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

Heterogeneous network and cognitive radio technology were introduced as part of 5G architecture to improve spectrum utilization and user capacity. Admitting secondary users in the wireless network is a challenge in the deployment of 5G. This paper presents a learning algorithm based on the multi-armed bandit technique to intelligently associate secondary users to the secondary base stations available in the network. Availing spectrum to secondary base stations and their associated users is done through a repeated auction game. The multi-armed bandit algorithm applied to the problem of user association enables secondary users oblivious about the channel environment to achieve higher data rates through association with the adequate secondary base station. Simulation results show a noticeably superior performance of the proposed multi-armed bandit algorithm compared to the matching game and random selection.

Multi-Armed Bandit and Repeated Auction Approaches for User Admission and Channel Allocation in 5G HetNets

In this paper, a reduced-size dielectric resonator antenna with switchable diversity patterns is proposed. Ring- and linear-shaped slots are etched in the ground plane of the antenna so as to generate two modes $TE_{\delta 11}^x$
 and $TE_{\delta 12}^x$
 at a center frequency of 19 GHz. Moreover, two groups of PIN diodes are integrated into these slots to short one group of slots, and let the other group generates the required mode. Thus, the antenna is able to generate two switchable patterns with an envelope correlation coefficient of 0.4. Furthermore, the antenna size is reduced to half of its original size by placing a copper sheet over certain plane of the antenna structure. The antenna achieves wide bandwidths of 17.6–20.9 GHz (17.1 $\percnt $
 ) and 18.3–21.6 GHz (13.8 $\percnt $
 ) in cases of exciting $TE_{\delta 11}^x$
 and $TE_{\delta 12}^x$
 modes, respectively. The antenna also attainsa high gain of 7.1 and 3.2 dB at the center frequency.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/BC19A29E243CDB9FADB99BC9D7746880/S1759078719001466a.pdf/div-class-title-a-wideband-dielectric-resonator-antenna-with-switchable-diversity-patterns-div.pdf

A wideband dielectric resonator antenna with switchable diversity patterns

Abstract Capsule Networks (CapsNets) is a great approach for understanding data in the field of computer vision. CapsNets allow a deeper understanding of images compared to the traditional Convolutional Neural Networks. The first test for CapsNet was in digits recognition on the ‘MNIST’ dataset, where it successfully achieved high accuracy. CapsNets are reliable at deciphering overlapping digits. Deep Capsule Networks achieved state-of-the-art accuracy in CIFAR10 which isn’t achieved by shallow capsule networks. Despite all these accomplishments, Deep Capsule Networks are very slow due to the ‘Dynamic Routing’ algorithm. In this paper, Fast Embedded Capsule Network and Deep Fast Embedded Capsule Network are introduced, representing novel capsule network architectures that uses 1D convolution based dynamic routing with a fast element-wise multiplication transformation process. These architectures not only compete with the state-of-the-art methods in terms of accuracy in the capsule domain, but also excels in terms of speed, and reduced complexity. This is shown by the 58% reduction in the number of trainable parameters and 64% reduction in the average epoch time in the training process. Experimental results shows excellent and verified properties. 

/pdf/deep-and-shallow-fast-embedded-capsule-networks-going-faster-3im0wt43.pdf

Deep and shallow fast embedded capsule networks: going faster with capsules

Wave digital filter studies are used to design multirate IIR (infinite-impulse response) linear-phase filters. The resulting structures have low sensitivity to element values. A method for analyzing multirate filters in the time domain is described and used to produce a number of design examples. >

https://ieeexplore.ieee.org/iel5/143/3356/00112202.pdf

Linear-phase multirate IIR filters

In this paper, the dynamic range of the conventional differential drive rectifier is enhanced based on leakage current reduction technique, to solve the narrow dynamic range problem of the input power and improve the output rectifier performance. The proposed design uses auxiliary transistors to control the conductivity of the rectifying PMOS devices dynamically to reduce the reverse leakage current. Post layout simulations have been performed using 0.18 μm CMOS technology at 402 MHz with 10kΩ load resistor. The proposed rectifier increases the range of the RF input power about ten times compared to a conventional rectifier's dynamic range and reduces the reverse leakage current from ‒98 μA to ‒22 μA. It also boosts the output average voltage at high input power. In addition, the developed rectifier exhibits good performance over wide band of input frequencies up to 2.4 GHz. The proposed rectifier has a small area of about 0.19 mm2.

Mohammed Abo-Zahhad

Papers

Multi-Armed Bandit and Repeated Auction Approaches for User Admission and Channel Allocation in 5G HetNets

A wideband dielectric resonator antenna with switchable diversity patterns

Deep and shallow fast embedded capsule networks: going faster with capsules

Linear-phase multirate IIR filters

Enhancing Dynamic Range of RF-to-DC Rectifier using Leakage Current Reduction Technique