Bias-aided DD-LMS equalizer for optical multipath-interference-impaired high-speed PAM4 transmission systems.

Multipath interference (MPI) noise induces drastic fluctuations in high-speed 4-level pulse amplitude modulation (PAM4) intensity modulation direct detection (IMDD) systems, severely degrading the transmission performance. Here, we propose a bias-aided decision-directed least mean square (DD-LMS) equalizer to eliminate the MPI noise. In the simulation, the proposed bias-aided DD-LMS equalizer could adeptly track and compensate the MPI-impaired PAM4 signal, markedly improving the bit error rate (BER) performance under different MPI levels and laser linewidths.

Are 4f-Orbitals Engaged in Covalent Bonding Between Lanthanides and Triphenylphosphine Oxide? An Oxygen K-Edge X-ray Absorption Spectroscopy and Density Functional Theory Study.

The bonding covalency between trivalent lanthanides (Ln = La, Pr, Nd, Eu, Gd) and triphenylphosphine oxide (TPPO) is studied by X-ray absorption spectra (XAS) and density functional theory (DFT) calculations on the LnCl(TPPO) complexes. The O, P, and Cl K-edge XAS for the single crystals of LnCl(TPPO) were collected, and the spectra were interpreted based on DFT calculations. The O and P K-edge XAS spectra showed no significant change across the Ln series in the LnCl(TPPO) complexes, unlike the Cl K-edge XAS spectra.

Synthesis, Structure, and Theoretical Calculations on NpOBr.

The actinide-halogen complexes (AnOX, X = Cl, Br, and I) are the simplest and most representative compounds for studying the bonding nature of actinides with ligands. In this work, we attempted to synthesize the crystals of NpOX (X = Cl, Br, and I). The crystals of NpOCl and NpOBr were successfully synthesized, in which the structure of NpOBr was obtained for the first time.

Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms.

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment.

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography.

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.

Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis.

Objective: Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment.

Methods: We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients.

Open set recognition algorithm based on Conditional Gaussian Encoder.

For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the original form of the signal as much as possible and deep neural network is used to extract useful information. CG-Encoder adopts residual network structure and a new Kullback-Leibler (KL) divergence is defined.