Urine comprises around 1% of domestic sewage volume but holds 80% of total nitrogen. Source separation is a sustainable way to wastewater management than traditional way due to low energy cost and preventing certain pollutants into wastewater treatment plants. Currently, removing and recovering nitrogen from source-separated urine has attracted more and more interests. Of them, ion exchange was used for removal and recovery of nitrogen in the form of ammonia from synthetic urine for potential application as a fertilizer in agriculture. No previous research studies were conducted to investigate the removal and recovery of nitrogen from hydrolyzed urine by ion exchange using POPs (porous organic polymers). So this study focused on evaluating the performance of POPs and comparing with clinoptilolite in synthetic hydrolyzed urine in terms of adsorption capacity (isotherm), adsorption rate (kinetics), regeneration rate, and cost. The ammonium removal from hydrolyzed urine using POPs was rapid with a high capacity of 68.03 mg/g than clinoptilolite (15.36 mg/g), and the regeneration efficiency of clinoptilolite and POPs can achieve 91% and 95.3%, respectively based single time use result. Although POPs had the better performance at one time use and multiple times use, it also had high materials cost. Additionally, the capacity of POP was estimated using the integrated ion exchange regeneration process model as 30.24 mg/g and 28.65 mg/g on cycle 10 and cycle 24, respectively. The regeneration efficiency of POPs was predicated as 45.4% and 38.4% in cycle 10 and cycle 24, respectively. The predicted capacity decreased with the number of cycles, but remained at about 55% of virgin POPs after 24 cycles, indicating POPs can maintain good performance after multiple reuses than clinoptilolite.

The information age has brought with it a dual problem. In the first place, the ready access to mechanisms to capture and store vast amounts of data in all forms (text, audio, image and video), has resulted in a continued demand for ever more efficient means to store and transmit this data. In the second, the rapidly increasing store demands effective means to structure and access the data in an efficient and meaningful manner. In terms of audio data, the first challenge has traditionally been the realm of audio compression research that has focused on statistical, unstructured audio representations that obfuscate the inherent structure and semantic content of the underlying data. This has only served to further complicate the resolution of the second challenge resulting in access mechanisms that are either impractical to implement, too inflexible for general application or too low level for the average user. Thus, an artificial dichotomy has been created from what is in essence a dual problem. The founding motivation of this thesis is that, although the hypermedia model has been identified as the ideal, cognitively justified method for organising data, existing audio data representations and coding models provide little, if any, support for, or resemblance to, this model. It is the contention of the author that any successful attempt to create hyperaudio must resolve this schism, addressing both storage and information management issues simultaneously. In order to achieve this aim, an audio representation must be designed that provides compact data storage while, at the same time, revealing the inherent structure of the underlying data. Thus it is the aim of this thesis to present a representation designed with these factors in mind. Perhaps the most difficult hurdle in the way of achieving the aims of content-based audio coding and information management is that of auditory source separation. The MPEG committee has noted this requirement during the development of its MPEG-7 standard, however, the mechanics of "how" to achieve auditory source separation were left as an open research question. This same committee proposed that MPEG-7 would "support descriptors that can act as handles referring directly to the data, to allow manipulation of the multimedia material." While meta-data tags are a part solution to this problem, these cannot allow manipulation of audio material down to the level of individual sources when several simultaneous sources exist in a recording. In order to achieve this aim, the data themselves must be encoded in such a manner that allows these descriptors to be formed. Thus, content-based coding is obviously required. In the case of audio, this is impossible to achieve without effecting auditory source separation. Auditory source separation is the concern of computational auditory scene analysis (CASA). However, the findings of CASA research have traditionally been restricted to a limited domain. To date, the only real application of CASA research to what could loosely be classified as information management has been in the area of signal enhancement for automatic speech recognition systems. In these systems, a CASA front end serves as a means of separating the target speech from the background "noise". As such, the design of a CASA-based approach, as presented in this thesis, to one of the most significant challenges facing audio information management research represents a significant contribution to the field of information management. Thus, this thesis unifies research from three distinct fields in an attempt to resolve some specific and general challenges faced by all three. It describes an audio representation that is based on a sinusoidal model from which low-level auditory primitive elements are extracted. The use of a sinusoidal representation is somewhat contentious with the modern trend in CASA research tending toward more complex approaches in order to resolve issues relating to co-incident partials. However, the choice of a sinusoidal representation has been validated by the demonstration of a method to resolve many of these issues. The majority of the thesis contributes several algorithms to organise the low-level primitives into low-level auditory objects that may form the basis of nodes or link anchor points in a hyperaudio structure. Finally, preliminary investigations in the representation’s suitability for coding and information management tasks are outlined as directions for future research.

<p>Magnetic Resonance Imaging is an established technology for both imaging and</p><p>functional studies in clinical and research environments. The field is still very</p><p>research intense. Two major research areas are acquisition time and signal quality.</p><p>The last decade has provided tools for more efficient possibilities of trading these</p><p>factors against each other through parallel imaging.</p><p>In this thesis one parallel imaging method, Sensitivity Encoding for fast</p><p>MRI (SENSE) is examined. An alternative solution CCASENSE is developed.</p><p>CCASENSE reduces the acquisition time by estimating the sensitivity maps required</p><p>for SENSE to work instead of running a reference scan. The estimation</p><p>process is done by Blind Source Separation through Canonical Correlation Analysis.</p><p>It is shown that CCASENSE appears to estimate the sensitivity maps better</p><p>than ICASENSE which is a similar algorithm.</p>

