107 research outputs found
Cambio de hábitos y transformaciones territoriales en los corredores de alta velocidad ferroviaria. Resultados de una encuesta de viajeros en la línea Madrid-Barcelona
El tren de alta velocidad (TAV) está viviendo un auge importante en España. Ello implica una transformación territorial como resultado de una modificación del comportamiento de los viajeros debido a una mejora en el transporte ferroviario interurbano. En este contexto hay un elemento que ha obtenido una atención insuficiente. Se trata del acceso a la estación como parte fundamental del viaje puerta-a-puerta. Para hacer frente a la carencia de datos estadísticos sobre este aspecto, hemos realizado una encuesta a los viajeros de alta velocidad en la línea Madrid – Barcelona, que ayuda responder a la pregunta: ¿Cómo llega el viajero a la estación? Es imprescindible conocer este aspecto para planificar y realizar actuaciones de mejora intermodal adecuadas. Los resultados muestran que el acceso en vehículo privado es preponderante y que existe la necesidad de ofrecer más alternativas atractivas para acceder a las estaciones. Se concluye además que las estaciones centrales son preferibles, ya que generan una movilidad más sostenible.The High-Speed Train is experiencing a considerable growth in Spain. This implies a territorial transformation, result of a modification of the travelers’ behavior due to an improvement of the interurban railway transportation. In this context, an element that has not received the necessary attention is, namely, the access to the station as a basic part of the door-to-door travel. To face the lack of statistics on railway station access trips, a passenger survey on the high-speed railway line Madrid-Barcelona has been realized in order to respond to the question: How does the passenger get to the station?
The knowledge of this aspect is needed to plan and realize improvement on intermodal actions adequately. The results show a dominance of the private car and a need to offer more attractive alternatives to access the stations. Further conclusions drawn, depict that central stations should be preferred, as they generate a more sustainable mobility
System based on inertial sensors for behavioral monitoring of wildlife
Sensors Network is an integration of multiples
sensors in a system to collect information about different
environment variables. Monitoring systems allow us to
determine the current state, to know its behavior and
sometimes to predict what it is going to happen. This work
presents a monitoring system for semi-wild animals that
get their actions using an IMU (inertial measure unit) and
a sensor fusion algorithm. Based on an ARM-CortexM4
microcontroller this system sends data using ZigBee
technology of different sensor axis in two different
operations modes: RAW (logging all information into a SD
card) or RT (real-time operation). The sensor fusion
algorithm improves both the precision and noise
interferences.Junta de Andalucía P12-TIC-130
Interfacing PDM sensors with PFM spiking systems: application for Neuromorphic Auditory Sensors
In this paper we present a sub-system to convert
audio information from low-power MEMS microphones with
pulse density modulation (PDM) output into rate coded spike
streams. These spikes represent the input signal of a Neuromorphic
Auditory Sensor (NAS), which is implemented with Spike
Signal Processing (SSP) building blocks. For this conversion, we
have designed a HDL component for FPGA able to interface
with PDM microphones and converts their pulses to temporal
distributed spikes following a pulse frequency modulation (PFM)
scheme with an accurate configurable Inter-Spike-Interval. The
new FPGA component has been tested in two scenarios, first as a
stand-alone circuit for its characterization, and then it has been
integrated with a full NAS design to verify its behavior. This
PDM interface demands less than 1% of a Spartan 6 FPGA
resources and has a power consumption below 5mW.Ministerio de Economía y Competitividad TEC2016-77785-
Live Demonstration: Neuromorphic Row-by-Row Multi-convolution FPGA Processor-SpiNNaker architecture for Dynamic-Vision Feature Extraction
In this demonstration a spiking neural network
architecture for vision recognition using an FPGA spiking
convolution processor, based on leaky integrate and fire neurons
(LIF) and a SpiNNaker board is presented. The network has
been trained with Poker-DVS dataset in order to classify the
four different card symbols. The spiking convolution processor
extracts features from images in form of spikes, computes by
one layer of 64 convolutions. These features are sent to an
OKAERtool board that converts from AER to 2-7 protocol
to be classified by a spiking neural network deployed on a
SpiNNaker platform
A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker
The study and monitoring of the behavior of wildlife has always been
a subject of great interest. Although many systems can track animal positions
using GPS systems, the behavior classification is not a common task. For this
work, a multi-sensory wearable device has been designed and implemented to be
used in the Doñana National Park in order to control and monitor wild and semiwild
life animals. The data obtained with these sensors is processed using a
Spiking Neural Network (SNN), with Address-Event-Representation (AER)
coding, and it is classified between some fixed activity behaviors. This works
presents the full infrastructure deployed in Doñana to collect the data, the wearable
device, the SNN implementation in SpiNNaker and the classification
results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Performance evaluation over HW/SW co-design SoC memory transfers for a CNN accelerator
Many FPGAs vendors have recently included embedded
processors in their devices, like Xilinx with ARM-Cortex
