Characterisation of cascaded Raman-assisted fibre optical parametric amplifiers using WDM QPSK signals

We report the first WDM numerical characterisation of crosstalk growth in cascaded Raman-Assisted Fibre Optical Parametric Amplifiers (RA-FOPAs). A cascade of ten RA-FOPAs results in ∼13dB lower crosstalk than the equivalent cascade of conventional FOPAs.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Calcium/Calmodulin-Dependent Protein Kinase II Serves as a Biochemical Integrator of Calcium Signals for the Induction of Synaptic Plasticity

Repetitive Ca2+ transients in dendritic spines induce various forms of synaptic plasticity by transmitting information encoded in their frequency and amplitude. CaMKII plays a critical role in decoding these Ca2+ signals to initiate long-lasting synaptic plasticity. However, the properties of CaMKII that mediate Ca2+ decoding in spines remain elusive. Here, I measured CaMKII activity in spines using fast-framing two-photon fluorescence lifetime imaging. Following each repetitive Ca2+ elevations, CaMKII activity increased in a stepwise manner. This signal integration, at the time scale of seconds, critically depended on Thr286 phosphorylation. In the absence of Thr286 phosphorylation, only by increasing the frequency of repetitive Ca2+ elevations could high peak CaMKII activity or plasticity be induced. In addition, I measured the association between CaMKII and Ca2+/CaM during spine plasticity induction. Unlike CaMKII activity, association of Ca2+/CaM to CaMKII plateaued at the first Ca2+ elevation event. This result indicated that integration of Ca2+ signals was initiated by the binding of Ca2+/CaM and amplified by the subsequent increases in Thr286-phosphorylated form of CaMKII. Together, these findings demonstrate that CaMKII functions as a leaky integrator of repetitive Ca2+ signals during the induction of synaptic plasticity, and that Thr286 phosphorylation is critical for defining the frequencies of such integration.

An Epistemic Event-based Correlation Scheme for Pervasive Network Management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Degradation rates and productivity signals of organic-walled dinoflagellate cyst species in surface sediment samples

To understand the role of the ocean within the global carbon cycle, detailed information is required on key-processes within the marine carbon cycle; bio-production in the upper ocean, export of the produced material to the deep ocean and the storage of carbon in oceanic sediments. Quantification of these processes requires the separation of signals of net primary production and the rate of organic matter decay as reflected in fossil sediments. This study examines the large differences in degradation rates of organic-walled dinoflagellate cyst species to separate these degradation and productivity signals. For this, accumulation rates of cyst species known to be resistant (R-cysts) or sensitive (S-cysts) to aerobic degradation of 62 sites are compared to mean annual chlorophyll-a, sea-surface temperature, sea-surface salinity, nitrate and phosphate concentrations of the upper waters and deep-water oxygen concentrations. Furthermore, the degradation of sensitive cysts, as expressed by the degradation constant k and reaction time t, has been related to bottom water [O2]. The studied sediments were taken from the Arabian Sea, north-western African Margin (North Atlantic), western-equatorial Atlantic Ocean/Caraibic, south-western African margin (South Atlantic) and Southern Ocean (Atlantic sector). Significant relationships are observed between (a) accumulation rates of R-cysts and upper water chlorophyll-a concentrations, (b) accumulation rates of S-cysts and bottom water [O2] and (c) degradation rates of S-cysts (kt) and bottom water [O2]. Relationships that are extremely weak or are clearly insignificant on all confidence intervals are between (1) S-cyst accumulation rates and chlorophyll-a concentrations, sea-surface temperature (SST), sea-surface salinity (SSS), phosphate concentrations (P) and nitrate concentrations (N), (2) between R-cyst accumulation rates and bottom water [O2], SST, SSS, P and N, and between (3) kt and water depth. Co-variance is present between the parameters N and P, N, P and chlorophyll-a, oxygen and water depth. Correcting for this co-variance does not influence the significance of the relationship given above. The possible applicability of dinoflagellate cyst degradation to estimate past net primary production and deep ocean ventilation is discussed.

Subfornical organ neurons integrate cardiovascular and metabolic signals

The subfornical organ (SFO) is a critical circumventricular organ involved in the control of cardiovascular and metabolic homeostasis. Despite the abundant literature clearly demonstrating the ability of SFO neurons to sense and respond to a plethora of circulating signals that influence various physiological systems, investigation of how simultaneously sensed signals interact and are integrated in the SFO is lacking. In this study, we use patch clamp techniques to investigate how the traditionally classified ‘cardiovascular’ hormone angiotensin II (ANG), ‘metabolic’ hormone cholecystokinin (CCK) and ‘metabolic’ signal glucose interact and are integrated in the SFO. Sequential bath-application of CCK (10nM) and ANG (10nM) onto dissociated SFO neurons revealed that: 63% of responsive SFO neurons depolarized to both CCK & ANG; 25% depolarized to ANG only; and 12% hyperpolarized to CCK only. We next investigated the effects of glucose by incubating and recording neurons in either hypo-, normo- or hyperglycemic conditions for a minimum of 24 hours and comparing the proportions of responses to ANG (n=55) or CCK (n=83) application in each condition. A hyperglycemic environment was associated with a larger proportion of depolarizing responses to ANG (X2, p<0.05), and a smaller proportion of depolarizing responses along with a larger proportion of hyperpolarizing responses to CCK (X2, p<0.01). These data demonstrate that SFO neurons excited by CCK are also excited by ANG, suggesting that CCK may influence fluid intake or blood pressure via the SFO, complementary to the well-understood actions of ANG at this site. Additionally, the demonstration that glucose environment affects the responsiveness of neurons to both these hormones highlights the ability of SFO neurons to integrate multiple metabolic and cardiovascular signals to affect transmission of information from the circulation to the brain, which has important implications for this structure’s critical role regulation of autonomic function.

