Combined ultrasound and temperature sensor using a fibre Bragg grating

We demonstrate the feasibility of using in-fibre Bragg gratings to measure MHz acoustic fields and temperature simultaneously. We achieved a noise-limited pressure resolution of ˜4.5×10-4 Atm/vHz and a temperature resolution of 0.2°C.

Resonant cavity Fibre Bragg grating sensor interrogation

This thesis presents a novel high-performance approach to time-division-multiplexing (TDM) fibre Bragg grating (FBG) optical sensors, known as the resonant cavity architecture. A background theory of FBG optical sensing includes several techniques for multiplexing sensors. The limitations of current wavelength-division-multiplexing (WDM) schemes are contrasted against the technological and commercial advantage of TDM. The author’s hypothesis that ‘it should be possible to achieve TDM FBG sensor interrogation using an electrically switched semiconductor optical amplifier (SOA)’ is then explained. Research and development of a commercially viable optical sensor interrogator based on the resonant cavity architecture forms the remainder of this thesis. A fully programmable SOA drive system allows interrogation of sensor arrays 10km long with a spatial resolution of 8cm and a variable gain system provides dynamic compensation for fluctuating system losses. Ratiometric filter- and diffractive-element spectrometer-based wavelength measurement systems are developed and analysed for different commercial applications. The ratiometric design provides a low-cost solution that has picometre resolution and low noise using 4% reflective sensors, but is less tolerant to variation in system loss. The spectrometer design is more expensive, but delivers exceptional performance with picometre resolution, low noise and tolerance to 13dB system loss variation. Finally, this thesis details the interrogator’s peripheral components, its compliance for operation in harsh industrial environments and several examples of commercial applications where it has been deployed. Applications include laboratory instruments, temperature monitoring systems for oil production, dynamic control for wind-energy and battery powered, self-contained sub-sea strain monitoring.

Fibre Bragg grating sensor interrogation using an acousto-optic tunable filter and low-coherence interferometry

We describe how an acousto-optic tunable filter can be used to both demultiplex the signals from multiple fibre Bragg grating sensors and simultaneously provide wide bandwidth signal demodulation in a system using interferometric wavelength shift detection. In an experimental demonstration, the approach provided a noise limited strain resolution of 24.9 n epsilon Hz(-1/ 2) at 15 Hz.

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.

Combined ultrasound and temperature sensor using a fibre Bragg grating

The feasibility of in-fiber Bragg gratings for simultaneous acoustic field and temperature sensing was demonstrated. A noise-limited pressure resolution of about 4.5×10-4 Atm/√Hz and a temperature resolution of 0.2 °C was achieved.

Fibre Bragg grating sensor interrogation using an acousto-optic tunable filter and low-coherence interferometry

We describe how an acousto-optic tunable filter can be used to both demultiplex the signals from multiple fibre Bragg grating sensors and simultaneously provide wide bandwidth signal demodulation in a system using interferometric wavelength shift detection. In an experimental demonstration, the approach provided a noise limited strain resolution of 24.9 nε Hz -1/2 at 15 Hz. © 2007 IOP Publishing Ltd.

An adaptive code position modulation scheme for wireless sensor networks

In this letter, an energy-efficient adaptive code position modulation scheme is proposed for wireless sensor networks to provide the relatively stable bit error ratio (BER) performance expected by the upper layers. The system is designed with focus on the adaptive control of transmission power, which is adjusted based on the measured power density of background noise. Interfaces among the modulation module, packet scheduling module and upper layer are provided for flexible adjustments to adapt to the background noise and deliver expected application quality. Simulations with Signal Processing Worksystem (SPW) validate the effectiveness of the scheme. © 2005 IEEE.

Optical sensor interrogation with a blazed fiber Bragg grating and a charge-coupled device linear array

We present what is to our knowledge the first comprehensive investigation of the use of blazed fiber Bragg gratings (BFBGs) to interrogate wavelength division multiplexed (WDM) in-fiber optical sensor arrays. We show that the light outcoupled from the core of these BFBGs is radiated with sufficient optical power that it may be detected with a low-cost charge-coupled device (CCD) array. We present thorough system performance analysis that shows sufficient spectral-spatial resolution to decode sensors with a WDM separation of 75 ρm, signal-to-noise ratio greater than 45-dB bandwidth of 70 nm, and drift of only 0.1 ρm. We show the system to be polarization-state insensitive, making the BFBG-CCD spectral analysis technique a practical, extremely low-cost, alternative to traditional tunable filter approaches.

High accuracy interrogation of a WDM FBG sensor array using radiation modes from a B-FBG

We report the development of a WDM optical sensor array interrogation system using the radiation modes from a BFBG. We present results indicating 70nm bandwidth, with 0.2um RMS noise and a minimum WDM spacing of 30um. We further show the system to be polarization independent.

