Tao Tao

Transparent objects are prevalent in various everyday scenarios. However, their reflective and refractive optical properties present significant challenges for conventional optical sensors. This difficulty makes the task of generating dense depth maps from sparse depth maps and high-resolution RGB images a critical area of research. In this paper, we introduce SRNet-Trans, a novel two-stage depth completion framework specifically designed for transparent objects. The approach is structured into two stages, each primarily focused on leveraging semantic and depth information, respectively. In the first stage, RGB images and sparse depth maps are used to predict a relatively dense depth map. The second stage then takes the predicted depth from the first stage, along with the sparse depth map, to generate a final dense depth map. The depth information produced by the two stages is complementary, allowing for effective fusion of both outputs. To enhance the depth estimation process, we integrate a self-attention mechanism in the first stage to better capture semantic features and introduce geometric convolutional layers in the second stage to improve depth encoding accuracy. Additionally, we incorporate a global consistency-based fine depth recovery technique to further refine the final depth map. Extensive experiments on the large-scale real-world TransCG dataset demonstrate that SRNet-Trans outperforms current state-of-the-art methods in terms of depth estimation accuracy.

The widespread presence of security patterns in modern anti-forgery systems has given rise to an urgent need for reliable smartphone authentication. However, persistent recognition inaccuracies occur because of the inherent degradation of patterns during smartphone capture. These acquisition-related artifacts are manifested as both spectral distortions in high-frequency components and structural corruption in the spatial domain, which essentially limit current verification systems. This paper addresses these two challenges through four key innovative aspects: (1) It introduces a chromatic-adaptive coupled oscillation mechanism to reduce noise. (2) It develops a DFT-domain processing pipeline. This pipeline includes micro-feature degradation modeling to detect high-frequency pattern elements and directional energy concentration for characterizing motion blur. (3) It utilizes complementary spatial-domain constraints. These involve brightness variation for local consistency and edge gradients for local sharpness, which are jointly optimized by combining maximum a posteriori estimation and maximum likelihood estimation. (4) It proposes an adaptive graph-based partitioning strategy. This strategy enables spatially variant kernel estimation, while maintaining computational efficiency. Experimental results showed that our method achieved excellent performance in terms of deblurring effectiveness, runtime, and recognition accuracy. This achievement enables near real-time processing on smartphones, without sacrificing restoration quality, even under difficult blurring conditions.

With the demand for workflow processing driven by edge computing in the Internet of Things (IoT) and cloud computing growing at an exponential rate, task scheduling in heterogeneous distributed systems has become a key challenge to meet real-time constraints in resource-constrained environments. Existing studies now attempt to achieve the best balance in terms of time constraints, energy efficiency, and system reliability in Dynamic Voltage and Frequency Scaling environments. This study proposes a two-stage collaborative optimization strategy. With the help of an innovative algorithm design and theoretical analysis, the multi-objective optimization challenges mentioned above are systematically solved. First, based on a reliability-constrained model, we propose a topology-aware dynamic priority scheduling algorithm (EAWRS). This algorithm constructs a node priority function by incorporating in-degree/out-degree weighting factors and critical path analysis to enable multi-objective optimization. Second, to address the time-varying reliability characteristics introduced by DVFS, we propose a Fibonacci search-based dynamic frequency scaling algorithm (SEFFA). This algorithm effectively reduces energy consumption while ensuring task reliability, achieving sub-optimal processor energy adjustment. The collaborative mechanism of EAWRS and SEFFA has well solved the dynamic scheduling challenge based on DAG in heterogeneous multi-core processor systems in the Internet of Things environment. Experimental evaluations conducted at various scales show that, compared with the three most advanced scheduling algorithms, the proposed strategy reduces energy consumption by an average of 14.56% (up to 58.44% under high-reliability constraints) and shortens the makespan by 2.58–56.44% while strictly meeting reliability requirements.

Real-time 3D object detection is a cornerstone for the safe operation of drones and autonomous vehicles (AVs)—drones must avoid millimeter-scale power lines in cluttered airspace, while AVs require instantaneous recognition of pedestrians and vehicles in dynamic urban environments. Although significant progress has been made in detection methods based on point clouds, cameras, and multimodal fusion, the computational complexity of existing high-precision models struggles to meet the real-time requirements of vehicular edge devices. Additionally, during the model lightweighting process, issues such as multimodal feature coupling failure and the imbalance between classification and localization performance often arise. To address these challenges, this paper proposes a knowledge distillation framework for multimodal 3D object detection, incorporating attention guidance, rank-aware learning, and interactive feature supervision to achieve efficient model compression and performance optimization. Specifically: To enhance the student model’s ability to focus on key channel and spatial features, we introduce attention-guided feature distillation, leveraging a bird’s-eye view foreground mask and a dual-attention mechanism. To mitigate the degradation of classification performance when transitioning from two-stage to single-stage detectors, we propose ranking-aware category distillation by modeling anchor-level distribution. To address the insufficient cross-modal feature extraction capability, we enhance the student network’s image features using the teacher network’s point cloud spatial priors, thereby constructing a LiDAR-image cross-modal feature alignment mechanism. Experimental results demonstrate the effectiveness of the proposed approach in multimodal 3D object detection. On the KITTI dataset, our method improves network performance by 4.89% even after reducing the number of channels by half.

Metal products are widely used in industrial manufacturing, and the quality of metal products is becoming more and more demanding. At present, although there are many methods for detecting defects on metal surfaces, there are still various limitations. The limited number of defect samples, unpredictable defect characteristics, and the interference of metal grain bring great challenges to metal surface defect detection. For this reason, this paper proposes an unsupervised algorithm, FFnet, based on the fusion of frequency domain information, which introduces the frequency domain features into the unsupervised detection. A method of the adaptive enhancement of features in the frequency domain is proposed to make the features on the frequency domain more concerned with anomalies rather than textures. A scale-adaptive feature reconstruction module is used to effectively fuse the spatial and frequency domain features to fully utilize the information from different domains. In addition, a feature selection module is designed to improve the anomaly detection capability and reduce the computational redundancy by selecting the most representative subset of features. The proposed method outperforms other state-of-the-art methods on the connecting rod surface image dataset. In addition, in the generalization experiments of Kolektor Surface-Defect Dataset 2, our method also achieves optimal results and demonstrates strong generalization ability.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

In order to simplify the complexity and reduce the cost of the microphone array, this paper proposes a dual-microphone based sound localization and speech enhancement algorithm. Based on the time delay estimation of the signal received by the dual microphones, this paper combines energy difference estimation and controllable beam response power to realize the 3D coordinate calculation of the acoustic source and dual-microphone sound localization. Based on the azimuth angle of the acoustic source and the analysis of the independent quantity of the speech signal, the separation of the speaker signal of the acoustic source is realized. On this basis, post-wiener filtering is used to amplify and suppress the voice signal of the speaker, which can help to achieve speech enhancement. Experimental results show that the dual-microphone sound localization algorithm proposed in this paper can accurately identify the sound location, and the speech enhancement algorithm is more robust and adaptable than the original algorithm.