Optical imaging requires appropriate light sources. For image-guided surgery, in particular fluorescence-guided surgery, a high fluence rate, a long working distance, computer control, and precise control of wavelength are required. In this article, we describe the development of light-emitting diode (LED)-based light sources that meet these criteria. These light sources are enabled by a compact LED module that includes an integrated linear driver, heat dissipation technology, and real-time temperature monitoring. Measuring only 27 mm wide by 29 mm high and weighing only 14.7 g, each module provides up to 6,500 lx of white (400-650 nm) light and up to 157 mW of filtered fluorescence excitation light while maintaining an operating temperature < or = 50 degrees C. We also describe software that can be used to design multimodule light housings and an embedded processor that permits computer control and temperature monitoring. With these tools, we constructed a 76-module, sterilizable, three-wavelength surgical light source capable of providing up to 40,000 lx of white light, 4.0 mW/cm2 of 670 nm near-infrared (NIR) fluorescence excitation light, and 14.0 mW/cm2 of 760 nm NIR fluorescence excitation light over a 15 cm diameter field of view. Using this light source, we demonstrated NIR fluorescence-guided surgery in a large-animal model.

An attempt is made in this paper to present the application, design, and performance analysis of a novel optimal controller (OC) for automatic generation control (AGC) of interconnected two-area electrical power systems in a deregulated power environment with energy storage units. The OC is designed via full state vector feedback strategy to carry out the study. Swift acting energy storage units such as redox flow batteries (RFBs) are integrated into the power system models, and their efficacy in boosting AGC performance is executed and compared. Initially, the efficacy of OC is investigated in a restructured two-area single-source thermal system, and then, the study is extended to a proposed more realistic restructured two-area multi-source thermal-hydro-diesel-gas power system. It is observed that OC is able to satisfy the AGC requirement under different power contracts in the open electricity market and shows better performance in comparison to a recently published artificial cooperative search algorithm optimized proportional integral controller. MATLAB simulation results demonstrate the improvements in the dynamic performance of the system in the presence of RFB. Sensitivity analysis reveals the robustness of OC under ample variations in the system parameters, initial loading and size and positions of uncontracted load power demands.

No abstract

Meshes are important representations of physical 3D entities in the virtual world. Applications like rendering, simulations and 3D printing require meshes to be manifold so that they can interact with the world like the real objects they represent. Prior methods generate meshes with great geometric accuracy but poor manifoldness. In this work we propose Neural Mesh Flow (NMF) to generate two-manifold meshes for genus-0 shapes. Specifically, NMF is a shape auto-encoder consisting of several Neural Ordinary Differential Equation (NODE) [1] blocks that learn accurate mesh geometry by progressively deforming a spherical mesh. Training NMF is simpler compared to state-of-the-art methods since it does not require any explicit mesh-based regularization. Our experiments demonstrate that NMF faciliates several applications such as single-view mesh reconstruction, global shape parameterization, texture mapping, shape deformation and correspondence. Importantly, we demonstrate that manifold meshes generated using NMF are better-suited for physically-based rendering and simulation. Code and data will be released 1 .Preprint. Under review.

No abstract