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.

/pdf/wavelet-transforms-and-the-ecg-a-review-36a6zg0xg1.pdf

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

Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

/pdf/integrated-model-of-dna-sequence-numerical-representation-50atorqsfv.pdf

Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

With the development of MEMS and CMOS technologies, the implementation of a huge
number of wireless distributed micro-sensors that can be simply and rapidly deployed to
form highly redundant, self-configuring, and ad hoc sensor networks. Sensor nodes are
generally battery- devices, the critical facets to face concern are how to minimize energy
consumption of nodes, so that the lifetime of sensing nodes can be maximized. The first
step to achieve this goal is to know completely the sources of energy consumption in
wireless sensor networks. In this paper, sources of energy consumption at various
communication layers have been studied and investigated. Moreover, the energy
consumption for the components of a typical sensor node and the impact of
communication protocols stack on the energy consumption are discussed. In the sequel,
the sources of energy consumption in each communication layer individually are studied.
Then, a survey has been provided for existing energy models and the classification of
these models into physical layer, MAC layer and cross-layer energy models.

/pdf/a-comparative-study-of-energy-consumption-sources-for-4v4dm993qe.pdf

A Comparative Study of Energy Consumption Sources for Wireless Sensor Networks

Routing protocols are responsible for discovering and maintaining energy-efficient routes in wireless sensor networks (WSNs) to make reliable and efficient communication. The main aim of the routing protocol design is collecting data of the sensor field efficiently. In general, routing in WSNs can be classified into three groups: flat routing, hierarchical routing, and location routing. According to the literature, hierarchical routing has more advantages compared to other types, for example, hierarchical routing reduces the redundant data transmission and balances the load among the sensor nodes in an efficient way. Recently, many intelligent-based hierarchical routing protocols are developed for controlling the consumption power of WSNs. Selecting an appropriate routing protocol for specific applications is an important and difficult task for the designer of WSNs. Therefore, this chapter presents a comprehensive survey of the recently intelligent-based hierarchical routing protocols that are developed based on Particle Swarm Optimization, Ant Colony Optimization, Fuzzy Logic, Genetic Algorithm, and Artificial Immune Algorithm. These protocols will review in detail according to different metrics such as WSN type, node deployment, control manner, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated here depending on delay, network size, energy efficiency, and scalability with mentioning the advantages and drawbacks of each protocol.

A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks

Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals such as DNA symbolic sequences cannot be directly processed with digital signal processing (DSP) techniques. The only way to apply DSP in genomic field is the mapping of DNA symbolic sequences to numerical sequences. Hence, biological properties are reflected in a numerical domain. This opens a field to present a set of tools for solving genomic problems. In literature many techniques have been developed for numerical representation of DNA sequences. The main drawback of these techniques is that each nucleotide is represented by a numerical value depending on nucleotide type only ignoring its position in codon and DNA sequence. In this paper a new approach for DNA symbolic to numeric representation called Circular Mapping (CM) is introduced. It"s based on graphical representation of DNA sequence that maps each nucleotide by a complex numerical value depending not only on nucleotide type but also on its position in codons. The main applications of this method are the gene prediction that aims to locate the protein-coding regions and the classification of exons and introns in DNA sequences. The proposed approach showed significant improvement in exons and introns classification as compared with the existing techniques. The efficiency of this method in classification depends on the right choice of the mapping angle ( ) as indicated by the power spectral analysis results over the sequences of the human genome (GRch37/hg19).

/pdf/a-novel-circular-mapping-technique-for-spectral-3les5l4n91.pdf

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

This paper proposes a new biometric identifier for humans based on eye blinking waveform extracted from brain waves. Brain waves were recorded using Neurosky Mindwave headset from 25 volunteers. Two approaches are adopted for the pre-processing stage; the first approach uses empirical mode decomposition to isolate electro-oculogram signal from brain waves, then, extracts eye blinking signal. The second approach extracts eye blinking signal directly from brain waves. Features are extracted based on time delineation of the eye blinking waveform and classified using linear discriminant analysis. The best correct identification and equal error rates achieved are 98.51% and 2.5% for identification and verification modes respectively. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

Mohammed Abo-Zahhad

Papers

Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

A Comparative Study of Energy Consumption Sources for Wireless Sensor Networks

A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

A new biometric modality for human authentication using eye blinking