Real-time high-resolution heterodyne-based measurements of spectral dynamics in fibre lasers

Conventional tools for measurement of laser spectra (e.g. optical spectrum analysers) capture data averaged over a considerable time period. However, the generation spectrum of many laser types may involve spectral dynamics whose relatively fast time scale is determined by their cavity round trip period, calling for instrumentation featuring both high temporal and spectral resolution. Such real-time spectral characterisation becomes particularly challenging if the laser pulses are long, or they have continuous or quasi-continuous wave radiation components. Here we combine optical heterodyning with a technique of spatiooral intensity measurements that allows the characterisation of such complex sources. Fast, round-trip-resolved spectral dynamics of cavity-based systems in real-time are obtained, with temporal resolution of one cavity round trip and frequency resolution defined by its inverse (85 ns and 24 MHz respectively are demonstrated). We also show how under certain conditions for quasi-continuous wave sources, the spectral resolution could be further increased by a factor of 100 by direct extraction of phase information from the heterodyned dynamics or by using double time scales within the spectrogram approach.

Localized surface plasmon resonance biosensors for real-time biomolecular binding study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ∼7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Development of hardware in the loop real-time control techniques for hybrid power systems involving distributed demands and sustainable energy sources

The future power grid will effectively utilize renewable energy resources and distributed generation to respond to energy demand while incorporating information technology and communication infrastructure for their optimum operation. This dissertation contributes to the development of real-time techniques, for wide-area monitoring and secure real-time control and operation of hybrid power systems. ^ To handle the increased level of real-time data exchange, this dissertation develops a supervisory control and data acquisition (SCADA) system that is equipped with a state estimation scheme from the real-time data. This system is verified on a specially developed laboratory-based test bed facility, as a hardware and software platform, to emulate the actual scenarios of a real hybrid power system with the highest level of similarities and capabilities to practical utility systems. It includes phasor measurements at hundreds of measurement points on the system. These measurements were obtained from especially developed laboratory based Phasor Measurement Unit (PMU) that is utilized in addition to existing commercially based PMU’s. The developed PMU was used in conjunction with the interconnected system along with the commercial PMU’s. The tested studies included a new technique for detecting the partially islanded micro grids in addition to several real-time techniques for synchronization and parameter identifications of hybrid systems. ^ Moreover, due to numerous integration of renewable energy resources through DC microgrids, this dissertation performs several practical cases for improvement of interoperability of such systems. Moreover, increased number of small and dispersed generating stations and their need to connect fast and properly into the AC grids, urged this work to explore the challenges that arise in synchronization of generators to the grid and through introduction of a Dynamic Brake system to improve the process of connecting distributed generators to the power grid.^ Real time operation and control requires data communication security. A research effort in this dissertation was developed based on Trusted Sensing Base (TSB) process for data communication security. The innovative TSB approach improves the security aspect of the power grid as a cyber-physical system. It is based on available GPS synchronization technology and provides protection against confidentiality attacks in critical power system infrastructures. ^

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

Assessment of the cortisol awakening response: Real-time analysis and curvilinear effects of sample timing inaccuracy

The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting.

Real-time depth camera tracking with CAD models and ICP

In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Localized Surface Plasmon Resonance Biosensors for Real-Time Biomolecular Binding Study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ~7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Communication Architecture Design for Real-Time Networked Control Systems

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs