Recent advances in medical image segmentation have significantly improved segmentation accuracy. Nevertheless, the clinical deployment of large-scale segmentation networks remains constrained by challenges such as excessive parameter counts, complex architectures, and limited adaptability to diverse deployment environments. The absence of lightweight design further restricts their integration into resource-limited edge devices. To address these barriers, lightweight strategies have emerged as an effective solution. Structural optimization simplifies network architectures to reduce computational costs, while model compression techniques shrink model size without sacrificing performance. At the same time, hardware-level acceleration provides additional support for efficient inference in real-world scenarios. This review systematically summarizes recent lightweight methods for medical image segmentation from both software and hardware perspectives. Representative algorithmic approaches are highlighted, including pruning, quantization, knowledge distillation, and efficient network architectures, along with hardware-aware optimization strategies tailored for edge deployment. Moreover, we explored the mainstream approach of integrating large-scale models with lightweight technologies to achieve the optimal balance between segmentation accuracy and computational efficiency. Finally, current limitations and potential research directions are outlined to promote the translation of lightweight segmentation models into routine clinical workflows. By providing a structured reference, this review aims to support researchers and practitioners in advancing the efficient and practical application of medical image segmentation in clinical environments.

Visible light and thermal infrared tracking combines the characteristics of visible light and thermal infrared modalities to achieve robust target tracking in all-weather and all-day scenarios. However, most existing visible light and thermal infrared tracking methods rely on either full fine-tuning or attention mechanisms, which introduce a large number of parameters and are predominantly influenced by the visible modality. This results in challenges such as high computational complexity, slower processing speeds, and limited exploitation of multimodal information. To address these issues, this paper proposes a lightweight multimodal tracking model based on feature fusion and enhancement. The model consists of a feature fusion adapter and a joint enhancement adapter, designed to integrate and refine information across modalities. It employs a dual-stream transformer encoder with shared parameters across modality branches, utilizing a frozen pre-trained foundation model to independently extract features from visible light and thermal infrared inputs. The lightweight fusion adapter combines modality-specific information, while the joint enhancement adapter refines unimodal features, introducing only 0.23M trainable parameters. Experimental results on the LasHeR benchmark demonstrate that the proposed method outperforms prompt learning and other adapter-based methods, achieving a 4.4% improvement in PR and a 3.3% increase in SR while maintaining computational efficiency. With a real-time inference speed of 28.60 FPS, the proposed method balances accuracy and efficiency effectively. The source code will be available at https://github.com/huxue/MFJA.

In recent years, federated learning (FL) has received widespread attention for its ability to enable collaborative training across multiple clients while protecting user privacy, especially demonstrating significant value in scenarios such as medical data analysis, where strict privacy protection is required. However, most existing FL frameworks mainly focus on data heterogeneity without fully addressing the challenge of heterogeneous model aggregation among clients. To address this problem, this article proposes a novel FL framework called FedMKD. This framework introduces proxy models as a medium for knowledge sharing between clients, ensuring efficient and secure interactions while effectively utilizing the knowledge in each client's data. In order to improve the efficiency of asymmetric knowledge transfer between proxy models and private models, a hybrid feature-guided multilayer fusion knowledge distillation (MKD) learning method is proposed, which eliminates the dependence on public data. Extensive experiments were conducted using a combination of multiple heterogeneous models under diverse data distributions. The results demonstrate that FedMKD efficiently aggregates model knowledge.

Small-object detection remains challenging due to limited pixel information, blurred boundaries, and weak semantic cues. Although recent advances in multiscale fusion and attention mechanisms have led to improved performance, existing methods still struggle to preserve high-frequency structural details and achieve precise localization-particularly in dense, cluttered, or low-resolution scenarios. These limitations are primarily caused by the loss of fine-grained features during downsampling and the absence of region-aware focus mechanisms. Inspired by the human visual strategy of "see clearly and detect precisely," we propose PEFC-Net, a novel framework that enhances both perceptual clarity and localization accuracy for small-object detection. To mitigate structural degradation, we introduce the hybrid structural perception (HSP) module, which jointly encodes spatial gradients and localized frequency components through wavelet-based decomposition and edge-aware refinement. To further improve region-level focus, we design the axis-aligned focus calibration (AAFC) module, which captures long-range directional context via axis-sensitive pooling and adaptively refines attention with shape-aware calibration. Extensive experiments on four challenging benchmarks-VisDrone-2019, TT100K, NWPU VHR-10, and DIOR-demonstrate that PEFC-Net consistently outperforms state-of-the-art methods, delivering robust performance under occlusion, dense distribution, and scale variation.