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus

BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk. METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation. RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P < 0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at P < 0.05. CONCLUSION: This study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk

A Reliable Downscaling of ECG Signals for the Detection of T wave Heterogeneity Features

In cardiovascular disease the definition and the detection of the ECG parameters related to repolarization dynamics in post MI patients is still a crucial unmet need. In addition, the use of a 3D sensor in the implantable medical devices would be a crucial mean in the assessment or prediction of Heart Failure status, but the inclusion of such feature is limited by hardware and firmware constraints. The aim of this thesis is the definition of a reliable surrogate of the 500 Hz ECG signal to reach the aforementioned objective. To evaluate the worsening of reliability due to sampling frequency reduction on delineation performance, the signals have been consecutively down sampled by a factor 2, 4, 8 thus obtaining the ECG signals sampled at 250, 125 and 62.5 Hz, respectively. The final goal is the feasibility assessment of the detection of the fiducial points in order to translate those parameters into meaningful clinical parameter for Heart Failure prediction, such as T waves intervals heterogeneity and variability of areas under T waves. An experimental setting for data collection on healthy volunteers has been set up at the Bakken Research Center in Maastricht. A 16 – channel ambulatory system, provided by TMSI, has recorded the standard 12 – Leads ECG, two 3D accelerometers and a respiration sensor. The collection platform has been set up by the TMSI property software Polybench, the data analysis of such signals has been performed with Matlab. The main results of this study show that the 125 Hz sampling rate has demonstrated to be a good candidate for a reliable detection of fiducial points. T wave intervals proved to be consistently stable, even at 62.5 Hz. Further studies would be needed to provide a better comparison between sampling at 250 Hz and 125 Hz for areas under the T waves.

Developing a Theory of Pervasive Information Capture, Processing, Visualization and Documentation

Developing a theoretical framework for pervasive information environments is an enormous goal. This paper aims to provide a small step towards such a goal. The following pages report on our initial investigations to devise a framework that will continue to support locative, experiential and evaluative data from ‘user feedback’ in an increasingly pervasive information environment. We loosely attempt to outline this framework by developing a methodology capable of moving from rapid-deployment of software and hardware technologies, towards a goal of realistic immersive experience of pervasive information. We propose various technical solutions and address a range of problems such as; information capture through a novel model of sensing, processing, visualization and cognition.

Pervasive eHealth services a security and privacy risk awareness survey

The human factor is often recognised as a major aspect of cyber-security research. Risk and situational perception are identified as key factors in the decision making process, often playing a lead role in the adoption of security mechanisms. However, risk awareness and perception have been poorly investigated in the field of eHealth wearables. Whilst end-users often have limited understanding of privacy and security of wearables, assessing the perceived risks and consequences will help shape the usability of future security mechanisms. This paper present a survey of the the risks and situational awareness in eHealth services. An analysis of the lack of security and privacy measures in connected health devices is described with recommendations to circumvent critical situations.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Using electroencephalography signals to control acoustical processing

Current hearing-assistive technology performs poorly in noisy multi-talker conditions. The goal of this thesis was to establish the feasibility of using EEG to guide acoustic processing in such conditions. To attain this goal, this research developed a model via the constructive research method, relying on literature review. Several approaches have revealed improvements in the performance of hearing-assistive devices under multi-talker conditions, namely beamforming spatial filtering, model-based sparse coding shrinkage, and onset enhancement of the speech signal. Prior research has shown that electroencephalography (EEG) signals contain information that concerns whether the person is actively listening, what the listener is listening to, and where the attended sound source is. This thesis constructed a model for using EEG information to control beamforming, model-based sparse coding shrinkage, and onset enhancement of the speech signal. The purpose of this model is to propose a framework for using EEG signals to control sound processing to select a single talker in a noisy environment containing multiple talkers speaking simultaneously. On a theoretical level, the model showed that EEG can control acoustical processing. An analysis of the model identified a requirement for real-time processing and that the model inherits the computationally intensive properties of acoustical processing, although the model itself is low complexity placing a relatively small load on computational resources. A research priority is to develop a prototype that controls hearing-assistive devices with EEG. This thesis concludes highlighting challenges for future research.

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness

Second-order cyclostationarity-based detection and classification of LTE SC-FDMA signals for cognitive radio

Cognitive radio (CR) was developed for utilizing the spectrum bands efficiently. Spectrum sensing and awareness represent main tasks of a CR, providing the possibility of exploiting the unused bands. In this thesis, we investigate the detection and classification of Long Term Evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, which are used in uplink LTE, with applications to cognitive radio. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection and classification (in other words, to spectrum sensing and awareness). The proposed detection and classification algorithms provide a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithms is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results.