The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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Wavelet transforms and the ECG: a review.

Recommendations for the Standardization and Interpretation of the Electrocardiogram

In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

https://www.heartrhythmjournal.com/article/S1547-5271(07)00105-1/pdf

Recommendations for the Standardization and Interpretation of the Electrocardiogram: Part I: The Electrocardiogram and Its Technology A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology

Machine learning algorithms for wireless sensor networks: A survey

Display Omitted An Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) is proposed.UMBIC solves the hot spot problem and improves the lifetime of networks.UMBIC adjusts the intra-cluster and inter-cluster energy dissipation of clusters.UMBIC utilizes the multi-objective immune algorithm to finds the optimum clusters.Simulation experiments were conducted in MATLAB correctly. In multi-hop routing, cluster heads near the base station act as relays for far cluster heads and thus will deplete their energy very quickly. Thus, hot spots in the sensor field result. This paper introduces a new clustering algorithm named an Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) to solve the hot spot problem and improve the lifetime of small and large scale/homogeneous and heterogeneous wireless sensor networks with different densities. UMBIC protocol utilizes the Unequal Clustering Mechanism (UCM) and the Multi-Objective Immune Algorithm (MOIA) to adjust the intra-cluster and inter-cluster energy consumption. The UCM is used to partition the network into clusters of unequal size based on distance with reference to base station and residual energy. While the MOIA constructs an optimum clusters and a routing tree among them based on covering the entire sensor field, ensuring the connectivity among nodes and minimizing the communication cost of all nodes. The UMBIC protocol rotates the role of cluster heads among the nodes only if the residual energy of one of the current cluster heads less than the energy threshold, as a result the time computational and overheads are saved. Simulation results show that, compared with other protocols, the UMBIC protocol can effectively improve the network lifetime, solve the hot spot problem and balance the energy consumption among all nodes in the network. Moreover, it has less overheads and computational complexity.

An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

This paper presents a new Genetic Algorithm-based Energy-Efficient adaptive clustering hierarchy Protocol (GAEEP) to efficiently maximize the lifetime and to improve the stable period of Wireless Sensor Networks (WSNs). The new protocol is aimed at prolonging the lifetime of WSNs by finding the optimum number of cluster heads (CHs) and their locations based on minimizing the energy consumption of the sensor nodes using genetic algorithm. The operation of the GAEEP is broken up into rounds, where each round begins with a set-up phase, when the base station finds the optimum number of CHs and assigns members nodes of each CH, followed by a steady-state phase, when the sensed data are transferred to CHs and collected in frames; then these frames are transferred to the base station. The performance of the GAEEP is compared with previous protocols using Matlab simulation. Simulation results show that GAEEP protocol improves the network lifetime and stability period over previous protocols in both homogeneous and heterogeneous cases. Moreover, GAEEP protocol increases the reliability of clustering process because it expands the stability period and compresses the instability period.

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A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks

In digital signal processing (DSP),
Nyquistrate sampling completely describes a signal by exploiting its
bandlimitedness. Compressed Sensing (CS), also known as compressive sampling,
is a DSP technique efficiently acquiring and reconstructing a signal completely
from reduced number of measurements, by exploiting its compressibility. The
measurements are not point samples but more general linear functions of the
signal. CS can capture and represent sparse signals at a rate significantly
lower than ordinarily used in the Shannon’s sampling theorem. It is interesting
to notice that most signals in reality are sparse; especially when they are
represented in some domain (such as the wavelet domain) where many coefficients
are close to or equal to zero. A signal is called K-sparse, if it can be
exactly represented by a basis, , and a set of coefficients , where only K
coefficients are nonzero. A signal is called approximately K-sparse, if it can
be represented up to a certain accuracy using K non-zero coefficients. As an
example, a K-sparse signal is the class of signals that are the sum of K
sinusoids chosen from the N harmonics of the observed time interval. Taking the
DFT of any such signal would render only K non-zero values . An example of
approximately sparse signals is when the coefficients , sorted by magnitude,
decrease following a power law. In this case the sparse approximation
constructed by choosing the K largest coefficients is guaranteed to have an
approximation error that decreases with the same power law as the coefficients.
The main limitation of CS-based systems is that they are employing iterative
algorithms to recover the signal. The sealgorithms are slow and the hardware
solution has become crucial for higher performance and speed. This technique
enables fewer data samples than traditionally required when capturing a signal
with relatively high bandwidth, but a low information rate. As a main feature
of CS, efficient algorithms such as -minimization can be used for recovery.
This paper gives a survey of both theoretical and numerical aspects of
compressive sensing technique and its applications. The theory of CS has many
potential applications in signal processing, wireless communication,
cognitive radio and medical imaging.

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Compressive Sensing Algorithms for Signal Processing Applications: A Survey

In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto- regressive model. Two algorithms are implemented for auto- regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.

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Mohammed Abo-Zahhad

Papers

An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks

Compressive Sensing Algorithms for Signal Processing Applications: A Survey

A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals

ECG data compression using optimal non-orthogonal wavelet transform