An upstream cylindrical bluff body connected to a tip body via an aluminum cantilever beam was tested as energy harvester in a wind tunnel. The characteristics and behavior of the different tip body configurations and lengths of aluminum cantilever beam were studied to optimize design to extract wind energy. Particular attention was paid to measure vibration amplitude and frequency response as a function of reduced velocity. Dynamic response showed that the device's behavior was dependent on both tip body shape and cantilever beam length. Flow visualization tests showed that high amplitude vibration was obtainable when a vortex was fully formed on each side of the downstream tip body. This was exemplified in a symmetrical triangular prism tip body at L/D1 = 5, where its structure's vibration frequency was close to its natural frequency. At such configuration, electrical energy was captured using a polyvinylidene fluoride (PVDF) piezoelectric beam of different load resistances, where an optimized load resistance could be found for each Reynolds number. Although power output and efficiency obtained were considerably weak when compared to those of traditional wind turbine, the design merits further research to improve its performance under various circumstances.

Current robotic haptic object recognition...

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.