MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks

Solyman, Ahmed A. A.; Attar, Hani; Khosravi, Mohammad R.; Koyuncu, Baki

doi:10.1177/1550147720912950

Cited by 19 publications

(5 citation statements)

References 35 publications

(77 reference statements)

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“…The proposed model can be further applied in the future for the classification of another various cancer histology, reducing the time, cost, and effort of large searching space [40][41][42][43][44][45][46]. Moreover, the presented technology by the authors in [47][48][49][50] can be implemented to obtain better results.…”

Section: Discussionmentioning

confidence: 99%

A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

Deif

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively.

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Section: Discussionmentioning

confidence: 99%

A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

Deif

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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“…The future work is directed to implement advanced technology to obtain better results, such as signal processing technology to improve the quality in the obtained results such as the technology proposed by the authors in [67][68][69][70][71].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness R a values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments.

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“…Orthogonal Chirp Division Multiplexing (OCDM), where chirps of common duration sweep over a shared band, keeping constant values of frequency distance that ensure orthogonality [29], is a physical layer solution that provides bene its exploited for different application domains: underwater [30], radar [31], videobroadcasting [32], power lines [33]. Other contributions have addressed the complexity of the receiver [34] or applied MIMO to OCDM [35]. Moreover, as shown in [36], the OCDM principle can also be exploited at MAC layer to multiplex multiple users in the same time-frequency slot.…”

Section: Mac Protocolsmentioning

confidence: 99%

Terahertz networks for future Industrial Internet of Things

Sara¹,

Decarli²,

Cuozzo³

et al. 2023

ITU J-FET

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Wireless technology is expected to become a fundamental enabler to improve the efficiency, safety, and revenues of advanced manufacturing processes, as well as to realize new paradigms such as digital twins. The extremely challenging industrial scenario requires some technological shifts such as the adoption of the so far unexplored THz band. The purpose of this paper is to provide an overview of THz networks applied to the Industrial Internet of Things (IIoT). First, the main requirements of future industrial THz-based networks, challenges, and state-of-the-art are described. Subsequently, the key enabling technologies are introduced and discussed. Finally, we present some research directions for THz-based industrial networks.

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MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks

Cited by 19 publications

References 35 publications

A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Terahertz networks for future Industrial Internet of Things

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