44,148 research outputs found
SPARCS: Stream-processing architecture applied in real-time cyber-physical security
In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network
A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing
We propose a compressed sampling and dictionary learning framework for
fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is
generated from a model for the reflected sensor signal. Imperfect prior
knowledge is considered in terms of uncertain local and global parameters. To
estimate a sparse representation and the dictionary parameters, we present an
alternating minimization algorithm that is equipped with a pre-processing
routine to handle dictionary coherence. The support of the obtained sparse
signal indicates the reflection delays, which can be used to measure
impairments along the sensing fiber. The performance is evaluated by
simulations and experimental data for a fiber sensor system with common core
architecture.Comment: Accepted for publication in Journal of the Optical Society of America
A [ \copyright\ 2017 Optical Society of America.]. One print or electronic
copy may be made for personal use only. Systematic reproduction and
distribution, duplication of any material in this paper for a fee or for
commercial purposes, or modifications of the content of this paper are
prohibite
MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum
In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application
Generalized Inpainting Method for Hyperspectral Image Acquisition
A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding,
sparse models, CMOS, Fabry-P\'ero
CMOS Architectures and circuits for high-speed decision-making from image flows
We present architectures, CMOS circuits and CMOS chips to process image flows at very high speed. This is achieved by exploiting bio-inspiration and performing processing tasks in parallel manner and concurrently with image acquisition. A vision system is presented which makes decisions within sub-msec range. This is very well suited for defense and security applications requiring segmentation and tracking of rapidly moving objects
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