Sound Source Localization Using a Convolutional Neural Network and Regression Model

Sound-Based Localization Using LSTM Networks for Visually Impaired Navigation

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer's memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.

Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution

To extract the phase information from multiple receivers, the conventional sound source localization system involves substantial complexity in software and hardware. Along with the algorithm complexity, the dedicated communication channel and individual analog-to-digital conversions prevent an increase in the system's capability due to feasibility. The previous study suggested and verified the single-channel sound source localization system, which aggregates the receivers on the single analog network for the single digital converter. This paper proposes the improved algorithm for the single-channel sound source localization system based on the Gaussian process regression with the novel feature extraction method. The proposed system consists of three computational stages: homomorphic deconvolution, feature extraction, and Gaussian process regression in cascade. The individual stages represent time delay extraction, data arrangement, and machine prediction, respectively. The optimal receiver configuration for the three-receiver structure is derived from the novel similarity matrix analysis based on the time delay pattern diversity. The simulations and experiments present precise predictions with proper model order and ensemble average length. The nonparametric method, with the rational quadratic kernel, shows consistent performance on trained angles. The Steiglitz-McBride model with the exponential kernel delivers the best predictions for trained and untrained angles with low bias and low variance in statistics.

Applicability of Convolutional Neural Network for Estimation of Turbulent Diffusion Distance from Source Point

For locating the source of leaking gas in various engineering fields, several issues remain in the immediate estimation of the location of diffusion sources from limited observation data, because of the nonlinearity of turbulence. This study investigated the practical applicability of diffusion source-location prediction using a convolutional neural network (CNN) from leaking gas instantaneous distribution images captured by infrared cameras. We performed direct numerical simulation of a turbulent flow past a cylinder to provide training and test images, which are scalar concentration distribution fields integrated along the view direction, mimicking actual camera images. We discussed the effects of the direction in which the leaking gas flows into the camera’s view and the distance between the camera and the leaking gas on the accuracy of inference. A single learner created by all images provided an inference accuracy exceeding 85%, regardless of the inflow direction or the distance between the camera and the leaking gas within the trained range. This indicated that, with sufficient training images, a high-inference accuracy can be achieved, regardless of the direction of gas leakage or the distance between the camera and the leaking gas.

A Novel Strategy for Smart Building Convergence Based on the SmartLVGrid Metamodel

Smart buildings provide opportunities for technological transformations in building environments to improve resource management, comfort, and efficiency of the systems present in these facilities. For this, Internet of Things (IoT) solutions contribute, with monitoring and remote control features, to automate these environments. However, these solutions can promote the disposal or replacement of outdated but still-needed legacy systems. Thus, a reference model that uses retrofit techniques to update pre-existing systems would be an alternative to enable smart building convergence. The lack of models that advocate this type of strategy provides an opportunity for the emergence of methods capable of filling this gap. Thus, this work presents a strategy for implementing monitoring, control, and communication resources to achieve smart building convergence in legacy building systems. This strategy consists of the use of retrofit techniques based on the adaptation of the SmartLVGrid metamodel. To validate this proposal, we developed hardware platforms and, respectively, their firmware to implement the premises established in a legacy building lighting circuit. The results obtained present a new possibility of implementing smart buildings from the retrofit of legacy infrastructures, as the pre-existing building lighting circuit obtained new functionalities and was preserved as much as possible.