The recent advancements in large language models (LLMs) have significantly enhanced language understanding and content generation capabilities. However, the deployment of LLMs on resource-constrained Internet of Things (IoT) devices remains challenging due to their substantial computational and storage requirements. To address this issue, we propose a novel LLM pruning method, termed structurally-aware adaptive pruning (SAAP), to reduce computational and storage costs for LLMs while maintaining model performance. Specifically, SAAP first leverages maximum likelihood estimation to calibrate traditional structural importance metrics for LLM pruning. Next, it employs a Bayesian fusion approach to address the predictive uncertainty in multi-granularity metrics, enabling accurate assessments of structural importance for LLMs. Then, SAAP introduces a cross-layer importance alignment mechanism based on quantile mapping, which normalizes layer-wise importance scores to ensure consistent pruning from a global perspective. Furthermore, SAAP develops an efficient block-wise fine-tuning strategy for enhancing the performance of the LLM after pruning. To validate the effectiveness of SAAP, we conduct extensive experiments on nine open-source LLMs across two representative tasks—language modeling and zero-shot classification. Experimental results show that SAAP consistently outperforms several baseline methods, achieving accuracy improvements of 2.5%, 2.63%, and 2.44% on LLaMA-7B, Vicuna-7B, and LLaMA-13B when the pruning ratio is 50%. Finally, SAAP is implemented on a testbed—NVIDIA Jetson AGX Orin 32GB Developer Kit. Test results demonstrate that compared to the foundation LLM, SAAP enhances the inference speed by 86.86% at a pruning ratio of 50%, highlighting its potential for practical deployment on resource-constrained IoT devices.

All-in-focus(AIF) images, which contain comprehensive scene information with global sharpness, play a crucial role in high-precision light field (LF) measurement and computational imaging. However, generating AIF images from LF data typically requires accurate depth priors, which are often unavailable or unreliable in practice. To overcome this limitation, directly fusing a series of LF refocused images provides an effective alternative that eliminates the dependency on explicit depth estimation. Nevertheless, existing multi-focus image fusion(MFIF) methods are primarily designed for fusing image pairs with complementary focus, performing poorly when applied to stacks due to the error accumulation that occurs during iterative fusion. To this end, we propose a Frequency-Decoupled Stack Fusion Network (FDSNet) for high-precision depth-free LF AIF image generation. FDSNet incorporates a spatial-frequency joint feature extraction module that captures multi-scale spatial details while decoupling high- and low- frequency components to model textures and contextual information separately, thereby alleviating edge blurring caused by subtle focal variations and weak textures in transition regions. Moreover, a dual-stage cross-attention fusion module, following a coarse-to-fine strategy, suppresses artifacts, enhances edge fidelity, and enables simultaneous fusion of arbitrary numbers of refocused images, thereby avoiding error accumulation and computational redundancy. Extensive experiments on both synthetic and real LF datasets demonstrate that FDSNet achieves superior visual quality and quantitative performance. Additional experiments further demonstrate that FDSNet performs robustly under varying low-light and noisy conditions. These results validate that FDSNet delivers excellent fusion capability in terms of image clarity, detail preservation, noise resistance, and generalization, outperforming existing state-of-the-art methods.

In water tunnels, autonomous navigation of autonomous underwater vehicles (AUVs) is challenging under accumulated localization errors and severe acoustic perturbations constraints. An AUV water tunnel navigation (AUV-WTN) framework is proposed to address these challenges. AUV-WTN integrates a forward-looking sonar (FLS) image segmentation method based on the refined mask R-CNN (RM R-CNN) network with real-time trajectory planning that employs the dynamic trajectory homotopy method (DTHM). RM R-CNN is optimized to combine a mixed-frequency block (MFB) along with a weighted loss function. Additionally, precise region of interest pooling (PrRoI Pooling) is combined to mitigate the impact of false targets, blurred edges, and noise on segmentation accuracy. DTHM is proposed to reduce trajectory drift by dynamically updating path generation based on segmented FLS images. Experimental results demonstrate that RM R-CNN outperforms state-of-the-art (SOTA) methods, achieving a 10.9% improvement over Mask R-CNN in mask segmentation. The simulation platform and real AUV experiments indicate that the capability of AUV-WTN framework is effective in generating precise paths and ensuring collision-free navigation in tunnel environments.

This paper addresses the problem of Internet of Underwater Things (IoUT) information collection using an unmanned marine vehicles (UMVs) system. A time-synchronized distributed method (TSDM) is proposed to address the distributed Dubins traveling salesman problem (DTSP) using unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). Information collection missions are allocated to a hybrid algorithm that combines cluster and ant colony optimization (ACO) via a single USV and multiple AUVs. A pseudo-trajectory homotopy method (PTHM) has been developed to optimize the paths associated with the diving and rising of AUVs under kinematic constraints. An AUV information collection path planning (ICPP) algorithm is presented to optimize the paths and enhance the efficiency of information collection from sensor nodes for AUVs. A Dubins obstacle avoidance algorithm is proposed to reduce the impact of obstacles for path planning. Based on the TSDM, the AUVs are recovered by the USV when they ascend to the surface. The simulation results demonstrate that the UMVs system and path planning strategy effectively address the challenges of information collection path planning in the IoUT, particularly under the constraints of underwater communication.

Phishing website is becoming a major threat to the information security in Social Network. The attacks not only lessen the users' trust but also influence the benefit of the third party who develops the platform. In order to solve the time lag in phishing website passive detection, this paper proposes a solution to discover phishing website initiatively based on blacklist, in which the anomalies of its URL and WHOIS information are analyzed, and based on this, the heuristic rules that aim to generate suspicious URLs are made. In order to filter out noise sites in the suspicious set, a website filtering solution based on webpages image-layout is presented. We firstly propose a Ray Scan Method to generate the location feature of webpage images quickly, and then, we proposed a method of calculating the webpage layout similarity, which will be compared against the preset threshold to decide whether it will be filtered. The experimental results show that the solution successfully detects some phishing websites out before they are widely spread, and further, the webpage filtering method guarantees both high filtration ratio and high phishing website retention ratio.

Motion control system of underwater robot without rudder and wing is presented with hardware and software architecture. Considering coupling effects and thrust reduction of propellers, the control layer, perception layer and executive layer in underwater robot system architecture are modified. In control layer, a nonlinear controller is presented to handle coupling effects between the longitudinal dimension and other dimensions of underwater robot. The stability of the controller is verified with Lyapunov function. A virtual sonar based guidance law is proposed in perception layer to coordinate motions on different dimensions, which originates from the behavior of human beings. In executive layer a kind of thruster configuration for high speed traveling is introduced to handle thrust reduction of tunnel thrusters. Finally, the reliability and feasibility of the motion control system are demonstrated by sea trial