Specialized datasets designed for mining scenarios are the essential foundation for the development, operation, and research of intelligent mines. Currently, the available datasets focus primarily on open-pit mines, with a lack of specialized datasets for underground mines. This gap severely hinders the application of intelligent solutions in underground mines. This paper proposes a challenging semantic segmentation dataset focusing on underground mines, named the underground mine scenes semantic segmentation (UMSSS) dataset, which contains 4200 high-quality annotated images and 18 annotated categories. To accurately capture the diversity and complexity of mine environments, we collect data from over ten mines located in various geographical regions. The UMSSS dataset is the first open-source semantic segmentation dataset for underground mines, widely covering varying lighting scenarios and diverse underground objects. The comparative experiments extensively explore the characteristics of the UMSSS dataset, providing a detailed evaluation of various state-of-the-art algorithms on the UMSSS dataset.

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming method of solving mixed-integer linear programming (MILP) problems with a specific block structure. Nevertheless, BD still needs to solve an NP-hard quasi-integer programming master problem (MAP), which motivates us to harness the popular variational quantum algorithm (VQA) to assist BD. More specifically, we choose the popular quantum approximate optimization algorithm (QAOA) of the VQA family. We transfer the BD's MAP into a digital quantum circuit associated with a physically tangible problem-specific ansatz; and then solve it with the aid of a state-of-the-art digital quantum computer. Next, we evaluate the computational results and discuss the feasibility of the proposed algorithm. The hybrid approach advocated, which utilizes both classical and digital quantum computers, is capable of tackling many practical MILP problems in communication and networking, as demonstrated by a pair of case studies.

Queue counting using WiFi channel state information (CSI) faces challenges due to susceptibility to external factors and relies on ideal testing environments for current methods. We propose an efficient CSI recurrence plot (RP)-based framework for queue counting (CRPF-QC), containing a transformation module and a recognition module. The conversion module transforms the CSI into RP, distinct from traditional models using a single signal point as the unit for feature extraction, utilizing the signal changes at different timestamps as units for feature extraction and effectively preserving the amplitude and phase relationships between any two time points. In the recognition module, the convolutional neural network (CNN) and the long short-term memory (LSTM) network are combined to profoundly understand the internal structure and changes within the image. The proposed integration framework is adept in the automatic extraction of amplitude and phase features, therefore improving image recognition accuracy. Meanwhile, we explore dynamic changes in the queuing crowd detection based on the Fresnel zone theory, identifying individuals’ entering and exiting behaviors at different positions within the Fresnel zone and updating the count accordingly, which makes up for the shortcomings of the static model. Intensive evaluations demonstrate that CRPF-QC, employing just two layers of CNN and one layer of LSTM, excels in adapting to dynamic environmental changes, outperforming traditional queue counting methods. Additionally, the dynamic model attains a perfect 100% accuracy in both scenarios.

The Benders’ decomposition algorithm is a technique in mathematical programming for complex mixed-integer linear programming (MILP) problems with a particular block structure. The strategy of Benders’ decomposition can be described as a strategy of divide and conquer. The Benders’ decomposition algorithm has been employed in a variety of applications such as communication, networking, and machine learning. However, the master problem in Benders’ decomposition is still NP-hard, which motivates us to employ quantum computing. In the paper, we propose a hybrid quantum-classical Benders’ decomposition algorithm. We transfer the Benders’ decomposition’s master problem into the quadratic unconstrained binary optimization (QUBO) model and solve it by the state-of-the-art quantum annealer. Then, we analyze the computational results and discuss the feasibility of the proposed algorithm. Due to our reformulation in the master problem in Benders’ decomposition, our hybrid algorithm, which takes advantage of both classical and quantum computers, can guarantee the solution quality for solving MILP problems.

Network function virtualization (NFV) is a crucial technology for the 5G network development because it can improve the flexibility of employing hardware and reduce the construction of base stations. There are vast service chains in NFV to meet users’ requests, which are composed of a sequence of network functions. These virtual network functions (VNFs) are implemented in virtual machines by software and virtual environment. How to deploy VMs to process VNFs of the service chains as soon as possible when users’ requests are received is very challenging to solve by traditional algorithms on a large scale. Compared with traditional algorithms, quantum computing has better computational performance because of quantum parallelism. We build an integer linear programming model of the VNF scheduling problem with the objective of minimizing delays, and transfer it into the quadratic unconstrained binary optimization (QUBO) model. Our proposed heuristic algorithm employs a quantum annealer to solve the model. Finally, we evaluate the computational results and explore the feasibility of leveraging quantum computing to solve the VNF scheduling problem.