1,383 research outputs found
Design and Implementation of HD Wireless Video Transmission System Based on Millimeter Wave
With the improvement of optical fiber communication network construction and the improvement of camera technology, the video that the terminal can receive becomes clearer, with resolution up to 4K. Although optical fiber communication has high bandwidth and fast transmission speed, it is not the best solution for indoor short-distance video transmission in terms of cost, laying difficulty and speed.
In this context, this thesis proposes to design and implement a multi-channel wireless HD video transmission system with high transmission performance by using the 60GHz millimeter wave technology, aiming to improve the bandwidth from optical nodes to wireless terminals and improve the quality of video transmission. This thesis mainly covers the following parts:
(1) This thesis implements wireless video transmission algorithm, which is divided into wireless transmission algorithm and video transmission algorithm, such as 64QAM modulation and demodulation algorithm, H.264 video algorithm and YUV420P algorithm.
(2) This thesis designs the hardware of wireless HD video transmission system, including network processing unit (NPU) and millimeter wave module. Millimeter wave module uses RWM6050 baseband chip and TRX-BF01 rf chip. This thesis will design the corresponding hardware circuit based on the above chip, such as 10Gb/s network port, PCIE.
(3) This thesis realizes the software design of wireless HD video transmission system, selects FFmpeg and Nginx to build the sending platform of video transmission system on NPU, and realizes video multiplex transmission with Docker. On the receiving platform of video transmission, FFmpeg and Qt are selected to realize video decoding, and OpenGL is combined to realize video playback.
(4) Finally, the thesis completed the wireless HD video transmission system test, including pressure test, Web test and application scenario test. It has been verified that its HD video wireless transmission system can transmit HD VR video with three-channel bit rate of 1.2GB /s, and its rate can reach up to 3.7GB /s, which meets the research goal
Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods
Internet traffic classification aims to identify the type of application or protocol that generated
a particular packet or stream of packets on the network. Through traffic classification,
Internet Service Providers (ISPs), governments, and network administrators can
access basic functions and several solutions, including network management, advanced
network monitoring, network auditing, and anomaly detection. Traffic classification is
essential as it ensures the Quality of Service (QoS) of the network, as well as allowing
efficient resource planning.
With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer
data encapsulation, some classical classification methods have lost interest from the
scientific community. The limitations of traditional classification methods based on port
numbers and payload inspection to classify encrypted or obfuscated Internet traffic have
led to significant research efforts focused on Machine Learning (ML) based classification
approaches using statistical features from the transport layer. In an attempt to increase
classification performance, Machine Learning strategies have gained interest from the scientific
community and have shown promise in the future of traffic classification, specially
to recognize encrypted traffic.
However, ML approach also has its own limitations, as some of these methods have a
high computational resource consumption, which limits their application when classifying
large traffic or realtime
flows. Limitations of ML application have led to the investigation
of alternative approaches, including featurebased
procedures and statistical methods. In
this sense, statistical analysis methods, such as distances and divergences, have been used
to classify traffic in large flows and in realtime.
The main objective of statistical distance is to differentiate flows and find a pattern in
traffic characteristics through statistical properties, which enable classification. Divergences
are functional expressions often related to information theory, which measure the
degree of discrepancy between any two distributions.
This thesis focuses on proposing a new methodological approach to classify encrypted
or obfuscated Internet traffic based on statistical methods that enable the evaluation of
network traffic classification performance, including the use of computational resources
in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler
and
JensenShannon
divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances
were proposed. The following are the four main contributions to the advancement
of scientific knowledge reported in this thesis.
First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased
and payloadbased
methods are becoming obsolete due to the increasing use of traffic encryption and multilayer
data encapsulation. MLbased
methods are also becoming limited due to their computational
complexity. As an alternative, Support Vector Machine (SVM), which is also
an ML method, and the KolmogorovSmirnov
and Chisquared
tests can be used as reference
for statistical classification. In parallel, the possibility of using statistical methods
for Internet traffic classification has emerged in the literature, with the potential of good
results in classification without the need of large computational resources. The potential
statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance,
Wootters Distance, as well as KullbackLeibler
(KL) and JensenShannon
divergences.
