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1. Bio-mimetic high-speed target localization with fused frame and event vision for edge application

   https://doi.org/10.3389/fnins.2022.1010302  

   Lele, Ashwin Sanjay ; Fang, Yan ; Anwar, Aqeel ; Raychowdhury, Arijit ( November 2022 , Frontiers in Neuroscience)

   Evolution has honed predatory skills in the natural world where localizing and intercepting fast-moving prey is required. The current generation of robotic systems mimics these biological systems using deep learning. High-speed processing of the camera frames using convolutional neural networks (CNN) (frame pipeline) on such constrained aerial edge-robots gets resource-limited. Adding more compute resources also eventually limits the throughput at the frame rate of the camera as frame-only traditional systems fail to capture the detailed temporal dynamics of the environment. Bio-inspired event cameras and spiking neural networks (SNN) provide an asynchronous sensor-processor pair (event pipeline) capturing the continuous temporal details of the scene for high-speed but lag in terms of accuracy. In this work, we propose a target localization system combining event-camera and SNN-based high-speed target estimation and frame-based camera and CNN-driven reliable object detection by fusing complementary spatio-temporal prowess of event and frame pipelines. One of our main contributions involves the design of an SNN filter that borrows from the neural mechanism for ego-motion cancelation in houseflies. It fuses the vestibular sensors with the vision to cancel the activity corresponding to the predator's self-motion. We also integrate the neuro-inspired multi-pipeline processing with task-optimized multi-neuronal pathway structure in primates andmore »insects. The system is validated to outperform CNN-only processing using prey-predator drone simulations in realistic 3D virtual environments. The system is then demonstrated in a real-world multi-drone set-up with emulated event data. Subsequently, we use recorded actual sensory data from multi-camera and inertial measurement unit (IMU) assembly to show desired working while tolerating the realistic noise in vision and IMU sensors. We analyze the design space to identify optimal parameters for spiking neurons, CNN models, and for checking their effect on the performance metrics of the fused system. Finally, we map the throughput controlling SNN and fusion network on edge-compatible Zynq-7000 FPGA to show a potential 264 outputs per second even at constrained resource availability. This work may open new research directions by coupling multiple sensing and processing modalities inspired by discoveries in neuroscience to break fundamental trade-offs in frame-based computer vision 1 .« less
2. Single Frame Lidar and Stereo Camera Calibration Using Registration of 3D Planes

   https://doi.org/10.1109/IRC52146.2021.00026  

   Singandhupe, Ashutosh ; La, Hung Manh ( November 2021 , 2021 Fifth IEEE International Conference on Robotic Computing (IRC))

   This work focuses on finding the extrinsic parameters (rotation and translation) between the lidar and the stereo camera setups. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both the sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The planes extracted from the sensor’s data are used as reference data sets to find the relative transformation between the two sensors. We use our proposed method Correntropy Similarity Matrix Iterative Closest Point (CoSM-ICP) algorithm to estimate the relative transformation. In this work, we use a single frame of the point cloud data acquired from the lidar sensor and a single frame from the calibrated Stereo camera point cloud to perform this operation. We evaluate our approach on a simulated dataset since it has the freedom to evaluate under multiple configurations. Through results, we verify our approach under various configurations.
3. Application of Digital Image Correlation (DIC) to the Measurement of Strain Concentration of a PVA Dual-Crosslink Hydrogel Under Large Deformation

   https://doi.org/10.1007/s11340-019-00520-4  

   Liu, M. ; Guo, J. ; Hui, C.-Y. ; Zehnder, A. T. ( January 2019 , Experimental Mechanics)

   Hydrogels are a class of soft, highly deformable materials formed by swelling a network of polymer chains in water. With mechanical properties that mimic biological materials, hydrogels are often proposed for load bearing biomedical or other applications in which their deformation and failure properties will be important. To study the failure of such materials a means for the measurement of deformation fields beyond simple uniaxial tension tests is required. As a non-contact, full-field deformation measurement method, Digital Image Correlation (DIC) is a good candidate for such studies. The application of DIC to hydrogels is studied here with the goal of establishing the accuracy of DIC when applied to hydrogels in the presence of large strains and large strain gradients. Experimental details such as how to form a durable speckle pattern on a material that is 90% water are discussed. DIC is used to measure the strain field in tension loaded samples containing a central hole, a circular edge notch and a sharp crack. Using a nonlinear, large deformation constitutive model, these experiments are modeled using the finite element method (FEM). Excellent agreement between FEM and DIC results for all three geometries shows that the DIC measurements are accurate up tomore »strains of over 10, even in the presence of very high strain gradients near a crack tip. The method is then applied to verify a theoretical prediction that the deformation field in a cracked sample under relaxation loading, i.e. constant applied boundary displacement, is stationary in time even as the stress relaxes by a factor of three.« less
4. Stellar Populations in the Central 0.5 pc of Our Galaxy. III. The Dynamical Substructures

   https://doi.org/10.3847/1538-4357/acb939  

   Jia, Siyao ; Xu, Ningyuan ; Lu, Jessica R. ; Chu, D. S. ; O’Neil, K. Kosmo ; Drechsler, W. B. ; Hosek, Jr., M. W. ; Sakai, S. ; Do, T. ; Ciurlo, A. ; et al ( May 2023 , The Astrophysical Journal)

   We measure the 3D kinematic structures of the young stars within the central 0.5 pc of our Galactic Center using the 10 m telescopes of the W. M. Keck Observatory over a time span of 25 yr. Using high-precision measurements of positions on the sky and proper motions and radial velocities from new observations and the literature, we constrain the orbital parameters for each young star. Our results show two statistically significant substructures: a clockwise stellar disk with 18 candidate stars, as has been proposed before, but with an improved disk membership; and a second, almost edge-on plane of 10 candidate stars oriented east–west on the sky that includes at least one IRS 13 star. We estimate the eccentricity distribution of each substructure and find that the clockwise disk has 〈e〉 = 0.39 and the edge-on plane has 〈e〉 = 0.68. We also perform simulations of each disk/plane with incompleteness and spatially variable extinction to search for asymmetry. Our results show that the clockwise stellar disk is consistent with a uniform azimuthal distribution within the disk. The edge-on plane has an asymmetry that cannot be explained by variable extinction or incompleteness in the field. The orientation, asymmetric stellar distribution,more »and high eccentricity of the edge-on plane members suggest that this structure may be a stream associated with the IRS 13 group. The complex dynamical structure of the young nuclear cluster indicates that the star formation process involved complex gas structures and dynamics and is inconsistent with a single massive gaseous disk.

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5. Angular Velocity Estimation using Non-coplanar Accelerometer Array

   https://doi.org/10.1109/JSEN.2021.3107470  

   Maynard, Michael ; Vikas, Vishesh ( August 2021 , IEEE Sensors Journal)

   Over the last few decades, Gyro-Free Inertial Measurement Units (GF-IMUs) have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar spacial arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an Extended Kalman Filter (EKF) that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube.They analyze the estimation error for static and dynamic movement as the distance between the accelerometers (four accelerometers in cube-orientation) is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard errormore »of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using Vicon® to construct the ground truth angular velocity. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises.« less