Ultra-low latency communication technology for Augmented Reality application in mobile periphery computing

Nagu, Bharathiraja; Arjunan, Thiruneelakandan; Bangare, Manoj L.; Karuppaiah, Pradeepa; Kaur, Gaganpreet; Bhatt, Mohammed Wasim

doi:10.1515/pjbr-2022-0112

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…A continuous wavelet transform based scheme is presented in [21], which applies time frequency analysis to predict FoG. This study aims to offer a practical and efficient solution for long-term monitoring, addressing challenges in FoG prediction accuracy, false ratio occurrences, and time complexity [22][23][24][25]. The goal is to enable more reliable and timely interventions, positively impacting the lives of Parkinson's patients.…”

Section: Related Workmentioning

confidence: 99%

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Ezhilarasi Jegadeesan,

Senthil Kiumar Thillaigovindhan

2024

ARASET

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The problem of predicting Freeze of Gait (FoG) on Parkinson diseased patients has been well studied. There exists number of approaches in predicting FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration Gait pattern based FoG prediction model (TOAGP-FoG) is presented in this paper. The model designed to attach accelerometer sensors at different ankle and joints of the body. The sensor signals are recorded at different gait movement in long term. The sensory signals are passed to the central data server which tracks the movement signals. With the time variant signals stored by the model, the method generates Acceleration Gait Pattern with number of features. At each movement, the method analyses the patterns to compute FOG Risk Support (FoGRS) towards various gait movement. The FoG Risk Support is measured according to the movement forces produced by the patient for various gait movement in different time stamp and computes minimum gait force to be produced. Based on the FoGRS value, the method performs FoG prediction. The proposed method improves the performance of FoG prediction with higher accuracy. Other notable aspects of the suggested model include comparable performance, resiliency, real-time prediction capabilities, FOG-specific integration of data, and advanced deep learning methodologies for accurate prediction. The Special Features of TOAGP-FoG include the Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic FoG Risk Support (FoGRS), and Enriched Feature Extraction. The TOAGP-FoG model offers an important breakthrough in the predictive modelling of Parkinson's disease since it integrates several features such as temporal flexibility, dynamic FoGRS computation, adaptive thresholding, enriched feature extraction, and multi-sensor configurations.

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Section: Related Workmentioning

confidence: 99%

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Ezhilarasi Jegadeesan,

Senthil Kiumar Thillaigovindhan

2024

ARASET

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“…6G networks are anticipated to enable unprecedented mobile broadband speeds, lowlatency communications, and massive terminal connectivity. This positions 6G as a catalyst for technological developments in various fields, including autonomous driving, smart cities, virtual reality, and automated industries [4][5][6]. However, the huge resource requirements associated with handling large-scale networked devices, real-time applications, and high-speed data transfers present significant obstacles to the rational allocation and efficient use of resources.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-based 6G task offloading and cooperative computing resource allocation study

Tian,

Zhang,

et al. 2024

J Cloud Comp

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In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

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Chip‐Scale Dispersion Compensation of High‐Speed Data – Recent Progress and Future Perspectives

Chen,

Ong,

Tan

2024

Laser & Photonics Reviews

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High‐speed data movement in data center communications and telecommunications is the cornerstone of society's connectivity. It serves as a critical driver of economic activity, social networks, and education. Complementary metal‐oxide semiconductor compatible silicon‐based photonic integrated circuits have proliferated transceiver technology, owing to their ease of integration with application‐specific integrated circuits and mass manufacturability. Fiber impairments in the transmission of high‐speed data stem from both optical attenuation and optical dispersion. As data rates scale and modulation formats advance, the impact of fiber dispersion even at shorter reaches becomes more important to address. In this review article, recent advancements made in integrated, chip‐scale dispersion compensation solutions are covered. The focus on chip‐scale devices stems from their ability to be easily integrated within the transmitter or receiver chip of transceivers. Future perspectives on how these devices may become commonplace within transceivers and their potential impact are discussed.

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Ultra-low latency communication technology for Augmented Reality application in mobile periphery computing

Cited by 5 publications

References 29 publications

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Blockchain-based 6G task offloading and cooperative computing resource allocation study

Chip‐Scale Dispersion Compensation of High‐Speed Data – Recent Progress and Future Perspectives

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