Abstract Summary Management of raw-sequencing data and its pre-processing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized laboratory information management systems handling much more than just sequencing raw data. In this article, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation. Availability and implementation The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client software components are available via Bioconda. Supplementary information Supplementary data are available at Bioinformatics online.

Modern IP-based wide-area surveillance systems often build on networks of multi-modal, intelligent and mobile sensor units. Detection of complex events is performed on intelligent sensors and fusing input in the sensor units or centralized control room components. The domain of surveillance and public safety creates requirement for robustness and fault-tolerance. This article will present an automated intelligence architecture for mobile surveillance, which provides capabilities for combining on-board event detection in sensor units, centralized decision making on the server side, and automated exploitation of mobile surveillance unit positioning data. This architecture must be very reliable to provide services in the face of challenges such as natural disasters and fire, potentially damaging the infrastructure of the surveillance system. To increase its reliability and robustness, we study the introduction of a self-healing system into the architecture and examine the combined system’s operation in three case studies.

The Single Location Surveillance Point (SLSP) is a multi-sensor surveillance system that supports human security personnel in monitoring a physical indoors location. The system's Logical Decision Making Server (LDMS) component features rule-based automated logical deduction capabilities used for detecting threats and reacting to them. We have constructed a representation format for user definable surveillance rules based on the Extensible Markup Language (XML), with the aim of providing a solution that is both human-readable and expressive enough to support the features of the SLSP system. The syntax is capable of representing complex logical rules consisting of multiple conditions and reactive actions. The design features a vocabulary for rule definition, specific to the capabilities of the SLSP system. A prototype rule set has been successfully developed as a part of an SLSP prototype implementation.