Title: Enhancing biomimetic design of tap scanning sensors through high-resolution thermal camera-based behavioral studies
Researchers conventionally employ thermal imaging to monitor the health of animals, observe their habitat utilization, and track their activity patterns. These non-invasive methods can generate detailed images and offer valuable insights into behavior, movements, and environmental interactions. The aye-aye (Daubentonia madagascariensis), a rare and endangered lemur from Madagascar, possesses a uniquely slender third finger evolved for tapping surfaces at relatively high frequencies. The adaptation enables acoustic-based sensing to locate cavities with prey in trees to enhance their foraging abilities. The authors’ previous studies have demonstrated some descent simulating dynamic models of the aye-aye’s third digit referenced from limited data collected with monocular cameras, which can be challenging due to noisy and distorted images, impacting motion analysis adversely. In this proposed research, high-speed thermal cameras are employed to capture detailed finger position and orientation, providing a clearer understanding of the overall dynamic range. The improved biomimetic model aims to enhance tap-testing strategies in nondestructive evaluation for various inspection applications. more »« less
The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a special thin middle finger. The aye-aye’s third digit (the slenderest one) has a remarkably specific adaptation, allowing it to perform tap-scanning (Finger tapping) to locate small cavities beneath tree bark and extract woodboring larvae from it. This finger, as an exceptional active acoustic actuator, makes an aye-aye’s biological system an attractive model for Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in engineering sensory and NDE, little is known about the mechanism and movement of this unique finger. In this paper a simplified kinematic model was proposed to simulate the aye-aye’s middle finger motion.
The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a unique thin middle finger. Its slender third digit has a remarkably specific adaptation, allowing them to perform tap-scanning to locate small cavities beneath tree bark and extract wood-boring larvae from it. As an exceptional active acoustic actuator, this finger makes an aye-aye’s biological system an attractive model for pioneering Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in the aye-aye’s unique foraging and its potential contribution to the engineering sensory, little is known about the mechanism and dynamics of this unique finger. This paper used a motion-tracking approach for the aye-aye’s middle finger using simultaneous video graphic capture. To mimic the motion, a two-link robot arm model is designed to reproduce the trajectory. Kinematics formulations were proposed to derive the motion of the middle finger using the Lagrangian method. In addition, a hardware model was developed to simulate the aye-aye’s finger motion. To validate the model, different motion states such as trajectory paths and joint angles, were compared. The simulation results indicate the kinematics of the model were consistent with the actual finger movement. This model is used to understand the aye-aye’s unique tap-scanning process for pioneering new tap-testing NDE strategies for various inspection applications.
Tan, Sheng; Yang, Jie; Chen, Yingying
(, IEEE Transactions on Mobile Computing)
null
(Ed.)
Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios.
Liu, Yuhao; Gutierrez-Barragan, Felipe; Ingle, Atul; Gupta, Mohit; Velten, Andreas
(, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))
Reconstruction of high-resolution extreme dynamic range images from a small number of low dynamic range (LDR) images is crucial for many computer vision applications. Current high dynamic range (HDR) cameras based on CMOS image sensor technology rely on multiexposure bracketing which suffers from motion artifacts and signal-to-noise (SNR) dip artifacts in extreme dynamic range scenes. Recently, single-photon cameras (SPCs) have been shown to achieve orders of magnitude higher dynamic range for passive imaging than conventional CMOS sensors. SPCs are becoming increasingly available commercially, even in some consumer devices. Unfortunately, current SPCs suffer from low spatial resolution. To overcome the limitations of CMOS and SPC sensors, we propose a learning-based CMOS-SPC fusion method to recover high-resolution extreme dynamic range images. We compare the performance of our method against various traditional and state-of-the-art baselines using both synthetic and experimental data. Our method outperforms these baselines, both in terms of visual quality and quantitative metrics.
Balhara, Himanshu; Karthikeyan, Adithyaa; Hanchate, Abhishek; Nakkina, Tapan Ganatma; Bukkapatnam, Satish T.
(, Frontiers in Manufacturing Technology)
This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on variousin-situandex-situimaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM).In-situimaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered fromin-situimaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand,ex-situimaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.
Masurkar, Nihar, Nemati, Hamidreza, and Dehghan_Niri, Ehsan. Enhancing biomimetic design of tap scanning sensors through high-resolution thermal camera-based behavioral studies. Retrieved from https://par.nsf.gov/biblio/10529359. Web. doi:10.1117/12.3027268.
Masurkar, Nihar, Nemati, Hamidreza, and Dehghan_Niri, Ehsan.
"Enhancing biomimetic design of tap scanning sensors through high-resolution thermal camera-based behavioral studies". Country unknown/Code not available: SPIE. https://doi.org/10.1117/12.3027268.https://par.nsf.gov/biblio/10529359.
@article{osti_10529359,
place = {Country unknown/Code not available},
title = {Enhancing biomimetic design of tap scanning sensors through high-resolution thermal camera-based behavioral studies},
url = {https://par.nsf.gov/biblio/10529359},
DOI = {10.1117/12.3027268},
abstractNote = {Researchers conventionally employ thermal imaging to monitor the health of animals, observe their habitat utilization, and track their activity patterns. These non-invasive methods can generate detailed images and offer valuable insights into behavior, movements, and environmental interactions. The aye-aye (Daubentonia madagascariensis), a rare and endangered lemur from Madagascar, possesses a uniquely slender third finger evolved for tapping surfaces at relatively high frequencies. The adaptation enables acoustic-based sensing to locate cavities with prey in trees to enhance their foraging abilities. The authors’ previous studies have demonstrated some descent simulating dynamic models of the aye-aye’s third digit referenced from limited data collected with monocular cameras, which can be challenging due to noisy and distorted images, impacting motion analysis adversely. In this proposed research, high-speed thermal cameras are employed to capture detailed finger position and orientation, providing a clearer understanding of the overall dynamic range. The improved biomimetic model aims to enhance tap-testing strategies in nondestructive evaluation for various inspection applications.},
journal = {},
publisher = {SPIE},
author = {Masurkar, Nihar and Nemati, Hamidreza and Dehghan_Niri, Ehsan},
editor = {Lakhtakia, Akhlesh and Martín-Palma, Raúl J and Knez, Mato}
}
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