Radio frequency sensing based environmental monitoring  technology

Zhu, Xianxun; Zhang, Yang; Zhao, Zhiyang; Zuo, Jiancun

doi:10.1117/12.2541098

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…This process ensures the extraction of more nuanced and representative features from the data. Such a methodology significantly enhances the accuracy in classifying and predicting various sample types [5]. Among the quintessential models in deep neural networks, we find the convolutional neural network, deep belief network, and stacked autoencoder network, each offering unique approaches to data interpretation.…”

Section: An Overview Of Deep Learningmentioning

confidence: 99%

Integration of Reinforcement Learning and Deep Learning in Path Planning Games

2024

HSET

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This paper explores the dynamic intersection of Reinforcement Learning (RL) and Deep Learning (DL), emphasizing their collaborative strength in the realm of Deep Reinforcement Learning (DRL). The fusion of RL's strategy optimization through agent-environment interactions and DL's advanced perception and decision-making abilities results in DRL's powerful framework. This framework is extensively utilized to tackle intricate decision-making challenges across diverse fields. The paper begins by laying a solid theoretical foundation, explaining the core principles of both reinforcement learning and deep learning, and highlighting their synergistic potential. Further, the focus shifts to the practical implementation of the Deep Q Network (DQN) algorithm, particularly in the context of elementary path analysis problems. This segment starts with an accessible introduction to deep neural networks, paving the way for a deeper understanding of DQN. It then explores key aspects such as the utilization and accumulation of experiences, a critical component of the learning process in DRL. Additionally, the paper addresses the inherent limitations of the DQN algorithm, suggesting avenues for potential improvements and enhancements.

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Section: An Overview Of Deep Learningmentioning

confidence: 99%

Integration of Reinforcement Learning and Deep Learning in Path Planning Games

2024

HSET

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“…Rapid movement through the threshold area is preferable, ensuring prompt device usability, which in turn minimizes unwanted power wastage and heat production. S represents the amount of gate voltage change required to change the source leakage current by a factor of ten, and a smaller S means a faster turn-off rate [8]. The unit of S is usually mV/dec, which is the amount of change in gate voltage when the drain current changes by an order of magnitude (10 times).…”

Section: Higher Speedmentioning

confidence: 99%

Advantages of FINFET over traditional CMOS: Reasons and implications

Shan

2023

TNS

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In the relentless march of technological advancement, the semiconductor industry remains at the forefront of innovation. Among the myriad breakthroughs, FinFET technology stands out as a recent focal point in research. Serving as an avant-garde semiconductor manufacturing process, FinFET plays a pivotal role in enhancing chip performance, diminishing power consumption, and minimizing component size. At its core, FinFET is a distinct type of field-effect transistor (FET) that utilizes a thin silicon fin as the conducting channel. This structure has revolutionized the way transistors are designed, offering remarkable control over the current flow through the channel. This control is achieved by wrapping a gate material around the three visible sides of the fin, which provides superior switching behavior and leakage reduction. Beyond its foundational principles, FinFETs inherent characteristics offer numerous advantages. For instance, the technology paves the way for more densely packed transistors, enabling more powerful yet compact integrated circuits. Moreover, its innovative design leads to more energy-efficient chips, which are crucial for todays demanding computing and electronic environments.

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“…If the RFID device is not working alone, but in conjunction with other electronic devices, the issue of information becomes apparent. For example, an RFID Smart Vareflyt system can operate independently, but will not work if it combined with an existing data system [100]. This question occurs when the real data is different from the reported data, does not change, or involves correction from other sources.…”

Section: Interoperability Issuesmentioning

confidence: 99%

“…The most challenging problem in the case of Smart Vareflyt is not just the issue of data incorporation at the knowledge level, but also the exchange of information and resources and the application of competition law to the level of the company [100].…”

Section: Interoperability Issuesmentioning

confidence: 99%

The Role of RFID in Green IoT: A Survey on Technologies, Challenges and a Way Forward

Yusoff¹,

Ishak²,

Alezabi³

2021

Adv. sci. technol. eng. syst. j.

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The Internet of Things (IoT) is a technology that enables communication between everyday life using different sensor actuators that work together to identify, capture, and distribute critical data from the planet. Massive machines and devices are therefore linked and communicate with them. The use of resources in this area presents new challenges for this technology. The goal was to find a green IoT that focuses on energy efficiency and IoT efficiency. Green IoT is an energy-efficient way to reduce or eliminate the greenhouse effect of current applications. Radio Frequency Identification (RFID) is one of the Green IoT and Master IoT components that identifies a person or entity in a high-frequency electromagnetic spectrum when combining electromagnetic or electrostatic couplings. If the predictions are also correct, energy use issues arise as active battery-powered RFID detection needs to be addressed by incorporating new solutions for Green IoT technology. Past studies and assessments have attempted to evaluate RFID technology and its functions. Unfortunately, however, they concentrated on a single RFID view of technique and technology. This paper examines holistically and systematically the impact of RFID applications on green IoT, focusing on three categories: the challenges, environmental consequences, and the benefits of green IoT RFID applications. The impacts, performance and safety of RFID IoT applications have been carefully described. The benefits and examples of RFID applications, including their key advantages and disadvantages, are also discussed. Overall, this paper highlights the potential efforts of RFID to address existing Green IoT issues.

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Radio frequency sensing based environmental monitoring technology

Cited by 4 publications

References 7 publications

Integration of Reinforcement Learning and Deep Learning in Path Planning Games

Integration of Reinforcement Learning and Deep Learning in Path Planning Games

Advantages of FINFET over traditional CMOS: Reasons and implications

The Role of RFID in Green IoT: A Survey on Technologies, Challenges and a Way Forward

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