Trajectory and Power Design for Aerial Multi-User Covert Communications

Unmanned aerial vehicles (UAVs) can provide wireless access to terrestrial users, regardless of geographical constraints, and will be an important part of future communication systems. In this paper, a multi-user downlink dual-UAVs enabled covert communication system was investigated, in which a UAV transmits secure information to ground users in the presence of multiple wardens as well as a friendly jammer UAV transmits artificial jamming signals to fight with the wardens. The scenario of wardens being outfitted with a single antenna is considered, and the detection error probability (DEP) of wardens with finite observations is researched. Then, considering the uncertainty of wardens' location, a robust optimization problem with worst-case covertness constraint is formulated to maximize the average covert rate by jointly optimizing power allocation and trajectory. To cope with the optimization problem, an algorithm based on successive convex approximation methods is proposed. Thereafter, the results are extended to the case where all the wardens are equipped with multiple antennas. After analyzing the DEP in this scenario, a tractable lower bound of the DEP is obtained by utilizing Pinsker's inequality. Subsequently, the non-convex optimization problem was established and efficiently coped by utilizing a similar algorithm as in the single-antenna scenario. Numerical results indicate the effectiveness of our proposed algorithm.Comment: 30 pages, 9 figures, submitted to the IEEE journal for revie

Training Energy-Based Models with Diffusion Contrastive Divergences

Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which leads to an irreconcilable trade-off between the computational burden and the validity of the CD. Running MCMCs till convergence is computationally intensive. On the other hand, short-run MCMC brings in an extra non-negligible parameter gradient term that is difficult to handle. In this paper, we provide a general interpretation of CD, viewing it as a special instance of our proposed Diffusion Contrastive Divergence (DCD) family. By replacing the Langevin dynamic used in CD with other EBM-parameter-free diffusion processes, we propose a more efficient divergence. We show that the proposed DCDs are both more computationally efficient than the CD and are not limited to a non-negligible gradient term. We conduct intensive experiments, including both synthesis data modeling and high-dimensional image denoising and generation, to show the advantages of the proposed DCDs. On the synthetic data learning and image denoising experiments, our proposed DCD outperforms CD by a large margin. In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs

Galaxy populations in groups and clusters: evidence for a characteristic stellar mass scale at M∗∼109.5M⊙M_\ast\sim 10^{9.5}M_\odot

We use the most recent data release (DR9) of the DESI legacy imaging survey and SDSS galaxy groups to measure the conditional luminosity function (CLF) for groups with halo mass $M_{\rm h}\ge 10^{12}M_{\odot}$ and redshift $0.01\le z\le 0.08$, down to a limiting $r$-band magnitude of $M_{\rm r}=-10\sim-12$. For a given halo mass we measure the CLF for the total satellite population, as well as separately for the red and blue populations classified using the $(g-z)$ color. We find a clear faint-end upturn in the CLF of red satellites, with a slope $\alpha\approx-1.8$ which is almost independent of halo mass. This faint-end upturn is not seen for blue satellites and for the total population. Our stellar population synthesis modeling shows that the $(g-z)$ color provides a clean red/blue division, and that group galaxies in the red population defined by $(g-z)$ are all dominated by old stellar populations. The fraction of old galaxies as a function of galaxy luminosity shows a minimum at a luminosity $M_{\rm r}\sim-18$, corresponding to a stellar mass $M_\ast\sim10^{9.5}M_\odot$. This mass scale is independent of halo mass and is comparable to the characteristic luminosity at which galaxies show a dichotomy in surface brightness and size, suggesting that the dichotomy in the old fraction and in galaxy structure may have a common origin. The rising of the old fraction at the faint end for Milky Way (MW)-sized halos found here is in good agreement with the quenched fraction measured both for the MW/M31 system and from the ELVES survey. We discuss the implications of our results for the formation and evolution of low-mass galaxies, and for the stellar mass functions of low-mass galaxies to be observed at high redshift.Comment: 26 pages, 13 figures, accepted by Ap

An Improved Anisotropic Vector Preisach Model for Nonoriented Electrical Steel Sheet Based on Iron Loss Separation Theory

An improved anisotropic vector Preisach model is proposed in this paper to describe the hysteresis properties of nonoriented (NO) electrical steel sheet (ESS) under 50 Hz rotating magnetic fields. The proposed model consists of three components, static hysteresis component, eddy current component, and excess component, which is based on the iron loss separation theory. The static hysteresis component is constructed by the static vector Preisach model. The proposed model is identified by the measured hysteresis properties under 1 Hz and 50 Hz magnetic fields. Finally, the experimental results prove the effectiveness of the proposed anisotropic vector hysteresis model

Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

Saiyan: Design and Implementation of a Low-power Demodulator for LoRa Backscatter Systems

The radio range of backscatter systems continues growing as new wireless communication primitives are continuously invented. Nevertheless, both the bit error rate and the packet loss rate of backscatter signals increase rapidly with the radio range, thereby necessitating the cooperation between the access point and the backscatter tags through a feedback loop. Unfortunately, the low-power nature of backscatter tags limits their ability to demodulate feedback signals from a remote access point and scales down to such circumstances. This paper presents Saiyan, an ultra-low-power demodulator for long-range LoRa backscatter systems. With Saiyan, a backscatter tag can demodulate feedback signals from a remote access point with moderate power consumption and then perform an immediate packet retransmission in the presence of packet loss. Moreover, Saiyan enables rate adaption and channel hopping-two PHY-layer operations that are important to channel efficiency yet unavailable on long-range backscatter systems. We prototype Saiyan on a two-layer PCB board and evaluate its performance in different environments. Results show that Saiyan achieves 5 gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of Saiyan is around 93.2 uW. Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan