Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Synchronized Illumination Modulation for Digital Video Compositing

Informationsaustausch ist eines der Grundbedürfnisse der Menschen. Während früher dazu Wandmalereien,Handschrift, Buchdruck und Malerei eingesetzt wurden, begann man später, Bildfolgen zu erstellen, die als sogenanntes ”Daumenkino” den Eindruck einer Animation vermitteln. Diese wurden schnell durch den Einsatz rotierender Bildscheiben, auf denen mit Hilfe von Schlitzblenden, Spiegeln oder Optiken eine Animation sichtbar wurde, automatisiert – mit sogenannten Phenakistiskopen,Zoetropen oder Praxinoskopen. Mit der Erfindung der Fotografie begannen in der zweiten Hälfte des 19. Jahrhunderts die ersten Wissenschaftler wie Eadweard Muybridge, Etienne-Jules Marey und Ottomar Anschütz, Serienbildaufnahmen zu erstellen und diese dann in schneller Abfolge, als Film, abzuspielen. Mit dem Beginn der Filmproduktion wurden auch die ersten Versuche unternommen, mit Hilfe dieser neuen Technik spezielle visuelle Effekte zu generieren, um damit die Immersion der Bewegtbildproduktionen weiter zu erhöhen. Während diese Effekte in der analogen Phase der Filmproduktion bis in die achtziger Jahre des 20.Jahrhunderts recht beschränkt und sehr aufwendig mit einem enormen manuellen Arbeitsaufwand erzeugt werden mussten, gewannen sie mit der sich rapide beschleunigenden Entwicklung der Halbleitertechnologie und der daraus resultierenden vereinfachten digitalen Bearbeitung immer mehr an Bedeutung. Die enormen Möglichkeiten, die mit der verlustlosen Nachbearbeitung in Kombination mit fotorealistischen, dreidimensionalen Renderings entstanden, führten dazu, dass nahezu alle heute produzierten Filme eine Vielfalt an digitalen Videokompositionseffekten beinhalten. ...Besides home entertainment and business presentations, video projectors are powerful tools for modulating images spatially as well as temporally. The re-evolving need for stereoscopic displays increases the demand for low-latency projectors and recent advances in LED technology also offer high modulation frequencies. Combining such high-frequency illumination modules with synchronized, fast cameras, makes it possible to develop specialized high-speed illumination systems for visual effects production. In this thesis we present different systems for using spatially as well as temporally modulated illumination in combination with a synchronized camera to simplify the requirements of standard digital video composition techniques for film and television productions and to offer new possibilities for visual effects generation. After an overview of the basic terminology and a summary of related methods, we discuss and give examples of how modulated light can be applied to a scene recording context to enable a variety of effects which cannot be realized using standard methods, such as virtual studio technology or chroma keying. We propose using high-frequency, synchronized illumination which, in addition to providing illumination, is modulated in terms of intensity and wavelength to encode technical information for visual effects generation. This is carried out in such a way that the technical components do not influence the final composite and are also not visible to observers on the film set. Using this approach we present a real-time flash keying system for the generation of perspectively correct augmented composites by projecting imperceptible markers for optical camera tracking. Furthermore, we present a system which enables the generation of various digital video compositing effects outside of completely controlled studio environments, such as virtual studios. A third temporal keying system is presented that aims to overcome the constraints of traditional chroma keying in terms of color spill and color dependency. ..

