Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

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.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.

Enhancement of Construction Equipment Detection in Video Frames by Combining with Tracking

Vision-based object detection has been introduced in construction for recognizing and locating construction entities in on-site camera views. It can provide spatial locations of a large number of entities, which is beneficial in large-scale, congested construction sites. However, even a few false detections prevent its practical applications. In resolving this issue, this paper presents a novel hybrid method for locating construction equipment that fuses the function of detection and tracking algorithms. This method detects construction equipment in the video view by taking advantage of entities' motion, shape, and color distribution. Background subtraction, Haar-like features, and eigen-images are used for motion, shape, and color information, respectively. A tracking algorithm steps in the process to make up for the false detections. False detections are identified by catching drastic changes in object size and appearance. The identified false detections are replaced with tracking results. Preliminary experiments show that the combination with tracking has the potential to enhance the detection performance.

Construction Worker Detection in Video Frames for Initializing Vision Trackers

Monitoring the location of resources on large scale, congested, outdoor sites can be performed more efficiently with vision tracking, as this approach does not require any pre-tagging of resources. However, the greatest impediment to the use of vision tracking in this case is the lack of detection methods that are needed to automatically mark the resources of interest and initiate the tracking. This paper presents such a novel method for construction worker detection that localizes construction workers in video frames. The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively. The three cues are characterized by using background subtraction, the histogram of oriented gradients (HOG), and the HSV color histogram. The method has been tested on videos taken in various environments. The results demonstrate its suitability for automatic initialization of vision trackers.

Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

An anomaly detection approach is considered for the mine hunting in sonar imagery problem. The authors exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of 'normal' natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier. © 2012 The Institution of Engineering and Technology.

Graduate education for leadership and organisational change in sustainable development

This paper reflects on the motivation, method and effectiveness of teaching leadership and organisational change to graduate engineers. Delivering progress towards sustainable development requires engineers who are aware of pressing global issues (such as resource depletion, climate change, social inequity and an interdependent economy) since it is they who deliver the goods and services that underpin society within these constraints. They also must understand how to implement change in the organisations within which they will work. In recognition of this fact the Cambridge University MPhil in Engineering for Sustainable Development has focussed on educating engineers to become effective change agents in their professional field with the confidence to challenge orthodoxy in adopting traditional engineering solutions. This paper reflects on ten years of delivering a special module to review how teaching change management and leadership aspects of the programme have evolved and progressed over that time. As the students who embark on this professional practice have often extensive experience as practising engineers and scientists, many have already learned the limitations of their technical background when solving complex problems. Students often join the course recognising their need to broaden their knowledge of relevant cross-disciplinary skills. The programme offers an opportunity for these early to mid-career engineers to explore an ethical and value-based approach to bringing about effective change in their particular sectors and organisations. This is achieved through action learning assignments in combination with reflections on the theory of change to enable students to equip themselves with tools that help them to be effective in making their professional and personal life choices. This paper draws on feedback gathered from students during their participation on the programme and augments this with alumni reflections gathered some years after their graduation. These professionals are able to look back on their experience of the taught components and reflect on how they have been able to apply this key learning in their subsequent careers. Copyright © 2012 September.

Electrical detection of trapped charge displacements using an ultra-thin SOI percolation transistor

Electrical detection of solid-state charge qubits requires ultrasensitive charge measurement, typically using a quantum point contact or single-electron-transistor, which imposes strict limits on operating temperature, voltage and current. A conventional FET offers relaxed operating conditions, but the back-action of the channel charge is a problem for such small quantum systems. Here, we discuss the use of a percolation transistor as a measurement device, with regard to charge sensing and backaction. The transistor is based on a 10nm thick SOI channel layer and is designed to measure the displacement of trapped charges in a nearby dielectric. At cryogenic temperatures, the trapped charges result in strong disorder in the channel layer, so that current is constrained to a percolation pathway in sub-threshold conditions. A microwave driven spatial Rabi oscillation of the trapped charge causes a change in the percolation pathway, which results in a measurable change in channel current. © The Electrochemical Society.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

Direct detection of molecular biorecognition by dipole sensing mechanism.

This work investigates the feasibility of transducing molecular-recognition events into a measurable potentiometric signal. It is shown for the first time that biorecognition of acetylcholine (ACh) can be translated to conformational changes in the enzyme, acetylcholine-esterase (AChE), which in turn induces a measurable change in surface potential. Our results show that a highly sensitive detector for ACh can be obtained by the dilute assembly of AChE on a floating gate derived field effect transistor (FG-FET). A wide concentration range response is observed for ACh (10(-2)-10(-9)M) and for the inhibitor carbamylcholine CCh (10(-6)-10(-11)M). These enhanced sensitivities are modeled theoretically and explained by the combined response of the device to local pH changes and molecular dipole variations due to the enzyme-substrate recognition event.

