82,085 research outputs found
ITERL: A Wireless Adaptive System for Efficient Road Lighting
This work presents the development and construction of an adaptive street lighting system
that improves safety at intersections, which is the result of applying low-power Internet of Things
(IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the
Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet
protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements
are sent to a coordinator node that collects and passes them to a local controller, which then makes
decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights
are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree
of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to
maintain up-to-date information for the system, enabling preventive maintenance.Consejería de Fomento y Vivienda Junta de Andalucía G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI
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
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Creating datasets for Neuromorphic Vision is a challenging task. A lack of
available recordings from Neuromorphic Vision sensors means that data must
typically be recorded specifically for dataset creation rather than collecting
and labelling existing data. The task is further complicated by a desire to
simultaneously provide traditional frame-based recordings to allow for direct
comparison with traditional Computer Vision algorithms. Here we propose a
method for converting existing Computer Vision static image datasets into
Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving
the sensor rather than the scene or image is a more biologically realistic
approach to sensing and eliminates timing artifacts introduced by monitor
updates when simulating motion on a computer monitor. We present conversion of
two popular image datasets (MNIST and Caltech101) which have played important
roles in the development of Computer Vision, and we provide performance metrics
on these datasets using spike-based recognition algorithms. This work
contributes datasets for future use in the field, as well as results from
spike-based algorithms against which future works can compare. Furthermore, by
converting datasets already popular in Computer Vision, we enable more direct
comparison with frame-based approaches.Comment: 10 pages, 6 figures in Frontiers in Neuromorphic Engineering, special
topic on Benchmarks and Challenges for Neuromorphic Engineering, 2015 (under
review
Wireless and Physical Security via Embedded Sensor Networks
Wireless Intrusion Detection Systems (WIDS) monitor 802.11 wireless frames (Layer-2) in an attempt to detect misuse. What distinguishes a WIDS from a traditional Network IDS is the ability to utilize the broadcast nature of the medium to reconstruct the physical location of the offending party, as opposed to its possibly spoofed (MAC addresses) identity in cyber space. Traditional Wireless Network Security Systems are still heavily anchored in the digital plane of "cyber space" and hence cannot be used reliably or effectively to derive the physical identity of an intruder in order to prevent further malicious wireless broadcasts, for example by escorting an intruder off the premises based on physical evidence. In this paper, we argue that Embedded Sensor Networks could be used effectively to bridge the gap between digital and physical security planes, and thus could be leveraged to provide reciprocal benefit to surveillance and security tasks on both planes. Toward that end, we present our recent experience integrating wireless networking security services into the SNBENCH (Sensor Network workBench). The SNBENCH provides an extensible framework that enables the rapid development and automated deployment of Sensor Network applications on a shared, embedded sensing and actuation infrastructure. The SNBENCH's extensible architecture allows an engineer to quickly integrate new sensing and response capabilities into the SNBENCH framework, while high-level languages and compilers allow novice SN programmers to compose SN service logic, unaware of the lower-level implementation details of tools on which their services rely. In this paper we convey the simplicity of the service composition through concrete examples that illustrate the power and potential of Wireless Security Services that span both the physical and digital plane.National Science Foundation (CISE/CSR 0720604, ENG/EFRI 0735974, CIES/CNS 0520166, CNS/ITR 0205294, CISE/ERA RI 0202067
A Comprehensive Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems
The capabilities of a sensor network are strongly influenced by the operating system used on the sensor nodes. In general, two different sensor network operating system types are currently considered: event driven and multi-threaded. It is commonly assumed that event driven operating systems are more suited to sensor networks as they use less memory and processing resources. However, if factors other than resource usage are considered important, a multi-threaded system might be preferred. This paper compares the resource needs of multi-threaded and event driven sensor network operating systems. The resources considered are memory usage and power consumption. Additionally, the event handling capabilities of event driven and multi-threaded operating systems are analyzed and compared. The results presented in this paper show that for a number of application areas a thread-based sensor network operating system is feasible and preferable
An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors
Event-Driven vision sensing is a new way of sensing
visual reality in a frame-free manner. This is, the vision sensor
(camera) is not capturing a sequence of still frames, as in conventional
video and computer vision systems. In Event-Driven sensors
each pixel autonomously and asynchronously decides when to
send its address out. This way, the sensor output is a continuous
stream of address events representing reality dynamically continuously
and without constraining to frames. In this paper we present
an Event-Driven Convolution Module for computing 2D convolutions
on such event streams. The Convolution Module has been
designed to assemble many of them for building modular and hierarchical
Convolutional Neural Networks for robust shape and
pose invariant object recognition. The Convolution Module has
multi-kernel capability. This is, it will select the convolution kernel
depending on the origin of the event. A proof-of-concept test prototype
has been fabricated in a 0.35 m CMOS process and extensive
experimental results are provided. The Convolution Processor has
also been combined with an Event-Driven Dynamic Vision Sensor
(DVS) for high-speed recognition examples. The chip can discriminate
propellers rotating at 2 k revolutions per second, detect symbols
on a 52 card deck when browsing all cards in 410 ms, or detect
and follow the center of a phosphor oscilloscope trace rotating at
5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
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