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Subject: search.filters.subject.Signal processing

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    On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings
    (American Institute of Physics, 2024-07-18) Nair AS; Ghosh I; Fatoyinbo HO; Muni SS
    Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark-Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0-1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.
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    Reducing calibration time in motor imagery based brain-computer interface : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in School of Fundamental Sciences at Massey University, Palmerston North, New Zealand
    (Massey University, 2022) Singh, Amardeep
    Motor imagery (MI) based Electroencephalogram (EEG) Brain-computer interface (BCI) detects neural activity generated due to kinesthetic imagination of limbs from brain scalp and translate it into control commands for external devices. MI-BCIs are indeed very promising for people suffering from neuromuscular disorder, but still lack adoption as access modalities outside laboratories. The main reason that prevents EEG based MI-BCIs from being widely used is there long calibration time. Due to considerable inter-subject/inter-session and intra-session variations, a large number of training trials are collected to calibrate systems at the beginning of each MI-BCI session. This time consuming calibration is required to achieve good performance with the BCI system but causes fatigue to user and leaves less time for online BCI interactions. This thesis focuses on developing reliable signal processing and classification pipeline that reduce MI-BCI calibration time while keeping accuracy in an acceptable range. In the first part of the study, we have provided an extensive review of current state of art in designing a EEG based MI-BCI system. In doing so, I have created an architectural framework which brings together interdisciplinary concepts under a unified umbrella. We used this framework to identify key signal processing, features extraction and learning algorithms and their limitation that must be taken into consideration while designing novel pipeline for reducing calibration in MI-BCI. This architecture is also useful to understand current issues in BCI and to visualize the gaps to be filled by future studies in order to further improve BCI usability. In the second part of the study, we address long calibration issue in MI-BCI under two scenarios. First, when there is only few training trials from new subject (user) is available and no training data from previous sessions or other users is available. Second, reducing (inter-subjects/sessions non-satationarity) calibration time of new subject when there is previous sessions or other subjects data is available along with few trials from new subject. In order to contribute to the progress of reducing calibration in MI-BCI, we proposed novel signal processing and classification pipeline that uses spatial, spectral, temporal and geometrical properties of subject’s trial from EEG signals and achieve acceptable performance under reduce calibration setting.
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    The effects of anti-aliasing filters on system identification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Production Technology Department at Massey University
    (Massey University, 1992) Sadr-Nia, Mohammad Ali
    Research was conducted to determine the effect of anti-aliasing filters on the identification of dynamic systems. Systems were simulated in the continuous simulation package ESL. The system response to a PRBS (Pseudo Random Binary Sequence) was recorded. Simulated noise was added and passed through a number of simulated analog filters. The systems were identified using the MATLAB identification toolbox. Two standard filters (Butterworth and Chebychev) were used with cut-off frequencies between ffis (natural frequency of the system) and 20 times ffis. Results showed that carefully designed filters could improve the performance of the identification algorithm in the presence of non-white high frequency additive noise. However for noise free measurements the filters degraded the performance of identification algorithms. This performance could be observed in the identified models steady state error, overshoot and settling time when subject to a step input. In the experiments performed, the lowest order (and in one case second order) filters with cut-off frequency of ffin= 5ros, gave the best results. [From Summary]
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    The development of a portable Earth's field NMR system for the study of Antarctic sea ice : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Electronics at Massey University
    (Massey University, 2001) Dykstra, Robin
    A portable Nuclear Magnetic Resonance (NMR) spectrometer based on digital signal processor (DSP) technology has been developed and applied to the study of the structure of Antarctic sea ice. The portability of this system means that external sources of noise can be minimised and remote sites can be investigated. A new sea-ice probe has been developed in conjunction with the spectrometer allowing in-situ measurement of water content, relaxation times and self diffusion. The new probe minimises disturbances to the sea ice sample which have been a problem with previous techniques. The core of the spectrometer consists of a Motorola DSP56303 DSP which controls the NMR experiment under the supervison of a host computer which in this case is a PC laptop. Communication between host and DSP is via either a PCMCIA card or USB interface. DSP software runs the experiment, controls acquisition and performs digital filtering of the NMR data before sending it to the PC for analysis and display. The flexibility of the DSP based core means that this system could be adapted to other control applications with relative ease.
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    Frequency domain exploits for symmetric adaptive decorrelation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand
    (Massey University, 2014) Harris, Jonathan
    Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, restrictions not present for many other blind source separation (BSS) techniques, it is more ideally suited for some applications (for example, live sound mixing) due to the fact that no post-processing is required to ascertain which output corresponds with which source. Since the SAD algorithm is based on second-order statistics (SOS), it also tends to have a lower computational load when compared with those based on higher order statistics. In order to increase the e ciency of the SAD algorithm, a multibranched recursive structure is investigated. While a nominal gain in e ciency is attained, we move away from this approach in pursuit of more substantial gains. A frequency-domain symmetric adaptive decorrelation (FD-SAD) algorithm is proposed, with savings increasing not only with larger lter sizes, but also with increasing the number of sources. The convergence and stability of this new algorithm is proven. A trade-o of the FD-SAD algorithm is that it introduces a delay in the output, which renders the algorithm unsuitable for real-time applications. Therefore a hybrid approach is also proposed that does not su er from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally e cient than the pure frequency domain algorithm, it is signi cantly more e cient than the time-domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time-domain algorithm in a real-world environment. The proposed adaptations could also be used to extend other BSS approaches (such as Triple-N ICA for Convolutive mixtures (TRINICON) [1], which can also be based on SOS), and a comparison of the proposed methods with TRINICON is explored.
