Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.

BACKGROUND AIMS: Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost. METHODS: One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities. RESULTS/CONCLUSIONS: This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

The Canonical Robot Command Language (CRCL).

Industrial robots can perform motion with sub-millimeter repeatability when programmed using the teach-and-playback method. While effective, this method requires significant up-front time, tying up the robot and a person during the teaching phase. Off-line programming can be used to generate robot programs, but the accuracy of this method is poor unless supplemented with good calibration to remove systematic errors, feed-forward models to anticipate robot response to loads, and sensing to compensate for unmodeled errors. These increase the complexity and up-front cost of the system, but the payback in the reduction of recurring teach programming time can be worth the effort. This payback especially benefits small-batch, short-turnaround applications typical of small-to-medium enterprises, who need the agility afforded by off-line application development to be competitive against low-cost manual labor. To fully benefit from this agile application tasking model, a common representation of tasks should be used that is understood by all of the resources required for the job: robots, tooling, sensors, and people. This paper describes an information model, the Canonical Robot Command Language (CRCL), which provides a high-level description of robot tasks and associated control and status information.

Invited Article: Autonomous assembly of atomically perfect nanostructures using a scanning tunneling microscope.

A major goal of nanotechnology is to develop the capability to arrange matter at will by placing individual atoms at desired locations in a predetermined configuration to build a nanostructure with specific properties or function. The scanning tunneling microscope has demonstrated the ability to arrange the basic building blocks of matter, single atoms, in two-dimensional configurations. An array of various nanostructures has been assembled, which display the quantum mechanics of quantum confined geometries. The level of human interaction needed to physically locate the atom and bring it to the desired location limits this atom assembly technology. Here we report the use of autonomous atom assembly via path planning technology; this allows atomically perfect nanostructures to be assembled without the need for human intervention, resulting in precise constructions in shorter times. We demonstrate autonomous assembly by assembling various quantum confinement geometries using atoms and molecules and describe the benefits of this approach.