4,951 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    2P-BFT-Log: 2-Phase Single-Author Append-Only Log for Adversarial Environments

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    Replicated append-only logs sequentially order messages from the same author such that their ordering can be eventually recovered even with out-of-order and unreliable dissemination of individual messages. They are widely used for implementing replicated services in both clouds and peer-to-peer environments because they provide simple and efficient incremental reconciliation. However, existing designs of replicated append-only logs assume replicas faithfully maintain the sequential properties of logs and do not provide eventual consistency when malicious participants fork their logs by disseminating different messages to different replicas for the same index, which may result in partitioning of replicas according to which branch was first replicated. In this paper, we present 2P-BFT-Log, a two-phase replicated append-only log that provides eventual consistency in the presence of forks from malicious participants such that all correct replicas will eventually agree either on the most recent message of a valid log (first phase) or on the earliest point at which a fork occurred as well as on an irrefutable proof that it happened (second phase). We provide definitions, algorithms, and proofs of the key properties of the design, and explain one way to implement the design onto Git, an eventually consistent replicated database originally designed for distributed version control. Our design enables correct replicas to faithfully implement the happens-before relationship first introduced by Lamport that underpins most existing distributed algorithms, with eventual detection of forks from malicious participants to exclude the latter from further progress. This opens the door to adaptations of existing distributed algorithms to a cheaper detect and repair paradigm, rather than the more common and expensive systematic prevention of incorrect behaviour.Comment: Fixed 'two-phase' typ

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward

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    ‘Inner qualities versus inequalities’: A case study of student change learning about Aboriginal health using sequential, explanatory mixed methods

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    Racism and lack of self-determination in health care perpetuate injury and injustice to Aboriginal people. To instil cultural safety at individual, organisational, community and systems levels, a key site of action has been health professional education that seeks to elicit reflexivity, cultural humility and a working understanding of Aboriginal health concepts. Studies in Aboriginal community settings show Family Well Being (FWB) empowerment education is effective in supporting personal and collective reflexivity and transformation through empowering life skills development. Implementation of FWB within educational settings shows early signs of effectiveness among students. Yet knowledge of the steps and processes of student change is lacking. This mixed methods explanatory case study sought to measure and understand change in postgraduate students of a leading Australian university learning about Aboriginal health and wellbeing through blended delivery, including through face-to-face immersion in FWB in an urban classroom. Three interrelated studies investigated fidelity and acceptability of the program, measured and analysed growth and empowerment in students, and explained processes of change observed, through thematic analysis of asynchronous online discussions using lenses based on transformative learning and empowerment. Researcher reflexivity was promoted by Aboriginal supervision. Over six years, 194 students enrolled in two different Aboriginal public health courses, 85 of them in the FWB course. As well as achieving program fidelity and acceptability, pre/post-course change in students across a range of emotional empowerment, personal growth and life-long learning processes was measured in the FWB group. Thematic analysis revealed students’ fluid and recursive processes of transformative learning in their professional selves and capacities to act in domains important to Aboriginal health. This case study contributes new knowledge critical to strengthening health professional capabilities for ever more complex, uncertain and emotionally demanding sites of practice, and to work in empowering ways—with, not for, Aboriginal people and communities

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    The Entrenched Political Limitations of Australian Refugee Policy: A Case Study of the Australian Labor Party (2007-2013)

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    This thesis deconstructs Australia’s asylum and refugee policy trajectory under the Labor government between 2007 and 2013. For a short time after the 2007 election, in accordance with its promise to abolish the LNP’s Pacific Solution, Labor began to unwind certain policy structures of externalisation and deterrence that had been in place since the introduction of mandatory detention in 1992. By 2013 however, the ALP had declared that asylum seekers arriving by boat had no prospect of resettlement in Australia. This thesis analyses the political strategy of the ALP in rhetoric, policy choices and policy justifications to derive lessons from Labor’s mitigated challenge to the deterrence/externalisation paradigm. Critical Discourse Analysis is used to examine the political strategies of lead actors, particularly the ALP and the LNP, and to reconcile these strategies with policy outcomes such as irregular arrivals, detention figures, deaths at sea and compliance with obligations under international law. A central argument of this thesis is that Labor’s attempt to sustainably depart from the dominant externalisation paradigm was impaired, not by a lack of commitment to its stated program of reform, but rather by entrenched political limitations of the Australian context. These limitations include the LNP’s rigid partisanship and lack of policy compromise, the deep-rooted nature of mandatory detention, and the Australian public’s historical and continued support for controlled migration. A precise and detailed analysis of the impact of these limitations on Labor’s proposed reform fills a gap in academic knowledge about the political influences on policy action in Australian asylum and refugee policy. I contend that these limitations must be effectively engaged with in any attempt to reform the Australian asylum and refugee policy space

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book
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