2,080 research outputs found
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in
critical infrastructure construction, where the meticulous analysis of failures
becomes paramount in achieving the fundamental objectives of continuity,
security, and availability. This survey enriches the discourse on failures,
failure analysis, and countermeasures in the context of the next-generation
critical communication infrastructures. Through an exhaustive examination of
existing literature, we discern and categorize prominent research orientations
with focuses on, namely resource depletion, security vulnerabilities, and
system availability concerns. We also analyze constructive countermeasures
tailored to address identified failure scenarios and their prevention.
Furthermore, the survey emphasizes the imperative for standardization in
addressing failures related to Artificial Intelligence (AI) within the ambit of
the sixth-generation (6G) networks, accounting for the forward-looking
perspective for the envisioned intelligence of 6G network architecture. By
identifying new challenges and delineating future research directions, this
survey can help guide stakeholders toward unexplored territories, fostering
innovation and resilience in critical communication infrastructure development
and failure prevention
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the
environment and thus imaging plays the pivotal role in mobile vision. Recent
explosive progresses in Artificial Intelligence (AI) have shown great potential
to develop advanced mobile platforms with new imaging devices. Traditional
imaging systems based on the "capturing images first and processing afterwards"
mechanism cannot meet this unprecedented demand. Differently, Computational
Imaging (CI) systems are designed to capture high-dimensional data in an
encoded manner to provide more information for mobile vision systems.Thanks to
AI, CI can now be used in real systems by integrating deep learning algorithms
into the mobile vision platform to achieve the closed loop of intelligent
acquisition, processing and decision making, thus leading to the next
revolution of mobile vision.Starting from the history of mobile vision using
digital cameras, this work first introduces the advances of CI in diverse
applications and then conducts a comprehensive review of current research
topics combining CI and AI. Motivated by the fact that most existing studies
only loosely connect CI and AI (usually using AI to improve the performance of
CI and only limited works have deeply connected them), in this work, we propose
a framework to deeply integrate CI and AI by using the example of self-driving
vehicles with high-speed communication, edge computing and traffic planning.
Finally, we outlook the future of CI plus AI by investigating new materials,
brain science and new computing techniques to shed light on new directions of
mobile vision systems
Active thermography for the investigation of corrosion in steel surfaces
The present work aims at developing an experimental methodology for the analysis
of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in
reflexion configuration (RC).
The peculiarity of this AT approach consists in exciting by means of a laser source the sound
surface of the specimens and acquiring the thermal signal on the same surface, instead of the
corroded one: the thermal signal is then composed by the reflection of the thermal wave
reflected by the corroded surface. This procedure aims at investigating internal corroded
surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and
Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to
improve the experimental set up. Surface thermal profiles were acquired by an IR
thermocamera and means of salt spray testing; at set time intervals the specimens were
investigated by means of AT. Each duration corresponded to a surface damage entity and to a
variation in the thermal response. Thermal responses of corroded specimens were related to
the corresponding corrosion level, referring to a reference specimen without corrosion. The
entity of corrosion was also verified by a metallographic optical microscope to measure the
thickness variation of the specimens
Beyond the Frame: Single and mutilple video summarization method with user-defined length
Video smmarization is a crucial method to reduce the time of videos which
reduces the spent time to watch/review a long video. This apporach has became
more important as the amount of publisehed video is increasing everyday. A
single or multiple videos can be summarized into a relatively short video using
various of techniques from multimodal audio-visual techniques, to natural
language processing approaches. Audiovisual techniques may be used to recognize
significant visual events and pick the most important parts, while NLP
techniques can be used to evaluate the audio transcript and extract the main
sentences (timestamps) and corresponding video frames from the original video.
Another approach is to use the best of both domain. Meaning that we can use
audio-visual cues as well as video transcript to extract and summarize the
video. In this paper, we combine a variety of NLP techniques (extractive and
contect-based summarizers) with video processing techniques to convert a long
video into a single relatively short video. We design this toll in a way that
user can specify the relative length of the summarized video. We have also
explored ways of summarizing and concatenating multiple videos into a single
short video which will help having most important concepts from the same
subject in a single short video. Out approach shows that video summarizing is a
difficult but significant work, with substantial potential for further research
and development, and it is possible thanks to the development of NLP models
State-of-research on performance indicators for bridge quality control and management
The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406—“Quality specifications for roadway bridges, standardization at a European level,” aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices.Peer ReviewedPostprint (published version
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