Seungwook Oh, GyeongIk Shin, Hyunki Hong^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4555-4572, 2022, DOI:10.32604/cmc.2022.022086

Abstract Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps as the input sequences on… More >

Shafayat Hossain¹, Md. Iquebal Hossain Patwary¹, Sikder Sunbeam Islam¹, Sultan Mahmud^1,2, Norbahiah Binti Misran², Ali F. Almutairi³, Mohammad Tariqul Islam^2,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5817-5836, 2022, DOI:10.32604/cmc.2022.022042

Abstract This paper presents a new Double-E-Triple-H-Shaped NRI (negative refractive index) metamaterial (MM) for dual-band microwave sensing applications. Here, a horizontal H-shaped metal structure is enclosed by two face-to-face E-shaped metal structures. This double-E-H-shaped design is also encased by two vertical H-shaped structures along with some copper links. Thus, the Double-E-Triple-H-Shaped configuration is developed. Two popular substrate materials of Rogers RO 3010 and FR-4 were adopted for analyzing the characteristics of the unit cell. The proposed structure exhibits transmission resonance inside the S-band with NRI and ENG (Epsilon Negative) metamaterial properties, and inside the C-band with ENG and MNG (Mu Negative)… More >

Waleed Shahjehan¹, Abid Ullah¹, Syed Waqar Shah¹, Ayman A. Aly², Bassem F. Felemban², Wonjong Noh^3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5399-5411, 2022, DOI:10.32604/cmc.2022.022034

Abstract Millimeter wave communication works in the 30–300 GHz frequency range, and can obtain a very high bandwidth, which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation (5G). The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture. The resulting array gain can compensate for the path loss of the millimeter wave. Utilizing this feature, the millimeter wave massive multiple-input multiple-output (MIMO) system uses a large antenna array at the base station. It enables the transmission of multiple data streams,… More >

Ala Saleh Alluhaidan^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5949-5962, 2022, DOI:10.32604/cmc.2022.022028

Abstract Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the… More >

Mohammed Abd-Elnaby^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.022020

Abstract Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact… More >

Abdelhamied A. Ateya^1,2, Abeer D. Algarni¹, Andrey Koucheryavy³, Naglaa. F. Soliman^1,2,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4677-4699, 2022, DOI:10.32604/cmc.2022.021850

Abstract Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two… More >

Aurora Gonzalez-Vidal¹, Fernando Terroso-Sáenz^2,*, Antonio Skarmeta¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4355-4375, 2022, DOI:10.32604/cmc.2022.021492

Abstract Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces. The purpose of our work is to study, design and develop a parking-availability predictor that extracts the knowledge from human mobility data, based on the anonymized human displacements of an urban area, and also from weather conditions. Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution. However, access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related… More >

Vlastimil Slany¹, Petr Koudelka^1,*, Eva Krcalova¹, Jan Jobbagy², Lukas Danys³, Rene Jaros³, Zdenek Slanina³, Michal Prauzek³, Radek Martinek³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5201-5217, 2022, DOI:10.32604/cmc.2022.021349

Abstract The massive development of internet of things (IoT) technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth, smart city, agriculture or waste management. This ongoing development is further pushed forward by the gradual deployment of 5G networks. With 5G capable smart devices, it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT. Massive-IoT (low-power wide area network-LPWAN) enables improved network coverage, long device operational lifetime and a high density of connections. Despite all the advantages of massive-IoT technology, there are certain… More >

Noor Munir¹, Majid Khan^1,*, Mohammad Mazyad Hazzazi², Amer Aljaedi³, Sajjad Shaukat Jamal², Iqtadar Hussain⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4503-4522, 2022, DOI:10.32604/cmc.2022.021077

Abstract Nonlinear dynamics is a fascinating area that is intensely affecting a wide range of different disciplines of science and technology globally. The combination of different innovative topics of information security and high-speed computing has added new visions into the behavior of complex nonlinear dynamical systems which uncovered amazing results even in the least difficult nonlinear models. The generation of complex actions from a very simple dynamical method has a strong relation with information security. The protection of digital content is one of the inescapable concerns of the digitally advanced world. Today, information plays an important role in everyday life and… More >

Batyrkhan Omarov^1,2,3, Azhar Tursynova^1,*, Octavian Postolache⁴, Khaled Gamry⁵, Aidar Batyrbekov⁵, Sapargali Aldeshov^6,7, Zhanar Azhibekova⁹, Marat Nurtas^5,8, Akbayan Aliyeva⁶, Kadrzhan Shiyapov^10,11

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998

Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More >