Other

Rensselaer Polytechnic Institute

Jason Tyler Smith

Pingkun Yan

Ruoyang Yao

Marien Ochoa-Mendoza

Xavier Intes

One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Fluorescence lifetime imaging using deep learning

A cat litter box has a tray having a bottom from which sides upwardly extend to an upper lip and a lid for the tray which has a top from which sides downwardly extend to a lower lip configured to be supported upon the tray upper lip. The lid sides are formed with an opening through which cats may come and go. A ramp having a rough litter-dislodging surface is mounted in the tray extending upwardly from adjacent the tray bottom to adjacent the lid side wall opening.

Cat litter box

A threshold for a covered cat litter box having a side entry comprises a stand supporting an elevated cat landing field. A ramp having a rough litter-dislodging surface extends downwardly from the cat landing field. A barrier is provided for barring cats from jumping onto or off the sides of said ramp. Upon setting the threshold with the ramp accessible to the litter box entry cats may ingress and egress the liter box only via the threshold rough, liter dislodging ramp and landing field which dislodges litter from the paws.

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Troy, NY, United States of America

Jason Tyler Smith