Driver distraction detection is critical to reducing road traffic accidents and increasing the efficiency of advanced driver assistance systems. Real-time lightweight models are especially important for in-vehicle devices with limited computing resources. However, most existing methods focus on designing lighter network architectures and ignore the performance loss when detecting tiny targets. In order to realize the collaborative optimization of tiny target detection accuracy and network lightweight, a driver distraction detection method ATD 2 Net based on adaptive tiny target detection and lightweight networks is proposed. This method aims to reduce model complexity while fully capturing target features for accurate detection. ATD 2 Net consists of three core modules, Channel Reconstruction Perception Module (CRPM), Dynamic Spatial Self-locking Module (DSSM) and Structural Feedback Optimization Module (SFOM). CRPM reconfigures channels and reconstructs them into batch dimensions, uses parallel strategies to perceive interactive features between channels, and significantly enhances feature extraction capabilities. DSSM adopts dynamic locking and adaptive spatial selection mechanisms to capture multi-scale features while injecting adaptive spatial information. It effectively aggregates instance features and reduces the interference of conflicting information and background information, thereby improving the detection ability of tiny targets. SFOM uses dependency trees to model inter-layer relationships and integrate coupling parameters into groupings. It uses a sparse strategy to remove unimportant parameters, achieving lightweight modeling while balancing accuracy and speed. Experimental results show that ATD 2 Net is superior to the latest methods in driver distraction detection, showing excellent performance and good application prospects.

3D medical images are volumetric data that provide spatial continuity and multi-dimensional information. These features provide rich anatomical context. However, their anisotropy may result in reduced image detail along certain directions. This can cause blurring or distortion between slices. In addition, global or local intensity inhomogeneities are often observed. This may be due to limitations of the imaging equipment, inappropriate scanning parameters, or variations in the patient's anatomy. This inhomogeneity may blur lesion boundaries and may also mask true features, causing the model to focus on irrelevant regions. Therefore, a probability map-guided network for 3D volumetric medical image segmentation (3D-PMGNet) is proposed. The probability maps generated from the intermediate features are used as supervisory signals to guide the segmentation process. A new probability map reconstruction method is designed, combining dynamic thresholding with local adaptive smoothing. This enhances the reliability of high-response regions while suppressing low-response noise. A learnable channel-wise temperature coefficient is introduced to adjust the probability distribution to make it closer to the true distribution; in addition, a feature fusion method based on dynamic prompt encoding is developed. The response strength of the main feature maps is dynamically adjusted, and this adjustment is achieved through the spatial position encoding derived from the probability maps. The proposed method has been evaluated on four datasets. Experimental results show that the proposed method outperforms state-of-the-art 3D medical image segmentation methods. The source codes have been publicly released at https://github.com/ZHANGZIMENG01/3D-PMGNet.

Abstract Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T .

In the field of object detection, detecting small objects is an important and challenging task. However, most existing methods tend to focus on designing complex network structures, lack attention to global representation, and ignore redundant noise and dense distribution of small objects in complex networks. To address the above problems, this paper proposes a small object detection method based on global multi-level perception and dynamic region aggregation. The method achieves accurate detection by dynamically aggregating effective features within a region while fully perceiving the features. This method mainly consists of two modules: global multi-level perception module and dynamic region aggregation module. In the global multi-level perception module, self-attention is used to perceive the global region, and its linear transformation is mapped through a convolutional network to increase the local details of global perception, thereby obtaining more refined global information. The dynamic region aggregation module, devised with a sparse strategy in mind, selectively interacts with relevant features. This design allows aggregation of key features of individual instances, effectively mitigating noise interference. Consequently, this approach addresses the challenges associated with densely distributed targets and enhances the model’s ability to discriminate on a fine-grained level. This proposed method was evaluated on two popular datasets. Experimental results show that this method outperforms state-of-the-art methods in small object detection tasks, demonstrating good performance and potential applications.

Due to incomplete appearance features, the identity matching of occluded pedestrians under multiple cross-camera views is a long-term challenge. Although existing re-identification (re-ID) solutions of occluded pedestrians have made significant progress, most of them achieve accurate identity matching by extracting pedestrian appearance features from unoccluded areas. However, when a pedestrian is partially blocked by the body of another pedestrian, existing methods cannot accurately determine whether the unoccluded body parts belong to the target pedestrian, which brings great difficulties to pedestrian identity matching. To alleviate this problem, this paper introduces the idea of adversarial attack into occluded person re-ID and proposes an adversarial training framework that can defend against attacks from obstacles to resist the interference of obstacles on pedestrian identity matching. Unlike existing solutions, the proposed framework is not limited to extracting features of unoccluded human body areas to achieve occluded person re-ID, but explores how to make the re-ID model more resistant to obstacles. In the proposed framework, the occluded pedestrian images are regarded as adversarial examples and used to attack model training. If the trained model can defend against this kind of attack, its generalization is significantly improved, and the above-mentioned issues are also effectively solved. Specifically, a single-branch dual-stream collaborative network is designed. With the cooperation of the pre-trained verification guidance network, the model realizes the attack and defense of adversarial samples. This work broadens research horizons in robust model design of occluded person re-ID, and expands the scope of adversarial attacks. Compared with existing solutions, a lot of experimental results confirm that the proposed solution achieves better performance on two occluded re-ID datasets and two partial re-ID datasets.

