Fairness-Oriented Link Scheduling for a D2D-enabled LTE-U/Wi-Fi Coexistence Network

To avoid spectrum crunch and improve spectrum efficiency, the use of unlicensed spectra and the introduction of D2D communication will be areas of focus in communication development. However, in the existing unlicensed spectrum coexistence mechanism, different ways of communication are seen as hindering each other. In this paper, we deliberate the coexistence of a D2D-enabled LTE network with Wi-Fi under an unlicensed band. Unlike previous coexistence mechanisms, we allow co-channel transmission, and our goal is to make full use of the advantages of D2D proximity communication and achieve fairness in co-channel transmission. First, we modeled the coexistence network and derived the expressions coverage probability of all types of receivers. Based on the analytical model and simulation results, we prove that D2D communication can be exploited to achieve fairness requirements in co-channel transmission over the unlicensed band. We rephrase the fairness schedule problem as a mixed-integer nonlinear optimization problem for D2D density and transmit power, and we use an Ortho-MADS algorithm to solve it. The simulation results show that the proposed scheme can use D2D communication to improve the fairness of the system

Real-time optimal control for attitude-constrained solar sailcrafts via neural networks

This work is devoted to generating optimal guidance commands in real time for attitude-constrained solar sailcrafts in coplanar circular-to-circular interplanetary transfers. Firstly, a nonlinear optimal control problem is established, and necessary conditions for optimality are derived by the Pontryagin's Minimum Principle. Under some assumptions, the attitude constraints are rewritten as control constraints, which are replaced by a saturation function so that a parameterized system is formulated to generate an optimal trajectory via solving an initial value problem. This approach allows for the efficient generation of a dataset containing optimal samples, which are essential for training Neural Networks (NNs) to achieve real-time implementation. However, the optimal guidance command may suddenly change from one extreme to another, resulting in discontinuous jumps that generally impair the NN's approximation performance. To address this issue, we use two co-states that the optimal guidance command depends on, to detect discontinuous jumps. A procedure for preprocessing these jumps is then established, thereby ensuring that the preprocessed guidance command remains smooth. Meanwhile, the sign of one co-state is found to be sufficient to revert the preprocessed guidance command back into the original optimal guidance command. Furthermore, three NNs are built and trained offline, and they cooperate together to precisely generate the optimal guidance command in real time. Finally, numerical simulations are presented to demonstrate the developments of the paper

A New Smoothing Technique for Bang-Bang Optimal Control Problems

Bang-bang control is ubiquitous for Optimal Control Problems (OCPs) where the constrained control variable appears linearly in the dynamics and cost function. Based on the Pontryagin's Minimum Principle, the indirect method is widely used to numerically solve OCPs because it enables to derive the theoretical structure of the optimal control. However, discontinuities in the bang-bang control structure may result in numerical difficulties for gradient-based indirect method. In this case, smoothing or regularization procedures are usually applied to eliminating the discontinuities of bang-bang controls. Traditional smoothing or regularization procedures generally modify the cost function by adding a term depending on a small parameter, or introducing a small error into the state equation. Those procedures may complexify the numerical algorithms or degenerate the convergence performance. To overcome these issues, we propose a bounded smooth function, called normalized L2-norm function, to approximate the sign function in terms of the switching function. The resulting optimal control is smooth and can be readily embedded into the indirect method. Then, the simplicity and improved performance of the proposed method over some existing methods are numerically demonstrated by a minimal-time oscillator problem and a minimal-fuel low-thrust trajectory optimization problem that involves many revolutions.Comment: This paper has been accpted for presentation at the 2024 AIAA Scitec

Gait-based identification for elderly users in wearable healthcare systems

Abstract The increasing scope of sensitive personal information that is collected and stored in wearable healthcare devices includes physical, physiological, and daily activities, which makes the security of these devices very essential. Gait-based identity recognition is an emerging technology, which is increasingly used for the access control of wearable devices, due to its outstanding performance. However, gait-based identity recognition of elderly users is more challenging than that of young adults, due to significant intra-subject gait fluctuation, which becomes more pronounced with user age. This study introduces a gait-based identity recognition method used for the access control of elderly people-centred wearable healthcare devices, which alleviates the intra-subject gait fluctuation problem and provides a significant recognition rate improvement, as compared to available methods. Firstly, a gait template synthesis method is proposed to reduce the intra-subject gait fluctuation of elderly users. Then, an arbitration-based score level fusion method is defined to improve the recognition accuracy. Finally, the proposed method feasibility is verified using a public dataset containing acceleration signals from three IMUs worn by 64 elderly users with the age range from 50 to 79 years. The experimental results obtained prove that the average recognition rate of the proposed method reaches 96.7%. This makes the proposed method quite lucrative for the robust gait-based identification of elderly users of wearable healthcare devices

