Mathematical and computer modeling of electro-optic systems using a generic modeling approach

The conventional approach to modelling electro-optic sensor systems is to develop separate models for individual systems or classes of system, depending on the detector technology employed in the sensor and the application. However, this ignores commonality in design and in components of these systems. A generic approach is presented for modelling a variety of sensor systems operating in the infrared waveband that also allows systems to be modelled with different levels of detail and at different stages of the product lifecycle. The provision of different model types (parametric and image-flow descriptions) within the generic framework can allow valuable insights to be gained

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Micro systems technology

The emerging field of Micro Systems Technology is described. Micro Systems Technology can be seen as the meeting of disciplines, a product of convergence along different lines. Apart from the traditional and ever developing line of 'classical' precision engineering, there is a line along micro electronics, micro sensors and actuators. This is the line we focus on in this contribution. The third line worth mentioning is the one along the upcoming field of molecular engineering. The main purpose of this paper is to show the wealth of possibilities and consequently the need for 'integral design' management

An overview of in-flight plume diagnostics for rocket engines

An overview and progress report of the work performed or sponsored by LeRC toward the development of in-flight plume spectroscopy technology for health and performance monitoring of liquid propellant rocket engines are presented. The primary objective of this effort is to develop technology that can be utilized on any flight engine. This technology will be validated by a hardware demonstration of a system capable of being retrofitted onto the Space Shuttle Main Engines for spectroscopic measurements during flight. The philosophy on system definition and status on the development of instrumentation, optics, and signal processing with respect to implementation on a flight engine are discussed

Spacelab system analysis: A study of the Marshall Avionics System Testbed (MAST)

An analysis of the Marshall Avionics Systems Testbed (MAST) communications requirements is presented. The average offered load for typical nodes is estimated. Suitable local area networks are determined

Action recognition based on efficient deep feature learning in the spatio-temporal domain

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer ReviewedPostprint (author's final draft