Flexible multiterminal photoelectronic neurotransistors based on self‐assembled rubber semiconductors for spatiotemporal information processing

A significant step toward constructing high‐efficiency neuromorphic systems is the electronic emulation of advanced synaptic functions of the human brain.While previous studies have focused on mimicking the basic functions of synapses using single‐gate transistors,multigate transistors offer an opportunity to simulate more complex and advanced memory‐forming behaviors in biological synapses.In this study,a simple and general method is used to assemble rubber semiconductors into suspended two‐phase composite films that are transferred to the surface of the ion‐conducting membrane to fabricate flexible multiterminal photoelectronic neurotransistors.The suspended ion conductive film is used as the gate dielectrics and supporting substrate.The prepared devices exhibit excellent electrical stability and mechanical flexibility after being bent.Basic photoelectronic synaptic behavior and pulse‐dependent plasticity are emulated.Furthermore,the device realizes the spatiotemporally integrated electrical and optical stimuli to mimic spatiotemporal information processing.This study provides a promising direction for constructing more complex spiking neural networks and more powerful neuromorphic systems with brain‐like dynamic spatiotemporal processing functions.

AUTOMATIC SEGMENTATION OF VIDEO OBJECT PLANES IN MPEG-4 BASED ON SPATIO-TEMPORAL INFORMATION

Segmentation of semantic Video Object Planes (VOP's) from video sequence is a key to the standard MPEG-4 with content-based video coding. In this paper, the approach of automatic Segmentation of VOP's Based on Spatio-Temporal Information (SBSTI) is proposed.The proceeding results demonstrate the good performance of the algorithm.

Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.