This paper presents an automated, rapid performance-driven design approach for tuning miniaturized, implantable microstrip patch antenna (MPA). The proposed method automates the design process by integrating the simulation platform (e.g., HFSS) with a K-Nearest Neighbors (KNN) based on-patch pixel slot screening process. Using this method, a custom-designed MPA with dimensions of 6 × 6 × 0.635 mm3is proposed, operating at 2.45 GHz for wireless biotelemetry and achieving a wide bandwidth of 770 MHz. Compared to time-consuming full sweep antenna tuning methods, this approach achieves up to a 76% reduction in computing time and resource usage. Additionally, an L-shaped impedance matching circuit is implemented to match the antenna to the source’s characteristic impedance for high power transfer efficiency. The proposed antenna design method, enhanced by AI-driven optimization, offers a highly efficient and user-friendly approach for customized biotelemetry antennas. Integrated with cardiovascular implantable biosensors, the antenna plays a crucial role in enabling reliable wireless communication for the early detection and timely intervention within real-time health monitoring systems.

An electrocardiogram (ECG) is a medical test that records the electrical activity of the heart over a period of time. This non-invasive and painless test makes ECG an essential tool in cardiology for diagnosing and monitoring heart health. Phonocardiogram (PCG) are typically used as an adjunct to the expertise of healthcare professionals rather than as a standalone diagnostic tool. By applying ECG signals to predict the corresponding PCG signals, no additional test for PCG is needed. These predictions can assist doctors in making informed decisions about patient care and treatment plans by merely collecting ECG signals. Physical reservoir computing (RC), as a bio-inspired algorithm, has received growing research interest. Under the term neuromorphic, physical RC processes the raw signals in the analogue domain with in-memory computing, thus reducing massive power consumption. At the same time, RC is a special type of Recurrent Neural Network (RNN) that is suitable for time-dependent signal processing. The ECG/PCG dataset we used here was experimentally collected by 10 people. We applied a recently proposed physical RC architecture called Rotating Neuron Reservoir and achieved an average NRMSE of 0.3690 for prediction, aiming to propose and construct a neuromorphic processing core for the ECG-to-PCG prediction task.

Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We derive the reciprocity relationship of Mueller matrix and formulate new reciprocity constraint that takes polarization state into account. We also utilize polarimetric constraints and extend them to the case of perspective projection. For the recovery of surface depths and normals, we incorporate reciprocity constraint with diffuse/specular polarimetric constraints in a unified optimization framework. For depth estimation, we further propose to utilize the consistency of diffuse angle of polarization. For normal estimation, we develop a normal refinement strategy based on degree of linear polarization. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.

Particle Imaging Velocimetry (PIV) is a classical method that estimates fluid flow by analyzing the motion of injected particles. To reconstruct and track the swirling particles is a difficult computer vision problem, as the particles are dense in the fluid volume and have similar appearances. Further, tracking a large number of particles is particularly challenging due to heavy occlusion. Here we present a low-cost PIV solution that uses compact lenslet-based light field cameras as imaging device. We develop novel optimization algorithms for dense particle 3D reconstruction and tracking. As a single light field camera has limited capacity in resolving depth (z-dimension measurement), the resolution of 3D reconstruction on the x-y plane is much higher than along the z-axis. To compensate for the imbalanced resolution in 3D, we use two light field cameras positioned at an orthogonal angle to capture particle images. In this way, we can achieve high-resolution 3D particle reconstruction in the full fluid volume. For each time frame, we first estimate particle depths under a single viewpoint by exploiting the focal stack symmetry of light field. We then fuse the recovered 3D particles in two views by solving a linear assignment problem (LAP). Specifically, we propose an anisotropic point-to-ray distance as matching cost to handle the resolution mismatch. Finally, given a sequence of 3D particle reconstructions over time, we recover the full-volume 3D fluid flow with a physically-constrained optical flow, which enforces local motion rigidity and fluid incompressibility. We perform comprehensive experiments on synthetic and real data for ablation and evaluation. We show that our method recovers full-volume 3D fluid flows of various types. Two-view reconstruction results achieves higher accuracy than those with one view only.

We present a 3D shape reconstruction method that leverages both photometric and polarimetric cues. Unlike many active methods that require controlled lighting condition, our method can be used under unknown and uncontrolled natural illumination (both indoor and outdoor). We use two circularly polarized spotlights to boost the polarization cues corrupted by the environment lighting, as well as to provide photometric cues. We solve surface normals with two polarization images by combining the polarimetric and photometric constraints. To mitigate the effect of uncontrolled environment light in photometric constraints, we es-timate a lighting proxy map and iteratively refine the normal and lighting estimation. We perform experiments under various natural illumination conditions and compare our results with state-of-the-arts photometric stereo and shape from polarization methods. Our method achieves good accuracy and can be used in flexible environment.

Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.

Given a set of multiple view videos, which records the motion trajectory of an object, we propose to find out the objects' kinematic formulas with neural rendering techniques. For example, if the input multiple view videos record the free fall motion of an object with different initial speed v, the network aims to learn its kinematics: Δ=vt-1over 2 gt2, where Δ, g and t are displacement, gravitational acceleration and time. To achieve this goal, we design a novel framework consisting of a motion network and a differentiable renderer. For the differentiable renderer, we employ Neural Radiance Field (NeRF) since the geometry is implicitly modeled by querying coordinates in the space. The motion network is composed of a series of blending functions and linear weights, enabling us to analytically derive the kinematic formulas after training. The proposed framework is trained end to end and only requires knowledge of cameras' intrinsic and extrinsic parameters. To validate the proposed framework, we design three experiments to demonstrate its effectiveness and extensibility. The first experiment is the video of free fall and the framework can be easily combined with the principle of parsimony, resulting in the correct free fall kinematics. The second experiment is on the large angle pendulum which does not have analytical kinematics. We use the differential equation controlling pendulum dynamics as a physical prior in the framework and demonstrate that the convergence speed becomes much faster. Finally, we study the explosion animation and demonstrate that our framework can well handle such black-box-generated motions.

Recovering the shape and reflectance of non-Lambertian surfaces remains a challenging problem in computer vision since the view-dependent appearance invalidates traditional photo-consistency constraint. In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces of various materials in one shot. Our CMSLF system consists of an array of cameras arranged on concentric circles where each ring captures a specific spectrum. Coupled with a multi-spectral ring light, we are able to sample viewpoint and lighting variations in a single shot via spectral multiplexing. We further show that our concentric camera and light source setting results in a unique single-peak pattern in specularity variations across viewpoints. This property enables robust depth estimation for specular points. To estimate depth and multi-spectral reflectance map, we formulate a physics-based reflectance model for the CMSLF under the surface camera (S-Cam) representation. Extensive synthetic and real experiments show that our method outperforms the state-of-the-art shape reconstruction methods, especially for non-Lambertian surfaces.