International Journal of Innovative Computing and Applications (7 papers in press)

Regular Issues

• Wireless Inertial Motion Capture System and Pose Trajectory Fusion Algorithm for Animation and Film Design  
  by Shifeng Wang 
  Abstract: The film and animation industry increasingly demand real-time performance and accuracy. Traditional optical motion capture methods face limitations due to environmental lighting and equipment constraints, often failing to meet high production standards. To address this issue, this study proposes a motion capture system that integrates an error-state Kalman filter and improved adaptive damped least squares. The system fuses data from multiple inertial measurement unit sensors through the error-state Kalman filter and optimises pose trajectories with the improved adaptive damped least squares, significantly enhancing motion capture performance. Experimental results show that the proposed system achieves a pose estimation error converging to 1.5 degrees within 2.5 s, and the trajectories closely match the optical ground truth. During dynamic tasks, the system exhibited absolute orientation errors of 2.44
  Keywords: Motion capture system; Pose trajectory fusion algorithm; Inertial measurement unit; Kalman filter; Animation and film design.
  DOI: 10.1504/IJICA.2026.10078298
   
  
• A Framework for Indoor Visual Element Extraction Using an Improved Attention Generative Adversarial Network  
  by Miao Nie, Zhongping Sun 
  Abstract: Traditional generative adversarial networks (GAN) suffer from insufficient structural modelling and loss of details in indoor visual element extraction. This study proposes an improved attention GAN model framework that integrates spatial attention mechanisms and channel attention allocation mechanisms. This framework guides the generator to continuously optimise the feature expression process through the supervised feedback mechanism of the discriminator, thereby improving the accuracy and completeness of visual element extraction, ensuring feature recognition and reconstruction in complex image scenes. The experimental outcomes showed that the algorithms element extraction accuracy was 93.5%, the edge segmentation accuracy was 91.2%, and it exhibited stronger robustness in interference environments. The indoor element extraction model achieved an element extraction accuracy of over 90.3% on different datasets, demonstrating good convergence and generalisation ability. The structural similarity of the extracted element feature images reached 0.94, with a PSNR of 37.4 dB. The above results indicate that the improved attention GAN model can effectively identify various elements in complex indoor scenes and maintain a high degree of detail restoration in indoor visual element extraction. This study provides more reliable technical support for indoor environment understanding and intelligent design.
  Keywords: Indoor visual element extraction; Generate adversarial networks; Attention mechanism; Spatial attention; Channel attention.
  DOI: 10.1504/IJICA.2026.10078660
   
  
• Intelligent inspection and monitoring of minor defects in complex slopes using unmanned aerial vehicles based on improved YOLOv11-PEW architecture  
  by Yong Lu, Li Tian, Sheng Yuan 
  Abstract: To address the issues of missed detection of small targets, interference from complex backgrounds, and blurred boundaries in visual inspection of geological disasters, this paper proposes the YOLOv11-PEW model: it introduces a P2 microscopic inspection head to retain shallow features to reduce texture loss, embeds an efficient multi-scale attention (EMA) module to calibrate feature weights across space to suppress background noise, and uses the Wise-IoU (WIoU) loss function (LF) to optimize boundary regression accuracy. Experimental findings demonstrate that YOLOv11-PEW achieves a mAP@0.5 of 90.1% on the self-built slope dataset, a 5.8% improvement over the baseline (YOLOv11n). Furthermore, while ensuring compliance with the real-time inference standard for embedded edge devices (>30 FPS), it significantly improves the detection accuracy of extremely small targets (Area <32 x 32) by 14.6%. Visualisation analysis (Grad-CAM) further confirms that the model can accurately focus on the disease itself from diffuse background noise, demonstrating excellent robustness.
  Keywords: highway slope protection; detection of minor defects; YOLOv11; attention mechanism; unmanned aerial vehicle remote sensing.
  DOI: 10.1504/IJICA.2026.10078678
   
  
• Deep Learning-Based Traffic Flow Multi-Source Heterogeneous Information Fusion and Emergency Management Response Mechanism  
  by Sirui Chen 
  Abstract: Accurate perception of fabric deformation is an important prerequisite for applying wearable sensor networks to intelligent clothing design. However, purely data-driven methods lack physical consistency, making it difficult for the wearable sensor network to perceive and predict fabric deformation accurately. To address these issues, this paper proposes a physical-informed joint simulation-recognition sensor framework for fabric. The core idea of achieving a precise perception of fabric deformation by calibrating fabric physical properties in the sensing data, focusing on key parts prone to deformation, and optimising model performance through closed-loop feedback is realised. Experimental results show that the proposed method achieves 0.941 in prediction accuracy for the fabric deformation degree and 0.86 in recognition accuracy for clothing fit, which are 5.5% and 8.9% higher than those of the optimal method. This research presents a new model for applying wearable sensor networks to the perception of fabric deformation.
  Keywords: Traffic Flow; Multi-source Heterogeneous Information Fusion; Emergency Management Response; Multi-head Attention Mechanism; Graph Convolutional Network.
  DOI: 10.1504/IJICA.2026.10079541
   
