Hanyu Jiang

TL;DR: This paper develops several methods to represent modulated signals in data formats with gridlike topologies for the CNN and demonstrates the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

TL;DR: This paper proposes a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering and builds a feature set fusing deep features, morphological features, texture features, and density features.

TL;DR: Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection.

TL;DR: CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications and achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features.

TL;DR: A scalable computational framework based on modeling biochemical reactions in explicit 3D space, that is suitable for studying the behavior of large and complex biological pathways, and introduces the Parallel Select algorithm that is key to breaking the sequential bottleneck limiting the performance of most other tools designed to study biochemical interactions.