Synthesis and study of evolutionary optimised sensor linearisation with translinear &amp; FPGA circuits

Sundararajan, S.; Naduvil, Madhusoodanan Kottarthil; Abudhahir, A.; Karuna, K T Gandhi; Noble, G. I.

doi:10.1080/00207217.2021.1941287

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Zhang et al [9] extend game theory principles to symmetric MEC-enabled vehicular networks, showcasing its adaptability in different vehicular scenarios.Ashraf et al [10] conduct an analysis of link selection challenges in underwater routing protocols, shedding light on the complexities of communication in submerged environments. Sundararajan et al [11] enhance sensor linearity by implementing translinear circuits with piecewise and neural network models, contributing to improved sensor performance. Khan et al [12] focus on location-based reverse data delivery between infrastructure and vehicles, presenting a novel approach to data transmission in vehicular networks.Nguyen et al [13] explore cooperative task offloading and block mining in blockchain-based edge computing using multi-agent deep reinforcement learning, offering insights into collaborative computing paradigms.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, To quantify the degree of deviation between each server's load and the overall average load, this paper employs the load balancing rate as a metric to evaluate the load balance of edge servers. In this context, x represents the average load of all edge servers, x i signifies the load of the i-th edge server, and the overall system load balancing rate L n is computed using Formula (11).…”

Section: Load Calculationmentioning

confidence: 99%

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Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning

Sun,

Chen,

2024

Mathematics

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As distributed computing evolves, edge computing has become increasingly important. It decentralizes resources like computation, storage, and bandwidth, making them more accessible to users, particularly in dynamic Telematics environments. However, these environments are marked by high levels of dynamic uncertainty due to frequent changes in vehicle location, network status, and edge server workload. This complexity poses substantial challenges in rapidly and accurately handling computation offloading, resource allocation, and delivering low-latency services in such a variable environment. To address these challenges, this paper introduces a “Cloud–Edge–End” collaborative model for Telematics edge computing. Building upon this model, we develop a novel distributed service offloading method, LSTM Muti-Agent Deep Reinforcement Learning (L-MADRL), which integrates deep learning with deep reinforcement learning. This method includes a predictive model capable of forecasting the future demands on intelligent vehicles and edge servers. Furthermore, we conceptualize the computational offloading problem as a Markov decision process and employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach for autonomous, distributed offloading decision-making. Our empirical results demonstrate that the L-MADRL algorithm substantially reduces service latency and energy consumption by 5–20%, compared to existing algorithms, while also maintaining a balanced load across edge servers in diverse Telematics edge computing scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Load Calculationmentioning

confidence: 99%

Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning

Sun,

Chen,

2024

Mathematics

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Enhancing sensor linearity through the translinear circuit implementation of piecewise and neural network models

Seenivasaan,

Madhusoodanan Kottarthil

2023

ELECTRENG

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<abstract><p>The performance of the control system relies on the linearity of the sensor, which can be influenced by various factors such as aging and alterations in material properties. However, current sensor linearization techniques, such as utilizing neural networks and piecewise regression models in the digital domain, suffer from issues like errors, excessive power consumption, and slow response times. To address these constraints, this investigation employs a translinear based analog circuit to realize neural networks and piecewise regression models for the purpose of linearizing the selected sensors. A conventional feed-forward back propagation network is constructed and trained using the Levenberg-Marquardt algorithm. The developed linearization algorithm is implemented using a translinear circuit, where the trained weights, biases, and sensor output are fed as input current sources into the current-mode circuit. Further in this work, the piecewise regression model is designed and implemented using a translinear circuit and the breakpoint is determined using 'R' language. The simulation results indicate that the implementation of the current-mode circuit with metal-oxide-semiconductor field-effect transistors (MOSFETs) for the neural network algorithm leads to a substantial reduction in full-scale error as compared to the piecewise current mode model. Additionally, a performance analysis was conducted to compare the utilization of current-mode circuits with digital approaches for the linearization of sensors. The proposed translinear implementation surpasses the other researcher's work by delivering notable results. It showcases a significant improvement in linearity, ranging from 60% to 80%, for the selected sensors. Furthermore, the proposed implementation excels not only in linearity but also in terms of both response speed and power consumption. The improvement in the linearity of the sensor can be enhanced further by replacing the MOSFETs with bipolar transistors or any versatile materials such as gallium arsenide or gallium nitride-based transistors.</p></abstract>

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Synthesis and study of evolutionary optimised sensor linearisation with translinear & FPGA circuits

Cited by 2 publications

References 21 publications

Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning

Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning

Enhancing sensor linearity through the translinear circuit implementation of piecewise and neural network models

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