In the recent years, Independent Component Analysis (ICA) has become a fundamental tool in adaptive signal and data processing, especially in the field of Blind Source Separation (BSS). Even though there exist some methods for which an algebraic solution to the ICA problem may be found, other iterative methods are very popular. Among them is the class of information-theoretic approaches, laying on entropies. The associated objective functions are maximized based on optimization schemes, and on gradient-ascent techniques in particular. Two major issues in this field are the following: 1) Does the global maximum point of these entropic objectives correspond to a satisfactory solution of BSS ? and 2) as gradient techniques are used, optimization algorithms look in fact for local maximum points, so what about the meaning of these local optima from the BSS problem point of view? Even though there are some partial answers to these questions in the literature, most of them are based on simulation and conjectures; formal developments are often lacking. This thesis aims at filling this lack and providing intuitive justifications, too. We focus the analysis on Rényi's entropy-based contrast functions. Our results show that, generally speaking, Rényi's entropy is not a suitable contrast function for BSS, even though we recover the well-known results saying that Shannon's entropy-based objectives are contrast functions. We also show that the range-based contrast functions can be built under some conditions on the sources. The BSS problem is stated in the first chapter, and viewed under the information (theory) angle. The two next chapters address specifically the above questions. Finally, the last chapter deals with range-based ICA, the only ``entropy-based contrast' which, based on the enclosed results, is also a <i>discriminant</i> contrast function, in the sense that it is theoretically free of spurious local optima. Geometrical interpretations and surprising examples are given. The interest of this approach is confirmed by testing the algorithm on the MLSP 2006 data analysis competition benchmark; the proposed method outperforms the previously obtained results on large-scale and noisy mixture samples obtained through ill-conditioned mixing matrices.

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This dissertation discusses a method that will aid in diagnosing the root causes of product and process variability in complex manufacturing processes when large quantities of multivariate in-process measurement data are available. As in any data mining application, this dissertation has as its objective the extraction of useful information from the data. A linear structured model, similar to the standard factor analysis model, is used to generically represent the variation patterns that result from the root causes. Blind source separation methods are investigated to identify spatial variation patterns in manufacturing data. Further, the existing blind source separation methods are extended, enhanced and improved to be a more effective, accurate and widely applicable method for manufacturing variation diagnosis. An overall strategy is offered to guide the use of the presented methods in conjunction with alternative methods.

Hearing aid devices are used to help people with hearing impairment. The number of people that requires hearingaid devices are possibly constant over the years, however the number of people that now have access to hearing aiddevices increasing rapidly. The hearing aid devices must be small, consume very little power, and be fairly accurate.Even though it is normally more important for the user that hearing impairment look good (are discrete). Once thehearing aid device prescribed to the user, she/he needs to train and adjust the device to compensate for the individualimpairment.We are within the framework of this project researching on hearing aid devices that can be trained by the hearingimpaired person her-/himself. This project is about finding suitable noise cancellation algorithm for the hearing-aiddevice. We consider several types of algorithms like, microphone array signal processing, Independent ComponentAnalysis (ICA) based on double microphone called Blind Source Separation (BSS) and DRNPE algorithm.We run this current and most sophisticated and robust algorithms in certain noise backgrounds like Cocktail noise,street, public places, train, babble situations to test the efficiency. The BSS algorithm was well in some situation andgave average results in some situations. Where one microphone gave steady results in all situations. The output isgood enough to listen targeted audio.The functionality and performance of the proposed algorithm is evaluated with different non-stationary noisebackgrounds. From the performance results it can be concluded that, by using the proposed algorithm we are able toreduce the noise to certain level. SNR, system delay, minimum error and audio perception are the vital parametersconsidered to evaluate the performance of algorithms. Based on these parameters an algorithm is suggested forheairng-aid. / Hearing-Aid

Magnetic Resonance Imaging is an established technology for both imaging and functional studies in clinical and research environments. The field is still very research intense. Two major research areas are acquisition time and signal quality. The last decade has provided tools for more efficient possibilities of trading these factors against each other through parallel imaging. In this thesis one parallel imaging method, Sensitivity Encoding for fast MRI (SENSE) is examined. An alternative solution CCASENSE is developed. CCASENSE reduces the acquisition time by estimating the sensitivity maps required for SENSE to work instead of running a reference scan. The estimation process is done by Blind Source Separation through Canonical Correlation Analysis. It is shown that CCASENSE appears to estimate the sensitivity maps better than ICASENSE which is a similar algorithm.

Blind source separation methods such as independent component analysis (ICA) and second order blind identification (SOBI) have shown considerable potential in the area of ambient vibration system identification. The objective of these methods is to separate the modal responses, or sources, from the measured output responses, without the knowledge of excitation. Several frequency domain and time domain methods have been proposed and successfully implemented in the literature. Whereas frequency-domain methods pose several challenges typical of dealing with signals in the frequency-domain, popular time-domain methods such as NExT/ERA and SSI pose limitations in dealing with noise, low sensor density, modes having low energy content, or in dealing with systems having closely-spaced modes, such as those found in structures with passive energy dissipation devices, for example, tuned mass dampers.Motivated by these challenges, the current research focuses on developing methods to address the problem of separability of sources with low energy content, closely-spaced modes, and under-determined blind identification, that is, when the number of response measurements is less than the number of sources. These methods, requiring the time and frequency diversities of the measured outputs, are referred to as hybrid time and time-frequency source separation methods. The hybrid methods are classified into two categories. In the first one, the basic principles of modified SOBI are extended using the stationary wavelet transform (SWT) in order to improve the separability of sources, thereby improving the quality of identification. In the second category, empirical mode decomposition is employed to extract the intrinsic mode functions from measurements, followed by an estimation of the mode shape matrix using iterative and/or non iterative procedures within the framework of modified-SOBI. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of these hybrid approaches to structural system identification problems.

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