Quantum information processing (QIP) offers a substantial speed advantage over classical processing, which is particularly promising in the fields of quantum artificial intelligence and quantum machine learning (QAI/QML). This study focuses on ...

Accurate node localization is essential in wireless sensor networks (WSNs) for effective data analysis and the successful operation of applications like environmental monitoring and disaster management. Range-free methods like the distance vector-...

With the rapid development of quantum machine learning, the Quantum Long Short-Term Memory (QLSTM) has been found to exhibit faster convergence characteristics in time series prediction problems. Currently, it has found applications in fields such ...

Event-related potentials (ERPs) represent the electroencephalographic responses to specific stimuli and are crucial for analyzing and understanding the processing of conscious activities within the human brain. Their classification is of ...

The present era has witnessed the wide deployment of reconfigurable hardware or Field Programmable Gate Arrays (FPGAs) in edge and cloud platforms. With its ability of dynamic partial reconfiguration at runtime, FPGAs provide the apt environment ...

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these ...

In the pursuit of high-performance designs for error-resilient applications, approximate computing emerges as a key strategy. This paper introduces an innovative approximate multiplier, leveraging two highly efficient compressors. These ...

The Variational Onsager Neural Networks-based Fair proof-of-reputation consensus for blockchain with Transaction Prioritisation for Smart Cities (VONN-FPORC-TP-SC) is proposed for transaction prioritisation in smart cities. Blockchain, as a ...

To effectively respond to severe seismic events, accurate and efficient models for predicting structural performance are essential. In this study, a multi-task adaptive learning framework that integrates physical information from nonlinear ...

This paper investigates the efficient application of half-precision floating-point (FP16) arithmetic on GPUs for boosting LU decompositions in double (FP64) precision. Addressing the motivation to enhance computational efficiency, we introduce two ...

Situation assessment is an important basis for achieving autonomous decision-making in air combat. The ever-increasing multi-source fusion information perceived by situation assessment system poses a computational challenge to current airborne ...

In this paper, we propose different alternatives for convolutional neural networks (CNNs) segmentation, addressing inference processes on computing architectures composed by multiple Edge TPUs. Specifically, we compare the inference performance ...$\mathrm{}$

Advancing intelligent mining and enhancing the reliability of the physical information systems in mines is a current objective for the coal mining industry. System resilience reflects the system’s ability to handle external and internal ...

Electrical load forecasting is of the utmost significance in the power business since it may serve as a reference for downstream operations such as power grid dispatch, resulting in substantial financial advantages. Presently, the urban energy ...

Currently, deep learning methods have become prevalent in the field of electrical load forecasting. These approaches have shown a great potential to map complex nonlinear feature interactions. However, many existing electrical load forecasting ...

k-Nearest Neighbors (k-NN) is one of the most widely used classification algorithms in real-world machine learning applications such as computer vision, speech recognition, and data mining. Massive high-dimensional datasets, reasonable accuracy of ...

Containerization is increasingly recognized as a valuable virtualization strategy for edge computing infrastructures, favored for its small footprint and low overhead. However, extracting the best out of containerization requires efficient ...

As the world endeavors to shift toward sustainable energy solutions, the pivotal role of solar energy, specifically photovoltaics, becomes increasingly evident. This study investigates the critical task of accurately predicting photovoltaics power ...

The node localization problem of wireless sensor networks (WSNs) is addressed in this article with a node localization algorithm designed using fuzzy logic and a nature-inspired algorithm. The coordinates of target nodes are obtained using fuzzy ...

Currently, Raft, as an mainstream consensus mechanism, has received widespread attention. Partition consensus can reduce the number of nodes involved in a single consensus and improve consensus efficiency. However, existing algorithms suffer from ...