An intelligent forex monitoring system

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network

An auto-switch for multisampling of a wire beam electrode corrosion monitoring system

An auto-switch device has been designed, constructed, and tested for multisensor corrosion measurements. The auto-switch is able to connect/disconnect the selected wire beam electrode and/or electrode pairs under computer control. A computer controlled analog switch-array consisting of 100 inputs has been used as an essential part of the switch bank. The switch-bank is an essential component of a corrosion-monitoring instrument that is expected to find wide application in industry. The performance of the switch-bank is discussed and its effect on the signals is illustrated.

Using an Electronic Monitoring System to Link Offspring Provisioning and Foraging Behavior of a Wild Passerine

Although the costs of parental care are at the foundations of optimal-parental-investment theory, our understanding of the nature of the underlying costs is limited by the difficulty of measuring variation in foraging effort. We simultaneously measured parental provisioning and foraging behavior in a free-living population of Zebra Finches (Taeniopygia guttata) using an electronic monitoring system. We fitted 145 adults with a passive transponder tag and remotely recorded their visits to nest boxes and feeders continuously over a 2-month period. After validating the accuracy of this monitoring system, we studied how provisioning and foraging activities varied through time (day and breeding cycle) and influenced the benefits (food received by the offspring) and costs (interclutch interval) of parental care. The provisioning rates of wild Zebra Finches were surprisingly low, with an average of only one visit per hour throughout the day. This was significantly lower than those reported for this model species in captivity and for most other passerines in the wild. Nest visitation rate only partially explained the amount of food received by the young, with parental foraging activity, including the minimum distance covered on foraging trips, being better predictors. Parents that sustained higher foraging activity and covered more distance during the first breeding attempt took longer to renest. These results demonstrate that in some species matching foraging activity with offspring provisioning may provide a better estimate of the true investment that individuals commit to a reproductive attempt.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

On-line tool condition monitoring and control system in forging processes

Cold bulk metal forming has made large-scale production of small complex solid parts economically feasible. Tooling used in metal forming poses many uncertainties in the preliminary cost estimation and production process and continual tool replacement and maintenance dramatically reduces productivity and raises manufacturing cost. In order to tackle this, an on-line tool condition monitoring system using artificial neural network (ANN) to integrate information from multiple sensors for forging process has been developed. Together with the force, acoustic emission signals and process conditions, information developed from theoretical models is integrated into the ANN tool monitoring system to predict tool life and provide the maintenance schedule.

Measurement and data transmission validity of a multi-biosensor system for real-time remote exercise monitoring among cardiac patients

Background: Remote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.

Objective: Our aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.

Methods: Participants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.

Results: Median heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all P≤.003) due to the large number of observations. Measurement reliability was generally excellent (rho=.87-.97, all P<.001; intraclass correlation coefficient [ICC]=.94-.98, all P<.001; coefficient of variation [CV]=2.24-7.94%), although respiratory rate measurement reliability was poor among AF participants (rho=.43, P<.001; ICC=.55, P<.001; CV=16.61%). Data loss was minimal (<5%) when all system components were active; however, instability of the network hosting the remote data capture server resulted in data loss at the remote Internet server during some trials.

Conclusions: System validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.

Acoustic condition monitoring for cold metal forming

One of the most important objectives of cold metal forming research is to develop techniques that enable better manufacturing efficiencies. Within this monitoring of tooling condition is vital to providing high quality manufacturing. The objective of this research is to determine the signature derived from Acoustic Emission (AE) sensors, in order to establish the current condition of a machine tool, as applied to bolt-making. From here we aim to develop and implement an on-line condition monitoring tool for the cold forming process. A review of the literature has shown that much research into AE has been successfully applied in metal cutting operations; such as milling, drilling and turning, but little research has been done related to metal forming. This appears to be due to the complexity of obtaining consistent signals using Acoustic Emission systems, because the presence of noise in many forms. This paper will detail many of the AE signals acquired and analysed through our research. The extensive results indicate this form of condition monitoring is not suitable for metal forming in its current configuration. Further tests are proposed to enable such research to move forward, so a condition monitoring system can be established.

Acceptability, impact and sustainability of the national pilot quality assurance system for self management programs

Chronic condition self-management education and training interventions such as the Stanford Self Management Programs (SMP) have the capacity to improve health and quality of life of people with chronic conditions whilst reducing the use of health services. This is in line with the outcomes from the recent Council of Australian Governments’ meeting where it was indicated that self-management will be a centrepiece in forthcoming chronic disease initiatives.
Aim: report on a large national pilot quality assurance program involving the implementation and of an evaluation and quality monitoring system for SMPs including the provision of structured feedback to courses course leaders and service providers. During 2005/06 the quality assurance program was implemented at 11 diverse organisations across Australia. The program involved assisting organisations apply the 42-item Health Education Impact Questionnaire (HEIQ), a chronic disease health education outcome measure, and then observe and evaluate the value and impact of the quality program. Interviews with course leaders (n=60) and course participants (n=35) have elicited views about course quality and feedback processes.
Results: The evaluation revealed enablers and barriers to effective implementation and sustainability. Important enablers were:
- Course Leaders and organisations valued an Australia-wide system that provided feedback on course
quality and the impact on participants.
- Course Leaders were strongly personally motivated to respond appropriately to HEI-Q course
report feedback.
- Completing the questionnaire provided participants with the opportunity to reflect on issues that
emerge in the course content and reflect on their progression at the end of the SSMP.
Sustainability issues included:
- Organisations and course leaders require support, training and flexibility on how to administer and
manage the use of the HEI-Q.
- Availability of administrative resources in organisations to support the quality assurance activities.
- The requirement that course leaders are trained in interpreting HEI-Q course report data.
A quality improvement framework was developed which identified the actions required of key stakeholders to
support effective implementation.
Discussion: With the increasing endorsement of SMP across sectors it is important that course quality is known, is acceptable, and is communicated to stakeholders to inform and engender confidence in the SSMP. To effectively implement and sustain a quality improvement program for SMP, the processes and tools for measuring outcomes need to be responsive, flexible and easily integrated into the organisation and delivery of programs.

Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.