Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Structured Light LiDAR is susceptible to lens scattering and temperature fluctuations, resulting in some level of distortion in the captured point cloud image. To address this problem, this paper proposes a high-performance 3D point cloud Least Mean Square filter based on Decision Tree, which is called the D−LMS filter for short. The D−LMS filter is an adaptive filtering compensation algorithm based on decision tree, which can effectively distinguish the signal region from the distorted region, thus optimizing the distortion of the point cloud image and improving the accuracy of the point cloud image. The experimental results clearly demonstrate that our proposed D−LMS filtering algorithm significantly improves accuracy by optimizing distorted areas. Compared with the 3D point cloud least mean square filter based on SVM, the accuracy of the proposed D−LMS filtering algorithm is improved from 86.17% to 92.38%, the training time is reduced by 1317 times and the testing time is reduced by 1208 times....
To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edgedeployment settings, this study proposes a cost-effective Cantonese ASR system based on LoRA fine-tuning and INT8 quantization. First, Whisper-tiny is parameter-efficiently fine-tuned on the Common Voice zh-HK training set using LoRA with rank = 8. Only 1.6% of the original weights are updated, reducing the character error rate (CER) from 49.5% to 11.1%, a performance close to full fine-tuning (10.3%), while cutting the training memory footprint and computational cost by approximately one order of magnitude. Next, the fine-tuned model is compressed into a 60 MB INT8 checkpoint via dynamic quantization in ONNX Runtime. On a MacBook Pro M1 Max CPU, the quantized model achieves an RTF = 0.20 (offline inference 5 × real-time) and 43% lower latency than the FP16 baseline; on an NVIDIA A10 GPU, it reaches RTF = 0.06, meeting the requirements of high-concurrency cloud services. Ablation studies confirm that the LoRA-INT8 configuration offers the best trade-off among accuracy, speed, and model size. Limitations include the absence of spontaneous-speech noise data, extreme-hardware validation, and adaptive LoRA structure optimization. Future work will incorporate large-scale self-supervised pre-training, tone-aware loss functions, AdaLoRA architecture search, and INT4/NPU quantization, and will establish an mJ/char energy–accuracy curve. The ultimate goal is to achieve CER ≤ 8%, RTF < 0.1, and mJ/char < 1 for low-power real-time Cantonese ASR in practical IoT scenarios....
The increasing complexity and scale of service-oriented architectures in cloud computing have heightened the demand for intelligent, decentralized, and adaptive service composition techniques. This study proposes an advanced framework that integrates a Multi-Agent System (MAS) with a novel hybrid metaheuristic optimization method, the Integrated Particle-Ant Algorithm (IPAA), to achieve efficient, scalable, and Quality of Service (QoS)- aware service composition. The IPAA dynamically combines the global search capabilities of Particle Swarm Optimization (PSO) with the local exploitation strength of Ant Colony Optimization (ACO), thereby enhancing convergence speed and solution quality. The proposed system is structured into three logical layers—agent, optimization, and infrastructure—facilitating autonomous decision-making, distributed coordination, and runtime adaptability. Extensive simulations using a synthetic cloud service dataset demonstrate that the proposed approach significantly outperforms traditional optimization methods, including standalone PSO, ACO, and random composition strategies, across key metrics such as utility score, execution time, and scalability. Moreover, the framework enables real-time monitoring and automatic re-optimization in response to QoS degradation or Service-Level Agreement (SLA) violations. Through decentralized negotiation and minimal communication overhead, agents exhibit high resilience and flexibility under dynamic service availability. These results collectively suggest that the proposed IPAA-based framework provides a robust, intelligent, and scalable solution for service composition in complex cloud computing environments....
Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve performance and increase availability to manage cloud computing resources. The combination of ML and cloud architectures balances the workload and ensures reliability. This research discusses cloud architectures that use ML to run different algorithms to predict the improvement in the cloud architectures by using a cloud computing resource dataset. The dataset is used with different classifiers with the same ML framework that is discussed in this paper; the ML framework has a sequence to provide the steps of the model training and testing and uses different techniques and methods for the better performance of the cloud architectures. The researchers used various ML techniques to create a model for predicting the workload. To enhance the model’s performance and flexibility, we used a regression-based dataset that was recently updated, which was used with different ML approaches to predict better performance in the cloud architectures. By using the Generalized Linear Model, we achieved the highest performance. The R2 value refers to the goodness of the model and its performance. Using cloud datasets and machine learning with cloud architectures enhances performance using the different techniques in this paper, resulting in a more generalizable model with overfitting risk. This study focuses on refining the execution of cloud architectures with the help of ML....
This paper proposes a structure-aware compression technique for efficient compression of high-resolution synthetic aperture radar (SAR)-based point clouds by quantitatively analyzing the directional characteristics of local structures. The proposed method computes the angular difference between the principal eigenvector of each point and those of its neighboring points, selectively removing points with low contribution to directional preservation and retaining only structurally significant feature points. The method demonstrates superior information preservation performance through various compression evaluation metrics such as entropy, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Additionally, the SHREC’19 human mesh dataset is employed to further assess the generality and robustness of the proposed approach. The results show that the proposed method can maximize data efficiency while preserving the core information of the point cloud through a novel directionality-based structural preservation strategy....
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