Analysing environmental acoustic data through collaboration and automation

Monitoring environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; online collaboration, manual, automatic and human-in-the loop analysis.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Development of zonal cartilage constructs : effects of chondrocyte subpopulation, compressive stimulation, and culture models

Articular cartilage is a highly resilient tissue located at the ends of long bones. It has a zonal structure, which has functional significance in load-bearing. Cartilage does not spontaneously heal itself when damaged, and untreated cartilage lesions or age-related wear often lead to osteoarthritis (OA). OA is a degenerative condition that is highly prevalent, age-associated, and significantly affects patient mobility and quality of life. There is no cure for OA, and patients usually resort to replacing the biological joint with an artificial prosthesis. An alternative approach is to dynamically regenerate damaged or diseased cartilage through cartilage tissue engineering, where cells, materials, and stimuli are combined to form new cartilage. However, despite extensive research, major limitations remain that have prevented the wide-spread application of tissue-engineered cartilage. Critically, there is a dearth of information on whether autologous chondrocytes obtained from OA patients can be used to successfully generate cartilage tissues with structural hierarchy typically found in normal articular cartilage. I aim to address these limitations in this thesis by showing that chondrocyte subpopulations isolated from macroscopically normal areas of the cartilage can be used to engineer stratified cartilage tissues and that compressive loading plays an important role in zone-dependent biosynthesis of these chondrocytes. I first demonstrate that chondrocyte subpopulations from the superficial (S) and middle/deep (MD) zones of OA cartilage are responsive to compressive stimulation in vitro, and that the effect of compression on construct quality is zone-dependent. I also show that compressive stimulation can influence pericelluar matrix production, matrix metalloproteinase secretion, and cytokine expression in zonal chondrocytes in an alginate hydrogel model. Subsequently, I focus on recreating the zonal structure by forming layered constructs using the alginate-released chondrocyte (ARC) method either with or without polymeric scaffolds. Resulting zonal ARC constructs had hyaline morphology, and expressed cartilage matrix molecules such as proteoglycans and collagen type II in both scaffold-free and scaffold-based approaches. Overall, my findings demonstrate that chondrocyte subpopulations obtained from OA joints respond sensitively to compressive stimulation, and are able to form cartilaginous constructs with stratified organization similar to native cartilage using the scaffold-free and scaffold-based ARC technique. The ultimate goal in tissue engineering is to help provide improved treatment options for patients suffering from debilitating conditions such as OA. Further investigations in developing functional cartilage replacement tissues using autologous chondrocytes will bring us a step closer to improving the quality of life for millions of OA patients worldwide.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

The calculation of acoustic indices to characterise acoustic recordings of the environment

This technical report describes the methods used to obtain a list of acoustic indices that are used to characterise the structure and distribution of acoustic energy in recordings of the natural environment. In particular it describes methods for noise reduction from recordings of the environment and a fast clustering algorithm used to estimate the spectral richness of long recordings.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

A spatiotemporal analysis of gait freezing and the impact of pedunculopontine nucleus stimulation

Gait freezing is an episodic arrest of locomotion due to an inability to take normal steps. Pedunculopontine nucleus stimulation is an emerging therapy proposed to improve gait freezing, even where refractory to medication. However, the efficacy and precise effects of pedunculopontine nucleus stimulation on Parkinsonian gait disturbance are not established. The clinical application of this new therapy is controversial and it is unknown if bilateral stimulation is more effective than unilateral. Here, in a double-blinded study using objective spatiotemporal gait analysis, we assessed the impact of unilateral and bilateral pedunculopontine nucleus stimulation on triggered episodes of gait freezing and on background deficits of unconstrained gait in Parkinson’s disease. Under experimental conditions, while OFF medication, Parkinsonian patients with severe gait freezing implanted with pedunculopontine nucleus stimulators below the pontomesencephalic junction were assessed during three conditions; off stimulation, unilateral stimulation and bilateral stimulation. Results were compared to Parkinsonian patients without gait freezing matched for disease severity and healthy controls. Pedunculopontine nucleus stimulation improved objective measures of gait freezing, with bilateral stimulation more effective than unilateral. During unconstrained walking, Parkinsonian patients who experience gait freezing had reduced step length and increased step length variability compared to patients without gait freezing; however, these deficits were unchanged by pedunculopontine nucleus stimulation. Chronic pedunculopontine nucleus stimulation improved Freezing of Gait Questionnaire scores, reflecting a reduction of the freezing encountered in patients’ usual environments and medication states. This study provides objective, double-blinded evidence that in a specific subgroup of Parkinsonian patients, stimulation of a caudal pedunculopontine nucleus region selectively improves gait freezing but not background deficits in step length. Bilateral stimulation was more effective than unilateral.

