Development of molecular imprinted polymer interdigital sensor for C-terminal telopeptide of type I collagen

This paper presents a label-free and non-invasive technique for selective detection of C-terminal telopeptide type I collagen (CTx-I) by employing Electrochemical Impedance Spectroscopy to measure sample impedance. Molecular imprinted polymer, containing artificial recognition sites for CTx-was prepared by precipitation polymerization using CTx-I peptide as a template, methacrylic acid as a functional monomer and ethylene glycol methacrylate as the cross-linker. A high penetration depth planar interdigital sensor was functionalized by a self-assembled monolayer along with the synthesized MIP. Different concentrations of CTx-I sample solutions were tested using the proposed sensing system. High-Performance Liquid chromatography diode array system was used to validate the results.5 page(s

Training traffic light behavior with end-to-end learning

In this work, we study neural network architectures that will reduce the number of infractions made by autonomous-driving agents. These agents control vehicles by providing future waypoints directly from a forward-facing camera. Building on top of the teacher-student approach of Cheating by Segmentation, we investigate the impact of Pyramid Pooling Module and Feature Pyramid Network with the aim to learn more representative features. We run our experiment with CARLA simulator and show that pyramid perception modules have a positive impact in reducing the number of traffic light infractions and collisions

Training traffic light behavior with end-to-end learning

In this work, we study neural network architectures that will reduce the number of infractions made by autonomous-driving agents. These agents control vehicles by providing future waypoints directly from a forward-facing camera. Building on top of the teacher-student approach of Cheating by Segmentation, we investigate the impact of Pyramid Pooling Module and Feature Pyramid Network with the aim to learn more representative features. We run our experiment with CARLA simulator and show that pyramid perception modules have a positive impact in reducing the number of traffic light infractions and collisions

Compact descriptor for local feature using dominating centre‐symmetric local binary pattern

The authors propose a terse texture feature, called the dominant centre‐symmetric local binary pattern (DCSLBP), which has similar distinctiveness and half dimension compared against original centre‐symmetric local binary pattern (CS‐LBP). On the basis of DCSLBP histogram and an improved construction, a compact descriptor for local feature is presented. To assess the proposed descriptor with the state‐of‐the‐art in performance and dimension, the authors extend it to two variants with different dimensions using the existing method. These descriptors are compared with scale‐invariant feature transform (SIFT), multisupport region rotation and intensity monotonic invariant descriptor (MRRID), orthagonal combination local binary pattern (OC‐LBP) in interest region matching and in the application of object recognition. The experiments demonstrate the proposed descriptor's compactness and robustness to various image transformations, especially to large illumination change