The automatic defect detection for fabric images is an essential mission in textile industry. However, there are some inherent difficulties in the detection of fabric images, such as complexity of the background and the highly uneven scales of defects. Moreover, the trade-off between accuracy and speed should be considered in real applications. To address these problems, we propose a novel model based on YOLOv4 to detect defects in fabric images, called Feature Augmentation YOLO (FA-YOLO). In terms of network structure, FA-YOLO adds an additional detection head to improve the detection ability of small defects and builds a powerful Neck structure to enhance feature fusion. First, to reduce information loss during feature fusion, we perform the residual feature augmentation (RFA) on the features after dimensionality reduction by using 1×1 convolution. Afterward, the attention module (SimAM) is embedded into the locations with rich features to improve the adaptation ability to complex backgrounds. Adaptive spatial feature fusion (ASFF) is also applied to output of the Neck to filter inconsistencies across layers. Finally, the cross-stage partial (CSP) structure is introduced for optimization. Experimental results based on three real industrial datasets, including Tianchi fabric dataset (72.5% mAP), ZJU-Leaper fabric dataset (0.714 of average F1-score) and NEU-DET steel dataset (77.2% mAP), demonstrate the proposed FA-YOLO achieves competitive results compared to other state-of-the-art (SoTA) methods.

Radio resource is the bottleneck for current multimedia wireless networks. Intelligent traffic control strategies can be enforced to optimize resource allocation so as to enhance network performance. In this study, dynamic control scheme for non-real-time traffic and autonomic control schemes for multimedia traffic are proposed to guarantee the required quality of service (QoS) in the inference-dominated high-speed wireless environment. Both handoff priority and terminal mobility are also taken into consideration. The performance of the state-dependent multidimensional birth-death process is derived by the efficient matrix-analytic methods (MAMs). Compared with the previous results, this paper shows that the proposed control methods can be used for both real-time and non-real-time multimedia traffic in order to meet the required performance without degrading the quality of multimedia services. These results are also important for the design of evolving multimedia wireless systems as well as network optimization.

Future cellular systems are envisioned to support mixed traffic, and ultimately multimedia services. However, a mixture of voice and data requires novel service mechanisms that can guarantee quality of service. In order to transfer high-speed data, multislot channel allocation is seen as a favoured solution to the present systems with the least compromise to circuit- switched services. This paper evaluates the performance of narrowband voice calls and multislot data packet transmission in such integrated systems by using a matrix-analytic approach. This method achieves quadratic convergence compared to the conventional spectral methods. Mobility is also considered in a prioritized cellular environment where frequent handoff has the potential of degrading data performance. The voice call distribution, data packets throughput, delay and waiting time distribution are derived. Moreover, a new multiple priority-based distributed control algorithm and a voice rate control scheme are enforced to mitigate the queuing congestion of data packets. The numerical results derived from this study show that larger data packets incur longer latency and the use of these flexible schemes can improve the overall performance.