Novel dual transient temperature modulation technique for multi-vapour detection

Here we present a novel signal processing technique for a square wave thermally-modulated carbon black/polymer composite chemoresistor. The technique consists of only two mathematical operations: summing the off-transient and on-transient conductance signals; and subtracting the steady-state conductance signal. A single carbon black/polyvinylpyrrolidone composite chemo -resistor was fabricated and used to demonstrate the validity of the technique. Classification of water, methanol and ethanol vapours was successfully performed using only the peak time of the resultant curves. Quantification of the different vapours was also possible using the height of the peaks, because it was linearly proportional to concentration. This technique does not require zero-gas calibration and thus is superior to previously reported methods. ©2009 IEEE.

A novel, sensitive potentiometric hydrocarbon sensor for high-vacuum applications.

A potentiometric device based on interfacing a solid electrolyte oxygen ion conductor with a thin platinum film acts as a robust, reproducible sensor for the detection of hydrocarbons in high- or ultrahigh-vacuum environments. Sensitivities in the order of approximately 5 x 10(-10) mbar are achievable under open circuit conditions, with good selectivity for discrimination between n-butane on one hand and toluene, n-octane, n-hexane, and 1-butene on the other hand. The sensor's sensitivity may be tuned by operating under constant current (closed circuit) conditions; injection of anodic current is also a very effective means of restoring a clean sensing surface at any desired point. XPS data and potentiometric measurements confirm the proposed mode of sensing action: the steady-state coverage of Oa, which sets the potential of the Pt sensing electrode, is determined by the partial pressure and dissociative sticking probability of the impinging hydrocarbon. The principles established here provide the basis for a viable, inherently flexible, and promising means for the sensitive and selective detection of hydrocarbons under demanding conditions.

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.

Dynamic modulation of detection window in conducting polymer based biosensors.

Here we demonstrate a novel application that employs the ion exchange properties of conducting polymers (CP) to modulate the detection window of a CP based biosensor under electrical stimuli. The detection window can be modulated by electrochemically controlling the degree of swelling of the CP associated with ion transport in and out of the polymer. We show that the modulation in the detection window of a caffeine imprinted polypyrrole biosensor, and by extension other CP based biosensors, can be achieved with this mechanism. Such dynamic modulation in the detection window has great potential for the development of smart biosensors, where the sensitivity of the sensor can be dynamically optimized for a specific test solution.

Detection of molecular interactions with modified ferrocene self-assembled monolayers.

Ferrocene-terminated self-assembled monolayers (Fc-SAMs) are one of the most studied molecular aggregates on metal electrodes. They are easy to fabricate and provide a stable and reproducible system to investigate the effect of the microenvironment on the electron transfer parameters. We propose a novel application for Fc-SAMs, the detection of molecular interactions, based on the modification of the SAM with target-specific receptors. Mixed SAMs were fabricated by coimmobilization on Au electrodes of thiolated alkane chains with three different head groups: hydroxy terminating head group, ferrocene head group, and a functional head group such as biotin. Upon binding, the intrinsic electric charge of the target (e.g., streptavidin) modifies the electrostatic potential at the plane of electron transfer, causing a shift in the formal potential E degrees '. The SAMs were characterized by AC voltammetry. The detection mechanism is confirmed by measurements of formal potential as a function of electrolyte pH.

Single-photon detection using a quantum dot field effect transistor

A novel device for detection of single photons based on a GaAs/AlGaAs modulation doped field effect transistor (MODFET) which does not rely on avalanche processes is proposed. The optimal channel electron densities and quantum dot parameters for detection of single photons are discussed.

Novel designs for Penning ion traps

A number of alternative designs are presented for Penning ion traps suitable for quantum information processing (QIP) applications with atomic ions. The first trap design is a simple array of long straight wires, which allows easy optical access. A prototype of this trap has been built to trap Ca+ and a simple electronic detection scheme has been employed to demonstrate the operation of the trap. Another trap design consists of a conducting plate with a hole in it situated above a continuous conducting plane. The final trap design is based on an array of pad electrodes. Although this trap design lacks the open geometry of the other traps described above, the pad design may prove useful in a hybrid scheme in which information processing and qubit storage take place in different types of trap. The behaviour of the pad traps is simulated numerically and techniques for moving ions rapidly between traps are discussed. Future experiments with these various designs are discussed. All of the designs lend themselves to the construction of multiple trap arrays, as required for scalable ion trap QIP.

Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

Post-earthquake structural safety evaluations are currently performed manually by a team of certified inspectors and/or structural engineers. This process is time-consuming and costly, keeping owners and occupants from returning to their businesses and homes. Automating these evaluations would enable faster, and potentially more consistent, relief and response processes. In order to do this, the detection of exposed reinforcing steel is of utmost significance. This paper presents a novel method of detecting exposed reinforcement in concrete columns for the purpose of advancing practices of structural and safety evaluation of buildings after earthquakes. Under this method, the binary image of the reinforcing area is first isolated using a state-of-the-art adaptive thresholding technique. Next, the ribbed regions of the reinforcement are detected by way of binary template matching. Finally, vertical and horizontal profiling are applied to the processed image in order to filter out any superfluous pixels and take into consideration the size of reinforcement bars in relation to that of the structural element within which they reside. The final result is the combined binary image disclosing only the regions containing rebar overlaid on top of the original image. The method is tested on a set of images from the January 2010 earthquake in Haiti. Preliminary test results convey that most exposed reinforcement could be properly detected in images of moderately-to-severely damaged concrete columns.

Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.