Zhiyong Xiao

Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao et al.

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.

Jinzhe Pan, Jingqing Wang, Zelin Yun et al.

The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasingly complex wireless environments while ensuring network Quality of Service (QoS) performance. This paper explores the potential integration of advanced Artificial Intelligence (AI) methods into the design of Wi-Fi MAC protocols. We propose AI-MAC, an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency. By intelligently predicting and managing interference, AI-MAC aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS. Our experimental results demonstrate that AI-MAC significantly reduces both interference and latency, paving the way for more reliable and efficient wireless communications in the increasingly crowded ISM band.

Xinle Gao, Linghui Ye, Zhiyong Xiao

With the rapid development of society and continuous advances in science and technology, the food industry increasingly demands higher production quality and efficiency. Food image classification plays a vital role in enabling automated quality control on production lines, supporting food safety supervision, and promoting intelligent agricultural production. However, this task faces challenges due to the large number of parameters and high computational complexity of Vision Transformer models. To address these issues, we propose a lightweight food image classification algorithm that integrates a Window Multi-Head Attention Mechanism (WMHAM) and a Spatial Attention Mechanism (SAM). The WMHAM reduces computational cost by capturing local and global contextual features through efficient window partitioning, while the SAM adaptively emphasizes key spatial regions to improve discriminative feature representation. Experiments conducted on the Food-101 and Vireo Food-172 datasets demonstrate that our model achieves accuracies of 95.24% and 94.33%, respectively, while significantly reducing parameters and FLOPs compared with baseline methods. These results confirm that the proposed approach achieves an effective balance between computational efficiency and classification performance, making it well-suited for deployment in resource-constrained environments.

Haotian Chen, Zhiyong Xiao

In the field of food image processing, efficient semantic segmentation techniques are crucial for industrial applications. However, existing large-scale Transformer-based models (such as FoodSAM) face challenges in meeting practical deploymentrequirements due to their massive parameter counts and high computational resource demands. This paper introduces TUNable Adapter module (Swin-TUNA), a Parameter Efficient Fine-Tuning (PEFT) method that integrates multiscale trainable adapters into the Swin Transformer architecture, achieving high-performance food image segmentation by updating only 4% of the parameters. The core innovation of Swin-TUNA lies in its hierarchical feature adaptation mechanism: it designs separable convolutions in depth and dimensional mappings of varying scales to address the differences in features between shallow and deep networks, combined with a dynamic balancing strategy for tasks-agnostic and task-specific features. Experiments demonstrate that this method achieves mIoU of 50.56% and 74.94% on the FoodSeg103 and UECFoodPix Complete datasets, respectively, surpassing the fully parameterized FoodSAM model while reducing the parameter count by 98.7% (to only 8.13M). Furthermore, Swin-TUNA exhibits faster convergence and stronger generalization capabilities in low-data scenarios, providing an efficient solution for assembling lightweight food image.

Lulu Liu, Zhiyong Xiao

Food is not only essential to human health but also serves as a medium for cultural identity and emotional connection. In the context of precision nutrition, accurately identifying and classifying food images is critical for dietary monitoring, nutrient estimation, and personalized health management. However, fine-grained food classification remains challenging due to the subtle visual differences among similar dishes. To address this, we propose Feature-Enhanced TResNet (FE-TResNet), a novel deep learning model designed to improve the accuracy of food image recognition in fine-grained scenarios. Built on the TResNet architecture, FE-TResNet integrates a Style-based Recalibration Module (StyleRM) and Deep Channel-wise Attention (DCA) to enhance feature extraction and emphasize subtle distinctions between food items. Evaluated on two benchmark Chinese food datasets-ChineseFoodNet and CNFOOD-241-FE-TResNet achieved high classification accuracies of 81.37% and 80.29%, respectively. These results demonstrate its effectiveness and highlight its potential as a key enabler for intelligent dietary assessment and personalized recommendations in precision nutrition systems.

Desheng Li, Chaoliang Liu, Zhiyong Xiao

Accurate segmentation of polyps from colonoscopy images is crucial for the early diagnosis and treatment of colorectal cancer. Most existing deep learning-based polyp segmentation methods adopt an Encoder-Decoder architecture, and some utilize multi-task frameworks that incorporate auxiliary tasks like classification to improve segmentation. However, these methods often need more labeled data and depend on task similarity, potentially limiting generalizability. To address these challenges, we propose CL-Polyp, a contrastive learning-enhanced polyp segmentation network. Our method uses contrastive learning to enhance the encoder's extraction of discriminative features by contrasting positive and negative sample pairs from polyp images. This self-supervised strategy improves visual representation without needing additional annotations. We also introduce two efficient, lightweight modules: the Modified Atrous Spatial Pyramid Pooling (MASPP) module for improved multi-scale feature fusion, and the Channel Concatenate and Element Add (CA) module to merge low-level and upsampled features for {enhanced} boundary reconstruction. Extensive experiments on five benchmark datasets-Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-show that CL-Polyp consistently surpasses state-of-the-art methods. Specifically, it enhances the IoU metric by 0.011 and 0.020 on the Kvasir-SEG and CVC-ClinicDB datasets, respectively, demonstrating its effectiveness in clinical polyp segmentation.

Youshan Zhang, Brian D. Davison, Vivien W. Talghader et al.

Transmission electron microscopy (TEM) is one of the primary tools to show microstructural characterization of materials as well as film thickness. However, manual determination of film thickness from TEM images is time-consuming as well as subjective, especially when the films in question are very thin and the need for measurement precision is very high. Such is the case for head overcoat (HOC) thickness measurements in the magnetic hard disk drive industry. It is therefore necessary to develop software to automatically measure HOC thickness. In this paper, for the first time, we propose a HOC layer segmentation method using NASNet-Large as an encoder and then followed by a decoder architecture, which is one of the most commonly used architectures in deep learning for image segmentation. To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result. To measure the thickness of the segmented HOC layer, we propose a regressive convolutional neural network (RCNN) model as well as orthogonal thickness calculation methods. Experimental results demonstrate a higher dice score for our model which has lower mean squared error and outperforms current state-of-the-art manual measurement.

Chuanhai Zhang, Kurt Loken, Zhiyu Chen et al.

Deep convolutional neural network (DCNN) is the state-of-the-art method for image segmentation, which is one of key challenging computer vision tasks. However, DCNN requires a lot of training images with corresponding image masks to get a good segmentation result. Image annotation software which is easy to use and allows fast image mask generation is in great demand. To the best of our knowledge, all existing image annotation software support only drawing bounding polygons, bounding boxes, or bounding ellipses to mark target objects. These existing software are inefficient when targeting objects that have irregular shapes (e.g., defects in fabric images or tire images). In this paper we design an easy-to-use image annotation software called Mask Editor for image mask generation. Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes. Mask Editor also supports drawing bounding polygons, drawing bounding boxes, drawing bounding ellipses, painting, erasing, super-pixel-marking, image cropping, multi-class masks, mask loading, and mask modifying.