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  • Open Access

    ARTICLE

    Design of Low Power Transmission Gate Based 9T SRAM Cell

    S. Rooban1, Moru Leela1, Md. Zia Ur Rahman1,*, N. Subbulakshmi2, R. Manimegalai3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1309-1321, 2022, DOI:10.32604/cmc.2022.023934

    Abstract Considerable research has considered the design of low-power and high-speed devices. Designing integrated circuits with low-power consumption is an important issue due to the rapid growth of high-speed devices. Embedded static random-access memory (SRAM) units are necessary components in fast mobile computing. Traditional SRAM cells are more energy-consuming and with lower performances. The major constraints in SRAM cells are their reliability and low power. The objectives of the proposed method are to provide a high read stability, low energy consumption, and better writing abilities. A transmission gate-based multi-threshold single-ended Schmitt trigger (ST) 9T SRAM cell in a bit-interleaving structure without… More >

  • Open Access

    ARTICLE

    Multi-dimensional Security Range Query for Industrial IoT

    Abdallah Abdallah1, Ayman A. Aly2, Bassem F. Felemban2, Imran Khan3, Ki-Il Kim4,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 157-179, 2022, DOI:10.32604/cmc.2022.023907

    Abstract The Internet of Things (IoT) has allowed for significant advancements in applications not only in the home, business, and environment, but also in factory automation. Industrial Internet of Things (IIoT) brings all of the benefits of the IoT to industrial contexts, allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy. The expansion of the IoT has been set by serious security threats and obstacles, and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control. A privacy-preserving multi-dimensional secure query technique for fog-enhanced IIoT… More >

  • Open Access

    ARTICLE

    Dimensionality and Angular Disparity Influence Mental Rotation in Computer Gaming

    Akanksha Tiwari1,*, Ram Bilas Pachori1,2, Premjit Khanganba Sanjram1,3,4

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 887-905, 2022, DOI:10.32604/cmc.2022.023886

    Abstract Computer gaming is one of the most common activities that individuals are indulged in their usual activities concerning interactive system-based entertainment. Visuospatial processing is an essential aspect of mental rotation (MR) in playing computer-games. Previous studies have explored how objects’ features affect the MR process; however, non-isomorphic 2D and 3D objects lack a fair comparison. In addition, the effects of these features on brain activation during the MR in computer-games have been less investigated. This study investigates how dimensionality and angular disparity affect brain activation during MR in computer-games. EEG (electroencephalogram) data were recorded from sixty healthy adults while playing… More >

  • Open Access

    ARTICLE

    Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

    Aymen Saad1, Israa S. Kamil2, Ahmed Alsayat3, Ahmed Elaraby4,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 561-576, 2022, DOI:10.32604/cmc.2022.023878

    Abstract COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its… More >

  • Open Access

    ARTICLE

    Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

    S. Karthik1, Robin Singh Bhadoria2, Jeong Gon Lee3,*, Arun Kumar Sivaraman4, Sovan Samanta5, A. Balasundaram6, Brijesh Kumar Chaurasia7, S. Ashokkumar8

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 243-259, 2022, DOI:10.32604/cmc.2022.023864

    Abstract Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters… More >

  • Open Access

    ARTICLE

    Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

    Anita Gehlot1, Rajesh Singh1, Sweety Siwach2, Shaik Vaseem Akram1, Khalid Alsubhi3, Aman Singh4,*, Irene Delgado Noya4,5, Sushabhan Choudhury2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 999-1015, 2022, DOI:10.32604/cmc.2022.023861

    Abstract Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino… More >

  • Open Access

    ARTICLE

    Identification and Classification of Crowd Activities

    Manar Elshahawy1, Ahmed O. Aseeri2,*, Shaker El-Sappagh3,4, Hassan Soliman1, Mohammed Elmogy1, Mervat Abu-Elkheir5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 815-832, 2022, DOI:10.32604/cmc.2022.023852

    Abstract The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density… More >

  • Open Access

    ARTICLE

    A Framework for e-Voting System Based on Blockchain and Distributed Ledger Technologies

    Shahid Hussain Danwar, Javed Ahmed Mahar*, Aneela Kiran

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 417-440, 2022, DOI:10.32604/cmc.2022.023846

    Abstract Election allows the voter of a country to select the most suitable group of candidates to run the government. Election in Pakistan is simply paper-based method but some certain political and socio-economic issues turn that simple process in complicated and disputes once. Solutions of such problems are consisting of many methods including the e-voting system. The e-voting system facilitates the voters to cast their votes by electronic means with very easy and convenient way. This also allows maintaining the security and secrecy of the voter along with election process. Electronic voting reduces the human-involvement throughout the process from start to… More >

  • Open Access

    ARTICLE

    QoS in FANET Business and Swarm Data

    Jesús Hamilton Ortiz1, Carlos Andrés Tavera Romero2,*, Bazil Taha Ahmed3, Osamah Ibrahim Khalaf4

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1877-1899, 2022, DOI:10.32604/cmc.2022.023796

    Abstract This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks (FANET). Each drone has the ability to send and receive information (like a router); and can behave as a hierarchical node whit the intregration of three protocols: Multiprotocol Label Switch (MPLS), Fast Hierarchical AD Hoc Mobile (FHAM) and Internet Protocol version 6 (IPv6), in conclusion MPLS + FHAM + IPv6. The metrics analyzed in the FANET are: delay, jitter, throughput, lost and sent packets/received. Testing process was carried out with swarms composed of 10, 20,… More >

  • Open Access

    ARTICLE

    Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

    S. Sreedhar Kumar1, Syed Thouheed Ahmed2,*, Qin Xin3, S. Sandeep4, M. Madheswaran5, Syed Muzamil Basha2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 281-299, 2022, DOI:10.32604/cmc.2022.023693

    Abstract This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and… More >

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