417 research outputs found
Ultrasound Signal Processing: From Models to Deep Learning
Medical ultrasound imaging relies heavily on high-quality signal processing
algorithms to provide reliable and interpretable image reconstructions.
Hand-crafted reconstruction methods, often based on approximations of the
underlying measurement model, are useful in practice, but notoriously fall
behind in terms of image quality. More sophisticated solutions, based on
statistical modelling, careful parameter tuning, or through increased model
complexity, can be sensitive to different environments. Recently, deep learning
based methods have gained popularity, which are optimized in a data-driven
fashion. These model-agnostic methods often rely on generic model structures,
and require vast training data to converge to a robust solution. A relatively
new paradigm combines the power of the two: leveraging data-driven deep
learning, as well as exploiting domain knowledge. These model-based solutions
yield high robustness, and require less trainable parameters and training data
than conventional neural networks. In this work we provide an overview of these
methods from the recent literature, and discuss a wide variety of ultrasound
applications. We aim to inspire the reader to further research in this area,
and to address the opportunities within the field of ultrasound signal
processing. We conclude with a future perspective on these model-based deep
learning techniques for medical ultrasound applications
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction
Spatial dissection of a soundfield using spherical harmonic decomposition
A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches.
To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield.
We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset
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