To address the challenges in fine-grained image classification stemming from significant intra-class variations and subtle inter-class differences, as well as the performance instability caused by simplistic fusion of deep and shallow features in conventional methods, this paper proposes a long–short range features network (LSNet), a novel gated convolutional model based on long/short-range feature integration. The model innovatively reformulates the convolution process as a sequential task, leveraging the specific order of each convolutional module in the network architecture to implement long–short-term memory network (LSTM)-based gating mechanisms for layer-wise feature selection and adaptive fusion between modules. LSNet achieves phased integration of spatial-static features from convolutional models and temporal-dynamic characteristics from recurrent models, effectively resolving the interference between deep and shallow features. Experimental results demonstrate that under equivalent parameter counts, LSNet achieves classification accuracy improvements of 2.58% and 4.80% over the baseline model on the FGVC-Aircraft and Stanford Cars datasets, with final accuracies reaching 90.15% and 91.97%, respectively, while its architecture allows seamless integration into any deep convolutional model. Gradient-based class activation mapping (Grad-CAM) visualizations further validate the model’s capability to capture discriminative subtle features. Additionally, we have successfully applied LSNet in intelligent livestock farming, achieving precise facial recognition of pigs in high-density breeding environments. The model achieves a recognition accuracy of 92.82% on the pig face recognition dataset. This research not only provides an effective solution for fine-grained classification but also demonstrates extensibility to other vision tasks requiring refined feature extraction.

As a crucial research direction in the field of computer vision, fine-grained visual classification (FGVC) aims to distinguish different subcategories belonging to the same base category. Its core challenge lies in effectively addressing subtle inter-class differences and significant intra-class differences. Existing methods usually have shortcomings in the collaboration among detail preservation, feature selection, and long-range dependency modeling. To this end, this paper proposes a novel multi-branch collaborative network named MBCNet. This network adopts a three-branch structure design: The backbone path integrates a hierarchical separated attention mechanism, which applies spatial attention and channel attention in the shallow and deep layers respectively to achieve accurate feature selection; the gating branch uses LSTM units to model spatial long-range dependencies; the detail preservation branch retains high-frequency texture features through the combination of dilated convolutions. Through an innovative forward-backward cross-attention mechanism, the model realizes bidirectional information interaction between the gating branch and the backbone path, forming interactive feedback of spatial features and temporal features. Experiments on multiple standard finegrained datasets and a self-built pig face recognition dataset demonstrate that MBCNet significantly improves classification accuracy while maintaining a reasonable computational complexity, which effectively verifies the superiority of the proposed method.

The traditional method for calculating the morphometric parameters of geese is mainly manual, which faces problems such as low efficiency and large errors. This paper proposes a non-contact method that uses a depth camera to obtain 3D point clouds for three-dimensional reconstruction of geese and automatically calculates the morphometric parameters. Firstly, depth images are captured from multiple perspectives using the depth cameras. Then, point cloud matching and 3D reconstruction are completed by using relevant preprocessing methods and the ICP algorithm. Finally, the key anatomical points of the goose body (such as the tip of the beak, tail, sole of the foot) were extracted in combination with morphological features, and the calculation models of parameters such as height, tibial length, semi-diving length, and webbed foot length were constructed. By comparing with the physical model of the measured geese, the accuracy of several body measurements of the geese obtained by the method in this experiment was all above 97%.

Collaborative perception allows connected vehicles to share sensor data through Vehicle-to-Vehicle (V2V), helping each vehicle to see beyond its own sensing range and make safer driving decisions. However, the large volume of perception data brings heavy computational pressure, and centralized offloading methods struggle to respond quickly in dynamic and decentralized vehicular networks. To address this challenge, we propose an intelligent distributed task offloading framework built upon non-cooperative game theory. In this framework, each vehicle acts as an autonomous decision-maker that selects its offloading strategy using only local observations, without relying on any central controller. A lightweight game-theoretic optimization algorithm is designed to iteratively adjust strategies and reach a Nash equilibrium, achieving real-time task allocation with minimal communication overhead. Simulation results show that our method significantly reduces perception latency and achieves better load balancing compared with fixed and random allocation schemes. These findings demonstrate that the proposed approach provides a practical solution for real-time collaborative perception in highly dynamic vehicular environments.

The intensification and automation of the pig farming industry have created an urgent need for cost-effective and efficient identification of individual pigs. Pig identification is crucial for disease prevention and control, pork quality traceability, genetic breeding, and insurance services. To address the challenges faced by existing noncontact pig face recognition models in overcoming strong environmental interference in pigsties and the minimal differences among pig faces, this paper proposes a convolutional neural network based on multi-granularity semantic analysis (MGSNet). By integrating pixel-level, component-level, and object-level semantic features, the model significantly improves recognition performance in complex scenarios. Specifically, the model addresses challenges such as environmental interference and high similarity among individual pigs. Experimental results show that the algorithm achieves a high test accuracy of 92.50% on a dataset of 10 pigs collected from actual pig farms, with lightweight network parameters. Through deconvolution and gradient-weighted class activation mapping techniques, the feature extraction process of the model is visually interpretable, providing reliable technical support for farmers. The research findings can be directly applied to precision feeding, disease monitoring, breeding optimization, and other scenarios, promoting the comprehensive adoption of smart agriculture.

