2,532 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Graph Filters for Signal Processing and Machine Learning on Graphs
Filters are fundamental in extracting information from data. For time series
and image data that reside on Euclidean domains, filters are the crux of many
signal processing and machine learning techniques, including convolutional
neural networks. Increasingly, modern data also reside on networks and other
irregular domains whose structure is better captured by a graph. To process and
learn from such data, graph filters account for the structure of the underlying
data domain. In this article, we provide a comprehensive overview of graph
filters, including the different filtering categories, design strategies for
each type, and trade-offs between different types of graph filters. We discuss
how to extend graph filters into filter banks and graph neural networks to
enhance the representational power; that is, to model a broader variety of
signal classes, data patterns, and relationships. We also showcase the
fundamental role of graph filters in signal processing and machine learning
applications. Our aim is that this article provides a unifying framework for
both beginner and experienced researchers, as well as a common understanding
that promotes collaborations at the intersections of signal processing, machine
learning, and application domains
Optimization and Applications of Modern Wireless Networks and Symmetry
Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications
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A machine learning approach for clinical gait analysis and classification of polymyalgia rheumatica using myoelectric sensors
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe study focuses on Polymyalgia Rheumatica (PMR), an autoimmune musculoskeletal disease primarily affecting the shoulder blade and hip muscles in older adults, particularly women aged 50 and above. The research aims to address two main challenges: the need for more clarity on the disease's pathophysiology and the challenge of identifying disease severity in patients. The study introduces a novel approach involving movement assessment, by designing a low-cost MyoTracker system, and using electromyography (EMG) features to understand the impact on patients' hip muscles. A clinical trial was conducted at Komfo Anokye Teaching Hospital in Ghana, where the study employed a qualitative research approach to monitor movement patterns. Participants were tasked to perform exercises comprising of gait, knee lifting, and knee extension with sensors attached to the hip muscles.
This research unfolds in three iterations, the first investigation involved hip muscular imbalances where the significant difference between patients and healthy controls in the maximum voluntary contraction (MVC) values was recorded. The bilateral difference computed between the left and right hip in patients exhibited 15% MVC on average compared to the healthy control group's 6%, indicating substantial hip muscular imbalances. The second iteration involved a movement assessment to identify specific movement patterns in patients. Support Vector Machine (SVM) achieves 85% accuracy for gait exercises, while Decision Tree (DT) performs less efficiently at 70%. SVM also excels in knee lifting exercises (70% accuracy), outperforming DT (60%). Based on hip muscle activation, patients' movement patterns significantly differ from healthy controls. In the third iteration, deep learning techniques, specifically RNN-LSTM and Vision Transformer (ViT), classify PMR disease severity based on EMG features. The study's results carry significant clinical implications with the evidence of hip muscular imbalances aiding in designing tailored rehabilitation protocols. Importantly, this study uses a cost-effective method for determining disease severity, enabling predictions about patients with severe PMR conditions. The key contribution of this thesis is the identification of patientsâ specific movement patterns and the determination of PMR severity among patients. Other contributions are the detection of hip muscular imbalance in patients and the design of rehabilitation protocols to address hip muscular imbalances and improve patients' range of motion, enhancing overall well-being. In conclusion, this comprehensive study leverages innovative approaches, from a MyoTracker system for movement assessment to deep learning models, to unravel the complexities of PMR disease. The collaboration with medical experts emphasises the potential real-world impact of this research in enhancing the treatment and recovery processes for individuals.Ghana Scholarship Secretaria
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems
Anomaly detection is a crucial task in complex distributed systems. A
thorough understanding of the requirements and challenges of anomaly detection
is pivotal to the security of such systems, especially for real-world
deployment. While there are many works and application domains that deal with
this problem, few have attempted to provide an in-depth look at such systems.
In this survey, we explore the potentials of graph-based algorithms to identify
anomalies in distributed systems. These systems can be heterogeneous or
homogeneous, which can result in distinct requirements. One of our objectives
is to provide an in-depth look at graph-based approaches to conceptually
analyze their capability to handle real-world challenges such as heterogeneity
and dynamic structure. This study gives an overview of the State-of-the-Art
(SotA) research articles in the field and compare and contrast their
characteristics. To facilitate a more comprehensive understanding, we present
three systems with varying abstractions as use cases. We examine the specific
challenges involved in anomaly detection within such systems. Subsequently, we
elucidate the efficacy of graphs in such systems and explicate their
advantages. We then delve into the SotA methods and highlight their strength
and weaknesses, pointing out the areas for possible improvements and future
works.Comment: The first two authors (A. Danesh Pazho and G. Alinezhad Noghre) have
equal contribution. The article is accepted by IEEE Transactions on Knowledge
and Data Engineerin
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