Embedded fibre Bragg grating array sensors in aluminium alloy matrix by ultrasonic consolidation

Fibre Bragg Grating (FBG) array sensors have been successfully embedded in aluminium alloy matrix by ultrasonic consolidation (UC) technique. The temperature and loading responses of the embedded FBG arrays have been systematically characterised. The embedded grating sensors exhibit an average temperature sensitivity of ~36pm/°C, which is three times higher than that of normal FBGs, and a loading responsivity of ~0.1nm/kg within the dynamic range from 0kg to 3kg. This initial experiment clearly demonstrates that FBG array sensors can be embedded in metal matrix together with other passive and active fibres to fabricate smart materials to monitor the operation and health of engineering structures.

Miles v Insitu Cleaning 2012

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Ultrasonic field and temperature sensor based on short in-fibre Bragg gratings

The authors demonstrate that in-fibre Bragg gratings may be successfully used to measure megahertz acoustic fields if the grating length is sufficiently short and the optical fibre is appropriately desensitised. A noise-limited pressure resolution of 4.5 × 10 –3 atm vHz was found. The capability to simultaneously act as a temperature sensor is also demonstrated.

An energy-efficient route to the rapid synthesis of organically-modified SBA-15 via ultrasonic template removal

A low energy route for the removal of Pluronic P123 surfactant template during the synthesis of SBA-15 mesoporous silicas is explored. The conventional reflux of the hybrid inorganic-organic intermediate formed during co-condensation routes to Pr-SOH-SBA-15 is slow, utilises large solvent volumes, and requires 24 h to remove ∼90% of the organic template. In contrast, room temperature ultrasonication in a small methanol volume achieves the same degree of template extraction in only 5 min, with a 99.9% energy saving and 90% solvent reduction, without compromising the textural, acidic or catalytic properties of the resultant Pr-SOH-SBA-15. © 2014 The Royal Society of Chemistry.

Ultrasonic hydrophone based on short in-fiber Bragg gratings

We investigate the feasibility of using in-fiber Bragg gratings for measuring acoustic fields in the megahertz range. We found that the acoustic coupling from the ultrasonic field to the grating leads to the formation of standing waves in the fiber. Because of these standing waves, the system response is complex and, as we show, the grating does not act as an effective probe. However, significant improvement in its performance can be gained by use of short gratings coupled with an appropriate desensitization of the fiber. A noise-limited pressure resolution of ˜4.5 × 10-3 atm/vHz was found.

Embedded fibre Bragg grating array sensors in aluminium alloy matrix by ultrasonic consolidation

Fibre Bragg Grating (FBG) array sensors have been successfully embedded in aluminium alloy matrix by ultrasonic consolidation (UC) technique. The temperature and loading responses of the embedded FBG arrays have been systematically characterised. The embedded grating sensors exhibit an average temperature sensitivity of ~36pm/°C, which is three times higher than that of normal FBGs, and a loading responsivity of ~0.1nm/kg within the dynamic range from 0kg to 3kg. This initial experiment clearly demonstrates that FBG array sensors can be embedded in metal matrix together with other passive and active fibres to fabricate smart materials to monitor the operation and health of engineering structures.

The effect of self-focusing on laser space-debris cleaning

A ground-based laser system for space-debris cleaning will use powerful laser pulses that can self-focus while propagating through the atmosphere. We demonstrate that for the relevant laser parameters, this self-focusing can noticeably decrease the laser intensity on the target. We show that the detrimental effect can be, to a great extent, compensated for by applying the optimal initial beam defocusing. The effect of laser elevation on the system performance is discussed.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Ultrasonic measurement of anisotropic reflectivity from Water-Phenolic CE Interface

In this contribution, we experimentally test the effects of azimuth and tilt angle on the acoustic reflectivity of a liquid- anisotropic solid interface. For this study, we are using a large source transducer, and acquired data for samples with different tilt angles. We use Phenolic CE material, which is known to have orthorhombic symmetry. Our results show that changes of the tilt angle produce important variations on the reflectivity that are larger as the tilt increases. The most remarkable feature is the change of the critical angle with the azimuth, which shows a larger spread for larger tilts. The spectral components of the acquired waveforms also show characteristic features linked to the location of the critical angle, we particularly observed a drop in the peak frequency. These observations suggest that care must be taken about the interpretation and inversion of observed incidence and azimuth dependent seismic reflectivities and critical angles in obtaining information on a formation's anisotropy. Zip archive contains four segy files: - LAB_TI00, is not tilted sample in contact with water, - LAB_TI30, is 30degrees tilted sample in contact with water, - LAB_TI45, is 45 degrees tilted sample in contact with water, - LAB_TI90, is 90 degrees tilted sample in contact with water.

The effect of ultrasonic waves on the phase behavior of a surfactant-brine-oil system

Acknowledgements The authors would like to gratefully acknowledge and appreciate the School of Engineering, University of Aberdeen, Aberdeen, Scotland, UK, for the provision of the laboratory facilities necessary for completing this work.

The effect of ultrasonic waves on the phase behavior of a surfactant-brine-oil system

Acknowledgements The authors would like to gratefully acknowledge and appreciate the School of Engineering, University of Aberdeen, Aberdeen, Scotland, UK, for the provision of the laboratory facilities necessary for completing this work.

Woman cleaning apartment window

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].