8,436 research outputs found

    Colour technologies for content production and distribution of broadcast content

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    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

    k-Means

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    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Security and Privacy Problems in Voice Assistant Applications: A Survey

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    Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

    Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS

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    When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis

    Anticholinergic use in the UK: longitudinal trends and associations with cognitive outcomes

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    Observational studies have shown an association between the use of anticholinergic drugs and various negative health outcomes. However, when studying cognitive outcomes, there is great heterogeneity in previous results. The objectives of the present thesis are threefold. First, to explore the longitudinal patterns of anticholinergic prescribing in the UK. Second, to examine the association between anticholinergic burden and dementia. Third, to probe the relationship between anticholinergic burden, general cognitive ability, and brain structural MRI in relatively healthy participants. Chapter 1 provides an overview of the role of acetylcholine as a neurotransmitter in the human body. It begins with a description of its molecular characteristics and continues with a summary of anatomical and cellular features of cholinergic pathways in the brain. The chapter concludes with a description of the relevance of cholinergic processing in cognition and Alzheimer’s disease. Chapter 2 gives a summary of anticholinergic drugs. It describes the history of anticholinergic compounds and their present use in medicine. It then appraises the tools used to gauge the anticholinergic potency of drugs. I conclude the Chapter by evaluating the available evidence on the effects of anticholinergic drugs on various important health outcomes. Chapter 3 focuses on UK Biobank, the sample used in all analyses presented in this thesis. The chapter briefly describes the conception of the study, the timeline of assessments, and the available variables. I focus in my descriptions on the variables that were used in the present thesis, especially cognitive tests, brain imaging, and linked health data. Chapters 4 to 6 present the empirical work conducted as part of this thesis. Chapter 4 presents an analysis of anticholinergic prescribing trends in UK primary care from the year 1990 to 2015. I first calculate an anticholinergic burden (AChB) according to 13 different anticholinergic scales and an average to derive a “Meta-scale”. I then describe the prevalence of anticholinergic prescribing and its longitudinal trend for all scales. I use different plots of age-, period- and cohort effects on the AChB according to the Meta-scale to evaluate the contributions of these effects to the linear longitudinal trend. The study finds AChB to have increased 9-fold over 25 years and that this effect was attributable to both age- and cohort/period-related changes. In other words, ageing of the sample is not sufficient to explain the increase in anticholinergic prescribing; cohort- or period-effects must have contributed to the observed changes. Chapter 5 explores the relationship between anticholinergic prescribing and dementia. Previous studies on this topic had provided varied results. One of the goals of the present study was to probe potential factors for this heterogeneity. We find that greater AChB according to most of the studied anticholinergic scales (standardised HRs range: 1.027-1.125), as well as the slope of anticholinergic change (HR=1.094; 95% CI: 1.068-1.119), are associated with dementia. However, we find that not all drug classes are associated with dementia. Antidepressants (HR=1.11, 95% CI=1.07-1.17), antiepileptics (HR=1.07, 95% CI=1.04-1.11), and the antidiuretic furosemide (HR=1.06, 95% CI=1.02-1.10) exhibit the strongest effects. Interestingly, when exploring the effects of groups of anticholinergic drugs with different anticholinergic potencies, only the moderate potency group shows significant associations with dementia (HR=1.10, 95% CI=1.05-1.15). Chapter 6 examines the association between AChB, general cognitive ability, and brain structural MRI. It aims both to explore the potential sources of heterogeneity in previous work, as well as to expand on it by studying relatively healthy community-dwelling adults. We study brain structural MRI in a much bigger sample (at least 5x bigger) and use many more outcomes than previous studies. We find weak, but significant associations between AChB and general cognitive ability, and with 7/9 individual cognitive tests (standardised betas (β) range: -0.039, -0.003). Again, AChB in only some drug classes is associated with lower general cognitive ability, especially β-lactam antibiotics (β=-0.035, pFDR. Finally, chapter 7 summarizes the findings presented in chapters 4 to 6. The chapter also provides a critique of the sample and of my approach when conducting the analyses presented in the present thesis. The chapter concludes by discussing suggestions for future work on this topic

    Deep Learning Enabled Semantic Communication Systems

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    In the past decades, communications primarily focus on how to accurately and effectively transmit symbols (measured by bits) from the transmitter to the receiver. Recently, various new applications appear, such as autonomous transportation, consumer robotics, environmental monitoring, and tele-health. The interconnection of these applications will generate a staggering amount of data in the order of zetta-bytes and require massive connectivity over limited spectrum resources but with lower latency, which poses critical challenges to conventional communication systems. Semantic communication has been proposed to overcome the challenges by extracting the meanings of data and filtering out the useless, irrelevant, and unessential information, which is expected to be robust to terrible channel environments and reduce the size of transmitted data. While semantic communications have been proposed decades ago, their applications to the wireless communication scenario remain limited. Deep learning (DL) based neural networks can effectively extract semantic information and can be optimized in an end-to-end (E2E) manner. The inborn characteristics of DL are suitable for semantic communications, which motivates us to exploit DL-enabled semantic communication. Inspired by the above, this thesis focus on exploring the semantic communication theory and designing semantic communication systems. First, a basic DL based semantic communication system, named DeepSC, is proposed for text transmission. In addition, DL based multi-user semantic communication systems are investigated for transmitting single-modal data and multimodal data, respectively, in which intelligent tasks are performed at the receiver directly. Moreover, a semantic communication system with a memory module, named Mem-DeepSC, is designed to support both memoryless and memory intelligent tasks. Finally, a lite distributed semantic communication system based on DL, named L-DeepSC, is proposed with low complexity, where the data transmission from the Internet-of-Things (IoT) devices to the cloud/edge works at the semantic level to improve transmission efficiency. The proposed various DeepSC systems can achieve less data transmission to reduce the transmission latency, lower complexity to fit capacity-constrained devices, higher robustness to multi-user interference and channel noise, and better performance to perform various intelligent tasks compared to the conventional communication systems
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