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

A Graphical-based educational simulation tool for Wireless Sensor Networks

A novel method is proposed for the synthesis of low-sensitivity digital filters meeting any prescribed transfer function. The method is based on extracting an orthogonal matrix from the filter state matrix, resulting in structures that need n Givens rotations and at most n + 1 multipliers. Thus the proposed realization is canonic in the sense that it has the same degrees of freedom as the original transfer function. It is also shown that when the filter transfer function is a reciprocal reactant bounded function, it can be decomposed into allpass functions that need only Givens rotations for their realization. As the basic module in the realization is the Givens rotation, the CORDIC computation algorithm can be applied directly. This means considerable savings in computation time and complexity. It also results in structures that are less sensitive to the effects of finite word length. Illustrative examples, including the design and synthesis of linear phase selective filters, are given to show the extremely low sensitivity with respect to finite word length of the resulting realizations when compared with other methods.

Synthesis of low‐sensitivity orthogonal digital filters

In massive multiple-input multiple-output frequency division duplexing systems, the user equipment should independently estimate the massive downlink channels state information and then feed them back to the base station. This process results in a large signaling overhead. Deep learning approaches tried to overcome this challenge using neural networks as an autoencoder to learn the mapping between the input and corresponding output. However, this type of learning consumes massive training datasets to learn. Also, it can not make use of the learning through the internal information within the tasks and thus, it can not reach the convergence quickly as its parameters are randomly initialized. In this paper, we introduce a meta learner-based autoencoder for tackling the feedback overhead. The proposed approach is mainly based on finding a good initialization of the parameters of the autoencoder to adapt rapidly to new tasks with a few number of samples. The results show that the proposed autoencoder based on the meta-learner method outperforms the state of the art with a margin.

A Meta Learner Autoencoder for Channel State Information Feedback in Massive MIMO Systems

The introduction of cognitive radio technology in licensed 5G networks could significantly enhance the overall system capacity and number of served users. In this context, this work discusses the admission of new cognitive radio secondary users (SUs) in the fifth generation (5G) Heterogeneous Networks (HetNets) as well as the allocation of the channels over the secondary cognitive network, using a many-to-one matching game and auctions theory. Simulation results show that the used matching algorithm for users admission is of low complexity as well as the existence of a Walrasian equilibrium point for the channels allocation problem.

Cognitive Radio Users Admission and Channels Allocation in 5G HetNets: A College-based Matching and Auction Game Approach

This paper is studied the effect of load representation, in the static point of view, on the performance of a UPFC connected in power system. The UPFC performance include the estimation of the UPFC control variables and converters powers that required to achieve certain operation condition, the lines power flow, the node voltages of the line at which, UPFC is connected, the total system power loss and the system voltage stability. Also the effect of load representation on UPFC optimal placement has been illustrated.

http://ieeexplore.ieee.org/iel5/5353767/5372314/05372356.pdf

Mohammed Abo-Zahhad

Papers

A Graphical-based educational simulation tool for Wireless Sensor Networks

Synthesis of low‐sensitivity orthogonal digital filters

A Meta Learner Autoencoder for Channel State Information Feedback in Massive MIMO Systems

Cognitive Radio Users Admission and Channels Allocation in 5G HetNets: A College-based Matching and Auction Game Approach

Effect of load representation on UPFC performance and optimal placement