Time Reversal Enabled Fiber-Optic Time Synchronization

Over the past few decades, fiber-optic time synchronization (FOTS) has provided fundamental support for the efficient operation of modern society. Looking toward the future beyond fifth-generation/sixth-generation (B5G/6G) scenarios and very large radio telescope arrays, developing high-precision, low-complexity and scalable FOTS technology is crucial for building a large-scale time synchronization network. However, the traditional two-way FOTS method needs a data layer to exchange time delay information. This increases the complexity of system and makes it impossible to realize multiple-access time synchronization. In this paper, a time reversal enabled FOTS method is proposed. It measures the clock difference between two locations without involving a data layer, which can reduce the complexity of the system. Moreover, it can also achieve multiple-access time synchronization along the fiber link. Tests over a 230 km fiber link have been carried out to demonstrate the high performance of the proposed method

SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity

To address the challenge of increasing network size, researchers have developed sparse models through network pruning. However, maintaining model accuracy while achieving significant speedups on general computing devices remains an open problem. In this paper, we present a novel mobile inference acceleration framework SparseByteNN, which leverages fine-grained kernel sparsity to achieve real-time execution as well as high accuracy. Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning. It designs multiple sparse patterns for different operators. Combined with our proposed whole network rearrangement strategy, the schema achieves a high compression rate and high precision at the same time. (b) Inference engine co-optimized with the sparse pattern. The conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for ARM and WebAssembly. Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet-v1 outperform strong dense baselines on the efficiency-accuracy curve. Experimental results on Qualcomm 855 show that for 30% sparse MobileNet-v1, SparseByteNN achieves 1.27x speedup over the dense version and 1.29x speedup over the state-of-the-art sparse inference engine MNN with a slight accuracy drop of 0.224%. The source code of SparseByteNN will be available at https://github.com/lswzjuer/SparseByteN

Measuring pollutant emissions of cattle breeding and its spatial-temporal variation in China

The rapid development of animal husbandry has resulted in serious pollution issues in the livestock and poultry breeding industry, increasing the cost of environmental management. This issue is particularly prominent in China due to its rapid economic development, significant domestic consumption, and aggressive carbon neutrality targets. This study analyses pollution emissions and spatial-temporal variation in China's cattle breeding industry. Using an emission coefficient method and panel data of 31 Chinese provinces/municipalities between 2002 and 2017, we measure the total volume of pollutant emissions from China's cattle breeding industry and five major pollutants: chemical oxygen demand, total nitrogen, total phosphorus, copper, and zinc. We also analyse the dynamic variation of the spatial distribution. The results show that both the total emissions volume and emissions of the five major pollutants have decreased to different extents, among which chemical oxygen demand has decreased the fastest. Spatial divergence is strengthened as the heavy pollution areas have moved from the southeast to the northwest of the country. This study contributes to current research by its focus on the cattle breading industry and by our improvements to the pollutant emission measurement method

Media use degree and depression: A latent profile analysis from Chinese residents

Background: Previous studies have emphasized the media as an essential channel for understanding information about depression. However, they have not divided groups according to the degree of media use to study their differences in depression. Therefore, this study aims to explore the influence of media use on depression and the influencing factors of depression in people with different media use degrees. Methods: Based on seven items related to media use, a total of 11, 031 respondents were categorized by the frequency of media use using latent profile analysis (LPA). Secondly, multiple linear regression analyzes were conducted to analyze the effects of depression in people with different degrees of media use. Finally, factors influencing depression among people with different degrees of media use were explored separately. Results: All respondents were classified into three groups: media use low-frequency (9.7%), media use general (67.1%), and media use high-frequency (23.2%). Compared with media use general group, media use low-frequency (β = 0.019, p = 0.044) and media use high-frequency (β = 0.238, p < 0.001) groups are significantly associated with depression. The factors influencing depression in the population differed between media use low-frequency, media use general, and media use high-frequency groups. Conclusion: The government and the appropriate departments should develop targeted strategies for improving the overall health status of people with different media use degrees