Chang Su, Liangliang Zhao^*, Dongbing Xiang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.071319 - 09 December 2025

Abstract To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from… More >

Le Wang¹, Bing Xu^1,*, Peng Liu², En Yuan¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.070794 - 09 December 2025

Abstract Deep learning has been recognized as an effective method for indoor positioning. However, most existing real-valued neural networks (RVNNs) treat the two constituent components of complex-valued channel state information (CSI) as real-valued inputs, potentially discarding useful information embedded in the original CSI. In addition, existing positioning models generally face the contradiction between computational complexity and positioning accuracy. To address these issues, we combine graph neural network (GNN) with complex-valued neural network (CVNN) to construct a lightweight indoor positioning model named CGNet. CGNet employs complex-valued convolution operation to directly process the original CSI data, fully exploiting… More >

Weiping Zeng¹, Xiangping Bryce Zhai^1,2,3,*, Cheng Sun¹, Liusha Jiang^1,2, Yicong Du³, Xuefeng Yan^1,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070777 - 09 December 2025

Abstract With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude More >

Wei Liu^1,*, Ruiyang Wang¹, Guangwei Liu²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070328 - 09 December 2025

Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… More >

Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >

Xin Ma^1,2, Jin Lei^3,4,*, Chenying Pei⁴, Chunming Wu⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069353 - 09 December 2025

Abstract This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model More >

Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.071047 - 10 November 2025

Abstract With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, More >

Zeeshan Ali Haider¹, Inam Ullah^2,*, Ahmad Abu Shareha³, Rashid Nasimov⁴, Sufyan Ali Memon^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.071042 - 10 November 2025

Abstract The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network.… More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Haixia Wang, Huan Zhang, Xiuling Wang, Xule Xin, Zhiguo Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069340 - 10 November 2025

Abstract Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and More >