Improving Acceptance to Sensory Substitution: A study on the V2A-SS Learning Model based on Information Processing Learning Theory.

The visual sensory organ (VSO) serves as the primary channel for transmitting external information to the brain; therefore, damage to the VSO can severely limit daily activities. Visual-to-Auditory Sensory Substitution (V2A-SS), an innovative approach to restoring vision, offers a promising solution by leveraging neuroplasticity to convey visual information via auditory channels. Advances in information technology and artificial intelligence mitigate technical challenges such as low resolution and limited bandwidth, thereby enabling broader applicability of V2A-SS.

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution.

Visual-auditory sensory substitution has demonstrated great potential to help visually impaired and blind groups to recognize objects and to perform basic navigational tasks. However, the high latency between visual information acquisition and auditory transduction may contribute to the lack of the successful adoption of such aid technologies in the blind community; thus far, substitution methods have remained only laboratory-scale research or pilot demonstrations. This high latency for data conversion leads to challenges in perceiving fast-moving objects or rapid environmental changes.

Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory.

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device.