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Issue:2026 - Vol. 11 - Iss.4
Published:-
Articles:12 articles
International Journal of Advanced Network, Monitoring and Controls
IJANMC
The journal aims to providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills, especially in the fields of advanced network, future network, monitoring, sensors and controls. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed, Only original… More
2026 Vol.11 Iss.4 (12 articles)
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202604Cover
IJANMC 2026, 11(4), 0-0;
202604Content
IJANMC 2026, 11(4), 0-0;
Research paper
Design of Joint Congestion Simulation Experiment Based on Cross-Layer Collaboration in VANETs
Abstract: Aiming at the link-level congestion caused by frequent network topology changes and limited communication resources and the node-level congestion caused by overload of cluster head nodes in VANETs, this paper proposes a Joint Congestion Strategy based on Cross-layer Collaboration in VANETs (JCSCCV). By designing a joint congestion overall architecture for cross-layer collaboration, a multi-criteria double cluster head dynamic clustering strategy based on motion similarity and a multi-path congestion-free routing al…
Keywords: Vanets; Cross-Layer Collaboration; Congestion Strategy; Dynamic Clustering; Multipath Congestion-Free Routing
IJANMC 2026, 11(4), 1-16;
https://doi.org/10.58244/ijanmc.260001
Research paper
Traffic Target Integrated Detection and Spatial Visualization System Based on Dual YOLOv11 Parallel Architecture
Abstract: To address the challenges of insufficient collaborative detection accuracy for traffic signs and vehicles, weak spatial visualization, and the imbalance between system integration and lightweight deployment in intelligent transportation scenarios, this paper proposes a traffic-target integrated detection and spatial visualization system based on a dual YOLOv11 parallel architecture. Following the technical route of independent training, parallel inference, and result fusion for dedicated traffic-sign and general-pu…
Keywords: Yolov11; Dual-Model Parallelism; Traffic Sign Detection; Vehicle Detection; Spatial Visualization; Digital Twin; Intelligent Transportation; Pyqt5
IJANMC 2026, 11(4), 17-33;
https://doi.org/10.58244/ijanmc.260002
Research paper
Neural-Enhanced Quantized Min-Sum Successive Cancellation Decoding for Polar Codes
Abstract: Polar codes have been adopted as the channel coding scheme for the 5G NR control channel, yet achieving hardware-efficient decoding without sacrificing error-correction performance remains a challenge. The Min-Sum(MS) approximation simplifies the successive cancellation (SC) f-function at a cost of systematic magnitude overestimation, conventional correction schemes—Normalized Min-Sum(NMS) and Offset Min-Sum(OMS)—rely on fixed parameters optimized under full-precision conditions, rendering them structurally unable…
Keywords: Successive; Cancellation; Decoding; Fixed-Point Quantization; Neural Network
IJANMC 2026, 11(4), 34-46;
https://doi.org/10.58244/ijanmc.260003
Research paper
A Drone Image Object Detection Algorithm Based on MSA-YOLOv10
Abstract: To address the problems of frequent missed detections and false detections caused by the small object scale, dense distribution, complex backgrounds, and susceptibility to occlusion in UAV aerial images, this.paper proposes MSA-YOLOv10, an improved object detection algorithm based on YOLOv10n. First, a Triple Feature Encoder (TFE) is introduced into the neck to enhance the representation of ulti-scale detail information. Second, a Scale Sequence Feature Fusion (ScalSeq) module is combined with a Channel and Positio…
Keywords: UAV Aerial Imagery; Object Detection; Yolov10n; Multi-Scale Feature Fusion; Localization Quality Estimation; Small-Object Detection
IJANMC 2026, 11(4), 47-63;
https://doi.org/10.58244/ijanmc.260004
Research paper
Resolution-Stable Arbitrary Style Transfer via Cross-Scale Low-Frequency Attention Anchoring
Abstract: Changes in content resolution can cause StyTR-2 to shift its attention correspondences and output texture organization. To address this problem, we propose a training-free cross-scale low-frequency attention-response anchoring method. A frozen low-resolution branch provides a reference whose decoder responses are spatially aligned with those of the high-resolution branch. Only the low-frequency component of the cross-scale response difference is injected, correcting global correspondences while retaining high-resol…
