Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Civil and Environmental Engineering Challenges for Data Sensing and Analysis

The objective of this study was to identify challenges in civil and environmental engineering that can potentially be solved using data sensing and analysis research. The challenges were recognized through extensive literature review in all disciplines of civil and environmental engineering. The literature review included journal articles, reports, expert interviews, and magazine articles. The challenges were ranked by comparing their impact on cost, time, quality, environment and safety. The result of this literature review includes challenges such as improving construction safety and productivity, improving roof safety, reducing building energy consumption, solving traffic congestion, managing groundwater, mapping and monitoring the underground, estimating sea conditions, and solving soil erosion problems. These challenges suggest areas where researchers can apply data sensing and analysis research.

Grand Challenges for Data Sensing and Analysis

A diverse group of experts proposed the 9 grand challenges outlined in this booklet. This expert task force was assembled by the ASCE TCCIT Data Sensing and Analysis (DSA) Committee and endorsed by the TRB AFH10(1) Construction IT joint subcommittee at the request of their membership. The task force did not rank the challenges selected, nor did it endorse particular approaches to meeting them. Rather than attempt to include every important goal for data sensing and analysis, the panel chose opportunities that were both achievable and sustainable to help people and the planet thrive. The panel’s conclusions were reviewed by several subject-matter experts. The DSA is offering an opportunity to comment on the challenges by contacting the task force chair via email at becerik@usc.edu.

Mode-localized displacement sensing

We report the construction of a new class of micromachined displacement sensors that employ the phenomenon of vibration-mode localization for monitoring minute inertial displacements. It is demonstrated both theoretically and experimentally that the eigenstate-shifted output signal of such mode-localized displacement sensors may be as high as 1000 times greater than corresponding resonant-frequency variations that serve as the output in the more traditional vibratory resonant micromechanical displacement/motion sensors. The high parametric sensitivities attainable in such mode-localized displacement sensors, together with their inherent advantages of improved environmental robustness and electrical tunability, suggest an alternative approach in achieving improved sensitivity and stability in high-resolution displacement transduction. © 1992-2012 IEEE.

A Novel Passive UHF RFID Real Time Location Sensing System

Ubiquitous in-building Real Time Location Systems (RTLS) today are limited by costly active radio frequency identification (RFID) tags and short range portal readers of low cost passive RFID tags. We, however, present a novel technology locates RFID tags using a new approach based on (a) minimising RFID fading using antenna diversity, frequency dithering, phase dithering and narrow beam-width antennas, (b) measuring a combination of RSSI and phase shift in the coherent received tag backscatter signals and (c) being selective of use of information from the system by, applying weighting techniques to minimise error. These techniques make it possible to locate tags to an accuracy of less than one metre. This breakthrough will enable, for the first time, the low-cost tagging of items and the possibility of locating them at relatively high precision.

SOI sensing technologies for harsh environment

This paper reviews and addresses certain aspects of Silicon-On-Insulator (SOI) technologies for a harsh environment. The paper first describes the need for specialized sensors in applications such as (i) domestic and other small-scale boilers, (ii) CO2 Capture and Sequestration, (iii) oil & gas storage and transportation, and (iv) automotive. We describe in brief the advantages and special features of SOI technology for sensing applications requiring temperatures in excess of the typical bulk silicon junction temperatures of 150oC. Finally we present the concepts, structures and prototypes of simple and smart micro-hotplate and Infra Red (IR) based emitters for NDIR (Non Dispersive IR) gas sensors in harsh environments. © 2012 IEEE.

Reconstruction of arbitrary biochemical reaction networks: A compressive sensing approach

Reconstruction of biochemical reaction networks (BRN) and genetic regulatory networks (GRN) in particular is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with ODE model descriptions containing polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated parameters. To solve the network reconstruction problem, we cast it as a compressive sensing (CS) problem and use sparse Bayesian learning (SBL) algorithms as a computationally efficient and robust way to obtain its solution. © 2012 IEEE.

A Novel System for Reducing Turbo-Lag by Injection of Compressed Gas into the Exhaust Manifold

A key challenge in achieving good transient performance of highly boosted engines is the difficulty of accelerating the turbocharger from low air flow conditions (“turbo lag”). Multi-stage turbocharging, electric turbocharger assistance, electric compressors and hybrid powertrains are helpful in the mitigation of this deficit, but these technologies add significant cost and integration effort. Air-assist systems have the potential to be more cost-effective. Injecting compressed air into the intake manifold has received considerable attention, but the performance improvement offered by this concept is severely constrained by the compressor surge limit. The literature describes many schemes for generating the compressed gas, often involving significant mechanical complexity and/or cost. In this paper we demonstrate a novel exhaust assist system in which a reservoir is charged during braking. Experiments have been conducted using a 2.0 litre light-duty Diesel engine equipped with exhaust gas recirculation (EGR) and variable geometry turbine (VGT) coupled to an AC transient dynamometer, which was controlled to mimic engine load during in-gear braking and acceleration. The experimental results confirm that the proposed system reduces the time to torque during the 3rd gear tip-in by around 60%. Such a significant improvement was possible due to the increased acceleration of turbocharger immediately after the tip-in. Injecting the compressed gas into the exhaust manifold circumvents the problem of compressor surge and is the key enabler of the superior performance of the proposed concept.

A novel system for reducing turbo-lag by injection of compressed gas into the exhaust manifold

A key challenge in achieving good transient performance of highly boosted engines is the difficulty of accelerating the turbocharger from low air flow conditions (turbo lag). Multi-stage turbocharging, electric turbocharger assistance, electric compressors and hybrid powertrains are helpful in the mitigation of this deficit, but these technologies add significant cost and integration effort. Air-assist systems have the potential to be more cost-effective. Injecting compressed air into the intake manifold has received considerable attention, but the performance improvement offered by this concept is severely constrained by the compressor surge limit. The literature describes many schemes for generating the compressed gas, often involving significant mechanical complexity and/or cost. In this paper we demonstrate a novel exhaust assist system in which a reservoir is charged during braking. Experiments have been conducted using a 2.0 litre light-duty Diesel engine equipped with exhaust gas recirculation (EGR) and variable geometry turbine (VGT) coupled to an AC transient dynamometer, which was controlled to mimic engine load during in-gear braking and acceleration. The experimental results confirm that the proposed system reduces the time to torque during the 3rd gear tip-in by around 60%. Such a significant improvement was possible due to the increased acceleration of turbocharger immediately after the tip-in. Injecting the compressed gas into the exhaust manifold circumvents the problem of compressor surge and is the key enabler of the superior performance of the proposed concept. Copyright © 2013 SAE International.

Nanotubes complexed with DNA and proteins for resistive-pulse sensing

We use a resistive-pulse technique to analyze molecular hybrids of single-wall carbon nanotubes (SWNTs) wrapped in either single-stranded DNA or protein. Electric fields confined in a glass capillary nanopore allow us to probe the physical size and surface properties of molecular hybrids at the single-molecule level. We find that the translocation duration of a macromolecular hybrid is determined by its hydrodynamic size and solution mobility. The event current reveals the effects of ion exclusion by the rod-shaped hybrids and possible effects due to temporary polarization of the SWNT core. Our results pave the way to direct sensing of small DNA or protein molecules in a large unmodified solid-state nanopore by using nanofilaments as carriers. © 2013 American Chemical Society.