Parametric Considerations and Dielectric Materials Impacts on the Performance of 10 GHzSIW-LWA for Respiration Monitoring

Sharma, Manvinder; Harjinder, Singh; Pandey, Digvijay

doi:10.1007/s11664-022-09482-1

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Cited by 3 publications

References 24 publications

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Target identification and control model of autopilot for passive homing missiles

Sharma,

Singh,

Gupta

et al. 2023

Multimed Tools Appl

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Target identification and control model of autopilot for passive homing missiles

Sharma,

Singh,

Gupta

et al. 2023

Multimed Tools Appl

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A Comprehensive Review on Algorithms of Image Processing for Autonomous and Electric Vehicles

Gollapudi,

Bhaumik,

Pandey

et al. 2023

Handbook of Research on Thrust Technologies’ Effect on Image Processing

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The development of autonomous electric vehicles has gained significant attention due to their potential to reduce carbon emissions and improve road safety. Image processing has become an important tool in the development of these vehicles, enabling them to detect and respond to objects and obstacles in their environment. In this review paper, we explore the use of image processing in electric vehicles and driverless cars, with a focus on the various techniques proposed by authors. The comparison of the performance and effectiveness of different approaches, including deep learning, computer vision, and sensor fusion, in detecting and recognizing objects in the environment. Our review highlights the advantages and limitations of each technique and their potential for future development in the field of electric vehicles. Overall, image processing has shown to be a promising solution for the development of safe and efficient autonomous electric vehicles.

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A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain

Talwar,

Sharma,

Sonia

2024

Advances in Logistics, Operations, and Management Science

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An intelligent supply chain is essential in the continuously changing environment of the healthcare industry because it combines modern technology, data analytics, and artificial intelligence. Artificial intelligence-driven radiomics enables the extraction of intricate details from medical images, allowing for the early detection and diagnosis of cancer. These algorithms can identify subtle patterns and features in imaging data that might go unnoticed by human observers. Early detection is critical for improving survival rates and treatment outcomes. In this chapter, a review is done on convolutional neural networks (CNNs), transfer learning, ensemble models, radiomics features and machine learning, deep learning for histopathology, multi-modal integration, risk assessment models, and real-time image analysis. The review compresses work on parameters like cancer type, dataset size, accuracy, complexity, and applications of these AI techniques.

show abstract

Parametric Considerations and Dielectric Materials Impacts on the Performance of 10 GHzSIW-LWA for Respiration Monitoring

Cited by 3 publications

References 24 publications

Target identification and control model of autopilot for passive homing missiles

Target identification and control model of autopilot for passive homing missiles

A Comprehensive Review on Algorithms of Image Processing for Autonomous and Electric Vehicles

A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain

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