Due to the importance of practical applications, unsupervised domain adaptation (UDA) person re-identification (re-ID) has attracted increasing attention. However, most of existing methods often lack the multi-view information reasoning and ignore the domain discrepancy of the pedestrian images with the same identity, which constrain the further improvement of recognition performance. So, this paper proposes a triple adversarial learning and multi-view imaginative reasoning network (TAL-MIRN) for UDA person re-ID, which consists of a multi-view imaginative reasoning module (IRM) and a triple adversarial learning module (TALM). IRM makes the classified pedestrian identity features from a single-view image extracted by a feature encoder consistent with the classification results of the aggregated multi-view pedestrian identity features, so the strong multi-view imaginative reasoning ability of the feature encoder is obtained. TALM is composed by the adversarial learning between the camera classifier and feature encoder, adversarial learning of joint distribution alignment, and adversarial learning of the difference between two classifiers used in classification. In particular, the domain-invariant features at camera level are guaranteed by the adversarial learning between the feature extractor and camera classifier. The joint alignment of identity and domain is achieved by the competition between the feature extractor and classifier integrated with identity and domain. The discriminability and robustness of the learned features are enhanced by playing a MinMax game between two different identity classifiers. Furthermore, a simple normalization operation named as cross normalization (CN) is proposed to increase both modeling and generalization capability of the proposed TAL-MIRN across multiple domains. The proposed TAL-MIRN is applied to five benchmark datasets, and the comparative experimental results confirm its superiority over the state-of-the-art methods. The related source codes is available athttps://github.com/lhf12278/TALM-IRM.

Although existing domain-adaptive person re-identification (re-ID) methods have achieved competitive performance, most of them highly rely on the reliability of pseudo-label prediction, which seriously limits their applicability as noisy labels cannot be avoided. This paper designs a Transformer framework based on body part-level domain alignment to solve the above-mentioned issues in domain-adaptive person re-ID. Different parts of the human body (such as head, torso, and legs) have different structures and shapes. Therefore, they usually exhibit different characteristics. The proposed method makes full use of the dissimilarity between different human body parts. Specifically, the local features from the same body part are aggregated by the Transformer to obtain the corresponding class token, which is used as the global representation of this body part. Additionally, a Transformer layer-embedded adversarial learning strategy is designed. This strategy can simultaneously achieve domain alignment and classification of the class token for each human body part in both target and source domains by an integrated discriminator, thereby realizing domain alignment at human body part level. Compared with existing domain-level and identity-level alignment methods, the proposed method has a stronger fine-grained domain alignment capability. Therefore, the information loss or distortion that may occur in the feature alignment process can be effectively alleviated. The proposed method does not need to predict pseudo labels of any target sample, so the negative impact caused by unreliable pseudo labels on re-ID performance can be effectively avoided. Compared with state-of-the-art methods, the proposed method achieves better performance on the datasets that are in line with real-world scene settings. The source codes of this paper will be available at https://github.com/lhf12278/BPDA.

Machine learning related research in transient control has drawn considerable attention with the rapid increase in data measurement from power grids. Two key components, the control algorithm and system structure, work together to determine the control performance. The design of control laws, the selection of phase measurement units, the allocation of power resources, and the scheduling of communication topology in limited cyber-physical resources need to be considered. Many existing scheduling or planning schemes specialized for control structure are designed based on various linearized analytical models or the optimization of steady states. However, the transient dynamics of power grids are nonlinear and parts of these dynamics are usually unknown. Linearized analytical models cannot represent the transient dynamics of power grids with large disturbances. This article proposes a sparse neural network based reinforcement learning scheme to optimize the control system structure for the transient stability enhancement of power grids with energy storage systems. One adjustable group sparse weight matrix is introduced to formulate both control structure and actor–critic networks. This strategy enables the proposed scheme to simultaneously schedule the control system structure and design the control laws by online learning without solving any combinational optimization problems or requiring any linearized analytical models. The sufficient conditions of learning stability, control stability, and group sparsity are thoroughly studied by mathematical analysis. The proposed scheme is simulated on an IEEE 118-bus test system for verification. The simulation results confirm the feasibility, advantages, and adaptability of the proposed method.

