Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are encouraged to construct and participate in sharing results and networking identical or improved versions of the SDMSI for safety, security and drop-in-place scientific image sensor networking

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

A Versatile Sensor Data Processing Framework for Resource Technology

Die Erweiterung experimenteller Infrastrukturen um neuartige Sensor eröffnen die Möglichkeit, qualitativ neuartige Erkenntnisse zu gewinnen. Um diese Informationen vollständig zu erschließen ist ein Abdecken der gesamten Verarbeitungskette von der Datenauslese bis zu anwendungsbezogenen Auswertung erforderlich. Eine Erweiterung bestehender wissenschaftlicher Instrumente beinhaltet die strukturelle und zeitbezogene Integration der neuen Sensordaten in das Bestandssystem. Das hier vorgestellte Framework bietet durch seinen flexiblen Ansatz das Potenzial, unterschiedliche Sensortypen in unterschiedliche, leistungsfähige Plattformen zu integrieren. Zwei unterschiedliche Integrationsansätze zeigen die Flexibilität dieses Ansatzes, wobei einer auf die Steigerung der Sensitivität einer Anlage zur Sekundärionenmassenspektroskopie und der andere auf die Bereitstellung eines Prototypen zur Untersuchung von Rezyklaten ausgerichtet ist. Die sehr unterschiedlichen Hardwarevoraussetzungen und Anforderungen der Anwendung bildeten die Basis zur Entwicklung eines flexiblen Softwareframeworks. Um komplexe und leistungsfähige Applikationsbausteine bereitzustellen wurde eine Softwaretechnologie entwickelt, die modulare Pipelinestrukturen mit Sensor- und Ausgabeschnittstellen sowie einer Wissensbasis mit entsprechenden Konfigurations- und Verarbeitungsmodulen kombiniert.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and OutlookNovel sensors with the ability to collect qualitatively new information offer the potential to improve experimental infrastructure and methods in the field of research technology. In order to get full access to this information, the entire range from detector readout data transfer over proper data and knowledge models up to complex application functions has to be covered. The extension of existing scientific instruments comprises the integration of diverse sensor information into existing hardware, based on the expansion of pivotal event schemes and data models. Due to its flexible approach, the proposed framework has the potential to integrate additional sensor types and offers migration capabilities to high-performance computing platforms. Two different implementation setups prove the flexibility of this approach, one extending the material analyzing capabilities of a secondary ion mass spectrometry device, the other implementing a functional prototype setup for the online analysis of recyclate. Both setups can be regarded as two complementary parts of a highly topical and ground-breaking unique scientific application field. The requirements and possibilities resulting from different hardware concepts on one hand and diverse application fields on the other hand are the basis for the development of a versatile software framework. In order to support complex and efficient application functions under heterogeneous and flexible technical conditions, a software technology is proposed that offers modular processing pipeline structures with internal and external data interfaces backed by a knowledge base with respective configuration and conclusion mechanisms.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and Outloo

TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world