Sensors for Road Vehicles of the Future

Jiménez, Felipe

doi:10.3390/s23010022

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Without a doubt, the breadth of the different technological blocks implies that the training of engineers in the field of autonomous and connected driving represents a challenge that is either addressed superficially [22] or involves a very heavy and multidisciplinary teaching workload [16], that could be explained in specific programs [51, 57]. In both approaches, multidisciplinarity is quite challenging, as students may not be aware of all technologies involved, so teaching strategies must be carefully selected to provide the appropriate level considering the subject objectives within the Bachelor or Master framework.…”

Section: Introductionmentioning

confidence: 99%

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Jiménez,

Clavijo

2024

Comp Applic In Engineering

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The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.

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Section: Introductionmentioning

confidence: 99%

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Jiménez,

Clavijo

2024

Comp Applic In Engineering

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Artificial Intelligence‐Based Medical Sensors for Healthcare System

Chen

Cui

Haick

et al. 2023

Advanced Sensor Research

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The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio‐economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI‐enabled medical sensors for off‐body detection, near‐body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis.

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Effect of SiO₂ protective layer on LiNbO₃ structured SAW resonators and temperature characteristics study

Qiang,

Wenlong,

Chi

et al. 2023

J. Micromech. Microeng.

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In this paper, using LiNbO3 as the piezoelectric substrate, Pt as the electrode and SiO2 as the protective layer, a multi-physics field coupled finite element model was used to investigate the effect of the protective layer thickness on the Rayleigh wave propagation characteristics, and the relationship between the protective layer thickness and the frequency temperature coefficient TCF and electromechanical coupling coefficient K2 is established. The frequency characteristics of the device and the microscopic changes of the electrode and substrate structures under high temperature were analyzed experimentally. The results show that no acoustic mode shift occurs when the normalized thickness is less than 31.25%; As the thickness of the protective layer increases, the vibration displacement in the direction of L wave, SH wave and SV wave decreases, and the thickness of the protective layer can be increased appropriately to reduce the interference of SH wave to Rayleigh wave; |TCF| decreases with increasing thickness of the protective layer; Changes in the thickness of the protective layer at different temperatures lead to fluctuations in K2; As the temperature increases, the fluctuation of the resonant frequency of the SAW resonator increases; The SiO2protective layer can effectively protect LiNbO3 materials while improving the high-temperature working stability of Pt electrodes.

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Sensors for Road Vehicles of the Future

Abstract: New vehicles include several systems that improve their safety, comfort, and performance [...]

Cited by 5 publications

References 15 publications

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Effect of SiO₂ protective layer on LiNbO₃ structured SAW resonators and temperature characteristics study

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Sensors for Road Vehicles of the Future

Abstract: New vehicles include several systems that improve their safety, comfort, and performance [...]

Cited by 5 publications

References 15 publications

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Effect of SiO2 protective layer on LiNbO3 structured SAW resonators and temperature characteristics study

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Product

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Effect of SiO₂ protective layer on LiNbO₃ structured SAW resonators and temperature characteristics study