Domain invariance and discrimination of learned features as two crucial factors affect the performance of unsupervised domain adaptation (UDA) person re-identification (Re-ID). Person attributes (such as “backpack”, “boots”, “handbag”, etc) remaining unchanged across multiple domains have been used as mid-level visual-semantic information in UDA person Re-ID. As two main challenges, both misalignment of attribute-related regions across multiple images and domain shift between source and target domains affect the learning of domain-invariant features (DIF). To address the above two challenges, this article proposes to take advantage of the stability of person attributes and the complementarity of person attributes and the corresponding low-level visual features to guide the learning of discriminative DIF. Specifically, the proposed solution contains the generation of latent attribute-correlated visual features (GLAVF), DIF learning under the guidance of person attributes, and the alignment of person attributes corresponding to the local regions of pedestrian images. Due to the gap between person attributes and visual features, person attributes are first converted into latent attribute-correlated visual features (LAVF) without any specific domain information in GLAVF, and then LAVF are used as the substitutions of person attributes to guide the learning of DIF. To enhance the discrimination of learned features, the proposed solution mainly explores the alignment between person attributes and corresponding local regions, and the alignment of the same person attributes across multiple pedestrian images. A fully connected layer is used to achieve the above two types of alignment in the proposed framework, which reduces the adverse impacts of inference information and ensures the semantic consistency between person attributes and corresponding local regions across multiple pedestrian images. The effectiveness of the proposed solution is confirmed on four existing datasets by comparative experiments.

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

A new algebraic system, test algebra (TA), is proposed for identifying faults in combinatorial testing for software-as-a-service (SaaS) applications. In the context of cloud computing, SaaS is a new software delivery model, in which mission-critical applications are composed, deployed, and executed on cloud platforms. Testing SaaS applications is challenging because new applications need to be tested once they are composed, and prior to their deployment. A composition of components providing services yields a configuration providing an SaaS application. While individual components in the configuration may have been thoroughly tested, faults still arise due to interactions among the components composed, making the configuration faulty. When there are k components, combinatorial testing algorithms can be used to identify faulty interactions with t or fewer components, for some threshold 2 ≤ t ≤ k on the size of interactions considered. In general, these methods do not identify specific faults, but rather indicate the presence or absence of some faults. To identify specific faults, an adaptive testing regime repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not. In order to perform such testing in a loosely coupled distributed environment such as the cloud, it is imperative that testing results can be combined from many different servers. The TA defines rules to permit results to be combined, and to identify the faulty interactions. Using the TA, configurations can be tested concurrently on different servers and in any order. The TA always keeps the high reduction rate of potential faulty configurations in fault location analysis.

Testing-as-a-Service (TaaS) is a software testing service in a cloud that can leverage the computation power provided by the cloud. Specifically, a TaaS can be scaled to large and dynamic workloads, executed in a distributed environment with hundreds of thousands of processors, and these processors may support concurrent and distributed test execution and analysis. This paper proposes an autonomous decentralized combinatorial testing system based on Adaptive Reasoning (AR) and Test Algebra (TA) for Combinatorial Testing (CT). AR performs testing and identifies faulty interactions, and TA eliminates related configurations from testing and there can be carried out concurrently. By combining these two, it is possible to perform large CT. We performed experiments with 2^10 components and 98:34% of configurations have been eliminated out of total number of configurations by AR and TA analysis.

Cloud storage provides a virtually unlimited storage spaces for customers. Customers can combine their data storages from different types of cloud storage following their own requirements. End customers often stuck in choosing desirable configuration from different types of cloud storage. How to spend the minimum costs on using the highly efficient cloud storage? How to balance the relationship between the expenditures and performance? This paper proposes a cost-effective optimal configuration model using the repeated game model that can provide optimal configuration solutions to customers. The data mining techniques are used in provisioning on cloud storage. Classification helps users to find the related data and trend analysis assists users to mine the future trend on data storage. A simulate experiment is discussed and verify the correctness of the proposed model.

As cloud services need a fair pricing for both service providers and customers. If the price is too high, the customer may not use it, if the price is too low, service providers have less incentive to develop services. This paper proposes a novel pricing framework for cloud services using game theory (Cournot Duopoly, Cartel, and Stackelberg models) and data mining techniques (clustering and classification, e.g., SVM (Support Vector Machine)) to determine optimal prices for cloud services. The framework is dynamic because the price is determined based on recent usage data and available resources, it is also intelligent as it takes into various economic models into consideration, it is benign because it considers two conflicting parties, service providers and consumers, into consideration at the same time, and it is customizable based on various pricing strategies proposed by service providers and usage patterns as exhibited by consumers. Linear regression is used in various game theory models to determine the optimal price. A global pricing union (GPU) framework is proposed to achieve the best practice of game theory models. Based on the proposed technique, this paper applies this pricing framework to a case study in cloud services, and demonstrates that the prices obtained meet the requirement of traditional supply-demand analysis. In other words, the price obtained is good enough.