Keywords: Arbitrary Style Transfer; Stytr-2; Cross-Scale Attention; Low-Frequency Anchoring; Resolution Stability
IJANMC 2026, 11(4), 64-77;
https://doi.org/10.58244/ijanmc.260005
Research paper
Fusion of Edge‑Computing Technology for Smart‑Security Face‑Recognition System Development
Abstract: Traditional centralized face recognition systems suffer five critical engineering drawbacks: high network transmission latency, high risk of facial biometric privacy leakage, excessive cloud hardware and operation‑maintenance costs, service failure under network disconnection, and poor recognition robustness under complex illumination and occlusion conditions. In this paper, MobileFaceNet is taken as the backbone network. An ECA efficient one‑dimensional channel attention module is embedded to construct a CNN‑ViT h…
Keywords: Edge Computing; Smart Security; Face Recognition; Mobilefacenet; ECA Attention; CNN‑Vit; Model Lightweighting; Ablation Experiment
IJANMC 2026, 11(4), 78-91;
https://doi.org/10.58244/ijanmc.260006
Research paper
A Real-Time Stitching Method for Dual-View Street-View Images Based on a TensorRT Hybrid Inference Architecture
Abstract: Deep image stitching networks deliver strong alignment quality, yet their inference latency keeps them below the frame rate that dual-view street-view applications demand. This paper reports a segmented hybrid inference architecture built on TensorRT. The partition criterion is the numerical sensitivity of each sub-graph: compute-bound but numerically stable modules are handed to TensorRT, while sensitive modules stay in PyTorch. Using the UDIS2 unsupervised deep image stitching algorithm as its base, the architect…
Keywords: Deep Learning; Image Stitching; Tensorrt Acceleration; Mixed-Precision Inference; UDIS2; Real-Time Vision System
IJANMC 2026, 11(4), 92-105;
https://doi.org/10.58244/ijanmc.260007
Research paper
Coordinated Training- and Inference-Time Boundary Optimization for Urban Scene Semantic Segmentation: An Empirical Analysis
Abstract: Boundary displacement remains common around road edges, thin objects, and distant road users in urban scene semantic segmentation. We examine how boundary supervision during training combines with boundary correction at inference, using DeepLabV3 Plus with a ResNet-101 backbone. Training uses active boundary loss and weight averaging across three random seeds. Inference combines horizontal-flip augmentation with SegFix at native image resolution. Matched experiments compare cross-entropy and boundary-aware training…
Keywords: Urban Scene Semantic Segmentation; Deeplabv3+; Active Boundary Loss; Test-Time Augmentation; Boundary Evaluation
IJANMC 2026, 11(4), 106-122;
https://doi.org/10.58244/ijanmc.260008
Research paper
A Comparative Study of Machine Learning Algorithms for Driver Behavior Classification Using Sensor-Based Features
Abstract: Driver behavior classification is a critical component of intelligent transportation systems, enabling proactive road safety interventions and personalized driver assistance. This paper presents a comparative evaluation of five supervised machine learning algorithms — Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, and Gradient Boosting — applied to the task of classifying three driving behaviors: Normal, Drowsy, and Aggressive. Features are extracted from multi-sensor vehicle da…
Keywords: Driver Behavior Classification; Machine Learning; Sensor Fusion; UAH-DriveSet; UAH-DriveSet; SVM; Gradient Boosting; Intelligent Transportation Systems
IJANMC 2026, 11(4), 123-129;
https://doi.org/10.58244/ijanmc.260009
Two-Stage Deep Learning Framework for Automatic Evaluation of Hard-Pen Chinese Calligraphy
Abstract: Intelligent evaluation of hard-pen Chinese calligraphy is crucial for smart education, online learning, and automated grading systems. Traditional manual assessment is inefficient, subjective, and difficult to scale. A novel two-stage deep learning framework for automatic handwriting quality assessment is proposed in this study. In the first stage, a convolutional AutoEncoder is employed to generate pseudo-labels based on reconstruction error, enabling unsupervised evaluation without manual annotation. In the secon…
Keywords: Deep Learning; Autoencoder; Resnet18; Mobilenetv2; Chinese Calligraphy Evaluation; Pseudo-Label; Two-Stage Assessment
IJANMC 2026, 11(4), 130-139;
https://doi.org/10.58244/ijanmc.260010