Weighted least squares with expectation-maximization algorithm for burst detection in U.K. water distribution systems

Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectationmaximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g., water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company's records of pipeline reparation work. © 2014 American Society of Civil Engineers.

An ELISA-like time-resolved fluorescence immunoassay for microcystin detection

Background: A time-resolved fluorescence immunoassay (TRFIA), based on anti-microcystin-LR (MCLR) monoclonal antibodies (MAbs) and europium-labeled antimouse IgG conjugate, was first developed for microcystin detection. Methods: Anti-MCLR MAbs were prepared by a standard method, and the attained MAbs showed a good cross reactivity with MCLR, MCRR and MCYR. The TRFIA was performed in an indirect competitive mode. The detection method of TRFIA was compared with indirect competitive enzyme-linked immunosorbent assay (ELISA) and high-performance liquid chromatography (HPLC). Results: The TRFIA exhibited a typical sigmoidal response for MCLR at concentrations of 0.005-50 ng/ml, with a wide quantitative range between 0.01 and 10 ng/ml, indicating the broadest detective range and the most sensitive of all the methods for microcystins (MCs) detection. Additionally, the TRFIA maintained good reliability through its quantitative range, as evidenced by low coefficients of variation (1.6-12.2%). The toxin data of algal samples assayed from TRFIA were in the same range as those with ELISA and HPLC, implying that the method was reliable and practical for the detection of MCs. Conclusions: The TRFIA may offer a valuable alternative or a substitute for conventional ELISA for microcystin detection. (C) 2004 Elsevier B.V. All rights reserved.

Comparison between visual and passive acoustic detection of finless porpoises in the Yangtze river, China

Recently, sonar signals and other sounds produced by cetaceans have been used for acoustic detection of individuals and groups in the wild. However, the detection probability ascertained by concomitant visual survey has not been demonstrated extensively. The finless porpoises (Neophocaena phocaenoides) have narrow band and high-frequency sonar signals, which are distinctive from background noises. Underwater sound monitoring with hydrophones (B&K8103) placed along the sides of a research vessel, concurrent with visual observations was conducted in the Yangtze River from Wuhan to Poyang Lake in 1998 in China. The peak to peak detection threshold was set at 133 dB re 1 mu Pa. With this threshold level, porpoises could be detected reliably within 300 m of the hydrophone. In a total of 774-km cruise, 588 finless porpoises were sighted by visual observation and 44 864 ultrasonic pulses were recorded by the acoustical observation system. The acoustic monitoring system could detect the presence of the finless porpoises 82% of the time. A false alarm in the system occurred with a frequency of 0.9%. The high-frequency acoustical observation is suggested as an effective method for field surveys of small cetaceans, which produce high-frequency sonar signals. (C) 2001 Acoustical Society of America.

Measuring spin diffusion of electrons in bulk n-GaAs using circularly dichromatic absorption difference spectroscopy of spin gratings

Circular dichromatic absorption difference spectroscopy is developed to measure the spin diffusion dynamics of electrons in bulk n-GaAs. This spectroscopy has higher detection sensitivity over homodyne detection of spin-grating-diffracted signal. A model to describe circular dichromatic absorption difference signal is derived and used to fit experimental signal to retrieve decaying rate of spin gratings. A spin diffusion constant of D-s=201 +/- 25 cm(2)/s for bulk n-GaAs has been measured at room temperature using this technique and is close to electron diffusion constant (D-c), which is much different from the case in GaAs quantum wells where D-s is markedly less than D-c.

Transfer and detection of single electrons using metal-oxide-semiconductor field-effect transistors

A single-electron turnstile and electrometer circuit was fabricated on a silicon-on-insulator substrate. The turnstile, which is operated by opening and closing two metal-oxide-semiconductor field-effect transistors (MOSFETs) alternately, allows current quantization at 20 K due to single-electron transfer. Another MOSFET is placed at the drain side of the turnstile to form an electron storage island. Therefore, one-by-one electron entrance into the storage island from the turnstile can be detected as an abrupt change in the current of the electrometer, which is placed near the storage island and electrically coupled to it. The correspondence between the quantized current and the single-electron counting was confirmed.