199 research outputs found
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Estrategias de visión por computador para la estimación de pose en el contexto de aplicaciones robóticas industriales: avances en el uso de modelos tanto clásicos como de Deep Learning en imágenes 2D
184 p.La visión por computador es una tecnología habilitadora que permite a los robots y sistemas autónomos percibir su entorno. Dentro del contexto de la industria 4.0 y 5.0, la visión por ordenador es esencial para la automatización de procesos industriales. Entre las técnicas de visión por computador, la detección de objetos y la estimación de la pose 6D son dos de las más importantes para la automatización de procesos industriales. Para dar respuesta a estos retos, existen dos enfoques principales: los métodos clásicos y los métodos de aprendizaje profundo. Los métodos clásicos son robustos y precisos, pero requieren de una gran cantidad de conocimiento experto para su desarrollo. Por otro lado, los métodos de aprendizaje profundo son fáciles de desarrollar, pero requieren de una gran cantidad de datos para su entrenamiento.En la presente memoria de tesis se presenta una revisión de la literatura sobre técnicas de visión por computador para la detección de objetos y la estimación de la pose 6D. Además se ha dado respuesta a los siguientes retos: (1) estimación de pose mediante técnicas de visión clásicas, (2) transferencia de aprendizaje de modelos 2D a 3D, (3) la utilización de datos sintéticos para entrenar modelos de aprendizaje profundo y (4) la combinación de técnicas clásicas y de aprendizaje profundo. Para ello, se han realizado contribuciones en revistas de alto impacto que dan respuesta a los anteriores retos
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
Colour technologies for content production and distribution of broadcast content
The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model
METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION
We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for
imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively.
Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness,
speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city
Immersivity in Music Performance with Original Compositions
The aim of this study is to critically investigate immersivity in music performance. It will evaluate how the combination of the performance space, musical material and delivery methods can produce unique and valuable sonic experiences to an audience. This investigative process hopes to highlight what compositional and spatial characteristics define this performance paradigm, with the goal to provide a taxonomy of key characteristics which creators must consider when devising an immersive music performance
Deep Learning for Video Object Segmentation:A Review
As one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural networks have shown outstanding performance improvements in many computer vision applications, with video object segmentation being one of the most advocated and intensively investigated. In this paper, we present a systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches. Concretely, we start by introducing the definition, background concepts and basic ideas of algorithms in this field. Subsequently, we summarise the datasets for training and testing a video object segmentation algorithm, as well as common challenges and evaluation metrics. Next, previous works are grouped and reviewed based on how they extract and use spatial and temporal features, where their architectures, contributions and the differences among each other are elaborated. At last, the quantitative and qualitative results of several representative methods on a dataset with many remaining challenges are provided and analysed, followed by further discussions on future research directions. This article is expected to serve as a tutorial and source of reference for learners intended to quickly grasp the current progress in this research area and practitioners interested in applying the video object segmentation methods to their problems. A public website is built to collect and track the related works in this field: https://github.com/gaomingqi/VOS-Review
Feasibility Study of High-Level Synthesis : Implementation of a Real-Time HEVC Intra Encoder on FPGA
High-Level Synthesis (HLS) on automatisoitu suunnitteluprosessi, joka pyrkii parantamaan tuottavuutta perinteisiin suunnittelumenetelmiin verrattuna, nostamalla suunnittelun abstraktiota rekisterisiirtotasolta (RTL) käyttäytymistasolle. Erilaisia kaupallisia HLS-työkaluja on ollut markkinoilla aina 1990-luvulta lähtien, mutta vasta äskettäin ne ovat alkaneet saada hyväksyntää teollisuudessa sekä akateemisessa maailmassa. Hidas käyttöönottoaste on johtunut pääasiassa huonommasta tulosten laadusta (QoR) kuin mitä on ollut mahdollista tavanomaisilla laitteistokuvauskielillä (HDL). Uusimmat HLS-työkalusukupolvet ovat kuitenkin kaventaneet QoR-aukkoa huomattavasti.
Tämä väitöskirja tutkii HLS:n soveltuvuutta videokoodekkien kehittämiseen. Se esittelee useita HLS-toteutuksia High Efficiency Video Coding (HEVC) -koodaukselle, joka on keskeinen mahdollistava tekniikka lukuisille nykyaikaisille mediasovelluksille. HEVC kaksinkertaistaa koodaustehokkuuden edeltäjäänsä Advanced Video Coding (AVC) -standardiin verrattuna, saavuttaen silti saman subjektiivisen visuaalisen laadun. Tämä tyypillisesti saavutetaan huomattavalla laskennallisella lisäkustannuksella. Siksi reaaliaikainen HEVC vaatii automatisoituja suunnittelumenetelmiä, joita voidaan käyttää rautatoteutus- (HW ) ja varmennustyön minimoimiseen.
