Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

,,Location-based Services“ – Personalisierung mobiler Dienste durch Verortung

Im Zeitalter der Informationsgesellschaft, so wurde postuliert, spielten r?umliche Distanzen keine Rolle mehr und unsere r?umliche Mobilit?t n?hme ab. Die Mobilit?t in den letzten Jahren, speziell die Freizeitmobilit?t, hat hingegen zugenommen. Die Bereitstellung ortsbezogener Dienste – ,,Location-based Services“ – unterstützt und f?rdert dieses Verhalten.     

Linear demosaicing inspired by the human visual system.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.     

The impact of roasting on the ochratoxin A content of coffee

Roasting coffee led to a drop in the ochratoxin A (OTA) concentration, as measured by the reference method, especially for dark type roasts. The way the beverage was prepared also affected the OTA content, which could paradoxically be higher than that of the initial roasted coffee. Assays on the thermal stability of pure OTA showed that it ought to be found in larger quantities in roasted coffee. This suggested that OTA was masked by reactions with the substrate during roasting. The absence of OTA in green coffee is therefore the best guarantee of safety.     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.