9,465 research outputs found
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management
Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
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
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing
With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
Toward a Minor Tech: A Peer-reviewed Newspaper, Volume 12, Issue 1, 2023
Following a process of open exchanges and a three-day research workshop in London, at London South Bank University and King’s College, London, the publication brings together researchers who address the problems of technological scale, thinking through the potentials of ‘the minor’; or what we are referring to as minor (or minority) tech. As such, the publication sets out to question the universal ideals of technology and its problems of scale, extending it to follow the three main characteristics identified in Deleuze and Guattari’s essay (Toward a Minor Literature), namely deterritorialization, political immediacy, and collective value.Â
Contributions by Christian Ulrik Andersen, Geoff Cox, Camille Crichlow, Mateus Domingos, Feminist Servers (mara karagianni & nate wessalowski), Teodora Sinziana Fartan, Susanne Förster, Inte Gloerich, Daniel Chávez Heras, Macon Holt, Jung-Ah Kim, Edoardo Lomi, Inga Luchs, Gabriel Menotti, Alasdair Milne, Anna Mladentseva, Shusha Niederberger, Søren Bro Pold, Roel Roscam Abbing, Winnie Soon, Magdalena Tyżlik-Carver, Varia, Jack Wilson, xenodata co-operative (Yasemin Keskintepe & Alexandra Anikina), Sandy Di Yu, Freja Kir.
Design & Production: Manetta Berends and Simon Browne (Varia)
Advanced Nanomaterials for Electrochemical Energy Conversion and Storage
This book focuses on advanced nanomaterials for energy conversion and storage, covering their design, synthesis, properties and applications in various fields. Developing advanced nanomaterials for high-performance and low-cost energy conversion and storage devices and technologies is of great significance in order to solve the issues of energy crisis and environmental pollution. In this book, various advanced nanomaterials for batteries, capacitors, electrocatalysis, nanogenerators, and magnetic nanomaterials are presente
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
We develop a novel graph-based trainable framework to maximize the weighted
sum energy efficiency (WSEE) for power allocation in wireless communication
networks. To address the non-convex nature of the problem, the proposed method
consists of modular structures inspired by a classical iterative suboptimal
approach and enhanced with learnable components. More precisely, we propose a
deep unfolding of the successive concave approximation (SCA) method. In our
unfolded SCA (USCA) framework, the originally preset parameters are now
learnable via graph convolutional neural networks (GCNs) that directly exploit
multi-user channel state information as the underlying graph adjacency matrix.
We show the permutation equivariance of the proposed architecture, which is a
desirable property for models applied to wireless network data. The USCA
framework is trained through a stochastic gradient descent approach using a
progressive training strategy. The unsupervised loss is carefully devised to
feature the monotonic property of the objective under maximum power
constraints. Comprehensive numerical results demonstrate its generalizability
across different network topologies of varying size, density, and channel
distribution. Thorough comparisons illustrate the improved performance and
robustness of USCA over state-of-the-art benchmarks.Comment: Published in IEEE Transactions on Wireless Communication
A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey
The growing interest in the Metaverse has generated momentum for members of
academia and industry to innovate toward realizing the Metaverse world. The
Metaverse is a unique, continuous, and shared virtual world where humans embody
a digital form within an online platform. Through a digital avatar, Metaverse
users should have a perceptual presence within the environment and can interact
and control the virtual world around them. Thus, a human-centric design is a
crucial element of the Metaverse. The human users are not only the central
entity but also the source of multi-sensory data that can be used to enrich the
Metaverse ecosystem. In this survey, we study the potential applications of
Brain-Computer Interface (BCI) technologies that can enhance the experience of
Metaverse users. By directly communicating with the human brain, the most
complex organ in the human body, BCI technologies hold the potential for the
most intuitive human-machine system operating at the speed of thought. BCI
technologies can enable various innovative applications for the Metaverse
through this neural pathway, such as user cognitive state monitoring, digital
avatar control, virtual interactions, and imagined speech communications. This
survey first outlines the fundamental background of the Metaverse and BCI
technologies. We then discuss the current challenges of the Metaverse that can
potentially be addressed by BCI, such as motion sickness when users experience
virtual environments or the negative emotional states of users in immersive
virtual applications. After that, we propose and discuss a new research
direction called Human Digital Twin, in which digital twins can create an
intelligent and interactable avatar from the user's brain signals. We also
present the challenges and potential solutions in synchronizing and
communicating between virtual and physical entities in the Metaverse
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