The traditional covering problem has two assumptions: “all or nothing coverage” and “individual coverage”. There are settings where the two assumptions may be unrealistic. In this research, we relax the two assumptions and study the cooperative covering facility location problem with demand uncertainty. Previous research on the covering problem has not considered cooperative covering under uncertain demand, particularly our approach to effectiveness maximization and offering full control of the conservatism of the model. We propose a cooperative covering model to maximize effectiveness, including the quality per dollar as a criterion. Then, the demand at each node is assumed to be uncertain, and the robust counterparts of the deterministic model are presented by considering the different degrees of conservatism of the robust solution. More importantly, the non-linear programming is transformed into equivalent linear programming by introducing auxiliary variables and using duality theory. The numerical examples show that the optimal location of the facility is affected by the protection level and the decision maker can make a trade-off between conservatism and effectiveness in an uncertain environment. Moreover, when the protection level is large, the objective function value makes a smaller sacrifice to get high robustness. In addition, two ways to measure the price of robustness are defined. The facility location decision can be made by evaluating the relative decrease in effectiveness compared to the nominal demand model or by evaluating the relative increase in effectiveness compared to the complete protection model.

Grant-free random access (RA) utilizing massive multiple-input multiple-output (MIMO) technology has attracted considerable attention in recent years due to its potential to enhance spectral efficiency. This paper introduces an innovative and advanced approach for the joint detection of users and estimation of channels in grant-free RA. The approach incorporates two distinct preamble structures: the single orthogonal preamble (SOP) and the concatenated orthogonal preamble (COP). The proposed algorithms make full use of the inherent quasiorthogonal characteristic of massive MIMO, thereby enabling the accurate estimation of user channels while effectively avoiding collisions in the preambles. As a result, these algorithms generate highly precise estimations of user channels. To substantiate the effectiveness of the proposed algorithms, this paper provides an extensive theoretical analysis and presents a comprehensive set of experimental results. These findings offer robust evidence for the efficacy of the algorithms in substantially bolstering the performance of grant-free RA. Additionally, we have conducted further research and analysis, which has led to additional insights and refinements in our proposed approach. Moreover, the experimental results validate the statistical significance and reliability of the performance enhancements achieved by these algorithms. Moreover, the proposed approach exhibits robustness in scenarios with different levels of user density and varying channel conditions. Through a thorough analysis of these scenarios, we showcase the versatility and applicability of our algorithms in real-world environments.

Compared to traditional images and 2D videos, 3D videos are more likely to cause distortion drift problems in data hiding. In this paper, an improved scheme for high capacity and efficient data hiding in 3D videos based on Multi-view coding (MVC) standard is proposed, which avoids the problems of distortion drift. To improve the visual quality of data hiding, two selection modes are provided to limit the distortion drift. By modifying the selected coefficients of 4×4 quantized discrete cosine transforms (QDCT) in macroblocks and hiding data into b4-frames, the proposed scheme will effectively prevent distortion drift which caused by intra prediction and inter prediction. Moreover, the proposed algorithm maintains great randomness by using two random sequences. Several benchmark 3D video sequences of different resolutions and diverse contents are used for experimental evaluation. It is experimentally proved that the algorithm has greater embedding capacity and higher efficient than the previous methods.

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities.