7 research outputs found
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
Low-power neuromorphic sensor fusion for elderly care
Smart wearable systems have become a necessary part of our daily life with applications ranging from entertainment to healthcare. In the wearable healthcare domain, the development of wearable fall recognition bracelets based on embedded systems is getting considerable attention in the market. However, in embedded low-power scenarios, the sensor’s signal processing has propelled more challenges for the machine learning algorithm. Traditional machine learning method has a huge number of calculations on the data classification, and it is difficult to implement real-time signal processing in low-power embedded systems. In an embedded system, ensuring data classification in a low-power and real-time processing to fuse a variety of sensor signals is a huge challenge. This requires the introduction of neuromorphic computing with software and hardware co-design concept of the system. This thesis is aimed to review various neuromorphic computing algorithms, research hardware circuits feasibility, and then integrate captured sensor data to realise data classification applications. In addition, it has explored a human being benchmark dataset, which is following defined different levels to design the activities classification task. In this study, firstly the data classification algorithm is applied to human movement sensors to validate the neuromorphic computing on human activity recognition tasks. Secondly, a data fusion framework has been presented, it implements multiple-sensing signals to help neuromorphic computing achieve sensor fusion results and improve classification accuracy. Thirdly, an analog circuits module design to carry out a neural network algorithm to achieve low power and real-time processing hardware has been proposed. It shows a hardware/software co-design system to combine the above work. By adopting the multi-sensing signals on the embedded system, the designed software-based feature extraction method will help to fuse various sensors data as an input to help neuromorphic computing hardware. Finally, the results show that the classification accuracy of neuromorphic computing data fusion framework is higher than that of traditional machine learning and deep neural network, which can reach 98.9% accuracy. Moreover, this framework can flexibly combine acquisition hardware signals and is not limited to single sensor data, and can use multi-sensing information to help the algorithm obtain better stability
Sistemas de posicionamento baseados em comunicação por luz para ambientes interiores
The demand for highly precise indoor positioning systems (IPSs) is growing
rapidly due to its potential in the increasingly popular techniques of the
Internet of Things, smart mobile devices, and artificial intelligence. IPS
becomes a promising research domain that is getting wide attention due to its
benefits in several working scenarios, such as, industries, indoor public
locations, and autonomous navigation. Moreover, IPS has a prominent
contribution in day-to-day activities in organizations such as health care
centers, airports, shopping malls, manufacturing, underground locations, etc.,
for safe operating environments. In indoor environments, both radio frequency
(RF) and optical wireless communication (OWC) based technologies could be
adopted for localization. Although the RF-based global positioning system,
such as, Global positioning system offers higher penetration rates with
reduced accuracy (i.e., in the range of a few meters), it does not work well in
indoor environments (and not at all in certain cases such as tunnels, mines,
etc.) due to the very weak signal and no direct access to the satellites. On the
other hand, the light-based system known as a visible light positioning (VLP)
system, as part of the OWC systems, uses the pre-existing light-emitting
diodes (LEDs)-based lighting infrastructure, could be used at low cost and
high accuracy compared with the RF-based systems. VLP is an emerging
technology promising high accuracy, high security, low deployment cost,
shorter time response, and low relative complexity when compared with RFbased
positioning.
However, in indoor VLP systems, there are some concerns such as,
multipath reflection, transmitter tilting, transmitter’s position, and orientation
uncertainty, human shadowing/blocking, and noise causing the increase in
the positioning error, thereby reducing the positioning accuracy of the system.
Therefore, it is imperative to capture the characteristics of different VLP
channel and properly model them for the dual purpose of illumination and
localization. In this thesis, firstly, the impact of transmitter tilting angles and
multipath reflections are studied and for the first time, it is demonstrated that
tilting the transmitter can be beneficial in VLP systems considering both line of
sight (LOS) and non-line of sight transmission paths. With the transmitters
oriented towards the center of the receiving plane, the received power level is
maximized due to the LOS components. It is also shown that the proposed
scheme offers a significant accuracy improvement of up to ~66% compared
with a typical non-tilted transmitter VLP. The effect of tilting the transmitter on
the lighting uniformity is also investigated and results proved that the
uniformity achieved complies with the European Standard EN 12464-1.
After that, the impact of transmitter position and orientation uncertainty on
the accuracy of the VLP system based on the received signal strength (RSS)
is investigated. Simulation results show that the transmitter uncertainties have
a severe impact on the positioning error, which can be leveraged through the
usage of more transmitters. Concerning a smaller transmitter’s position
epochs, and the size of the training set. It is shown that,
the ANN with Bayesian regularization outperforms the traditional RSS
technique using the non-linear least square estimation for all values of signal
to noise ratio.
Furthermore, a novel indoor VLP system is proposed based on support
vector machines and polynomial regression considering two different
multipath environments of an empty room and a furnished room. The results
show that, in an empty room, the positioning accuracy improvement for the
positioning error of 2.5 cm are 36.1, 58.3, and 72.2 % for three different
scenarios according to the regions’ distribution in the room. For the furnished
room, a positioning relative accuracy improvement of 214, 170, and 100 % is
observed for positioning error of 0.1, 0.2, and 0.3 m, respectively. Ultimately,
an indoor VLP system based on convolutional neural networks (CNN) is
proposed and demonstrated experimentally in which LEDs are used as
transmitters and a rolling shutter camera is used as receiver. A detection
algorithm named single shot detector (SSD) is used which relies on CNN (i.e.,
MobileNet or ResNet) for classification as well as position estimation of each
LED in the image. The system is validated using a real-world size test setup
containing eight LED luminaries. The obtained results show that the maximum
average root mean square positioning error achieved is 4.67 and 5.27 cm with
SSD MobileNet and SSD ResNet models, respectively. The validation results
show that the system can process 67 images per second, allowing real-time
positioning.A procura por sistemas de posicionamento interior (IPSs) de alta precisão tem
crescido rapidamente devido ao seu interesse nas técnicas cada vez mais
populares da Internet das Coisas, dispositivos móveis inteligentes e
inteligência artificial. O IPS tornou-se um domínio de pesquisa promissor que
tem atraído grande atenção devido aos seus benefícios em vários cenários de
trabalho, como indústrias, locais públicos e navegação autónoma. Além disso,
o IPS tem uma contribuição destacada no dia a dia de organizações, como,
centros de saúde, aeroportos, supermercados, fábricas, locais subterrâneos,
etc. As tecnologias baseadas em radiofrequência (RF) e comunicação óptica
sem fio (OWC) podem ser adotadas para localização em ambientes interiores.
Embora o sistema de posicionamento global (GPS) baseado em RF ofereça
taxas de penetração mais altas com precisão reduzida (ou seja, na faixa de
alguns metros), não funciona bem em ambientes interiores (e não funciona
bem em certos casos como túneis, minas, etc.) devido ao sinal muito fraco e
falta de acesso direto aos satélites. Por outro lado, o sistema baseado em luz
conhecido como sistema de posicionamento de luz visível (VLP), como parte
dos sistemas OWC, usa a infraestrutura de iluminação baseada em díodos
emissores de luz (LEDs) pré-existentes, é um sistemas de baixo custo e alta
precisão quando comprado com os sistemas baseados em RF. O VLP é uma
tecnologia emergente que promete alta precisão, alta segurança, baixo custo
de implantação, menor tempo de resposta e baixa complexidade relativa
quando comparado ao posicionamento baseado em RF.
No entanto, os sistemas VLP interiores, exibem algumas limitações, como, a
reflexão multicaminho, inclinação do transmissor, posição do transmissor e
incerteza de orientação, sombra/bloqueio humano e ruído, que têm como
consequência o aumento do erro de posicionamento, e consequente redução
da precisão do sistema. Portanto, é imperativo estudar as características dos
diferentes canais VLP e modelá-los adequadamente para o duplo propósito de
iluminação e localização. Esta tesa aborda, primeiramente, o impacto dos
ângulos de inclinação do transmissor e reflexões multipercurso no
desempenho do sistema de posicionamento. Demonstra-se que a inclinação
do transmissor pode ser benéfica em sistemas VLP considerando tanto a linha
de vista (LOS) como as reflexões. Com os transmissores orientados para o
centro do plano recetor, o nível de potência recebido é maximizado devido aos
componentes LOS. Também é mostrado que o esquema proposto oferece
uma melhoria significativa de precisão de até ~66% em comparação com um
sistema VLP de transmissor não inclinado típico. O efeito da inclinação do
transmissor na uniformidade da iluminação também é investigado e os
resultados comprovam que a uniformidade alcançada está de acordo com a
Norma Europeia EN 12464-1.
O impacto da posição do transmissor e incerteza de orientação na precisão
do sistema VLP com base na intensidade do sinal recebido (RSS) foi também investigado. Os resultados da simulação mostram que as incertezas do
transmissor têm um impacto severo no erro de posicionamento, que pode ser
atenuado com o uso de mais transmissores. Para incertezas de
posicionamento dos transmissores menores que 5 cm, os erros médios de
posicionamento são 23.3, 15.1 e 13.2 cm para conjuntos de 4, 9 e 16
transmissores, respetivamente. Enquanto que, para a incerteza de orientação
de um transmissor menor de 5°, os erros médios de posicionamento são 31.9,
20.6 e 17 cm para conjuntos de 4, 9 e 16 transmissores, respetivamente.
O trabalho da tese abordou a investigação dos aspetos de projeto de um
sistema VLP indoor no qual uma rede neuronal artificial (ANN) é utilizada para
estimativa de posicionamento considerando um canal multipercurso. O estudo
considerou a influência do ruído como indicador de desempenho para a
comparação entre diferentes abordagens de projeto. Três algoritmos de treino
de ANNs diferentes foram considerados, a saber, Levenberg-Marquardt,
regularização Bayesiana e algoritmos de gradiente conjugado escalonado,
para minimizar o erro de posicionamento no sistema VLP. O projeto da ANN foi
otimizado com base no número de neurónios nas camadas ocultas, no número
de épocas de treino e no tamanho do conjunto de treino. Mostrou-se que, a
ANN com regularização Bayesiana superou a técnica RSS tradicional usando
a estimação não linear dos mínimos quadrados para todos os valores da
relação sinal-ruído.
Foi proposto um novo sistema VLP indoor baseado em máquinas de vetores
de suporte (SVM) e regressão polinomial considerando dois ambientes
interiores diferentes: uma sala vazia e uma sala mobiliada. Os resultados
mostraram que, numa sala vazia, a melhoria da precisão de posicionamento
para o erro de posicionamento de 2.5 cm são 36.1, 58.3 e 72.2% para três
cenários diferentes de acordo com a distribuição das regiões na sala. Para a
sala mobiliada, uma melhoria de precisão relativa de posicionamento de 214,
170 e 100% é observada para erro de posicionamento de 0.1, 0.2 e 0.3 m,
respetivamente.
Finalmente, foi proposto um sistema VLP indoor baseado em redes neurais
convolucionais (CNN). O sistema foi demonstrado experimentalmente usando
luminárias LED como transmissores e uma camara com obturador rotativo
como recetor. O algoritmo de detecção usou um detector de disparo único
(SSD) baseado numa CNN pré configurada (ou seja, MobileNet ou ResNet)
para classificação. O sistema foi validado usando uma configuração de teste
de tamanho real contendo oito luminárias LED. Os resultados obtidos
mostraram que o erro de posicionamento quadrático médio alcançado é de
4.67 e 5.27 cm com os modelos SSD MobileNet e SSD ResNet,
respetivamente. Os resultados da validação mostram que o sistema pode
processar 67 imagens por segundo, permitindo o posicionamento em tempo
real.Programa Doutoral em Engenharia Eletrotécnic
MOCAST 2021
The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications