Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Design of Petroleum Physical Properties Prediction Application

Based on the results of previous studies regarding the modeling of the physical properties of petroleum, a mathematical model has been built to calculate the prediction of the physical properties of petroleum. The prediction is based on viscosity, interfacial tension, and density data from the EOR laboratory in UPN Veteran Yogyakarta. The model still cannot be used independently without the Python environment, so to be used easily by more users, the model must be built into an independent application that can be installed on the user's device. In this research, the application design for the physical properties of petroleum prediction application will be carried out. The application is built using the Multivariate Polynomial Regression method according to the model to predict the physical properties of petroleum, and uses Naïve Bayes to classify the petroleum, and will be the changing result of the physical properties of petroleum modeling that has been made in a previous study. The shift from model to the application requires several adjustments, including user interface, system, and database adjustments which are implemented as the designs of application. . The design is done before the application is built to suit user needs as the result of the research.

Combining Action Observation Treatment with a Brain–Computer Interface System: Perspectives on Neurorehabilitation

Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain–computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson’s disease patients, and children with cerebral palsy.

Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.