A cores, together with programmable logic cells. These devices
are known as Programmable System on Chip (PSoC). Their ARM
cores (embedded in the processing system or PS) communicates
with the programmable logic cells (PL) using ARM-standard AXI
buses. In this paper we analyses the performance of exhaustive
data transfers between PS and PL for a Xilinx Zynq FPGA
in a co-design real scenario for Convolutional Neural Networks
(CNN) accelerator, which processes, in dedicated hardware, a
stream of visual information from a neuromorphic visual sensor
for classification. In the PS side, a Linux operating system is
running, which recollects visual events from the neuromorphic
sensor into a normalized frame, and then it transfers these
frames to the accelerator of multi-layered CNNs, and read results,
using an AXI-DMA bus in a per-layer way. As these kind of
accelerators try to process information as quick as possible, data
bandwidth becomes critical and maintaining a good balanced
data throughput rate requires some considerations. We present
and evaluate several data partitioning techniques to improve the
balance between RX and TX transfer and two different ways
of transfers management: through a polling routine at the userlevel
of the OS, and through a dedicated interrupt-based kernellevel
driver. We demonstrate that for longer enough packets,
the kernel-level driver solution gets better timing in computing a
CNN classification example. Main advantage of using kernel-level
driver is to have safer solutions and to have tasks scheduling in
the OS to manage other important processes for our application,
like frames collection from sensors and their normalization.Ministerio de Economía y Competitividad TEC2016-77785-
Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA
Deep Learning algorithms have become state-of-theart
methods for multiple fields, including computer vision, speech
recognition, natural language processing, and audio recognition,
among others. In image vision, convolutional neural networks
(CNN) stand out. This kind of network is expensive in terms of
computational resources due to the large number of operations required
to process a frame. In recent years, several frame-based chip
solutions to deploy CNN for real time have been developed. Despite
the good results in power and accuracy given by these solutions, the
number of operations is still high, due the complexity of the current
network models. However, it is possible to reduce the number of
operations using different computer vision techniques other than
frame-based, e.g., neuromorphic event-based techniques. There exist
several neuromorphic vision sensors whose pixels detect changes
in luminosity. Inspired in the leaky integrate-and-fire (LIF) neuron,
we propose in this manuscript an event-based field-programmable
gate array (FPGA) multiconvolution system. Its main novelty is the
combination of a memory arbiter for efficient memory access to
allowrow-by-rowkernel processing. This system is able to convolve
64 filters across multiple kernel sizes, from 1 × 1 to 7 × 7, with
latencies of 1.3 μs and 9.01 μs, respectively, generating a continuous
flow of output events. The proposed architecture will easily fit
spike-based CNNs.Ministerio de Economía y Competitividad TEC2016-77785-
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA
Neural networks algorithms are commonly used to
recognize patterns from different data sources such as audio or
vision. In image recognition, Convolutional Neural Networks are
one of the most effective techniques due to the high accuracy they
achieve. This kind of algorithms require billions of addition and
multiplication operations over all pixels of an image. However,
it is possible to reduce the number of operations using other
computer vision techniques rather than frame-based ones, e.g.
neuromorphic frame-free techniques. There exists many neuromorphic
vision sensors that detect pixels that have changed
their luminosity. In this study, an event-based convolution engine
for FPGA is presented. This engine models an array of leaky
integrate and fire neurons. It is able to apply different kernel
sizes, from 1x1 to 7x7, which are computed row by row, with a
maximum number of 64 different convolution kernels. The design
presented is able to process 64 feature maps of 7x7 with a latency
of 8.98 s.Ministerio de Economía y Competitividad TEC2016-77785-
Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets
Although it is not a novel topic, pattern recognition has
become very popular and relevant in the last years. Different classification
systems like neural networks, support vector machines or even
complex statistical methods have been used for this purpose. Several
works have used these systems to classify animal behavior, mainly in an
offline way. Their main problem is usually the data pre-processing step,
because the better input data are, the higher may be the accuracy of the
classification system. In previous papers by the authors an embedded
implementation of a neural network was deployed on a portable device
that was placed on animals. This approach allows the classification to
be done online and in real time. This is one of the aims of the research
project MINERVA, which is focused on monitoring wildlife in Do˜nana
National Park using low power devices. Many difficulties were faced when
pre-processing methods quality needed to be evaluated. In this work, a
novel pre-processing evaluation system based on self-organizing maps
(SOM) to measure the quality of the neural network training dataset is
presented. The paper is focused on a three different horse gaits classification
study. Preliminary results show that a better SOM output map
matches with the embedded ANN classification hit improvement.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-
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