Piecewise companding transform assisted optical-OFDM systems for indoor visible light communications

In visible light communications (VLCs) relying on intensity-modulation and direct detection (IM/DD), the conversion from electrical signals to optical signals and the limited dynamic range of the light-emitting diodes (LEDs) constitute the fundamental impediments in the way of high-integrity communications, especially when orthogonal frequency-division multiplexing (OFDM) is employed. In IM/DD VLCs, only real-valued positive signals are used for signal transmission. However, the Fourier transform of OFDM systems is operated in the complex domain. In order to meet the requirements of the IM/DD VLCs, the complex-to-real conversion is achieved at the cost of reducing the bandwidth efficiency. Moreover, OFDM signals experience a high peak-to-average power ratio; hence, typically clipping is used for confining the positive-valued signals within the LED's dynamic range. However, hard clipping leads to the loss of orthogonality for optical OFDM (O-OFDM) signals, generating inter-carrier interference. As a result, the performance of the clipping-based O-OFDM systems may be severely degraded. In this paper, the concept of piecewise companding transform (CT) is introduced into the O-OFDM system advocated, forming the CTO-OFDM arrangement. We first investigate the general principles and design criteria of the piecewise CTO-OFDM. Based on our studies, three types of piecewise companders, namely, the constant probability sub-distribution function, linear PsDF (LPsDF), and the non-LPsDF-based CT, are designed. Furthermore, we investigate the nonlinear effect of hard clipping and of our CT on O-OFDM systems in the context of different scenarios by both analytical and simulation techniques. Our investigations show that the CTO-OFDM constitutes a promising signaling scheme conceived for VLCs, which exhibits a high bandwidth efficiency, high flexibility, high reliability, as well as a high data-rate, despite experiencing nonlinear distortions

Topological Crystalline Insulators with C2C_2 Rotation Anomaly

Based on first-principles calculations and symmetry-based indicator analysis, we find a class of topological crystalline insulators (TCIs) with $C_2$ rotation anomaly in a family of Zintl compounds, including $\mathrm{Ba}_{3}\mathrm{Cd}_{2}\mathrm{As}_{4}$, $\mathrm{Ba}_{3}\mathrm{Zn}_{2}\mathrm{As}_{4}$ and $\mathrm{Ba}_{3}\mathrm{Cd}_{2}\mathrm{Sb}_{4}$. The nontrivial band topology protected by coexistence of $C_2$ rotation symmetry and time-reversal symmetry $T$ leads to two surface Dirac cones at generic momenta on both top and bottom surfaces perpendicular to the rotation axis. In addition, ($d-2$)-dimensional helical hinge states are also protected along the hinge formed by two side surfaces parallel with the rotation axis. We develop a method based on Wilson loop technique to prove the existence of these surface Dirac cones due to $C_2$ anomaly and precisely locate them as demonstrated in studying these TCIs. The helical hinge states are also calculated. Finally, we show that external strain can be used to tune topological phase transitions among TCIs, strong Z$_2$ topological insulators and trivial insulators.Comment: 10 pages, 10 figure

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system