Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Effects of varied ionic calcium and phosphate on the proliferation, osteogenic differentiation and mineralization of human periodontal ligament cells in vitro

Background and Objective: A number of bone filling materials containing calcium (Ca++) and phosphate (P) ions have been used in the repair of periodontal bone defects; however, the effect that local release of Ca++ and P ions have on biological reactions is not fully understood. In this study, we investigated the effects of various levels of Ca++ and P ions on the proliferation, osteogenic differentiation, and mineralization of human periodontal ligament cells (hPDLCs). Materials and Methods: hPDLCs were obtained using an explant culture method. Defined concentrations and ratios of ionic Ca++ to inorganic P were added to standard culture and osteogenic induction media. The ability of hPDLCs to proliferate in these growth media was assayed using the Cell Counting Kit-8 (CCK-8). Cell apoptosis was evaluated by FITC-Annexin V/PI double staining method. Osteogenic differentiation and mineralization were investigated by morphological observations, alkaline phosphatase (ALP) activity, and Alizarin red S/von Kossa staining. The mRNA expression of osteogenic related markers was analyzed using a reverse transcriptase polymerase chain reaction (RT-PCR). Results: Within the ranges of Ca++ and P ions concentrations tested, we observed that increased concentrations of Ca++ and P ions enhanced cell proliferation and formation of mineralized matrix nodules; whereas ALP activity was reduced. The RT-PCR results showed that elevated concentrations of Ca++ and P ions led to a general increase of Runx2 mRNA expression and decreased ALP mRNA expression, but gave no clear trend on OCN mRNA levels. Conclusion: The concentrations and ratios of Ca++ and P ions could significantly influence proliferation, differentiation, and mineralization of hPDLCs. Within the range of concentrations tested, we found that the combination of 9.0 mM Ca++ ions and 4.5 mM P ions were the optimum concentrations for proliferation, differentiation, and mineralization in hPDLCs.

Neural interface systems : a new legal frontier?

In an age where digital innovation knows no boundaries, research in the area of brain-computer interface and other neural interface devices go where none have gone before. The possibilities are endless and as dreams become reality, the implications of these amazing developments should be considered. Some of these new devices have been created to correct or minimise the effects of disease or injury so the paper discusses some of the current research and development in the area, including neuroprosthetics. To assist researchers and academics in identifying some of the legal and ethical issues that might arise as a result of research and development of neural interface devices, using both non-invasive techniques and invasive procedures, the paper discusses a number of recent observations of authors in the field. The issue of enhancing human attributes by incorporating these new devices is also considered. Such enhancement may be regarded as freeing the mind from the constraints of the body, but there are legal and moral issues that researchers and academics would be well advised to contemplate as these new devices are developed and used. While different fact situation surround each of these new devices, and those that are yet to come, consideration of the legal and ethical landscape may assist researchers and academics in dealing effectively with matters that arise in these times of transition. Lawyers could seek to facilitate the resolution of the legal disputes that arise in this area of research and development within the existing judicial and legislative frameworks. Whether these frameworks will suffice, or will need to change in order to enable effective resolution, is a broader question to be explored.

Human neural cell interactions with orientated electrospun nanofibers in vitro

Aim: Electrospun nanofibers represent potent guidance substrates for nervous tissue repair. Development of nanofiber-based scaffolds for CNS repair requires, as a first step, an understanding of appropriate neural cell type-substrate interactions. Materials & methods: Astrocyte–nanofiber interactions (e.g., adhesion, proliferation, process extension and migration) were studied by comparing human neural progenitor-derived astrocytes (hNP-ACs) and a human astrocytoma cell line (U373) with aligned polycaprolactone (PCL) nanofibers or blended (25% type I collagen/75% PCL) nanofibers. Neuron–nanofiber interactions were assessed using a differentiated human neuroblastoma cell line (SH-SY5Y). Results & discussion: U373 cells and hNP-AC showed similar process alignment and length when associated with PCL or Type I collagen/PCL nanofibers. Cell adhesion and migration by hNP-AC were clearly improved by functionalization of nanofiber surfaces with type I collagen. Functionalized nanofibers had no such effect on U373 cells. Another clear difference between the U373 cells and hNP-AC interactions with the nanofiber substrate was proliferation; the cell line demonstrating strong proliferation, whereas the hNP-AC line showed no proliferation on either type of nanofiber. Long axonal growth (up to 600 µm in length) of SH-SY5Y neurons followed the orientation of both types of nanofibers even though adhesion of the processes to the fibers was poor. Conclusion: The use of cell lines is of only limited predictive value when studying cell–substrate interactions but both morphology and alignment of human astrocytes were affected profoundly by nanofibers. Nanofiber surface functionalization with collagen significantly improved hNP-AC adhesion and migration. Alternative forms of functionalization may be required for optimal axon–nanofiber interactions.

Calcium ions promote osteogenic differentiation and mineralization of human dental pulp cells : implication for pulp capping materials

Calcium (Ca) is the main element of most pulp capping materials and plays an essential role in mineralization. Different pulp capping materials can release various concentrations of Ca ions leading to different clinical outcomes. The purpose of this study was to investigate the effects of various concentrations of Ca ions on the growth and osteogenic differentiation of human dental pulp cells (hDPCs). Different concentrations of Ca ions were added to growth culture medium and osteogenic inductive culture medium. A Cell Counting Kit-8 (CCK-8) was used to determine the proliferation of hDPCs in growth culture medium. Osteogenic differentiation and mineralization were measured by alkaline phosphatase (ALP) assay, Alizarin red S/von kossa staining, calcium content quantitative assay. The selected osteogenic differentiation markers were investigated by quantitative real-time polymerase chain reaction (qRT-PCR). Within the range of 1.8–16.2 mM, increased concentrations of Ca ions had no effect on cell proliferation, but led to changes in osteogenic differentiation. It was noted that enhanced mineralized matrix nodule formation was found in higher Ca ions concentrations; however, ALP activity and gene expression were reduced. qRT-PCR results showed a trend towards down-regulated mRNA expression of type I collagen (COL1A2) and Runx2 at elevated concentrations of Ca ions, whereas osteopontin (OPN) and osteocalcin (OCN) mRNA expression was significantly up-regulated. Ca ions content in the culture media can significantly influence the osteogenic properties of hDPCs, indicating the importance of optimizing Ca ions release from dental pulp capping materials in order to achieve desirable clinical outcomes.

Using strategic movement to calibrate a neural compass : a spiking network for tracking head direction in rats and robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modeled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

The stimulation of proliferation and differentiation of periodontal ligament cells by the ionic products from Ca7Si2P2O16 bioceramics

The ultimate goal of periodontal tissue engineering is to produce predictable regeneration of alveolar bone, root cementum, and periodontal ligament, which are lost as a result of periodontal diseases. To achieve this goal, it is of great importance to develop novel bioactive materials which could stimulate the proliferation, differentiation and osteogenic/cementogenic gene expression of periodontal ligament cells (PDLCs) for periodontal regeneration. In this study, we synthesized novel Ca7Si2P2O16 ceramic powders for the first time by the sol–gel method and investigated the biological performance of PDLCs after exposure to different concentrations of Ca7Si2P2O16 extracts. The original extracts were prepared at 200 mg ml-1 and further diluted with serum-free cell culture medium to obtain a series of diluted extracts (100, 50, 25, 12.5 and 6.25 mg ml–1). Proliferation, alkaline phosphatase(ALP) activity, Ca deposition, and osteogenesis/cementogenesis-related gene expression (ALP, Col I, Runx2 and CEMP1) were assayed for PDLCs on days 7 and 14. The results showed that the ionic products from Ca7Si2P2O16 powders significantly stimulated the proliferation, ALP activity, Ca deposition and osteogenesis/cementogenesisrelated gene expression of PDLCs. In addition, it was found that Ca7Si2P2O16 powders had excellent apatite-mineralization ability in simulated body fluids. This study demonstrated that Ca7Si2P2O16 powders with such a specific composition possess the ability to stimulate the PDLC proliferation and osteoblast/cemenoblast-like cell differentiation, indicating that they are a promising bioactive material for periodontal tissue regeneration application.