Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications

Tausif, Mohd; Jain, Abhinandan; Khan, E.; Hasan, Mohd.

doi:10.1007/s11042-020-10258-0

Cited by 6 publications

(2 citation statements)

References 67 publications

(88 reference statements)

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“…IoMT aims to construct a smart framework for healthcare. And also, IoMT framework is a complex architecture and connects various components that interact with each other and offers many solutions to end-users [ 3 ]. IoMT enhances real-time health monitoring solutions and human–machine interaction that improves patient participation in decision-making [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

Yıldırım

Ci̇ci̇oğlu

Çalhan

2023

Med Biol Eng Comput

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The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Graphical abstract Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

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Section: Introductionmentioning

confidence: 99%

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

Yıldırım

Ci̇ci̇oğlu

Çalhan

2023

Med Biol Eng Comput

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“…Lifting-based hardware architectures [ 33 , 34 , 35 , 36 , 37 , 38 ] are usually simpler, have lower computational complexity and utilize less amount of logic and memory resources. The most efficient hardware architectures of the 1-D DWT and 2-D DWT are described in [ 28 ] and [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. The concept of the proposed 2-D DWT with 5/3 filters and its hardware architecture are presented in [ 61 , 62 , 63 ].…”

Section: Introductionmentioning

confidence: 99%

Digital Image Decoder for Efficient Hardware Implementation

Savić

Prokin

Rajović

et al. 2022

Sensors

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Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the-art realizations. The proposed digital image decoder has been implemented in a low-cost FPGA device and it has been shown that it requires at least 32% less memory resources in comparison to the other state-of-the-art decoders which can process high-definition frame size. The proposed solution also requires effectively lower memory size than state-of-the-art architectures which process frame size or tile size smaller than high-definition size. The presented digital image decoder has maximum operating frequency comparable with the highest maximum operating frequencies among the state-of-the-art solutions.

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Visual information processing and transmission in Wireless Multimedia Sensor Networks: a deep learning based practical approach

Rehman

Tariq

2022

Internet of Multimedia Things (IoMT)

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Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications

Cited by 6 publications

References 67 publications

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

Digital Image Decoder for Efficient Hardware Implementation

Visual information processing and transmission in Wireless Multimedia Sensor Networks: a deep learning based practical approach

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