58,210 research outputs found
PARISROC, a Photomultiplier Array Integrated Read Out Chip
PARISROC is a complete read out chip, in AMS SiGe 0.35 !m technology, for
photomultipliers array. It allows triggerless acquisition for next generation
neutrino experiments and it belongs to an R&D program funded by the French
national agency for research (ANR) called PMm2: ?Innovative electronics for
photodetectors array used in High Energy Physics and Astroparticles?
(ref.ANR-06-BLAN-0186). The ASIC (Application Specific Integrated Circuit)
integrates 16 independent and auto triggered channels with variable gain and
provides charge and time measurement by a Wilkinson ADC (Analog to Digital
Converter) and a 24-bit Counter. The charge measurement should be performed
from 1 up to 300 photo- electrons (p.e.) with a good linearity. The time
measurement allowed to a coarse time with a 24-bit counter at 10 MHz and a fine
time on a 100ns ramp to achieve a resolution of 1 ns. The ASIC sends out only
the relevant data through network cables to the central data storage. This
paper describes the front-end electronics ASIC called PARISROC.Comment: IEEE Nuclear Science Symposium an Medical Imaging Conference (2009
NSS/MIC
On-site forest fire smoke detection by low-power autonomous vision sensor
Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor
networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby
very early detection and precise location of forest fires. One of the fundamental requirements that
need to be met at the network nodes is reliable low-power on-site image processing. It greatly
simplifies the communication infrastructure of the network as only alarm signals instead of
complete images are transmitted, anticipating thus a very competitive cost. As a first
approximation to fulfill such a requirement, this paper reports the results achieved from field tests
carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled
burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was
successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system,
built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was
triggered despite the significant motion other than smoke present in the scene. Finally, as a further
step, we describe the preliminary laboratory results obtained from a prototype vision chip which
implements, at very low energy cost, some image processing primitives oriented to environmental
monitoring.Ministerio de Ciencia e Innovación 2006-TIC-2352, TEC2009-1181
Electrocardiogram (ECG/EKG) using FPGA
FPGAs (Field Programmable Gate Arrays) are finding wide acceptance in medical systems for their ability for rapid prototyping of a concept that requires hardware/software co-design, for performing custom processing in parallel at high data rates and be programmed in the field after manufacturing. Based on the market demand, the FPGA design can be changed and no new hardware needs to be purchased as was the case with ASICs (Application Specific Integrated Circuit) and CPLDs (Complex Programmable Logic Device). Medical companies can now move over to FPGAs saving cost and delivering highly-efficient upgradable systems. ECG (Electrocardiogram) is considered to be a must have feature for a medical diagnostic imaging system. This project attempts at implementing ECG heart-rate computation in an FPGA. This project gave me exposure to hardware engineering, learning about the low level chips like Atmel UC3A3256 micro-controller on an Atmel EVK1105 board which is used as a simulator for generating the ECG signal, the operational amplifiers for amplifying and level-shifting the ECG signal, the A/D converter chip for analog to digital conversion of the ECG signal, the internal workings of FPGA, how different hardware components communicate with each other on the system and finally some signal processing to calculate the heart rate value from the ECG signal
A geographically distributed bio-hybrid neural network with memristive plasticity
Throughout evolution the brain has mastered the art of processing real-world
inputs through networks of interlinked spiking neurons. Synapses have emerged
as key elements that, owing to their plasticity, are merging neuron-to-neuron
signalling with memory storage and computation. Electronics has made important
steps in emulating neurons through neuromorphic circuits and synapses with
nanoscale memristors, yet novel applications that interlink them in
heterogeneous bio-inspired and bio-hybrid architectures are just beginning to
materialise. The use of memristive technologies in brain-inspired architectures
for computing or for sensing spiking activity of biological neurons8 are only
recent examples, however interlinking brain and electronic neurons through
plasticity-driven synaptic elements has remained so far in the realm of the
imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where
memristors work as "synaptors" between rat neural circuits and VLSI neurons.
The two fundamental synaptors, from artificial-to-biological (ABsyn) and from
biological-to- artificial (BAsyn), are interconnected over the Internet. The
bNN extends across Europe, collapsing spatial boundaries existing in natural
brain networks and laying the foundations of a new geographically distributed
and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
NoCo: ILP-based worst-case contention estimation for mesh real-time manycores
Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-
65316-P and the HiPEAC Network of Excellence. It also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (agreement No. 772773). Carles Hernández
is jointly supported by the MINECO and FEDER funds
through grant TIN2014-60404-JIN. Jaume Abella has been
partially supported by the Spanish Ministry of Economy and
Competitiveness under Ramon y Cajal postdoctoral fellowship
number RYC-2013-14717. Enrico Mezzetti has been partially
supported by the Spanish Ministry of Economy and Competitiveness
under Juan de la Cierva-Incorporaci´on postdoctoral
fellowship number IJCI-2016-27396.Peer ReviewedPostprint (author's final draft
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
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