Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.

At-sea associations in foraging little penguins

Prey distribution, patch size, and the presence of conspecifics are important factors influencing a predator's feeding tactics, including the decision to feed individually or socially. Little is known about group behaviour in seabirds as they spend most of their lives in the marine environment where it is difficult to observe their foraging activities. In this study, we report on at-sea foraging associations of little penguins (Eudyptula minor) during the breeding season. Individuals could be categorised as (1) not associating; (2) associating when departing from and/or returning to the colony; or (3) at sea when travelling, diving or performing synchronised dives. Out of 84 separate foraging tracks, 58 (69.0%) involved associations with conspecifics. Furthermore, in a total of 39 (46.4%), individuals were found to dive during association and in 32 (38.1%), individuals were found to exhibit synchronous diving. These behaviours suggest little penguins forage in groups, could synchronise their underwater movements and potentially cooperate to concentrate their small schooling prey.

Research of a new type rectifier and voltage-multiplier piezoelectric energy harvester circuit

Traditional rectifier circuit can convert AC to DC, but some disadvantages can't be avoided, such as small output current, high power consumption, low conversion efficiency. This paper designs a new type of rectifier voltage-multiplier circuit named MR MOS circuit. It uses a low let-through resistance MOS tube to replace the conventional rectifier diode, and adds the voltage-multiplying factor to the synchronous input port. Therefore, it can improve the rectifier effect and increase the output voltage. By the simulation result of Synopsys Saber Platform, it shows that the new type circuit can implement the rectification and voltage-multiplying by the simulating output pulse voltage of nano fiber made in Deakin University as the source of excitation. It can provide the basic theoretical of the piezoelectric energy harvester (PEH) development, and has certain reference significance to the development of piezoelectricity technology. © (2014) Trans Tech Publications.

Distributed multi-agent scheme for reactive power management with renewable energy

This paper presents a new distributed multi-agent scheme for reactive power management in smart coordinated distribution networks with renewable energy sources (RESs) to enhance the dynamic voltage stability, which is mainly based on controlling distributed static synchronous compensators (DSTATCOMs). The proposed control scheme is incorporated in a multi-agent framework where the intelligent agents simultaneously coordinate with each other and represent various physical models to provide information and energy flow among different physical processes. The reactive power is estimated from the topology of distribution networks and with this information, necessary control actions are performed through the proposed proportional integral (PI) controller. The performance of the proposed scheme is evaluated on a 8-bus distribution network under various operating conditions. The performance of the proposed scheme is validated through simulation results and these results are compared to that of conventional PI-based DSTATCOM control scheme. From simulation results, it is found that the distributed MAS provides excellence performance for improving voltage profiles by managing reactive power in a smarter way.

Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement

For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.

Improving online communication for students in higher education contexts

The study reported here sought to identify Higher Education students’ preferred modes of online communication whilst studying a wholly online research subject at University. The teacher education student participants from an Australian university were required to collaboratively conduct inquiry research projects in groups whilst relying upon computer-mediated communication. How do students communicate as a collaborative research group whilst only meeting online? The data were collected via the use of online pre-test and post-test surveys conducted ‘prior to’ and ‘post’ involvement in the unit of study and descriptive statistical analysis was applied. The findings revealed that important influences affecting students’ choice of communication mode included their own views on the capacity of online communication, their prior experience and the availability and accessibility of the modes. Furthermore, it was found that when given a choice, students preferred the use of asynchronous forms of digital communication to synchronous forms. Recommendations for improving online teaching, learning and research contexts in Universities are provided and the importance of considering blended mode delivery for wholly online units is argued.

A multi-agent approach for enhancing transient stability of smart grids

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

E-learning opportunities and challenges for legal education in rural Victoria

Access to justice extends beyond consideration of the systems and institutions of justice; it includes infrastructure such as transport, health, education and communications. Rural, regional and remote (‘RRR’) communities are more likely to face difficulties in accessing advice and accurate information on laws and processes available for resolution of disputes. Perhaps more fundamentally, they rarely have a voice in effecting reforms in laws and related policies. For several decades, community legal centres, legal aid, courts, and a range of other institutions have used community legal education programs to improve knowledge and access to law and justice systems, services and organisations. The recent Productivity Commission Inquiry into Access to Justice Arrangements notes that, ‘Better coordination and greater quality control in the development and delivery of these [community legal education, legal information] services would improve their value and reach.’ At the same time, research into the professional needs of RRR legal practitioners has found that many of these practitioners face considerable difficulties accessing good quality continuing professional development (‘CPD’) and informal networking/support opportunities.6 Current and emerging internet-based technologies open up opportunities for legal organisations to better meet the educational needs of both rural communities and legal practitioners. Though limitations still exist at multiple levels, relatively low-cost, media-rich, synchronous and tailored education programs can now be delivered effectively in many rural and remote areas. However, complex layers of decisions are required to critically assess, harness and optimise technologies to best suit the needs of users, and to utilise teaching and learning techniques that best match the technologies and participant needs. Getting these elements — needs, technology and learning technique — right, nevertheless offers extraordinary opportunities. Sound decisions and good practices should enable state-wide and specialist law and justice-related services interested in improving their engagement with RRR communities to dramatically improve the reach and quality of outcomes, not only for distant participants but the spectrum of stakeholders.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

Online interprofessional education in dietetic students

Aim: The need for dietetic students to develop interprofessional collaborative practice capabilities is well recognised. The aim of this study was to examine the ability of an online interprofessional education (IPE) unit (using asynchronous and synchronous media) to improve dietetic students' confidence in understanding professional roles and attitudes associated with interprofessional practice along with exploring their experience in the unit. Methods: Final year master of dietetic students undertaking a compulsory online unit in IPE were invited to complete a questionnaire examining their confidence in understanding professional roles and their interprofessional attitudes both pre- and post-delivery of the unit, and to participate in semi-structured telephone interviews to explore their experience in the unit. Results: Thirty-five dietetic students completed the questionnaire pre- and post-unit, along with seven students undertaking a telephone interview. Their confidence in understanding the roles of other health professions (P = 0.000 to 0.014), self-assessment of interprofessional communication and teamwork skills (P = 0.002) and attitudes towards interprofessional interaction (P = 0.001) and interprofessional relationships (P = 0.002) increased significantly from pre- to post-unit. The students articulated positives about the experience (flexibility of the delivery, opportunity to reflect on personal factors related to teamwork, increasing their knowledge of other professions) and some challenges (lack of body language, some technological difficulties, the need for regular contribution to the teams' asynchronous discussion boards). Conclusions: This study suggests that although there can be some challenges in online IPE, it can be a positive experience and can improve dietetic students' collaborative practice attitudes and confidence in understanding other professional roles.

A comparative study of different power system stabilizers for dynamic stability analysis

Power system stabilizers (PSSs) are extensively used to ensure the dynamic stability of power systems through the modulation of excitation signals supplied to synchronous generators. This paper presents a comparative study of two different PSSs: STAB1 and IEEEST. The stabilizers are designed for the linearized model of a single machine infinite bus (SMIB) system with different loads. Both time-and frequency-domain simulations are carried out to investigate the performance of these stabilizers. For all PSSs, the time-domain simulations are performed by applying a three-phase short-circuit fault at the terminal of the synchronous generator. These simulation results are compared against the open-loop characteristics of the SMIB system where no PSS is implemented. Simulation results demonstrate that the speed-fed PSS provides more damping as compared to frequency- and power-fed stabilizers.

Child learning and development: concerns and perceptions of Chinese urban mothers

Child development is deservedly dominant in the discourses on education. In populated communities such as China’s, awareness of the prevalent ideas about child development within families is particularly important. Drawing on a series of conversations between parents and teachers through synchronous online text chat, this paper investigated the perceptions and concerns of Chinese urban parents on child development. The participants were mothers of three to six year old children from Changchun, China. Results were presented in terms of the nature of the questions the mothers raised and what they talked about when discussing their questions. Analyses of the mothers’ texts revealed their concerns on those elements of child development which challenged their roles in parenting, such as inappropriate social behaviours or regulated emotions. Data from the study provided insights into key characteristics of contemporary Chinese preschool children’s learning and development within families that might identify issues and trends of early childhood education on a larger contextual scope.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Robust adaptive backstepping excitation controller design for simple power system models with external disturbances

This paper presents a nonlinear robust adaptive excitation controller design for a simple power system model where a synchronous generator is connected to an infinite bus. The proposed controller is designed to obtain the adaption laws for estimating critical parameters of synchronous generators which are considered as unknown while providing the robustness against the bounded external disturbances. The convergence of different physical quantities of a single machine infinite bus (SMIB) system, with the proposed control scheme, is ensured through the negative definiteness of the derivative of Lyapunov functions. The effects of external disturbances are considered during formulation of Lyapunov function and thus, the proposed excitation controller can ensure the stability of the SMIB system under the variation of critical parameters as well as external disturbances including noises. Finally, the performance of the proposed scheme is investigated with the inclusion of external disturbances in the SMIB system and its superiority is demonstrated through the comparison with an existing robust adaptive excitation controller. Simulation results show that the proposed scheme provides faster responses of physical quantities than the existing controller.