Using EMD-FrFT filtering to mitigate high power interference in chirp tracking radars

This letter presents a new signal processing subsystem for conventional monopulse tracking radars that offers an improved solution to the problem of dealing with manmade high power interference (jamming). It is based on the hybrid use of empirical mode decomposition (EMD) and fractional Fourier transform (FrFT). EMD-FrFT filtering is carried out for complex noisy radar chirp signals to decrease the signal's noisy components. An improvement in the signal-to-noise ratio (SNR) of up to 18 dB for different target SNRs is achieved using the proposed EMD-FrFT algorithm

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results

Chirp-based multicarrier modulation

In this paper we demonstrate that in doubly-dispersive environments, a multicarrier (MC) system based on a fractional Fourier transform (FrFT) can achieve a better concentration of power near the main diagonal of the equivalent channel matrix compared to standard orthogonal frequency division multiplexing (OFDM). The resulting inter-symbol and inter-carrier interference in such a chirp- based MC system can therefore be suppressed with a reduced complexity equaliser. Simulations show that compared to an equalised OFDM system, the equalised FrFT-MC approach can either significantly reduce complexity or enhance performance in a time-varying environment

Low-complexity LSMR equalisation of FrFT-based multicarrier systems in doubly dispersive channels

The discrete fractional Fourier transform (FrFT) has been suggested to enhance performance over DFT-based multicarrier systems when transmitting over doubly-dispersive channels. In this paper, we propose a novel low-complexity equaliser for inter-symbol and inter-carrier interference arising in such multicarrier transmission system. Due to a lower spreading in the FrFT-domain compared to the DFTchannel matrix as compared to the DFT domain, the equaliser cam approximate the fractional-domain channel matrix by a band matrix. Further, we utilise the least squares minres (LSMR) algorithm in the calculation of the equalisation, which exhibits attractive numerical properties and low complexity. Simulation results demonstrate the superior performance of the proposed LSMR equaliser over benchmark schemes

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

<p>Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.</p> <p>Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.</p> <p>Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.</p&gt

The effects of F0, intensity and durational manipulations of the perception of stress in dysarthric speech

Marking stress plays an important role in conveying meaning and directing the listeners attentionto the important parts of a message. Extensive research has been conducted into howhealthy speakers produce stress, with the key phonetic cues acknowledged as F0, intensity andduration (Bolinger 1961). We also know that speakers with dysarthria experience problemsin marking stress successfully (Lowit et al., 2012, Patel & Campellone, 2009). However, wecurrently lack sufficiently specific information on these deficits and potential compensatorytechniques to allow Speech and Language Therapists (SLTs) to provide effective treatmentmethods to address stress production problems.In order to build an evidence base for intervention, it is essential to first establish therelationship between features of disordered stress production and their perceptual outcomes.In particular, we need to know which phonetic cues (or combinations thereof) are most salientand what degree of change needs to be achieved in order to signal stress effectively to listeners.This project aims to explore these questions in detail by performing perceptual experimentson data from disordered speakers that have been acoustically manipulated, in order to produceguidance to clinicians on how to improve their patients ability to signal stress successfully.We used contrastive stress sentences from 10 speakers with ataxic dysarthria. Each speakerproduced 30 sentences, i.e. 10 sentences (SVOA structures) across 3 conditions (stress on initial(S), medial (O), or final (A) target words). Sentences were perceptually scored by 5 listenersregarding the location of the stress target. We then chose 15 utterances where listeners hadbeen unable to identify the target, five for each of the sentence positions. These utteranceswere subsequently manipulated acoustically by incrementally increasing the F0, intensity andduration of the target words, in accordance with the degree of change observed in the healthycontrol group. In addition, pausing patterns as well intonation contours were altered. Themanipulated utterances were played to 50 listeners to evaluate what degree and combinationof alteration resulted in correct identification of the stress target.We will report on the patterns of impairment observed in the disordered speech samples,as well as the impact of the above manipulations on listener accuracy. This will provide informationfor future studies on stress production regarding focus of analysis, as well as guideclinicians on how best to address deficits in this area in their patients

A new spiking convolutional recurrent neural network (SCRNN) with applications to event-based hand gesture recognition

The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal relations from event-based sequence data are presented. The use of recurrent architecture enables the network to have a sampling window with an arbitrary length, allowing the network to exploit temporal correlations between event collections. Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based) data directly. The network structure is validated on the DVS gesture dataset and achieves a 10 class gesture recognition accuracy of 96.59% and an 11 class gesture recognition accuracy of 90.28%