Classification of Rigid and Non-Rigid Objects Using CNN

Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.

Robotic Manipulation System for Multi-Layer Fabric Stitching

Designing gripping and manipulation systems for soft materials is an interesting but challenging task that has been widely investigated in the robotic field recently. Soft materials require a departure from traditional methodologies proposed for grasping rigid objects. The presented work is step towards manipulating planar soft materials, namely garments. A work-cell to automate the stitching, involving the manipulation of a fabric cloth and a foam pad is described and simulated. The results of the simulation show the feasibility, and speeds comparable to humans. This work is part of SOFTMANBOT project, a cross-sectoral project funded in the EU Horizon 2020 framework.

Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.