Resonant cavity time-division-multiplexed fiber Bragg grating sensor interrogator

The first resonant-cavity time-division-multiplexed (TDM) fiber Bragg grating sensor interrogation system is reported. This novel design uses a pulsed semiconductor optical amplifier in a cyclic manner to function as the optical source, amplifier, and modulator. Compatible with a range of standard wavelength detection techniques, this optically gated TDM system allows interrogation of low reflectivity "commodity" sensors spaced just 2 m apart, using a single active component. Results demonstrate an exceptional optical signal-to-noise ratio of 36 dB, a peak signal power of over +7 dBm, and no measurable crosstalk between sensors. Temperature tuning shows that the system is fully stable with a highly linear response. © 2004 IEEE.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Autonomic and embedded wireless sensor protocols for critical infrastructures

The tragic events of September 11th ushered a new era of unprecedented challenges. Our nation has to be protected from the alarming threats of adversaries. These threats exploit the nation's critical infrastructures affecting all sectors of the economy. There is the need for pervasive monitoring and decentralized control of the nation's critical infrastructures. The communications needs of monitoring and control of critical infrastructures was traditionally catered for by wired communication systems. These technologies ensured high reliability and bandwidth but are however very expensive, inflexible and do not support mobility and pervasive monitoring. The communication protocols are Ethernet-based that used contention access protocols which results in high unsuccessful transmission and delay. An emerging class of wireless networks, named embedded wireless sensor and actuator networks has potential benefits for real-time monitoring and control of critical infrastructures. The use of embedded wireless networks for monitoring and control of critical infrastructures requires secure, reliable and timely exchange of information among controllers, distributed sensors and actuators. The exchange of information is over shared wireless media. However, wireless media is highly unpredictable due to path loss, shadow fading and ambient noise. Monitoring and control applications have stringent requirements on reliability, delay and security. The primary issue addressed in this dissertation is the impact of wireless media in harsh industrial environment on the reliable and timely delivery of critical data. In the first part of the dissertation, a combined networking and information theoretic approach was adopted to determine the transmit power required to maintain a minimum wireless channel capacity for reliable data transmission. The second part described a channel-aware scheduling scheme that ensured efficient utilization of the wireless link and guaranteed delay. Various analytical evaluations and simulations are used to evaluate and validate the feasibility of the methodologies and demonstrate that the protocols achieved reliable and real-time data delivery in wireless industrial networks.

Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space

Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.

Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks

In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

MODELING OF TRANSFER PATH FOR DETERMINATION OF COMBUSTION AND NOISE METRICS ON DIESEL ENGINES

Determination of combustion metrics for a diesel engine has the potential of providing feedback for closed-loop combustion phasing control to meet current and upcoming emission and fuel consumption regulations. This thesis focused on the estimation of combustion metrics including start of combustion (SOC), crank angle location of 50% cumulative heat release (CA50), peak pressure crank angle location (PPCL), and peak pressure amplitude (PPA), peak apparent heat release rate crank angle location (PACL), mean absolute pressure error (MAPE), and peak apparent heat release rate amplitude (PAA). In-cylinder pressure has been used in the laboratory as the primary mechanism for characterization of combustion rates and more recently in-cylinder pressure has been used in series production vehicles for feedback control. However, the intrusive measurement with the in-cylinder pressure sensor is expensive and requires special mounting process and engine structure modification. As an alternative method, this work investigated block mounted accelerometers to estimate combustion metrics in a 9L I6 diesel engine. So the transfer path between the accelerometer signal and the in-cylinder pressure signal needs to be modeled. Depending on the transfer path, the in-cylinder pressure signal and the combustion metrics can be accurately estimated - recovered from accelerometer signals. The method and applicability for determining the transfer path is critical in utilizing an accelerometer(s) for feedback. Single-input single-output (SISO) frequency response function (FRF) is the most common transfer path model; however, it is shown here to have low robustness for varying engine operating conditions. This thesis examines mechanisms to improve the robustness of FRF for combustion metrics estimation. First, an adaptation process based on the particle swarm optimization algorithm was developed and added to the single-input single-output model. Second, a multiple-input single-output (MISO) FRF model coupled with principal component analysis and an offset compensation process was investigated and applied. Improvement of the FRF robustness was achieved based on these two approaches. Furthermore a neural network as a nonlinear model of the transfer path between the accelerometer signal and the apparent heat release rate was also investigated. Transfer path between the acoustical emissions and the in-cylinder pressure signal was also investigated in this dissertation on a high pressure common rail (HPCR) 1.9L TDI diesel engine. The acoustical emissions are an important factor in the powertrain development process. In this part of the research a transfer path was developed between the two and then used to predict the engine noise level with the measured in-cylinder pressure as the input. Three methods for transfer path modeling were applied and the method based on the cepstral smoothing technique led to the most accurate results with averaged estimation errors of 2 dBA and a root mean square error of 1.5dBA. Finally, a linear model for engine noise level estimation was proposed with the in-cylinder pressure signal and the engine speed as components.