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  • Open Access



    Jiwon Yua , Seok-Won Kanga, Saeil Jeonb, Debjyoti Banerjeea,*

    Frontiers in Heat and Mass Transfer, Vol.3, No.1, pp. 1-9, 2012, DOI:10.5098/hmt.v3.1.3004

    Abstract Experiments were performed to study the forced convective heat transfer of de-ionized water (DI water) and aqueous nanofluids in a microchannel and temperature measurements were obtained using an array of nanosensors (i.e., thin film thermocouples or “TFT”). Heat flux values were calculated from the experimental measurements for temperature recorded by the TFT array. The experiments were performed for the different test fluids where the flow rate, mass concentration (of silica nanoparticles ~10-30 nm diameter) in the colloidal suspension and the wall temperature profile (as well as applied heat flux values) were varied parametrically.
    Anomalous enhancement of the convective heat… More >

  • Open Access


    Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

    M. F. Elkady1,2,*, E. M. El-Sayed2, Mahmoud Samy3, Omneya A. Koriem1, H. Shokry Hassan4,5

    Journal of Renewable Materials, Vol.12, No.2, pp. 369-380, 2024, DOI:10.32604/jrm.2023.046585

    Abstract In this study, green zinc oxide (ZnO)/polypyrrole (Ppy)/cellulose acetate (CA) film has been synthesized via solvent casting. This film was used as supporting material for glucose oxidase (GOx) to sensitize a glucose biosensor. ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant. ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole (Py) monomer using ferric chloride (FeCl3) as an oxidizing agent. The produced materials and the composite films were characterized using X-ray diffraction analysis (XRD), scanning electron microscope (SEM), Fourier transform infrared (FTIR) and thermogravimetric analysis (TGA). Glucose… More > Graphic Abstract

    Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

  • Open Access


    Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940

    Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires a wide range of body… More >

  • Open Access


    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this… More >

  • Open Access


    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088

    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly includes: (1) Construct a smart… More >

  • Open Access


    Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

    Tariq Qayyum1, Zouheir Trabelsi1,*, Asadullah Tariq1, Muhammad Ali2, Kadhim Hayawi3, Irfan Ud Din4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684

    Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality,… More >

  • Open Access


    Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

    Jianguo Wang1, Kuan Zhang1,*, Yuesheng Zhao2,*, Xiaoling Wang2, Muhammad Shamrooz Aslam2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506

    Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm,… More >

  • Open Access


    Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

    Fadhil Mukhlif1,*, Norafida Ithnin1, Roobaea Alroobaea2, Sultan Algarni3, Wael Y. Alghamdi2, Ibrahim Hashem4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 583-599, 2023, DOI:10.32604/cmc.2023.038757

    Abstract The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research… More >

  • Open Access


    Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

    Sakorn Mekruksavanich1, Narit Hnoohom2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549

    Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet and motion information from wearable… More >

  • Open Access


    Size Dependent Structures and Properties of Na0.5Bi0.5TiO3-Based Ceramics for Piezoelectric Sensors

    Pan Chen1,2,3, Baojin Chu1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09199

    Abstract Generally, film dielectric materials often exhibit size-dependent structure and electric properties. In this work, we demonstrate a similar behavior in bulk Na0.5Bi0.5TiO3 (NBT)-based polycrystalline ceramics. According to the results from X-ray diffraction, the (Na0.5Bi0.5)0.92Ba0.08Ti0.99Mg0.01O2.99 (NBT8M1.0) ceramic showed a complex structure that consists of rhombohedral, tetragonal and cubic symmetries. We found, when decreasing the thickness of a ϕ 10 mm NBT8M1.0 ceramic from 1230 μm to 230 μm, the ceramic showed increased content of cubic symmetry (CC) from 28% to 56%. Meanwhile, the piezoelectric response (d33) increased from 107 pC/N to 134 pC/N and the depolarization temperature (Td) decreased from 170… More >

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