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1. Aye-aye’s middle finger kinematic modeling during tap-scanning

   https://doi.org/10.1117/12.2612943  

   Kang, Jiming; Nemati, Hamidreza; Dehghan-Niri, Ehsan (April 2022, SPIE, Smart Structures/NDE, 300 Long Beach Blvd, Long Beach, CA, 2022)

   Lakhtakia, Akhlesh; Martín-Palma, Raúl J.; Knez, Mato (Ed.)

   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. 

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2. Aye-aye middle finger kinematic modeling and motion tracking during tap-scanning

   https://doi.org/10.1016/j.birob.2023.100134  

   Masurkar, Nihar; Kang, Jiming; Nemati, Hamidreza; Dehghan-Niri, Ehsan (December 2023, Biomimetic Intelligence and Robotics)

   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. 

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3. Enabling Fine-grained Finger Gesture Recognition on Commodity WiFi Devices

   https://doi.org/10.1109/TMC.2020.3045635  

   Tan, Sheng; Yang, Jie; Chen, Yingying (December 2020, 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. 

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4. NeRF-Enhanced Digital Twin for Building Anomaly Inspection Using Unmanned Aerial Systems (UASs)

   https://doi.org/10.7771/3067-4883.1831  

   Gholizadeh_Lonbar, Ahmad; Chen, Kaiwen (June 2025, CIB Conferences)

   Building facade inspections are crucial for ensuring structural integrity and occupant safety, traditionally requiring physical access that can be costly and risky. Recent advancements have seen the integration of Unmanned Aerial Systems (UASs) equipped with imaging technologies to facilitate these inspections. However, the primary challenge remains in the effective detection and precise localization of facade anomalies, such as cracks, stains, and specifically, thermal anomalies. This paper investigates the fusing of diverse data sources, specifically thermal infrared (IR) imaging and high-resolution RGB data, to enhance 3D registration of thermal anomalies. Utilizing a Computational Neural Radiance Field (NeRF) approach, it aims to reconstruct detailed 3D models of building facades. This integration improves the accuracy of anomaly detection by fusing the precise camera positions derived from the original RGB data to refine the alignment and visualization of thermal anamolies in both RGB and IR imagery. We utilized COLMAP for camera position estimation and NeRF for 3D registration, employing Structure-from-Motion (SfM) and NeRF methodologies to create detailed and scalable 3D models from 2D IR and RGB images. This approach integrates the precision of camera positioning with advanced 3D reconstruction techniques to enhance the visualization and analysis of building facades. Our method automates the registration and mapping of detected thermal anomalies in 2D images onto the reconstructed 3D models, improving the diagnostics of building facades by enabling precise localization and scaling of these anomalies. The findings demonstrate the potential of our approach to reduce inspection times and enhance the safety of diagnostic procedures through higher accuracy and less invasive methods. 

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5. Single-Photon Camera Guided Extreme Dynamic Range Imaging

   Liu, Yuhao; Gutierrez-Barragan, Felipe; Ingle, Atul; Gupta, Mohit; Velten, Andreas (January 2022, 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. 

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