Muhammad Naeem Zafar¹, Yunfei Yin^1,*, Junaid Abbas², Bayan Alabdullah³, Khaled Alnowaiser⁴, Yunyoung Nam⁵, Zepa Yang^5,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085136 - 13 August 2026

Abstract Accurate medical image classification increasingly relies on the joint modeling of global contextual semantics and fine-grained local structural cues, since many lesions are only reliably recognized when subtle local details are interpreted within their broader anatomical context. However, most recent hybrid CNN–Transformer and global–local frameworks still extract these features in separate streams and merge them only through late-stage static fusion, without explicit bidirectional interaction during representation learning. As a result, global context cannot effectively guide the refinement of subtle local structures, and local discriminative cues cannot recalibrate higher-level semantic reasoning before classification, which limits reciprocal… More >

Xiaosong Chang, Liang Shi^*, Ao Zhang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085087 - 13 August 2026

Abstract To ensure the reliability of biometric authentication, Face Anti-Spoofing (FAS) models must accurately detect presentation attacks. However, due to the highly complex distribution shifts caused by variations in style, cross-domain generalization remains a significant challenge. Test-Time Domain Generalization (TTDG) has recently surfaced as an innovative framework, facilitating the adaptation of unseen samples to source-domain characteristics through the strategic utilization of learned style bases. Nevertheless, existing TTDG methods optimize randomly initialized style bases solely through statistical objectives, leaving a critical research gap: the lack of explicit semantic constraints inevitably leads to hierarchical semantic inconsistency and weakens… More >

Qi Li^1,2, Sathish Kumar Selvaperumal^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085073 - 13 August 2026

Abstract Unmanned Aerial Vehicle (UAV) air-to-ground (A2G) communication is a core enabling technology for emerging low-altitude wireless applications. At the same time, accurate real-time channel emulation remains a key bottleneck restricting its large-scale engineering deployment. Conventional universal channel simulators exhibit limited fidelity when modeling UAV-specific fading characteristics and degrade real-time performance on resource-constrained hardware platforms. In this study, we develop a dedicated UAV A2G channel simulator based on a heterogeneous FPGA platform (Processing System (PS) + Programmable Logic (PL)). To achieve high-precision path-loss prediction, we train a lightweight backpropagation neural network (BPNN) using field-measured data in… More >

Yaolei Wang¹, Wangyan Li^1,*, Guoliang Wei²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085054 - 13 August 2026

Abstract With the rapid development of unmanned aerial vehicle (UAV) technologies, simultaneous localization and mapping (SLAM) has emerged as a key enabling paradigm for autonomous navigation and environmental perception. This paper presents a comprehensive survey of recent trends in UAV-based SLAM. First, we review the fundamental components of UAV-based SLAM systems, including commonly used onboard sensors and front-end odometry methods such as visual odometry, visual-inertial odometry, and LiDAR-inertial odometry, which provide reliable ego-motion estimation. Next, we summarize back-end methodologies that enhance estimation accuracy and global consistency, covering pose graph optimization, 3D reconstruction techniques, filter-based SLAM, fusion-based multi-UAV SLAM, More >

Bareera Anam, Muhammad Asim, Muhammad Nadeem Ali, Byung-Seo Kim^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084949 - 13 August 2026

Abstract The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport… More >

Zheng Yao¹, Jie Liu¹, Changjun Deng^2,3,*, Wang Lin^2,3

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084892 - 13 August 2026

Abstract Mobile edge computing (MEC) is an effective paradigm for supporting latency-sensitive and computation-intensive intelligent applications. However, in dynamic mobile-edge network scenarios, mobile terminals experience time-varying wireless links due to mobility. Tasks may also arrive unpredictably, while multiple terminals compete for limited edge resources. As a result, MEC systems may suffer from service congestion and unbalanced resource utilization, which increases end-to-end latency and energy consumption. This paper investigates cooperative task offloading in dynamic MEC networks. The considered system comprises one macro base station and multiple small base stations equipped with edge-computing resources. In each time slot,… More >

Manaswi Kulahara¹, Khadija Parwez², Faisal Alhwikem^3,*, Fawwad Hassan Jaskani⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084877 - 13 August 2026

Abstract Large Language Models (LLMs) have recently advanced in real-world commonsense reasoning, including understanding everyday object behaviors and inferring their attributes from text. However, they remain limited in reasoning about the real-world consequences of events, such as how object failures, obstructions, or structural changes affect the surrounding environment-especially without visual or sensorimotor input. Existing works like PIQA and NEWTON evaluate narrow sub-skills, such as whether an object action makes sense and whether object properties can be inferred, providing valuable benchmarks for commonsense and physical reasoning but offering limited evaluation of how events alter environmental functionality and downstream… More >

Adeel Iqbal^1,#,*, Muhammad Faisal Siddiqui^2,#,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084851 - 13 August 2026

Abstract Hop-constrained packet routing is a fundamental problem in wireless sensor networks (WSNs), where latency constraints, energy limitations, and practical feasibility requirements greatly restrict routing choices. Traditional methods based on shortest path and greedy routing have low complexity but cannot adapt to dynamic network changes well, while reinforcement learning for routing has the potential to adapt to network variations but has not been well explored in the hard hop-constrained setting. The current study attempts to fill the gap by modeling hop-constrained routing as the decision-making problem in a finite-horizon setting. An integrated simulation environment is proposed… More >

Diana Maria Popa, Simona-Vasilica Oprea^*, Adela Bâra

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084792 - 13 August 2026

Abstract This paper investigates how generative-artificial intelligence (AI) is influencing job requirements, skill compositions and sectoral dynamics across global labor markets. It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive component in the workplace. A large-scale, multi-source corpus of over 150,000 English-language job postings (2018–2025) is compiled from twelve open-access datasets and one public API. The analytical framework integrates lexical skill extraction, semantic framing, topic modeling and time-series forecasting. Skill mentions are categorized into five dimensions: AI_Data, Routine, Soft_Meta, Domain_Specific and Leadership,… More >

Kaisen Li¹, Yunwei Zhang^1,*, Guoying Sun¹, Bin Li^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084755 - 13 August 2026

Abstract With the development of vision-language pre-trained models, effectively exploiting high-level semantics and precisely controlling bitrate in learned image compression remains a challenging problem. Existing methods mainly rely on image feature modeling alone, making it difficult to jointly preserve fine-grained details and semantic consistency under a given bitrate budget. To address this issue, this paper proposes a learned image compression framework that integrates text-semantic guidance with content-aware bitrate control. The framework combines Bootstrapping Language-Image Pre-training (BLIP) and Contrastive Language-Image Pre-training (CLIP) to extract image semantic information, and performs conditional modulation on multi-scale visual features through feature-wise… More >