Second, we present a proposal and implementation of a classifier based on SVM for P2P
multimedia traffic, comparing the results with KolmogorovSmirnov
(KS) and Chisquare
tests. The results suggest that SVM classification with Linear kernel leads to a better classification
performance than KS and Chisquare
tests, depending on the value assigned to
the Self C parameter. The SVM method with Linear kernel and suitable values for the Self
C parameter may be a good choice to identify encrypted P2P multimedia traffic on the
Internet.
Third, we present a proposal and implementation of two classifiers based on KL Divergence
and Euclidean Distance, which are compared to SVM with Linear kernel, configured
with the standard Self C parameter, showing a reduced ability to classify flows based
solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean
methods were able to classify all tested applications, particularly streaming and P2P,
where for almost all cases they efficiently identified them with high accuracy, with reduced
consumption of computational resources. Based on the obtained results, it can be
concluded that KL and Euclidean Distance methods are an alternative to SVM, as these
statistical approaches can operate in realtime
and do not require retraining every time a
new type of traffic emerges.
Fourth, we present a proposal and implementation of a set of classifiers for encrypted
Internet traffic, based on JensenShannon
Divergence and Hellinger, Bhattacharyya, and
Wootters Distances, with their respective results compared to those obtained with methods
based on Euclidean Distance, KL, KS, and ChiSquare.
Additionally, we present a comparative
qualitative analysis of the tested methods based on Kappa values and Receiver
Operating Characteristic (ROC) curves. The results suggest average accuracy values above
90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa
values, with the exception of KS. This result indicates that these methods are viable options
to classify encrypted Internet traffic, especially Hellinger Distance, which showed
the best Kappa values compared to other classifiers. We conclude that the considered
statistical methods can be accurate and costeffective
in terms of computational resource
consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical
distances and divergences. We have shown that it is possible to classify and obtain
good results with statistical methods, balancing classification performance and the
use of computational resources in terms of CPU and memory. The validation of the proposal
supports the argument of this thesis, which proposes the implementation of statistical
methods as a viable alternative to Internet traffic classification compared to methods
based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que
gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de
tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede
podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento
avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é
essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear
com eficiência o uso de recursos.
Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento
de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de
investigação da comunidade científica. As limitações dos métodos tradicionais da classificação
com base no número da porta e na inspeção de carga útil payload para classificar
o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação
com foco em abordagens da classificação baseadas em técnicas de Aprendizagem
Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa
de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática
ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da
classificação de tráfego, principalmente no reconhecimento de tráfego cifrado.
No entanto, a abordagem em ML também têm as suas próprias limitações,
pois alguns
desses métodos possuem um elevado consumo de recursos computacionais, o que limita
a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações
no âmbito da aplicação de ML levaram à investigação de abordagens alternativas,
incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido,
os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados
para classificar tráfego em grandes fluxos e em tempo real.
A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar
um padrão nas características de tráfego através de propriedades estatísticas, que
possibilitam a classificação. As divergências são expressões funcionais frequentemente
relacionadas com a teoria da informação, que mede o grau de discrepância entre duas
distribuições quaisquer.
Esta tese focase
na proposta de uma nova abordagem metodológica para classificação de
tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite
avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos
computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores
de tráfego baseados nas Divergências de KullbackLeibler
e JensenShannon
e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse
os tese.
Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado
e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e
baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização
de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos
baseados em ML também está se tornando limitado em função da complexidade computacional.
Como alternativa, podese
utilizar a Máquina de Vetor de Suporte (SVM),
que também é um método de ML, e os testes de KolmogorovSmirnov
e Quiquadrado
como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura
a possibilidade de utilização de métodos estatísticos para classificação de tráfego
de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos
computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana,
Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e
JensenShannon.
Segundo, apresentamos uma proposta e implementação de um classificador baseado na
Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer),
comparando
os resultados com os testes de KolmogorovSmirnov
(KS) e Quiquadrado.
Os
resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor
desempenho da classificação do que os testes KS e Quiquadrado,
dependente do valor
atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados
para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par
(P2P) multimédia cifrado na Internet.
Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados
na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados
com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta
reduzida
capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes
em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram
capazes de classificar todas as aplicações testadas, destacandose
streaming e P2P, onde
para quase todos os casos foi eficiente identificálas
com alta precisão, com reduzido consumo
de recursos computacionais.Com base nos resultados obtidos, podese
concluir que
os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens
estatísticas podem operar em tempo real e não precisam de retreinamento cada vez
que surge um novo tipo de tráfego.
Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores
para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon
e nas Distâncias
de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados
com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado.
Além disso, apresentamos uma análise qualitativa comparativa dos
métodos testados com base nos valores de Kappa e Curvas Característica de Operação do
Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos
os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores
de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis
para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger,
que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais
classificadores. Concluise
que os métodos estatísticos considerados podem ser precisos e
económicos em termos de consumo de recursos computacionais para classificar o tráfego
da rede.
A nossa abordagem baseouse
na classificação de tráfego de rede Internet, focando em
distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter
bons resultados com métodos estatísticos, equilibrando desempenho de classificação e
uso de recursos computacionais em termos de CPU e memória. A validação da proposta
sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos
como alternativa viável à classificação de tráfego da Internet em relação aos métodos com
base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação
da Covilhã and at the Department
of Computer Science of the University of Beira Interior, and submitted to the
University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in
Computer Science and Engineering.
This work has been funded by Portuguese FCT/MCTES through national funds and, when
applicable, cofunded
by EU funds under the project UIDB/50008/2020, and by operation
Centro010145FEDER000019
C4
Centro
de Competências em Cloud Computing,
cofunded
by the European Regional Development Fund (ERDF/FEDER) through
the Programa Operacional Regional do Centro (Centro 2020). This work has also been
funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education)
within the Ministry of Education of Brazil under a scholarship supported by the
International Cooperation Program CAPES/COFECUB Project
9090134/
2013 at the
University of Beira Interior
Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the
demand for human point clouds with realistic and intricate details. The
compression of such data becomes challenging with overwhelming data amounts
comprising millions of points. Herein, we leverage the human geometric prior in
geometry redundancy removal of point clouds, greatly promoting the compression
performance. More specifically, the prior provides topological constraints as
geometry initialization, allowing adaptive adjustments with a compact parameter
set that could be represented with only a few bits. Therefore, we can envisage
high-resolution human point clouds as a combination of geometric priors and
structural deviations. The priors could first be derived with an aligned point
cloud, and subsequently the difference of features is compressed into a compact
latent code. The proposed framework can operate in a play-and-plug fashion with
existing learning based point cloud compression methods. Extensive experimental
results show that our approach significantly improves the compression
performance without deteriorating the quality, demonstrating its promise in a
variety of applications
Two-Stage Overfitting of Neural Network-Based Video Coding In-Loop Filter
