Maxwell Equation for the Coupled Spin-Charge Wave Propagation

We show that the dissipationless spin current in the ground state of the Rashba model gives rise to a reactive coupling between the spin and charge propagation, which is formally identical to the coupling between the electric and the magnetic fields in the 2+1 dimensional Maxwell equation. This analogy leads to a remarkable prediction that a density packet can spontaneously split into two counter propagation packets, each carrying the opposite spins. In a certain parameter regime, the coupled spin and charge wave propagates like a transverse "photon". We propose both optical and purely electronic experiments to detect this effect.Comment: 4 page

Information-Coupled Turbo Codes for LTE Systems

We propose a new class of information-coupled (IC) Turbo codes to improve the transport block (TB) error rate performance for long-term evolution (LTE) systems, while keeping the hybrid automatic repeat request protocol and the Turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing a few common information bits. We propose a feed-forward and feed-back decoding scheme and a windowed (WD) decoding scheme for decoding the whole TB by exploiting the coupled information between CBs. Both decoding schemes achieve a considerable signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and our proposed IC Turbo codes from the EXIT functions of underlying convolutional codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes is derived and calculated by the constructed EXIT charts. Numerical results show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for various code parameters at a TB error rate level of $10^{-2}$, which complies with the derived SNR gain upper bound.Comment: 13 pages, 12 figure

Revealing the relationship between human mobility and urban deprivation using geo-big data: a case study from London in the post-pandemic era

Investigating the association between human mobility and urban deprivation helps to understand the disparate routines of urban residents with different socioeconomic vulnerabilities. Though lots of research have revealed the difference in the population’s mobility behaviours impacted by social distancing measures during the COVID-19 pandemic since 2020, limited analytics focuses on the inequality in mobility recovery patterns of urban residents in the post-pandemic era. Using a large-scale geo-big data set (mobile phone GPS trajectories), we calculated the associations between the measured mobility recovery rate and urban deprivation indices (seven categories) in 4835 London communities (LSOAs) during the first four months of 2022. We show that mobility recovery is associated with urban deprivation (particularly the ‘Barriers to Housing and Services’ deprivation index) over the observed post-pandemic period. The results further demonstrate that the residents from higher deprived/vulnerable communities are likely to obtain lower mobility recovery rates in London

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti