Self-Powered Sensors: Applications, Challenges, and Solutions

Javaid, Shumaila; Fahim, Hamza; Zeadally, Sherali; He, Bin

doi:10.1109/jsen.2023.3241947

Cited by 20 publications

(7 citation statements)

References 280 publications

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“…Deep Learning deals with DNN architecture, where deep refers to figures of the hidden layers, and its main objective is to resolve learning problems by copying the functioning of the human brain [ 9 , 10 ]. Schemes employing DL have been developing and improving consistently, as have adjustments to the model structure.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

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Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

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Section: Deep Learning Techniquesmentioning

confidence: 99%

“…It combines the RMSProp and momentum approaches with a bias correction mechanism. Adam's update rule consists of the following steps, and, mathematically, it is defined in Equation (10).…”

mentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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“…Recent times have witnessed several review articles delving into the fusion of flexible mechanical sensors with the domain of deep learning, with a pronounced emphasis on the integration of the CRNN algorithm. However, it's notable that a majority of these reviews tend to focus on specific sensor types or application domains, ranging from self-powered mechanical sensors to stretchable sensors, piezoelectric acoustic sensors, flexible sensors for cardiac monitoring, tactile sensors, and even soft robotics [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Unlocking the potential: CRNN-driven flexible mechanical sensors for intelligent sensing and beyond

Wang

2024

ACE

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In our rapidly evolving society, intelligent technology has become a driving force across various sectors, leading to transformative progress. This paper explores the integration of Convolutional Recurrent Neural Network (CRNN) algorithms with flexible sensors, showcasing their remarkable potential in enhancing data processing and interpretation. The synergy between CRNN and flexible sensors holds great promise in diverse domains, including health monitoring and industrial automation. Our comprehensive evaluation of the CRNN model reveals exceptional performance in classification and regression tasks, highlighting its adaptability to complex and varied sensor data. Additionally, confusion matrix analysis provides insights into multi-class classification scenarios, reinforcing the model's reliability. A comparative analysis against traditional machine learning techniques demonstrates the superiority of CRNN in handling time-series data. In conclusion, the integration of CRNN with flexible sensors is set to revolutionize intelligent sensing technology, opening new avenues for innovation and problem-solving in various industries. This collaborative approach not only enhances data accuracy and reliability but also paves the way for groundbreaking technologies and applications yet to be imagined. The future of intelligent sensing is here, and it is both exciting and promising, with CRNN-driven flexible sensors leading the way towards a more intelligent and connected world.

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“…3,4 However, conventional energy supply devices such as batteries and capacitors can hardly withstand severe deformation due to their strong structural rigidity. 5,6 Moreover, current forms of commonly used energy supplies usually require periodic recharging or must be replaced after use, greatly limiting the potential practical application scenarios for all-weather use. There is a need to develop energy harvesting technology to address this obstacle and provide a sustainable power source for wearable electronics.…”

Section: Introductionmentioning

confidence: 99%

Highly Stretchable, Conductive, Adhesive, and Self-Powered Ionogel Sensor for Human Motion Detection, Signal Transmission, and Traffic Monitoring

Long,

Yuan,

Wang

2024

ACS Appl. Polym. Mater.

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Distinguished from traditional power sources such as batteries and capacitors, triboelectric nanogenerators have higher freedom of material selection and application flexibility and thus can fulfill the needs of flexible self-supplied electronic devices for advanced e-skin, human−computer interaction, and sensing. Here, a flexible (1100% of elongation at break, 0.2 MPa of breaking strength), highly conductive and adhesive (120 kPa), and environmentally stable ionogel was fabricated by introducing ionic liquids into a physical cross-linking polymer framework. The ionic liquid built a nonaqueous solvent system that made the ionogel environmentally stable. The introduction of amphiphilic ions both improved the mechanical properties of the polymer network and acted as ion channels to enhance the mobility of the ionic liquid and thus the electrical conductivity. Meanwhile, dynamic hydrogen bonding and dipole−dipole interaction made the ionogel have good adhesion properties. The ionogel can be applied as a flexible sensor for strain and pressure monitoring, and more importantly, the assembly of the ionogel with silicone rubber elastomer films into a sandwich-structured triboelectric nanogenerator enabled its application in scenarios such as self-supplied messaging and all-weather traffic monitoring, and it can also provide wired and wireless signal outputs with fast response (∼0.04 s). This work provides a direction for the application of gel-based electronic devices in the fields of intelligent human−computer interaction, information transfer, health monitoring, and traffic detection.

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Self-Powered Sensors: Applications, Challenges, and Solutions

Cited by 20 publications

References 280 publications

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Unlocking the potential: CRNN-driven flexible mechanical sensors for intelligent sensing and beyond

Highly Stretchable, Conductive, Adhesive, and Self-Powered Ionogel Sensor for Human Motion Detection, Signal Transmission, and Traffic Monitoring

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