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Nutritional status in Parkinson's disease patients undergoing deep-brain stimulation surgery : a pilot study

People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. Poorer outcomes are associated with higher amounts of weight loss (>5%) and lower levels of fat free mass. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=11 out of 16). The Scale for Outcomes of Parkinson’s disease –Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS), and a 3-day food diary were mailed to participants’ homes for completion prior to hospital admission. During admission, the Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed. Mean(±s.d.) PD duration from diagnosis and time since occurrence of PD symptoms was 9.0(±8.0) and 12(±8.8) years, respectively. Five participants reported unintentional weight loss (average loss of 15.6%). PD duration but not years since symptom onset significantly predicted PG-SGA scores (β=4.2, t(8)=2.7, p<.05). Both were positively correlated with PG-SGA score (r = .667, r=.587). On average, participants classified as well-nourished (SGA-A) (n=4) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass (FFMI) indices when compared to malnourished participants (SGA-B) (n=7). They also reported fewer non-motor symptoms on the SCOPA-AUT and MCAS. Three participants had previously received dietetic advice but not in relation to PD. These findings demonstrate that malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and a high prevalence of malnutrition.

Nutritional status in Parkinson’s disease patients undergoing deep brain stimulation surgery : a pilot study

Objectives: People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. The aim of this study was to assess the nutritional status of people with PD who may be at higher risk of malnutrition related to unsatisfactory symptom management with optimised medical therapy. Design: This was an observational study using a convenience sample. Setting: Participants were seen during their hospital admission for their deep brain stimulation surgery. Participants: People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=15). Measurements: The Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed to determine nutritional status. Results: Six participants (40%) were classified as moderately malnourished (SGA-B). Eight participants (53%) reported previous unintentional weight loss (average loss of 13.3%). On average, participants classified as well-nourished (SGA-A) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass indices (FFMI) when compared to malnourished participants (SGA-B). Five participants had previously received dietetic advice but only one in relation to unintentional weight loss. Conclusion: Malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and presence of nutrition impact symptoms. Improving nutritional status prior to surgery may improve surgical outcomes.

In vitro physical stimulation of tissue-engineered and native cartilage

Because of the limited availability of donor cartilage for resurfacing defects in articular surfaces, there is tremendous interest in the in vitro bioengineering of cartilage replacements for clinical applications. However, attaining mechanical properties in engineered cartilaginous constructs that approach those of native cartilage has not been previously achieved when constructs are cultured under free-swelling conditions. One approach toward stimulating the development of constructs that are mechanically more robust is to expose them to physical environments that are similar, in certain ways, to those encountered by native cartilage. This is a strategy motivated by observations in numerous short-term experiments that certain mechanical signals are potent stimulators of cartilage metabolism. On the other hand, excess mechanical loading can have a deleterious effect on cartilage. Culture conditions that include a physical stimulation component are made possible by the use of specialized bioreactors. This chapter addresses some of the issues involved in using bioreactors as integral components of cartilage tissue engineering and in studying the physical regulation of cartilage. We first consider the generation of cartilaginous constructs in vitro. Next we describe the rationale and design of bioreactors that can impart either mechanical deformation or fluid-induced mechanical signals.

Vestibular rehabilitation following the removal of an acoustic neuroma : a systematic review of randomized trials

An acoustic neuroma (also known as a vestibular schwannoma) is an intracranial tumour of the vestibular nerve that is commonly treated by surgical resection. Following resection of an acoustic neuroma, patients may experience a range of symptoms that include deficits in gaze stability, mobility and balance. Vestibular rehabilitation may be useful in reducing the severity and minimizing the impact of these symptoms.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Graphene-like nano-sheets/36° LiTaO3 surface acoustic wave hydrogen gas sensor

Presented is the material and gas sensing properties of graphene-like nano-sheets deposited on 36° YX lithium tantalate (LiTaO3) surface acoustic wave (SAW) transducers. The graphene-like nano-sheets were characterized via scanning electron microscopy (SEM), atomic force microscopy(AFM)and X-ray photoelectron spectroscopy (XPS). The graphenelike nano-sheet/SAW sensors were exposed to different concentrations of hydrogen (H2) gas in a synthetic air at room temperature. The developed sensors exhibit good sensitivity towards low concentrations of H2 in ambient conditions, as well as excellent dynamic performance towards H2 at room temperature.