WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that it developed an acoustic hologram reconstruction based on unsupervised wavefield deep learning to address the limitations of traditional acoustic hologram reconstruction methods and improve the efficiency and accuracy of acoustic data processing.
The key to WiMi’s unsupervised wavefield deep learning-based acoustic hologram reconstruction is it can automatically reconstruct holograms of acoustic data without supervised or human intervention. It is unique in that it utilizes an unsupervised learning approach to automatically learn patterns and features in acoustic wavefield data through deep learning algorithms. This not only dramatically improves the processing efficiency of acoustic data, but also enables applications in a variety of fields, including medical diagnostics, material testing, and non-destructive testing. The principle of the acoustic hologram reconstruction technique based on unsupervised wavefield deep learning is as follows:
Data acquisition: First, acoustic data needs to be acquired, which can capture the reflection, scattering or propagation of sound waves through sensors. These data include information such as the amplitude, frequency, and phase of the sound wave, usually recorded as a time series. These data constitute the acoustic wavefield data.
Data pre-processing: Acoustic wavefield data typically undergoes a number of pre-processing steps to remove noise, adjust the amplitude range of the data, etc. This ensures the quality and consistency of the data.
Wavefield deep learning model: This is the core part of the technology. A deep learning model is used to process the acoustic wavefield data. This model may be a convolutional neural network (CNN) or other neural network structure suitable for processing wavefield data.
Unsupervised learning: The key to this is the use of the unsupervised learning method. Unlike traditional supervised learning, unsupervised learning does not require data with labels to guide the training of the model. The acoustic wavefield data itself contains a wealth of information from which the model only needs to learn.
Feature learning: The deep learning model gradually learns the features and patterns in the data by warping the acoustic wavefield data. These features may include the frequency, wavelength, phase, amplitude, etc. of the sound waves. The model automatically recognizes which features are most important for the reconstruction of the acoustic hologram.
Acoustic hologram reconstruction: Once the model has learned enough features and patterns, it can use this information to generate acoustic holograms. Acoustic hologram is a visual representation of how sound waves interact and propagate into different objects or media. This process can be viewed as a process of reducing the information of the sound waves from the raw data.
Model optimization and tuning: During the training, the model may need to be optimized and tuned to ensure that the generated acoustic holograms are of high quality and accuracy. This may require the use of backpropagation algorithms and loss functions to tune the model parameters.
WiMi‘s unsupervised wavefield deep learning-based acoustic hologram reconstruction utilizes a deep learning model to automatically learn patterns and features in acoustic wavefield data, and then uses this information to generate acoustic holograms. Due to the application of unsupervised learning, it can be applied to a wide range of acoustic data reconstruction tasks without the need for large amounts of labeled training data. This approach is expected to improve the efficiency and accuracy of acoustic hologram reconstruction, bringing more innovation and application potential to the scientific field. It should be noted that specific deep learning architectures and algorithms may vary depending on the implementation of the technique, and thus detailed technical details require further research and development.
Acoustic hologram reconstruction has an important place in scientific research for exploring material properties, medical diagnosis and geological exploration. The development of this technology will push the frontiers of scientific research and help solve complex problems. In the medical field, the technology can improve ultrasound medical imaging, increase the accuracy of disease diagnosis, and help physicians better understand their patients’ conditions. This is important for improving patient health and the quality of healthcare. In engineering and manufacturing, acoustic hologram reconstruction can be used to detect defects in materials and structures, improve quality control on production lines, and reduce losses and maintenance costs. In the field of geological exploration, the technology can help explore subsurface resources, improve exploration efficiency and reduce wasted resources. This technology represents the future trend of automation and intelligence. It makes full use of the concepts of deep learning and unsupervised learning to make the processing of acoustic data more intelligent and automated.
SOURCE: PRNewswire
