Recent Projects

The rapid development of genome sequencing technology has fundamentally changed the way humans study the blueprints of life. With the continuous development of genome sequencing technology, the expense of sequencing has been largely reduced, and the efficient compression and transmission of big genome data have become the fundamental technology of the bioinformatics industry. This project aims to explore the inherent redundancy and utility within the genome data, and achieves high-efficiency compression and transmission of high-throughput genome data through the optimal representation with deep neural networks.

Generally speaking, there are three distinguished properties of Internet of Video Things (IoVT) sensor data 1) the data are acquired in an uncontrolled physical environment without sophisticated equipment and elaborate acquisition preparations; 2) the data are featured with high volume and low-value density, instead of being entirely valuable with high utility; 3) the data possess underlying regularities, as they are generated without human intervention instead of being carefully processed and edited. As such, we aim to develop a new scheme based on intelligent sensing at the front-end, in an effort to provide highly conceptual and extremely compact representation with the consideration of these characteristics. The pipeline is composed of four modules, including visual data tracing with representation in-loop, utility-oriented resource allocation and data-driven based inference at the encoder side, as well as unsupervised deep reconstruction in an effort to better reform the visual data at the decoder side.