurements and a flexible ring tire model. The radial deformation of the tread band was directly obtained from strain measurements based on the strain-deformation relationship. Tire parameters were identified by fitting the radial deformations from the flexible ring model to those derived from strain measurements. This approach removed the complex and repeated procedure to satisfy the contact constraints between the tread and the road surface in the traditional ring model. For validation purposes, circumferential strains were measured for three different tires on a Flat-Trac indoor test rig. And then, circumferential contact pressures and tire parameters were estimated based on these measurements. Identification using only model-based methods was conducted and comparison was made to the measured contact patch shapes. The comparison among identification methods and measurements shows good agreement. The proposed method of utilizing intelligent tire fused with physical tire model is expected to provide another tool to investigate tire characteristics. Moreover, tire properties identified using intelligent tires could be more closely linked to vehicle performance.
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