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

Location based services (LBS) and their applications are regarded as the mostgrowing services applied on communication systems. According to the greatenhancement in the mobile network platform and its related database servers,we are not only able to develop LBS applications but also to enhance theperformance of mobile networks. Future location prediction of mobile userresults in great enhancement in LBS applications and mobile networkperformance. Many researches were introduced to predict location of mobileuser. In [7] User Mobility Pattern (UMP) algorithm based on sequentialmining prediction has been developed to deal with future location prediction inmobile networks. The main limitations of this pattern are: map based intra cellprediction cannot be extracted and the timestamps aren't associated with rules.In [8], another method has been introduced using All frequent MovementPattern (ALLMOP) algorithm for extracting all frequent movement patternswithin movement Rule based Location Prediction method (RLP). RLP methodworks on GPS network map based platform without manipulating cell to cellmovement. So, it can't be used to enhance mobile network's resource allocationand mobility management. In this paper we proposed a new method, Intra CellMovement Prediction (ICMP), for mobile user's future location prediction onUniversal Mobile Telecommunication System (UMTS) environments to benefitfrom both intra and inter cell based techniques for both network and servicesenhancement. The proposed method depends on map based intra-cellprediction and utilizes the network database and positioning techniques inextracting user trajectories and movement rules. As a result, the user positioncan be predicted for the next movement step. The performance of the proposedalgorithm is evaluated through computer simulation and compared with that of[7] and [8]. The simulation results indicate that the proposed method is highlyefficient for both network and services' improvements as it is used to enhancelocation based services with satisfactory accuracy, network's resourceutilization, reduce the latency in accessing resources and mobilitymanagement.

https://jesaun.journals.ekb.eg/article_129386_5ce4778b19547cd8d4eb8a89571f999b.pdf

Map based intra-cell method for location prediction over umts network platform

In this paper a new synthesis method has been proposed for the design of 2D scaled state space filters with fixed point arithmetic. It is based on constructing a non-singular transformation matrix which minimizes the output noise power and maintains the signal-scaling condition, besides zeroing some of the entries of the state matrices. As a result, simultaneous savings in both additions and multiplications are achieved. Numerical results are included to illustrate the performance of the proposed structures and to compare them with other structures.

Design of two‐dimensional scaled state space filters with fixed point arithmetic

Wavelets have recently emerged as a powerful tool for signal compression, particularly in the areas of image, video, and audio compression. In this paper, we present a low-complexity wavelet-based audio compression algorithm that is capable of handling fairly arbitrary audio sources. The algorithm transforms the incoming audio data into the wavelet domain, and compresses data by exploring redundancy in the wavelet coefficients and exploiting the large runs of zeros in the transformed signal. Also there is a possibility of applying a threshold to the non-zero coefficients, thus a further increase in the number of zeros is expected. The audio signal is first preprocessed to scale down the wavelet coefficients. Then the preprocessed signal is wavelet transformed using a bi-orthogonal discrete wavelet transform (DWT) and threshold by applying energy compaction strategy. Encoding represents the threshold coefficients in compact form. A new encoding technique that is easy to implement, and that provides a reasonable compression ratio for a certain acceptable distortion level has been developed to encode the threshold DWT. So, a bit rate can be controlled such that the algorithm operates at virtually any pre-selected bit rate. The motivation of using the bi-orthogonal wavelet transform is that it permits the use of a much broader class of filters, and this class includes symmetric linear phase filters. The superior performance of this algorithm is also demonstrated by comparing it with two other popular audio compression techniques and this meets the requirements of multimedia computing.

High-quality low-complexity wavelet-based compression algorithm for audio signals

Taguchi Immune Algorithm (TIA) is based on both features of the biological immune system and the Taguchi method which increases the ability of the Immune Algorithm (IA) to find the global optimal solution in a nonlinear space. In the TIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. Two major tools are used in the Taguchi method; namely the Orthogonal Arrays (OAs) and the Signal to Noise Ratio (SNR). The effect of selecting the number of levels adopted in the construction of OAs on TIA is not studied before. So, this paper addresses the problem increasing the convergence speed of immune algorithm based two-dimensional recursive digital filters design process by adopting two, three and four levels OAs. For seek of comparison, the same computational experiments adopted in [1] are considered. Numerical results show that increasing the number of OA levels yields to faster convergence and better antibody genes selection in order to achieve the potential recombination, and consequently enhance the design process.

Design of Immune Algorithm based two-dimensional recursive digital filters using multi-level Orthogonal Arrays

This paper presents a new preclinical microwave hyperthermia system based on the combination among horn antenna and convex lenses. The horn antenna is designed to resonate at 3 GHz which is reported for hyperthermia therapy. Two convex lenses are designed and incorporated with the designed horn antenna and the reported breast phantom. The proposed system aims to heat up the tumor region above 42
 °
C while keeping the surrounding area temperature unchanged as possible to protect them from damage. Specific Absorption Rate (SAR) and breast phantom thermal distribution are studied with and without using lenses at different inclination angles. The proposed system has the advantage of heating up different zones separately while keeping other regions temperature unchanged. Simulation results show that, the breast temperature can be elevated by 10
 °
 C above the normal body temperature.

Mohammed Abo-Zahhad

Papers

Map based intra-cell method for location prediction over umts network platform

Design of two‐dimensional scaled state space filters with fixed point arithmetic

High-quality low-complexity wavelet-based compression algorithm for audio signals

Design of Immune Algorithm based two-dimensional recursive digital filters using multi-level Orthogonal Arrays

Convex lenses horn antenna microwave hyperthermia scheme