An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards.

Fire blight is an infectious disease found in apple and pear orchards. While managing the disease is critical to maintaining orchard health, identifying symptoms early is a challenging task which requires trained expert personnel. This paper presents an inspection technique that targets individual symptoms via deep learning and density estimation.

A Real-Time Inspection System for Industrial Helical Gears.

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.

Detecting Teeth Defects on Automotive Gears Using Deep Learning.

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear's integrity can become compromised and lead to catastrophic failure.

Performing Complex Tasks by Users With Upper-Extremity Disabilities Using a 6-DOF Robotic Arm: A Study.

In this paper, we report on the results of a study that was conducted to examine how users suffering from severe upper-extremity disabilities can control a 6 degrees-of-freedom (DOF) robotics arm to complete complex activities of daily living. The focus of the study is not on assessing the robot arm but on examining the human-robot interaction patterns. Three participants were recruited.

The impact of arithmetic representation on implementing MLP-BP on FPGAs: a study.

In this paper, arithmetic representations for implementing multilayer perceptrons trained using the error backpropagation algorithm (MLP-BP) neural networks on field-programmable gate arrays (FPGAs) are examined in detail. Both floating-point (FLP) and fixed-point (FXP) formats are studied and the effect of precision of representation and FPGA area requirements are considered. A generic very high-speed integrated circuit hardware description language (VHDL) program was developed to help experiment with a large number of formats and designs.

Combining expert neural networks using reinforcement feedback for learning primitive grasping behavior.

This paper present an architecture for combining a mixture of experts. The architecture has two unique features: 1) it assumes no prior knowledge of the size or structure of the mixture and allows the number of experts to dynamically expand during training, and 2) reinforcement feedback is used to guide the combining/expansion operation. The architecture is particularly suitable for applications when there is a need to approximate a many-to-many mapping.