The convolutional neural network (CNN) has recently achieved great breakthroughs in many
computer vision tasks. However, its application in fabric texture defects classification has not …

In this paper, we integrate some ideas of sparse autoencoder of deep learning into
compressed sensing (CS) theory, and set up a sparse autoencoder compressed sensing …

Base stations (BS) in a cellular network send unique synchronization signals based on their
physical layer cell-identity, in every frame. To synchronize with a BS, a mobile terminal (MT) …

Network densification by installing more cellular stations (cells) with smaller coverage is
promising to improve wireless capacity to meet the explosive growth of cellular traffic. The …

针对全球导航卫星系统(GNSS) 拒止环境下定位精度难以保证的问题, 该文设计了一种基于新
无线电(NR) 机会信号的定位方案, 并提出一种基于干扰消除子空间追踪(ICSP) 算法 …

We consider the problem of jointly recovering block sparse signals that share the same
support set, using multiple measurement vectors (MMVs). We consider the generalized MMV …

mmWave bands have large chunks of spectrum and are promising for satisfying the
unprecedented cellular traffic demands of future cellular networks. Unfortunately, the …

To strike a balance between energy efficiency and data quality control, this paper proposes
a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing …

According to the idea of deep learning, this paper designs a new multilayer long short-term
memory (LSTM) network method, a data driven model for sequence modeling. We use this …

One of the most critical challenges of future generation cellular networks is to meet the
unprecedented cellular traffic demands from mobile applications. mmWave has large …