Intelligent control has become an important research direction of a combine harvester. However, the impact of cleaning control parameters in rice–wheat combine harvesters on cleaning loss rate and impurity rate often tends to be contradictory. In this paper, an intelligent controlling model based on multi-objective optimization particle swarm (MOPSO) was constructed to solve this problem. The control model can real-time monitor the cleaning performance such as the cleaning loss rate and impurity rate and regulate the cleaning operation conditions such as the angle of the air distributor plate, the opening of the upper sieve and the fan speed. The field operation experiment of 10[Formula: see text]kg feeding rice–wheat combine harvester proves that the control model based on MOPSO is more effective than the model based on fuzzy control.

Nanodrones are insect-sized drones that could fly in complex environments and confined spaces, and act as an emerging tool for covert surveillance and intelligence attacks, which would become a potential threat to national security. Radar has the advantage of wide range, all-day, and all-weather detection ability, making it a means of detecting such threat. First, this paper introduces a pertinent multiple-input multiple-output (MIMO) millimeter-wave (MMW) radar system, with the advantages of low cost and high accuracy. It is utilized to detect three targets: nanodrone, small helicopter, and mechanical bird, through which more detailed features can be obtained. Then, the echo data of the three targets are processed and analyzed, and their distinct micro-Doppler characteristics were obtained. Finally, the Radar Transformer target classification network is used to classify and identify the targets. It has been confirmed that desired results could be achieved through the above process.

Cleaning control based on changes in cleaning loss rate and impurity rate has emerged as a hot topic in the research on intelligent control for rice and wheat combined harvesters. However, numerous operation parameters can lead to deviations beyond the normal range of cleaning loss rate and impurity rate. The impact of cleaning control parameters in rice and wheat combined harvesters on cleaning loss rate and impurity rate often tends to be contradictory. How to combine different contradictory cleaning control strategies to get a more widely used and better cleaning control strategy is a hot issue in the current cleaning control research. In this paper, the granularity of quotient space is introduced into the cleaning control based on operation parameters, and a cleaning control model based on granularity synthesis theory is proposed. This method first constructs a knowledge base for intelligent control of cleaning tailored to the cleaning loss rate and impurity rate using production rule representation, and considers that these control strategies constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the cleaning control strategy. The experimental results verify that the validity of the cleaning control based on granular computing is better than fuzzy control.

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.

In this paper, the operation process of cleaning of intelligent rice–wheat combine harvester is divided into two key steps: initial setting of cleaning device operation parameters and dynamic control of cleaning device operation parameters. Combined with the operation experience of cleaning control of agricultural machinery operators, the dynamic control knowledge-based system of cleaning device operation parameters was built based on production rule reasoning. The cleaning device of the rice–wheat combine harvester is intelligently controlled based on the dynamic monitoring and control system of the cleaning device operation quality and operation parameters, so as to achieve the purpose of controlling the cleaning operation quality of the rice–wheat combine harvester in the normal range. Through the field experiment results and analysis, it is proved that the intelligent control system of the cleaning device operation parameters based on the dynamic control knowledge-based system of cleaning device operation parameters can effectively keep the cleaning impurity content and loss rate of intelligent rice–wheat combine harvester in the normal range, so as to verify the effectiveness of the intelligent control knowledge-based system of the cleaning device operation parameters.

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction.

Placement of edge computing servers at the edge of the network can reduce task transmission delay. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the number of tasks offloaded to each edge server may be quite different, which will seriously affect the QoS of the system. To this end, we investigate the QoS improvement of the distributed edge computing system from the game-theoretic perspective and propose a multi-agent state-based learning algorithm. Firstly, by modeling the cost of an edge computing server as the deviation between its execution time and the system average execution time, we formulate the QoS improvement of the system as a state-based game where each agent competes to maximize its own utility. Then, we propose a multi-agent state-based learning algorithm to obtain the pure Nash equilibrium strategy of each agent. Finally, compared with the existing approaches, the experiments show that the proposed algorithm can improve the QoS of the distributed edge computing system.

Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementation of MatLoc, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and comparison of the proposed fingerprint form, and integrate them all in a practical system, which follows the classical fingerprinting framework and requires no more inputs than any previous RSS fingerprint based systems. Extensive experiments in different environments demonstrate that MatLoc significantly improves the accuracy in both localization and tracking scenarios by about 30% to 50% compared with five state-of-the-art approaches.

Sensorless sensing using wireless signals has been rapidly conceptualized and developed recently. Among numerous applications of WiFi-based sensing, human presence detection acts as a primary and fundamental function to boost applications in practice. Many complicated approaches have been proposed to achieve high detection accuracy, which, however, frequently omit various practical constraints like real-time capability, computation efficiency, sampling rates, deployment efforts, etc. A practical detection system that works in real world lacks. In this paper, we design and implement WiSH, a real-time system for contactless human detection that is applicable for whole-day usage. WiSH employs lightweight yet effective methods and thus enables detection under practical conditions even on resource-limited devices with very low signal sampling rates. We deploy WiSH on commodity desktops and customized tiny nodes in different everyday scenarios. The experimental results demonstrate superior performance of WiSH, achieving a detection accuracy of >98% using a sampling rate of 20Hz with an average detection delay of merely 1.5s, which renders it a promising system for real-world deployment.