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    Distributed image and video coding based on compressed sensing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Engineering at Massey University, New Zealand
    (Massey University, 2013) Baig, Muhammad Yousuf
    Conventional methods for encoding of images and videos is a complex process with high computational demands. They are designed for application scenarios where the signals concerned are encoded once and played back many times. However, new applications such as wireless video sensor networks demand low cost and low power cameras with limited computing resources. The focus of this thesis is on such image and video coding systems where the computational burden is shifted from the encoder to the decoder. Three separate coding schemes have been developed { two for videos and one for images. Together they form a framework for distributed coding which is based on the theory of compressed sensing and distributed coding. Compressed sensing is a relatively new theory for the acquisition of sparse signals that allows the sampling rate to be much lower than the Nyquist limit. Distributed coding is based on the theorem by Slepian and Wolf, and Wyner and Ziv. It allows different correlated parts of a signal to be encoded independently without loss of coding efficiency. The decoding of these separately encoded parts are then decoded jointly in order to exploit the correlation between them. The main characteristics of the coding scheme proposed in this thesis are: (1) they do not require the use of traditional codecs; (2) only compressed sensing measurements are used for encoding and decoding; (3) no motion estimation and compensation are involved for videos. The first proposed coding scheme is for the encoding of whole video frames. The compressed sensing measurement of individual frames are separately encoded. These frames are divided into key and non-key frames with the key frames encoded at a higher rate than non-key ones. While the key frames are decoded independently, the non-key ones are decoded with the help of side information generated from the measurements of the key frames. The most important part of the decoder is a simple, yet effective, side information generation method which requires only minimal computation. The side information generated is simply added to the measurements of the non-key frames for use with any compressed sensing reconstruction algorithm. The other two coding schemes are block-based coding methods. Each image or frame is divided into non-overlapping image blocks in a similar way it is done in some existing coding standards. The coding of the blocks are performed in a distributed manner by classifying them into key blocks and nonkey blocks. An adaptive encoding strategy based on block similarity is also developed. Experimental analyses using publicly available test images and videos show that the performances of the simpler codecs proposed are better than other existing compressed sensing based codecs. The video codecs also out-perform conventional distributed video codec in terms of simplicity, compression ratio and decoding complexity. The basis of these coding methods is on the correlation of frames or blocks. This correlation is established through experimental analyses. These analyses also showed that the minimum square error between any pair of them can be effectively used as a measure of correlation. In conjunction with the development of the codecs, a quantization scheme that is tailored to the statistics of CS measurements has also been proposed. This scheme yields better results than a uniform quantizer and those used for JPEG. The quantizer is also robust against different statistics of individual images. Separate experimental evaluations also show that structurally random matrices are the best sensing matrices for acquiring images and the sparse reconstruction by separable approximation (SpaRSA) algorithm produces the best reconstructed image quality.
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    Development and applications of a low-field portable NMR system : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Physics at Massey University, Manawatu, New Zealand
    (Massey University, 2011) Ward, Robert L
    Nuclear magnetic resonance (NMR) is a phenomenon similar to MRI in which radio frequency signals are used to excite and manipulate atomic nuclei within a static magnetic field. Following excitation, the nuclei return to equilibrium, all the while offering valuable molecular level information pertaining to the sample. Within the last decade, the development of small and inexpensive NMR spectrometers and permanent magnet NMR sensors has been a significant focus within the NMR community. More recently, application scientists have sought practical applications for the new technologies. In this thesis, a prototype NMR apparatus consisting of a spectrometer and 3.2MHz permanent magnet sensor was extended to enable scientifc measurements. This involved developing radio frequency electronic circuitry for the spectrometer front-end, and electromagnetic noise shielding and temperature regulation for the magnetic sensor. Experimental results confirmed that repeatable measurements using the modified apparatus were indeed possible. The NMR apparatus was thereafter successfully used to study flow, diffusion and kiwifruit using several different experimental techniques. A significantly larger effort was then expended upon the study of T2 relaxation in pectin model systems using pH as the adjustable parameter. The fascinating experimental results were successfully interpreted and modeled across three pH zones in terms of a proton chemical exchange model and molecular conformational changes. In addition, it was found that pectin carboxyl de-protonation was significantly less than expected. Further experiments performed upon galacturonic acid monomers, dimers and trimers appeared to further illuminate the pectin results. Future experiments are planned. Also while studying pectin solutions, an unexpected pH-dependent water transverse relaxation behavior was observed at both 3.2MHz and 400MHz. The only references found in the literature were from a small publication almost 50 years ago, and a 2011 publication. Altogether, this thesis contributed to original knowledge in several ways: it showed how a low- eld apparatus and single-sided sensor could be improved and utilized for a variety of scientific measurements; it showed both experimentally and theoretically how T2 for pectin solutions change with pH; it revealed an unexpected de-protonation limit for pectin molecules; it revealed a T2 pH dependence for water.