308 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Decaying DC offset current mitigation in phasor estimation applications: A Review
Decaying DC (DDC) offset current mitigation is a vital challenge in phasor current estimation since it causes malfunctioning/maloperation of measurements and protection systems. Due to the inductive nature of electric power systems, the current during fault inception cannot change immediately and it contains a transient oscillation. The oscillatory component acts similar to an exponential DC signal and its characteristics depend on the X/R ratio of the system, fault location, and fault impedance. DDC attenuates accurate phasor estimation, which is pivotal in protection systems. Therefore, the DDC must be eliminated from the fault current (FC) signal. This paper presents an overview of DDC mitigation methods by considering different groupsābefore the discrete Fourier transform (pre-DFT), after the discrete Fourier transform (post-DFT), the least square-based (LS-based), and other methods. Through a comprehensive review of the existing schemes, the effects of noise, harmonics, multiple DDCs (MDDCs), and off-nominal frequency (ONF) on the accuracy of DDC estimation, were recognized. A detailed discussion (along with some simulation results) are presented to address the main advantages/disadvantages of the past studies. Finally, this paper presents a few suggestions for future researchers, for researchers to investigate more implementable solutions in this field
Adaptive Equalisation for Impulsive Noise Environments
This thesis addresses the problem of adaptive channel equalisation in environments where the
interfering noise exhibits nonāGaussian behaviour due to impulsive phenomena. The family
of alpha-stable distributions has proved to be a suitable and flexible tool for the modelling of signals with impulsive nature. However,nonāGaussian alphaāstable signals have infinite variance, and signal processing techniques based on second order moments are meaningless in such environments.
In order to exploit the flexibility of the stable family and still take advantage of
the existing signal processing tools, a novel framework for the integration of the stable model
in a communications context is proposed, based on a finite dynamic range receiver. The performance
of traditional signal processing algorithms designed under the Gaussian assumption
may degrade seriously in impulsive environments. When this degradation cannot be tolerated,
the traditional signal processing methods must be revisited and redesigned taking into account
the nonāGaussian noise statistics. In this direction, the optimum feedāforward and decision
feedback Bayesian symbolābyāsymbol equalisers for stable noise environments are derived.
Then, new analytical tools for the evaluation of systems in infinite variance environments are
presented. For the centers estimation of the proposed Bayesian equaliser, a unified framework
for a family of robust recursive linear estimation techniques is presented and the underlying relationships
between them are identified. Furthermore, the direct clustering technique is studied
and robust variants of the existing algorithms are proposed. A novel clustering algorithm is also
derived based on robust location estimation. The problem of estimating the stable parameters
has been addressed in the literature and a variety of algorithms can be found. Some of these
algorithms are assessed in terms of efficiency, simplicity and performance and the most suitable
is chosen for the equalisation problem. All the building components of an adaptive Bayesian
equaliser are then put together and the performance of the equaliser is evaluated experimentally.
The simulation results suggest that the proposed adaptive equaliser offers a significant performance
benefit compared with a traditional equaliser, designed under the Gaussian assumption.
The implementation of the proposed Bayesian equaliser is simple but the computational complexity
can be unaffordable. However, this thesis proposes certain approximations which enable
the computationally efficient implementation of the optimum equaliser with negligible loss in
performance
Mathematics and Digital Signal Processing
Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems
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Microarray image processing: A novel neural network framework
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods.
Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri
Adaptive Algorithms for Intelligent Acoustic Interfaces
Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the ālast-mileā problem of telecommunications.
One of the main feature of immersive communications is the distant-talking,
i.e. the hands-free (in the broad sense) speech communications without bodyworn
or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms.
The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms.
In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals.
As regards linear adaptive algorithms, a class of adaptive filters based on the
sparse nature of the acoustic impulse response has been recently proposed.
We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature.
On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel.
Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications
Adaptive Algorithms for Intelligent Acoustic Interfaces
Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the ālast-mileā problem of telecommunications.
One of the main feature of immersive communications is the distant-talking,
i.e. the hands-free (in the broad sense) speech communications without bodyworn
or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms.
The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms.
In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals.
As regards linear adaptive algorithms, a class of adaptive filters based on the
sparse nature of the acoustic impulse response has been recently proposed.
We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature.
On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel.
Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications
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