Mobile Edge Computing (MEC) is an emerging network architecture designed to enhance Quality of Service (QoS) by bringing computational resources and storage closer to end users. In MEC environments, strategic placement of edge servers is crucial to minimize costs and optimize QoS. The Edge Server Placement (ESP) problem has been effectively addressed by using Multi-Armed Bandit (MAB) algorithms, known for their efficiency and adaptability, with the Upper Confidence Bound (UCB) algorithm being particularly prominent due to its stable performance and low dependency on parameters. However, UCB suffers excessive exploration and high uncertainty in reward estimation during its initial phase, leading to slow convergence. To overcome these challenges, we propose a novel Preference-based UCB (P-UCB) algorithm, which integrates the preference function into the UCB framework, drawing inspiration from the Gradient Bandit (GB) method. This modification not only accelerates convergence, but also improves overall efficiency. Furthermore, to address the Base Station Allocation (BSA) issue within the ESP context, we introduce a Weighted Base Station Allocation (WBSA) algorithm, which helps better manage access delay and workload balance. The P-UCB algorithm is evaluated through a comprehensive metric that based on access delay and workload balance, showing significant improvements over existing methods such as Multiple Choice (MC)-UCB, Q- Particle Swarm Optimization (QPSO), and Genetic Algorithm (GA). Experimental results on a real-world dataset show that P-UCB achieves a notable performance increase of at least 12.9%, effectively optimizing access delay and workload balance across various experimental settings, including the number of base stations, edge servers, and other relevant system parameters.

Accurate segmentation of small anatomical structures like the pubic symphysis in intrapartum ultrasound images is critical for clinical diagnosis, yet remains challenging due to inherent noise, speckle artifacts, low target-background contrast, and blurred boundaries. Established methods often fail to capture long-range dependencies while preserving the spatial continuity of small targets, leading to inaccurate boundary localization. To address these issues, we propose an Edge-Aware Network with Large Kernel Attention (EAN_LKA). Our approach integrates three novel components: a Spatial-Continuous Encoder which capture long-range dependencies via shift operations in order to avoid structural fragmentation caused by traditional patch-based mechanisms and to maintain pixel-level boundary coherence; a Cross-Scale Fusion Attention module which enhances semantic discriminability between target and background by refining multi-scale features between the encoder and decoder in order to effectively mitigate low-contrast boundary ambiguity; and a Large Kernel Attention Decoder which leverages extensive contextual perception to suppress noise interference while guiding detail-preserving boundary reconstruction. Experiments on two public MICCAI challenge datasets demonstrate that the EAN_LKA model significantly outperforms existing methods in small target segmentation tasks, with an average Dice score improvement of 1.18 % and a relative reduction in average surface distance of$\mathbf{3. 8 7 \%}$, fully validating its overall superiority.

Accurate segmentation of the fetal head and pubic symphysis in intrapartum ultrasound images and measurement of fetal angle of progression (AoP) are critical to both outcome prediction and complication prevention in delivery. However, due to poor quality of perinatal ultrasound imaging with blurred target boundaries and the relatively small target of the public symphysis, fully automated and accurate segmentation remains challenging. In this paper, we propse a dual-path boundary-guided residual network (DBRN), which is a novel approach to tackle these challenges. The model contains a multi-scale weighted module (MWM) to gather global context information, and enhance the feature response within the target region by weighting the feature map. The model also incorporates an enhanced boundary module (EBM) to obtain more precise boundary information. Furthermore, the model introduces a boundary-guided dual-attention residual module (BDRM) for residual learning. BDRM leverages boundary information as prior knowledge and employs spatial attention to simultaneously focus on background and foreground information, in order to capture concealed details and improve segmentation accuracy. Extensive comparative experiments have been conducted on three datasets. The proposed method achieves average Dice score of 0.908$\pm$0.05 and average Hausdorff distance of 3.396$\pm$0.66 mm. Compared with state-of-the-art competitors, the proposed DBRN achieves better results. In addition, the average difference between the automatic measurement of AoPs based on this model and the manual measurement results is 6.157$^{\circ }$, which has good consistency and has broad application prospects in clinical practice.

Inclement weather, haze, and fog severely decrease the performance of outdoor imaging systems. Due to a large range of the depth-of-field, most image dehazing or enhancement methods suffer from color distortions and halo artifacts when applied to real-world hazy outdoor scenes, especially those with the sky. To effectively recover details in both distant and nearby regions as well as to preserve color fidelity of the sky, in this study, we propose a novel image defogging and enhancement approach based on a replaceable plug-in segmentation module and region-adaptive processing. First, regions of the grayish sky, pure white objects, and other parts are separated. Second, a luminance-inverted multi-scale Retinex with color restoration (MSRCR) and region-ratio-based adaptive Gamma correction are applied to non-grayish and non-white areas. Finally, the enhanced regions are stitched seamlessly by using a mean-filtered region mask. Extensive experiments show that the proposed approach not only outperforms several state-of-the-art defogging methods in terms of both visibility and color fidelity, but also provides enhanced outputs with fewer artifacts and halos, particularly in sky regions.

The introduction of multi-core architectures generates a higher demand for parallelism in order to fully exploit the potential of modern computers. It is of vital importance that a compiler can allocate parallel workload in a cost-aware manner in order to achieve optimal performance on a multi-core architecture. This paper presents an adaptive OpenMP-based mechanism capable of generating a reasonable number of representative multi-threaded versions for a given loop, and selecting at runtime a suitable version to execute on a multi-core architecture. Preliminary experimental results show that, on average, it achieves 87% of the highest performance improvement across a whole spectrum of input sizes on two multi-core platforms.

This paper describes a portable, machine learning-based approach to Java optimisation. This approach uses an instance-based learning scheme to select good transformations drawn from Pugh's Unified Transformation Framework[11]. This approach was implemented and applied to a number of numerical Java benchmarks on two platforms. Using this scheme, we are able to gain over 70% of the performance improvement found when using an exhaustive iterative search of the best compiler optimisations. Thus we have a scheme that gives a high level of portable performance without any excessive compilations.