algorithmics and modular computations, Theory of Codes and Cryptography (3).From an analytical 1. RE Blahut. Theory and practice of error control codes. eecs.uottawa.ca/∼yongacog/courses/coding/ (3) R.E. Blahut,Theory and Practice of Error Control Codes, Addison Wesley, 1983. QA 268. Cached. Download as a PDF 457, Theory and Practice of Error Control CodesBlahut 1984 (Show Context). Citation Context..ontinued fractions.

Theory and practice of error control codes

Network Coding (NC) can significantly help increase the efficient use of network resources. However, directly applying NC to wireless networks is not an effective design solution. Wireless channels are prone to bit errors due to fading, possibly generating erroneous decisions at the relay nodes while applying NC. Furthermore, the broadcast nature of wireless channels may provide a direct communication link between source and destination nodes. In order to combat fading channel impairments, and also to exploit the inherent spatial diversity provided through the direct link, network coded cooperation (NCC) techniques should be incorporated to the NC systems for wireless networks. This tutorial provides an overview of the state-of-the-art in the NCC systems, combining the powerful tools of NC and cooperative communication, in order to increase data transmission rates and robustness of multiple source wireless networks.

A Tutorial on Network Coded Cooperation

Abstract In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers. 

/pdf/diagnosing-covid-19-using-artificial-intelligence-a-2dbink3v.pdf

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

/pdf/diagnosing-covid-19-using-artificial-intelligence-a-18wv8n1g.pdf

Successive relaying is a bandwidth-efficient scheme in cooperative networks. In this study, a novel distributed space-time coding (DSTC) transmission scheme for a two-path successive relay network is proposed that enjoys threshold-based selection relaying protocol. A new beamforming method is also employed alongside the proposed DSTC transmission scheme. Three scenarios are considered in this study. In the first scenario, single-input–single-output (SISO) relays are considered. In the second and third scenarios, multiple-input–multiple-output (MIMO) relays are used, but in the second case a transmission scheme named as quasi-DSTC scheme alongside employing space-time coding at each relay is proposed. A closed-form expression for the end-to-end bit error rate (BER) of the system is derived and it is shown that regardless of the fading in inter-relay channel, the proposed scheme achieves full diversity order even when there are decoding errors at the relay nodes. Simulation results show a significant saving in power of about 2.5, 3, and 4 dB at BER ( 10 − 4 ) compared with other conventional methods in the case of using SISO relays, MIMO relays in the second and third scenarios, respectively.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2017.1272

New beamforming and space-time coding for two-path successive decode and forward relaying

Since the outbreak of COVID-19, many efforts have been made to utilize the respiratory sounds and coughs collected by smartphones for training Machine Learning models to classify and distinguish COVID-19 sounds from healthy ones. Embedding those models into mobile applications or Internet of things devices can make effective COVID-19 pre-screening tools afforded by anyone anywhere. Most of the previous researchers trained their classifiers with respiratory sounds such as breathing or coughs, and they achieved promising results. We claim that using special voice patterns besides other respiratory sounds can achieve better performance. In this study, we used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status. A combination of models trained on different sounds can diagnose COVID-19 more accurately than a single model trained on cough or breathing only. Our results show that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained and evaluated separately on different sound types. Finally, this study aims to draw attention to the importance of the human voice alongside other respiratory sounds for the sound-based COVID-19 diagnosis.

Pay Attention to the Speech: COVID-19 Diagnosis using Machine Learning and Crowdsourced Respiratory and Speech Recordings

The maximum a posterior (MAP) decoding scheme is presented here for cooperative communication networks that adopt the parity forwarding as a cooperation protocol. The MAP decoder is optimal in the sense that it minimises the error probability. The authors consider a wireless network that is composed of two sources: two relays and a single destination. A closed-form expression is derived for upper bound on the bit error probability. The complexity of derivation comes from the fact that although the source generates data with equal probability, the data received at the destination does not have the same a priori probability. That is because of the error that occurs in the source-to-relay link. Therefore, the MAP decoding rule cannot be simplified to the maximum likelihood decoding rule. The results show that the analytical upper bound is very tight and almost coincides with the exact error probability obtained from simulations at higher values of the signal-to-noise ratio. Accordingly, the closed-form expression of the upper bound can be used to fully study and understand the diversity performance of the system.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2013.0851

Decoding scheme for relay networks with parity forwarding cooperation protocol

High resolution time-to-digital converter using low resources FPGA for time-of-flight measurement

This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier’s performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB. 

A hybrid model for automatic modulation classification based on residual neural networks and long short term memory

In this paper, we examine and investigate the effects of noise-induced transmission errors on the performance of image transmission over cooperative relay networks with various error-correction capabilities. MAP decoder is used to decode the data sent across the cooperative network. First, the relationships between lengths of the code, peak signal-to-noise ratio, and signal-to-noise ratio are analyzed. Second, the comparison between the performances of different channel coding is introduced in conjunction with the set partitioning in hierarchical trees algorithm as a source code. The genetic algorithm, as an efficient optimization technique, is applied for solving the problem of optimal unequal error protection method to reduce the transmission rate and to improve the transmitted image quality. Many useful and significant results that are introduced in this paper can be used in the enhancement of image transmission over the cooperative networks. Compared to traditional methods, the suggested method is more practical for the transmission of images with high-quality via smart devices.

https://jesaun.journals.ekb.eg/article_128637_ccb1a6f37e4bd1a1ff346e0b42478ec6.pdf

Safwat M. Ramzy

Papers

Pay Attention to the Speech: COVID-19 Diagnosis using Machine Learning and Crowdsourced Respiratory and Speech Recordings

Decoding scheme for relay networks with parity forwarding cooperation protocol

High resolution time-to-digital converter using low resources FPGA for time-of-flight measurement

A hybrid model for automatic modulation classification based on residual neural networks and long short term memory

Analysis of channel coding performance for image transmission over cooperative relay networks