An efficient soft demapper for APSK signals using extreme learning machine

Öztekin, Abdulkerim; Erçelebi, Ergün

doi:10.1007/s00521-018-3392-6

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…The obtained in‐phase and quadrature components of the complex symbols at the output of the demodulator are then subjected to a maximum‐likelihood (ML) detector. After the detection process, the output of the detector is passed through a demapper, which may involve several independent or iterative decoding stages depending on the coding algorithms used in the mapper [5]. Finally, decoded symbols are passed through a multiplexer to convert from parallel to serial and generate the estimate of the transmitted message sequence.…”

Section: Brief Review Of Apskmentioning

confidence: 99%

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Quadrature‐carrier amplitude phase shift keying

Öztekin

Erçelebi

2019

IET Communications

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Section: Brief Review Of Apskmentioning

confidence: 99%

“…Consequently, trying to keep the power consumption at a certain level will, unfortunately, end up in an increased BER. Besides, the other drawback is, of course, the cost of implementation complexity of both a high‐order transmitter and a receiver, as the complexity gradually increased with the size of the constellation [5].…”

Section: Introductionmentioning

confidence: 99%

Quadrature‐carrier amplitude phase shift keying

Öztekin

Erçelebi

2019

IET Communications

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“…In order to minimize the error that will occur during training in gradient-based approaches, the process of changing the given weights and biases continue until the most appropriate parameters are obtained. In the ELM method, the input weights and biases are given randomly, and the output weights are calculated accordingly [10]. Equation 1 can be rewritten in a more compact form as follows =…”

Section: B Extreme Learning Machinementioning

confidence: 99%

Automatic Positioning of Mobile Users via GSM Signal Measurements

Demir

Öztekin

2021

Balkan Journal of Electrical and Computer Engineering

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Today the need for mobile communication systems and the high increase in the number of users have also made the development of new generation mobile applications indispensable. Obtaining location information has been one of the most interesting and significant areas of improvement. The purpose of the services used to determine the location is generally to obtain the information of the users such as approximate location, speed, and time. The GPS is the most preferred and globally accurate positioning system among global positioning systems. However, in addition high installation cost of the system; galactic and meteorological factors, high buildings, other physical obstacles, and especially indoor areas are some of the main constraints that can lead to serious signal degradation and losses which may cause the system to be out of service. In this context, there is an urgent need for positioning systems that will be alternative and complementary to global positioning systems. The cellular network is widely used by almost everyone and its coverage area is increasing day by day. The network has been trained and tested in the simulation environment using machine learning algorithms, namely, extreme learning machine (ELM), generalized regression neural network (GRNN), and nearest neighborhood ( NN). When compared to other cellular localization methods in the literature, the proposed system performs positioning with much higher accuracies with distance error rates below a meter (m) at minimum, and between 76-216 m on average. The test results show that it can successfully localize the mobile users with a significant accuracy for indoor, where GPS signals are very weak or cannot be received at all; and it can also stand in the breach for outdoor, where GPS may be disabled for different reasons.

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“…This situation allows RNN to be easy and fast in data processing [32] and ensures a fine generalization achievement for a single FFNN [33]. Compared to the other known gradient-based learning algorithms, RNN has several advantages like the potential of reaching the minimum training error, operating with non-differentiable activation functions, and employing a single hidden layer [34]. Moreover, the number of observations is more than the number of neurons in the RNN hidden layer [35].…”

Section: Randomized Neural Networkmentioning

confidence: 99%

Detection of Covid-19 from X-ray Images via Ensemble of Features Extraction Methods Employing Randomized Neural Networks

Ertuğrul¹,

Acar²,

Öztekin³

et al. 2021

European Journal of Technic

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Artificial intelligence-based solutions have achieved significant successes in the field of health in recent years. These solutions have been started to be used for pre-diagnosis and decision support for a virus that spreads rapidly such as COVID-19 and thus creates fear and panic among the public. They also have augmented clinical expertise and thus have great potential to mitigate the virus outbreak burden of health experts. In this context, the load of healthcare workers can be significantly reduced through the help of an automatic diagnosis system of a high number of patients who apply to healthcare organizations with suspicion of disease. In this study, a machine-learning automatic diagnosis system exploiting x-ray images is proposed to detect diseases caused by COVID-19. The proposed system employs powerful texture features (Histogram of Oriented Gradients, Law's Texture Energy Measure, Gabor Wavelet Transform, Gray Level Co-Occurrence Matrix, and Local Binary Pattern) for the x-ray images to train a randomized neural network, a fast network, to establish a robust and fast diagnosis process for the virus. This study has raised the thesis that the mentioned image texture features extracted from the virus patients' images contain determinative indicators in two-dimensional space that make it possible to diagnose the disease. The proposed system contributes to the literature by using the tissue properties of x-ray images for the diagnosis of the virus. The disease is detected with an accuracy of %100 utilizing Law's Texture Energy Measure features and randomized neural network approach.

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An efficient soft demapper for APSK signals using extreme learning machine

Cited by 9 publications

References 23 publications

Quadrature‐carrier amplitude phase shift keying

Quadrature‐carrier amplitude phase shift keying

Automatic Positioning of Mobile Users via GSM Signal Measurements

Detection of Covid-19 from X-ray Images via Ensemble of Features Extraction Methods Employing Randomized Neural Networks

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