Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks

This paper aims at answering two fundamental questions: how area spectral efficiency (ASE) behaves with different system parameters; how to design an energy-efficient network. Based on stochastic geometry, we obtain the expression and a tight lower-bound for ASE of Poisson distributed networks considering multi-user MIMO (MU-MIMO) transmission. With the help of the lower-bound, some interesting results are observed. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another work of this paper is joint optimization of the base station (BS) density, the number of antennas and active users to minimize the network energy consumption. It is discovered that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near optimal performance.Comment: Submitted to IEEE Transactions on Wireless Communications, Major Revisio

Entry patterns of low‐cost carriers in Hong Kong and implications to the regional market

This study aims to provide a better understanding of Asia’s lowcost carriers (LCCs) by empirically analysing their route entry patterns in Hong Kong. Two alternative models have been tested, namely a standard probit model and a generalized least squares estimation. Consistent findings from the two models suggest that LCCs in Asia have a clear preference for high density routes, and the dominance of incumbent full service airlines (FSAs) and the lack of secondary airports are not critical to the growth of LCCs. However, government regulations and airport access are main impediment factors. Despite the adoption of long-distance lowcost models by the region’s airlines, geographic distance still plays an important role in LCCs’ entry decisions. For the growth of low-cost travel and associated benefits in the tourism industry and overall economy, it is important for governments in the region to liberalize aviation markets, provide sufficient airport capacity, and promote efficient allocation of airport slots

Efficient Halftoning via Deep Reinforcement Learning

Halftoning aims to reproduce a continuous-tone image with pixels whose intensities are constrained to two discrete levels. This technique has been deployed on every printer, and the majority of them adopt fast methods (e.g., ordered dithering, error diffusion) that fail to render structural details, which determine halftone's quality. Other prior methods of pursuing visual pleasure by searching for the optimal halftone solution, on the contrary, suffer from their high computational cost. In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach. Specifically, we formulate halftoning as a reinforcement learning problem, in which each binary pixel's value is regarded as an action chosen by a virtual agent with a shared fully convolutional neural network (CNN) policy. In the offline phase, an effective gradient estimator is utilized to train the agents in producing high-quality halftones in one action step. Then, halftones can be generated online by one fast CNN inference. Besides, we propose a novel anisotropy suppressing loss function, which brings the desirable blue-noise property. Finally, we find that optimizing SSIM could result in holes in flat areas, which can be avoided by weighting the metric with the contone's contrast map. Experiments show that our framework can effectively train a light-weight CNN, which is 15x faster than previous structure-aware methods, to generate blue-noise halftones with satisfactory visual quality. We also present a prototype of deep multitoning to demonstrate the extensibility of our method

Multiphoton and Fluorescence Lifetime Imaging Microscopy in Studying Nanoparticle Pharmacokinetics in Skin and Liver

The use of nanoparticles has increased in consumer products in recent decades; however, concerns regarding their safety remain. Zinc oxide is used in sunblocking and may generate free radicals in response to UV illumination, leading to DNA damage and an immunological response. With high‐resolution, high‐contrast imaging in biological tissue, multiphoton microscopy is able to separate nanoparticles signals from endogenous fluorophores. It has been proven to be very useful in imaging penetration of zinc oxide nanoparticles in skin and in combination with fluorescence lifetime imaging microscopy study cellular function as well. This chapter aims to review the use of these imaging techniques in studying the uptake and distribution of nanoparticles in skin and liver. Due to the questionable clinical use and possible toxicity of nanoparticles, it is important to study their pharmacokinetics. Some nanomaterials have been identified as relatively toxic to humans and a few metal nanoparticles have been reported to penetrate and be detected in blood. Multiphoton microscopy has high resolution and is able to visualize nanoparticles, due to their optical properties, in vivo. The addition of fluorescence lifetime imaging makes it possible to measure the physiochemical environment, with outputs that can be statistically analyzed, posing an advantage over fluorescence intensity imaging only