Modern video coding standards like the Versatile Video Coding (VVC) produce compression artefacts, due to their block-based, lossy compression techniques. These artefacts are mitigated to an extent by the in-loop filters inside the coding process. Neural Network (NN) based in-loop filters are being explored for the denoising tasks, and in recent studies, these NN-based loop filters are overfitted on test content to achieve a content-adaptive nature, and further enhance the visual quality of the video frames, while balancing the trade-off between quality and bitrate. This loop filter is a relatively low-complexity Convolutional Neural Network (CNN) that is pretrained on a general video dataset and then fine-tuned on the video that needs to be encoded. Only a set of parameters inside the CNN architecture, named multipliers, are fine-tuned, thus the bitrate overhead, that is signalled to the decoder, is minimized. The created weight update is compressed using the Neural Network Compression and Representation (NNR) standard. In this project, an exploration of high-performing hyperparameters was conducted, and the two-stage training process was employed to, potentially, further increase the coding efficiency of the in-loop filter. A first-stage model was overfitted on the test video sequence, it explored on which patches of the dataset it could improve the quality of the unfiltered video data, and then the second-stage model was overfitted only on these patches that provided a gain. The model with best-found hyperparameters saved on average 1.01% (Y), 4.28% (Cb), and 3.61% (Cr) Bjontegaard Delta rate (BD-rate) compared to the Versatile Video Coding (VVC) Test Model (VTM) 11.0 NN-based Video Coding (NNVC) 5.0, Random Access (RA) Common Test Conditions (CTC). The second-stage model, although exceeded the VTM, it underperformed with about 0.20% (Y), 0.23% (Cb), and 0.18% (Cr) BD-rate with regards to the first-stage model, due to the high bitrate overhead created by the second-stage model
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
Interdisciplinarity in the Age of the Triple Helix: a Film Practitioner's Perspective
This integrative chapter contextualises my research including articles I have published as well as one of the creative artefacts developed from it, the feature film The Knife That Killed Me. I review my work considering the ways in which technology, industry methods and academic practice have evolved as well as how attitudes to interdisciplinarity have changed, linking these to Etzkowitz and Leydesdorff’s ‘Triple Helix’ model (1995). I explore my own experiences and observations of opportunities and challenges that have been posed by the intersection of different stakeholder needs and expectations, both from industry and academic perspectives, and argue that my work provides novel examples of the applicability of the ‘Triple Helix’ to the creative industries. The chapter concludes with a reflection on the evolution and direction of my work, the relevance of the ‘Triple Helix’ to creative practice, and ways in which this relationship could be investigated further
Coherent and Holographic Imaging Methods for Immersive Near-Eye Displays
Lähinäytöt on suunniteltu tarjoamaan realistisia kolmiulotteisia katselukokemuksia, joille on merkittävää tarvetta esimerkiksi työkoneiden etäkäytössä ja 3D-suunnittelussa. Nykyaikaiset lähinäytöt tuottavat kuitenkin edelleen ristiriitaisia visuaalisia vihjeitä, jotka heikentävät immersiivistä kokemusta ja haittaavat niiden miellyttävää käyttöä. Merkittävänä ratkaisuvaihtoehtona pidetään koherentin valon, kuten laservalon, käyttöä näytön valaistukseen, millä voidaan korjata nykyisten lähinäyttöjen puutteita. Erityisesti koherentti valaistus mahdollistaa holografisen kuvantamisen, jota käyttävät holografiset näytöt voivat tarkasti jäljitellä kolmiulotteisten mallien todellisia valoaaltoja. Koherentin valon käyttäminen näyttöjen valaisemiseen aiheuttaa kuitenkin huomiota vaativaa korkean kontrastin häiriötä pilkkukuvioiden muodossa. Lisäksi holografisten näyttöjen laskentamenetelmät ovat laskennallisesti vaativia ja asettavat uusia haasteita analyysin, pilkkuhäiriön ja valon mallintamisen suhteen.
Tässä väitöskirjassa tutkitaan laskennallisia menetelmiä lähinäytöille koherentissa kuvantamisjärjestelmässä käyttäen signaalinkäsittelyä, koneoppimista sekä geometrista (säde) ja fysikaalista (aalto) optiikan mallintamista. Työn ensimmäisessä osassa keskitytään holografisten kuvantamismuotojen analysointiin sekä kehitetään hologrammien laskennallisia menetelmiä. Holografian korkeiden laskentavaatimusten ratkaisemiseksi otamme käyttöön holografiset stereogrammit holografisen datan likimääräisenä esitysmuotona. Tarkastelemme kyseisen esitysmuodon visuaalista oikeellisuutta kehittämällä analyysikehyksen holografisen stereogrammin tarjoamien visuaalisten vihjeiden tarkkuudelle akkommodaatiota varten suhteessa sen suunnitteluparametreihin. Lisäksi ehdotamme signaalinkäsittelyratkaisua pilkkuhäiriön vähentämiseksi, ratkaistaksemme nykyisten menetelmien valon mallintamiseen liittyvät visuaalisia artefakteja aiheuttavat ongelmat. Kehitämme myös uudenlaisen holografisen kuvantamismenetelmän, jolla voidaan mallintaa tarkasti valon käyttäytymistä haastavissa olosuhteissa, kuten peiliheijastuksissa.
Väitöskirjan toisessa osassa lähestytään koherentin näyttökuvantamisen laskennallista taakkaa koneoppimisen avulla. Kehitämme koherentin akkommodaatioinvariantin lähinäytön suunnittelukehyksen, jossa optimoidaan yhtäaikaisesti näytön staattista optiikka ja näytön kuvan esikäsittelyverkkoa. Lopuksi nopeutamme ehdottamaamme uutta holografista kuvantamismenetelmää koneoppimisen avulla reaaliaikaisia sovelluksia varten. Kyseiseen ratkaisuun sisältyy myös tehokkaan menettelyn kehittäminen funktionaalisten satunnais-3D-ympäristöjen tuottamiseksi. Kehittämämme menetelmä mahdollistaa suurten synteettisten moninäkökulmaisten kuvien datasettien tuottamisen, joilla voidaan kouluttaa sopivia neuroverkkoja mallintamaan holografista kuvantamismenetelmäämme reaaliajassa.
Kaiken kaikkiaan tässä työssä kehitettyjen menetelmien osoitetaan olevan erittäin kilpailukykyisiä uusimpien koherentin valon lähinäyttöjen laskentamenetelmien kanssa. Työn tuloksena nähdään kaksi vaihtoehtoista lähestymistapaa ristiriitaisten visuaalisten vihjeiden aiheuttamien nykyisten lähinäyttöongelmien ratkaisemiseksi joko staattisella tai dynaamisella optiikalla ja reaaliaikaiseen käyttöön soveltuvilla laskentamenetelmillä. Esitetyt tulokset ovat näin ollen tärkeitä seuraavan sukupolven immersiivisille lähinäytöille.Near-eye displays have been designed to provide realistic 3D viewing experience, strongly demanded in applications, such as remote machine operation, entertainment, and 3D design. However, contemporary near-eye displays still generate conflicting visual cues which degrade the immersive experience and hinders their comfortable use. Approaches using coherent, e.g., laser light for display illumination have been considered prominent for tackling the current near-eye display deficiencies. Coherent illumination enables holographic imaging whereas holographic displays are expected to accurately recreate the true light waves of a desired 3D scene. However, the use of coherent light for driving displays introduces additional high contrast noise in the form of speckle patterns, which has to be taken care of. Furthermore, imaging methods for holographic displays are computationally demanding and impose new challenges in analysis, speckle noise and light modelling.
This thesis examines computational methods for near-eye displays in the coherent imaging regime using signal processing, machine learning, and geometrical (ray) and physical (wave) optics modeling. In the first part of the thesis, we concentrate on analysis of holographic imaging modalities and develop corresponding computational methods. To tackle the high computational demands of holography, we adopt holographic stereograms as an approximative holographic data representation. We address the visual correctness of such representation by developing a framework for analyzing the accuracy of accommodation visual cues provided by a holographic stereogram in relation to its design parameters. Additionally, we propose a signal processing solution for speckle noise reduction to overcome existing issues in light modelling causing visual artefacts. We also develop a novel holographic imaging method to accurately model lighting effects in challenging conditions, such as mirror reflections.
In the second part of the thesis, we approach the computational complexity aspects of coherent display imaging through deep learning. We develop a coherent accommodation-invariant near-eye display framework to jointly optimize static display optics and a display image pre-processing network. Finally, we accelerate the corresponding novel holographic imaging method via deep learning aimed at real-time applications. This includes developing an efficient procedure for generating functional random 3D scenes for forming a large synthetic data set of multiperspective images, and training a neural network to approximate the holographic imaging method under the real-time processing constraints.
Altogether, the methods developed in this thesis are shown to be highly competitive with the state-of-the-art computational methods for coherent-light near-eye displays. The results of the work demonstrate two alternative approaches for resolving the existing near-eye display problems of conflicting visual cues using either static or dynamic optics and computational methods suitable for real-time use. The presented results are therefore instrumental for the next-generation immersive near-eye displays
EgoActive: Integrated wireless wearable sensors for capturing infant egocentric auditory-visual statistics and autonomic nervous system function ‘in the wild’
There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multi-modal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning
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