  
• A cooperative optimization method for coverage and scheduling of traffic WSN relay nodes considering carbon emission cost constraints  
  by Xiaxiao Tian 
  Abstract: To address the high energy consumption and carbon emissions of relay nodes in wireless sensor networks for traffic sensing, this paper proposes a collaborative optimisation method for relay coverage and scheduling under carbon emission cost constraints. This method constructs a joint scoring mechanism of coverage gain and carbon emission penalty, embeds carbon constraints during the node activation stage, and couples the coverage optimisation results with the dynamic duty cycle adjustment closed loop through carbon budget allocation, forming a collaborative decision-making link for coverage selection, carbon quota decomposition, and scheduling execution. Experimental results show that the proposed method converges within 40 iterations, achieving a coverage rate of 92%, which is superior to the multi-objective differential evolution algorithm (83%) and the particle swarm optimisation algorithm (75%). This method enables efficient and low-carbon operation of green transportation sensing systems, providing a feasible solution for their efficient and low-carbon operation.
  Keywords: Traffic wireless sensor network; Relay node; Carbon emission constraints; Coverage optimization; Scheduling coordination.
  DOI: 10.1504/IJICA.2026.10079636
   
  
• Hierarchical Asynchronous Federated Learning Method for Blockchain-enabled Vehicle Networking Integrating Spatio-temporal Trajectory Characteristics  
  by Li-ting Zhang, Yunfei Wang, Hou Fu Zhang, Ziyang Chen, Zeng Sheng 
  Abstract: As the Internet of Vehicles (IoV) transitions into a data-driven ecosystem, it encounters critical challenges regarding privacy preservation and resilience against model poisoning attacks. Conventional Federated Learning (FL) is constrained by the highly dynamic topology of vehicular networks and lacks intrinsic trust mechanisms to mitigate stealthy poisoning attacks. To bridge these gaps, we propose B-HAFL, a Blockchain-enabled Hierarchical Asynchronous Federated Learning framework. First, to address network efficiency, we construct a vehicle-to-cluster-to-cloud architecture that utilizes edge-side pre-aggregation, effectively reducing communication overhead by approximately 70%. Second, to fortify system security, we design a physical-logical fused multidimensional dynamic reputation model. This model not only incorporates FastDTW-based trajectory analysis to verify physical behaviour but also integrates a time decay factor to dynamically evaluate the timeliness and reliability of node contributions. Based on this, a reputation-driven differentiated weighted aggregation (RDWA) algorithm and a smart contract-based incentive mechanism are implemented. Extensive simulations on the UNSW-NB15 dataset demonstrate the frameworks robustness. Specifically, in a hostile environment with a 30% poisoning attack rate, B-HAFL maintains a classification accuracy of 92%. Furthermore, by incorporating the time decay factor, the models accuracy under the same 30% attack scenario is significantly improved to 95.8%, effectively preventing model collapse compared to baseline methods.
  Keywords: Internet of Vehicles (IoV); blockchain; asynchronous federated learning; secure data sharing; dynamic reputation; physical-logical fusion.
  DOI: 10.1504/IJICA.2027.10079755
   
  
• DHT Load Balancing Algorithm for Distributed File Systems  
  by Dejun Miao, Rongyan Xu, Jiusong Chen, Yan Wu 
  Abstract: To address load imbalance and node congestion in distributed file systems deployed in intelligent aviation cabins, a distributed hash table-based load balancing algorithm named MTTL-K is proposed. The algorithm integrates predictive data migration and adaptive threshold control into a decentralised storage architecture to improve balancing efficiency and stability. A dynamic migration mechanism is designed to optimise target-node selection and reduce unnecessary migrations. Experimental results show that the proposed algorithm reduces the load standard deviation to 3.69% after multiple migration rounds and decreases total migrations to 62% of the initial level. Compared with the conventional TTL-K algorithm, MTTL-K reduces the imbalance degree to approximately 0.5 within two minutes while maintaining low migration overhead. These results demonstrate that the proposed method improves load distribution, resource utilisation, and operational stability in distributed file systems.
  Keywords: MTTL-K algorithm; Distributed Hash Table; Dynamic Load Balancing; Predictive Migration; Aviation DFS System.
  DOI: 10.1504/IJICA.2027.10079885