Biological adhesion in wet environments: adaptations and mechanisms

Physiochemical conditions in water are fundamentally different to those in air; hence, organisms require special adaptations to adhere in wet environments. In my thesis, I have investigated three study systems to elucidate mechanisms for adhesion under wet conditions. In Chapters 2 and 3, I explore an aquatic insect (Diptera: Blephariceridae) that uses suction organs to attach to rocks in raging alpine torrents. Suction-based attachment is driven by physical processes requiring a pressure difference and a seal to maintain it. Through my investigations, I have identified several key principles for biological suction attachments to wet and rough surfaces. Using three-dimensional reconstructions of blepharicerid suction organs and in vivo visualisation of the adhesive contact zone, I found several internal and external morphological adaptations that are important for strong adhesion under water. Moreover, I characterised a mechanism for rapid detachment which is the first detailed account of an actively controlled detachment system in biological suction organs. In Chapter 4, I investigate the contribution of physical and chemical mechanisms to the powerful attachment of common limpets (Patella vulgata) to rocks in the intertidal zone. I demonstrate that suction is not the primary contributor to their attachment forces; rather, it is their adhesive pedal mucus that is responsible. This adhesive mucus comprises of a complex mixture of glycans and proteins, many of which share homology with adhesive secretions from other marine invertebrates, such as sea stars, sea anemones, and flatworms. In Chapters 5 and 6, I study the physical and chemical properties of sticky secretions from carnivorous pitcher plants (Nepenthes) that help to capture, retain, and digest insects. I show that the viscoelastic pitcher fluid readily adheres to but not easily dewets from insect cuticle, and forms stable filaments as the insect attempts to escape that require significant work to overcome. In addition, the surface tension is reduced in pitcher fluid compared to water, making insects sink more easily into the former and facilitating further wetting of the cuticle. Chemical characterisation of the pitcher fluid revealed that its sticky filamentous property is caused by a polysaccharide with a glucurono-mannan backbone structure, which is chemically stable and contains carboxylic groups for strong interactions. Glucurono-mannan are an understudied group of plant polysaccharides that are present in mucilaginous secretions from across the plant kingdom, including sticky capture fluids from other carnivorous plants. My findings show that pitcher plant fluid can be used as a study system for future investigations into the origins and functional role of glucurono-mannan in carnivorous plants. In summary, my thesis has identified novel adaptations and principles for biological adhesion under wet conditions using three selected study systems, hence expanding our understanding of the underlying physical and chemical mechanisms and providing inspiration for biomimetic adhesives with improved performance in wet environments.EU Horizon 2020 under Marie Skłodowska-Curie grant agreement No. 64286

Design Methods and Tools for Application-Specific Predictable Networks-on-Chip

As the complexity of applications grows with each new generation, so does the demand for computation power. To satisfy the computation demands at manageable power levels, we see a shift in the design paradigm from single processor systems to Multiprocessor Systems-on-Chip (MPSoCs). MPSoCs leverage the parallelism in applications to increase the performance at the same power levels. To further improve the computation to power consumption ratio, MPSoCs for embedded applications are heterogeneous and integrate cores that are specialized to perform the different functionalities of the application. With technology scaling, wire power consumption is increasing compared to logic, making communication as expensive as computation. Therefore customizing the interconnect is necessary to achieve energy efficiency. Designing an optimal application specific Network-on-Chip (NoC), that meets application demands, requires the exploration of a large design space. Automatic design and optimization of the NoC is required in order to achieve fast design closure, especially for heterogeneous MPSoCs. To continue to meet the computation requirements of future applications new technologies are emerging. Three dimensional integration promises to increase the number of transistors by stacking multiple silicon layers. This will lead to an increase in the number of cores of the MPSoCs resulting in increased communication demands. To compensate for the increase in the wire delay in new technology nodes as well as to reduce the power consumption further, multi-synchronous design is becoming popular. With multiple clock signals, different parts of the MPSoC can be clocked at different frequencies according to the current demands of the application and can even be shutdown when they are not used at all. This further complicates the design of the NoC.Many applications require different levels of guarantee from the NoC in order to perform their functionality correctly. As communication traffic patterns become more complex, the performance of the NoC can no longer be predicted statically. Therefore designing the interconnect network requires that such guarantees are provided during the dynamic operation of the system which includes the interaction with major subsystems (i.e., main memory) and not just the interconnect itself. In this thesis, I present novel methods to design application-specific NoCs that meet performance demands, under the constraints of new technologies. To provide different levels of Quality of Service, I integrate methods to estimate the NoC performance during the design phase of the interconnect topology. I present methods and architectures for NoCs to efficiently access memory systems, in order to achieve predictable operation of the systems from the point of view of the communication as well as the bottleneck target devices. Therefore the main contribution of the thesis is twofold: scientific as I propose new algorithms to perform topology synthesis and engineering by presenting extensive experiments and architectures for NoC design

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Temporal Context Modeling for Text Streams

There is increasing recognition that time plays an essential role in many information seeking tasks. This dissertation explores temporal models on evolving streams of text and the role that such models play in improving information access. I consider two cases: a stream of social media posts by many users for tweet search and a stream of queries by an individual user for voice search. My work explores the relationship between temporal models and context models: for tweet search, the evolution of an event serves as the context of clustering relevant tweets; for voice search, the user's history of queries provides the context for helping understand her true information need. First, I tackle the tweet search problem by modeling the temporal contexts of the underlying collection. The intuition is that an information need in Twitter usually correlates with a breaking news event, thus tweets posted during that event are more likely to be relevant. I explore techniques to model two different types of temporal signals: pseudo trend and query trend. The pseudo trend is estimated through the distribution of timestamps from an initial list of retrieved documents given a query, which I model through continuous hidden Markov approach as well as neural network-based methods for relevance ranking and sequence modeling. As an alternative, the query trend, is directly estimated from the temporal statistics of query terms, obviating the need for an initial retrieval. I propose two different approaches to exploit query trends: a linear feature-based ranking model and a regression-based model that recover the distribution of relevant documents directly from query trends. Extensive experiments on standard Twitter collections demonstrate the superior effectivenesses of my proposed techniques. Second, I introduce the novel problem of voice search on an entertainment platform, where users interact with a voice-enabled remote controller through voice requests to search for TV programs. Such queries range from specific program navigation (i.e., watch a movie) to requests with vague intents and even queries that have nothing to do with watching TV. I present successively richer neural network architectures to tackle this challenge based on two key insights: The first is that session context can be exploited to disambiguate queries and recover from ASR errors, which I operationalize with hierarchical recurrent neural networks. The second insight is that query understanding requires evidence integration across multiple related tasks, which I identify as program prediction, intent classification, and query tagging. I present a novel multi-task neural architecture that jointly learns to accomplish all three tasks. The first model, already deployed in production, serves millions of queries daily with an improved customer experience. The multi-task learning model is evaluated on carefully-controlled laboratory experiments, which demonstrates further gains in effectiveness and increased system capabilities. This work now serves as the core technology in Comcast Xfinity X1 entertainment platform, which won an Emmy award in 2017 for the technical contribution in advancing television technologies. This dissertation presents families of techniques for modeling temporal information as contexts to assist applications with streaming inputs, such as tweet search and voice search. My models not only establish the state-of-the-art effectivenesses on many related tasks, but also reveal insights of how various temporal patterns could impact real information-seeking processes

Overcoming the Intuition Wall: Measurement and Analysis in Computer Architecture

These are exciting times for computer architecture research. Today there is significant demand to improve the performance and energy-efficiency of emerging, transformative applications which are being hammered out by the hundreds for new computing platforms and usage models. This booming growth of applications and the variety of programming languages used to create them is challenging our ability as architects to rapidly and rigorously characterize these applications. Concurrently, hardware has become more complex with the emergence of accelerators, multicore systems, and heterogeneity caused by further divergence between processor market segments. No one architect can now understand all the complexities of many systems and reason about the full impact of changes or new applications. To that end, this dissertation presents four case studies in quantitative methods. Each case study attacks a different application and proposes a new measurement or analytical technique. In each case study we find at least one surprising or unintuitive result which would likely not have been found without the application of our method

Detection and Evaluation of Clusters within Sequential Data

Motivated by theoretical advancements in dimensionality reduction techniques we use a recent model, called Block Markov Chains, to conduct a practical study of clustering in real-world sequential data. Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees and can be deployed in sparse data regimes. Despite these favorable theoretical properties, a thorough evaluation of these algorithms in realistic settings has been lacking. We address this issue and investigate the suitability of these clustering algorithms in exploratory data analysis of real-world sequential data. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. In order to evaluate the determined clusters, and the associated Block Markov Chain model, we further develop a set of evaluation tools. These tools include benchmarking, spectral noise analysis and statistical model selection tools. An efficient implementation of the clustering algorithm and the new evaluation tools is made available together with this paper. Practical challenges associated to real-world data are encountered and discussed. It is ultimately found that the Block Markov Chain model assumption, together with the tools developed here, can indeed produce meaningful insights in exploratory data analyses despite the complexity and sparsity of real-world data.Comment: 37 pages, 12 figure