Tässä väitöskirjassa ehdotetaan HLS:n käyttöä koko enkooderin suunnitteluprosessissa. Dataintensiivisistä koodaustyökaluista, kuten intra-ennustus ja diskreetit muunnokset, myös enemmän kontrollia vaativiin kokonaisuuksiin, kuten entropiakoodaukseen. Avoimen lähdekoodin Kvazaar HEVC -enkooderin C-lähdekoodia hyödynnetään tässä työssä referenssinä HLS-suunnittelulle sekä toteutuksen varmentamisessa. Suorituskykytulokset saadaan ja raportoidaan ohjelmoitavalla porttimatriisilla (FPGA).
Tämän väitöskirjan tärkein tuotos on HEVC intra enkooderin prototyyppi. Prototyyppi koostuu Nokia AirFrame Cloud Server palvelimesta, varustettuna kahdella 2.4 GHz:n 14-ytiminen Intel Xeon prosessorilla, sekä kahdesta Intel Arria 10 GX FPGA kiihdytinkortista, jotka voidaan kytkeä serveriin käyttäen joko peripheral component interconnect express (PCIe) liitäntää tai 40 gigabitin Ethernettiä. Prototyyppijärjestelmä saavuttaa reaaliaikaisen 4K enkoodausnopeuden, jopa 120 kuvaa sekunnissa. Lisäksi järjestelmän suorituskykyä on helppo skaalata paremmaksi lisäämällä järjestelmään käytännössä minkä tahansa määrän verkkoon kytkettäviä FPGA-kortteja.
Monimutkaisen HEVC:n tehokas mallinnus ja sen monipuolisten ominaisuuksien mukauttaminen reaaliaikaiselle HW HEVC enkooderille ei ole triviaali tehtävä, koska HW-toteutukset ovat perinteisesti erittäin aikaa vieviä. Tämä väitöskirja osoittaa, että HLS:n avulla pystytään nopeuttamaan kehitysaikaa, tarjoamaan ennen näkemätöntä suunnittelun skaalautuvuutta, ja silti osoittamaan kilpailukykyisiä QoR-arvoja ja absoluuttista suorituskykyä verrattuna olemassa oleviin toteutuksiin.High-Level Synthesis (HLS) is an automated design process that seeks to improve productivity over traditional design methods by increasing design abstraction from register transfer level (RTL) to behavioural level. Various commercial HLS tools have been available on the market since the 1990s, but only recently they have started to gain adoption across industry and academia. The slow adoption rate has mainly stemmed from lower quality of results (QoR) than obtained with conventional hardware description languages (HDLs). However, the latest HLS tool generations have substantially narrowed the QoR gap.
This thesis studies the feasibility of HLS in video codec development. It introduces several HLS implementations for High Efficiency Video Coding (HEVC) , that is the key enabling technology for numerous modern media applications. HEVC doubles the coding efficiency over its predecessor Advanced Video Coding (AVC) standard for the same subjective visual quality, but typically at the cost of considerably higher computational complexity. Therefore, real-time HEVC calls for automated design methodologies that can be used to minimize the HW implementation and verification effort.
This thesis proposes to use HLS throughout the whole encoder design process. From data-intensive coding tools, like intra prediction and discrete transforms, to more control-oriented tools, such as entropy coding. The C source code of the open-source Kvazaar HEVC encoder serves as a design entry point for the HLS flow, and it is also utilized in design verification. The performance results are gathered with and reported for field programmable gate array (FPGA) .
The main contribution of this thesis is an HEVC intra encoder prototype that is built on a Nokia AirFrame Cloud Server equipped with 2.4 GHz dual 14-core Intel Xeon processors and two Intel Arria 10 GX FPGA Development Kits, that can be connected to the server via peripheral component interconnect express (PCIe) generation 3 or 40 Gigabit Ethernet. The proof-of-concept system achieves real-time.
4K coding speed up to 120 fps, which can be further scaled up by adding practically any number of network-connected FPGA cards.
Overcoming the complexity of HEVC and customizing its rich features for a real-time HEVC encoder implementation on hardware is not a trivial task, as hardware development has traditionally turned out to be very time-consuming. This thesis shows that HLS is able to boost the development time, provide previously unseen design scalability, and still result in competitive performance and QoR over state-of-the-art hardware implementations
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
Images on the Move
In contemporary society, digital images have become increasingly mobile. They are networked, shared on social media, and circulated across small and portable screens. Accordingly, the discourses of spreadability and circulation have come to supersede the focus on production, indexicality, and manipulability, which had dominated early conceptions of digital photography and film. However, the mobility of images is neither technologically nor conceptually limited to the realm of the digital. The edited volume re-examines the historical, aesthetical, and theoretical relevance of image mobility. The contributors provide a materialist account of images on the move - ranging from wired photography to postcards to streaming media
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