10,509 research outputs found
Recent developments in 2D materials for energy harvesting applications
The ever-increasing demand for energy as a result of the growing interest in applications, such as the Internet of Things and wearable systems, etc, calls for the development of self-sustained energy harvesting solutions. In this regard, 2D materials have sparked enormous interest recently, due to their outstanding properties, such as ultra-thin geometry, high electromechanical coupling, large surface area to volume ratio, tunable band gap, transparency and flexibility. This has given rise to noteworthy advancements in energy harvesters such as triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs) and photovoltaics based on 2D materials. This review introduces the properties of different 2D materials including graphene, transition metal dichalcogenides, MXenes, black phosphorus, hexagonal boron nitride, metal-organic frameworks and covalent-organic frameworks. A detailed discussion of recent developments in 2D materials-based PENG, TENG and photovoltaic devices is included. The review also considers the performance enhancement mechanism and importance of 2D materials in energy harvesting. Finally, the challenges and future perspectives are laid out to present future research directions for the further development and extension of 2D materials-based energy harvesters
Beam scanning by liquid-crystal biasing in a modified SIW structure
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
Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition
This work proposes an autonomous multi-robot exploration pipeline that
coordinates the behaviors of robots in an indoor environment composed of
multiple rooms. Contrary to simple frontier-based exploration approaches, we
aim to enable robots to methodically explore and observe an unknown set of
rooms in a structured building, keeping track of which rooms are already
explored and sharing this information among robots to coordinate their
behaviors in a distributed manner. To this end, we propose (1) a geometric cue
extraction method that processes 3D map point cloud data and detects the
locations of potential cues such as doors and rooms, (2) a spherical
decomposition for open spaces used for target assignment. Using these two
components, our pipeline effectively assigns tasks among robots, and enables a
methodical exploration of rooms. We evaluate the performance of our pipeline
using a team of up to 3 aerial robots, and show that our method outperforms the
baseline by 36.6% in simulation and 26.4% in real-world experiments
ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting
Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment.
The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented.
Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable
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Can I Get a Witness?—Living While Black Death is Trending
It is not uncommon for graphic scenes of violence and death to infiltrate our timelinesfrom retweets, reposts, and shares. I often question how much control do we really have over the images that enter our feed? In what ways are we affected and influenced by these images? How do we relate to these images and video clips that are played and replayed before us? In what ways are these images evoking or are related to past scenes of racist violence? In what ways are these racially violent moments captured in photos and videos and shared online speaking to a Black consciousness?
This project comparatively researches and examines the relationship between past modes and methods of Black trauma curation in the past, to contemporary modes of dissemination on social media in order to argue that contemporary uses of spaces such as Instagram, Facebook, and Twitter serve as an extension of previous scrapbooking methods. By comparing The Emmet Till Generation and their curation of trauma via scrapbooks which were used to galvanize social movements, and impact organizing efforts of the youth, The Trayvon Generation today uses social media in a similar fashion; to bear witness, to organize, and to curate digital memorials for the dead. Witnessing is further extended and complicated on digital platforms, providing an abundance of visual evidence that has proven to be vital in leading tp prosecutions and arrests of violent state officials, and perpetrators of extrajudicial violence.
These live or recorded moments of witnessing are used not only as evidence, but to inform the public. However, we have always known that it’s always happening somewhere, even if we aren’t around to witness it. With that said, what are the effects of having the duty, and the responsibility to bear witness? Paying particular attention to Black youth, this project examines their presence and usage of social media spaces. By analyzing young Black people’s use of social media platforms in relation to Darnella Frazier’s strategic use Facebook, this project examines how Black youth and witnessing is currently driving a cultural shift in entertainment media that highlights witnessing death as a significant milestone for Black youth that marks the transition between childhood and adulthood. It is also impacting entertainment media that is not marketed towards Black people, further highlighting Black witnessing of racialized violence at the intersection of technology as both a contemporary and future issue through its inclusion in contemporary media.Witnessing for Black people is framed as being both necessary and traumatic.
This project concludes with an in depth examination of speculative media to reveal the implications of both the present and the future intersections of race relations, state violence and technology. Through analyses of interviews, image circulation and dissemination, magazine articles, social media platforms, visual and speculative media, this dissertation works to address and attempts to answer the aforementioned questions
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
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