Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

Devasenapathy, Deepa; Madhumathy, P.; Umamaheshwari, R.; Pandey, Binay Kumar; Pandey, Digvijay

doi:10.1007/s42979-023-02410-y

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2024

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Cited by 48 publications

References 30 publications

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Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Saxena,

Agarwal,

Pandey

et al. 2024

Circ.Econ.Sust.

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Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Saxena,

Agarwal,

Pandey

et al. 2024

Circ.Econ.Sust.

View full text Add to dashboard Cite

Unleashing the Power of AI and Data Analysis

Singh,

Madaan,

Swapna

et al. 2024

Advances in Computational Intelligence and Robotics

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Revolutionizing industries and civilizations alike, data analysis and artificial intelligence (AI) have emerged as transformative forces in modern technology. Machine learning and other forms of artificial intelligence allow systems to gain knowledge from data, see trends, and make judgments with more independence and precision. To enhance AI's decision-making and forecasting capacities across a wide range of disciplines, data analysis is essential for mining massive datasets for actionable insights. Companies can improve their operations, provide better customer experiences, and come up with new goods and services with the help of AI and data analysis. The significance of ethically deploying AI is highlighted by factors like data privacy and bias prevention. Future technologies, such as healthcare diagnostics, autonomous systems, and smart cities, are expected to be transformed by the combination of AI and data analysis. Efforts in research, development, and governance must be coordinated if these areas are to realize their full potential, overcome obstacles.

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Comparative Study on Implementation of PID Control Architecture and Sliding Mode Control With IoT Integration on Inverted Pendulum Systems

Dawane,

Malwatkar

2024

Advances in Computational Intelligence and Robotics

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In this chapter, the issue of using sliding mode control to stabilize an inverted pendulum system is examined. The goal is to keep the pendulum in an upright position by giving the cart the proper control inputs. To achieve system stabilization, sliding mode control, a type of variable structure control, is used. This control strategy entails creating control systems with the intention of maintaining the system state within a restricted area close to a switching function. Numerous real-world situations, including aerial landing pads over water, walking balance systems for people, and cart inverted pendulum systems, make use of the dynamics of inverted pendulum systems. Due to its inherent instability and nonlinearity, designing a controller for this underactuated, non-minimum phase system is difficult.

show abstract

Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

Cited by 48 publications

References 30 publications

Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Unleashing the Power of AI and Data Analysis

Comparative Study on Implementation of PID Control Architecture and Sliding Mode Control With IoT Integration on Inverted Pendulum Systems

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