... the overall stability and efficiency of data transmission. To address the significant intra-class variation in isolated sign language words, a novel deep learning framework named EWCGR is proposed in this paper. The first layer of the wide convolutional kernel deep convolutional neural network ( WDCNN...

... tools. The research seeks to improve the robustness and accuracy of strip interpretation under diverse environmental and user-dependent conditions. Design/methodology/approach The proposed system incorporates a sequential deep learning framework, consisting of a convolutional neural network...

...Zhengdi Sun; Anle Mu; Ye Qian; Fuxiang Hao; Yupeng Wang; Wuyang Wang Purpose Cardiovascular diseases require continuous, noninvasive blood pressure ( BP ) monitoring. This study aims to develop a cuffless BP estimation framework using dual acoustic sensors and deep learning. Design...

... Limited Licensed re-use rights only Water level detection Raspberry Pi Image segmentation Deep learning Under the dual effects of global climate change and intensified human activities, water resources management has become a major issue of concern to the international community (Hoy...

.... However, its accuracy is severely hampered by motion artifacts (MAs), sensor location and subject-specific factors like skin tone and body mass index. While traditional signal processing methods offer partial solutions, deep learning ( DL ) models – particularly convolutional neural network, recurrent...

... with the lung EIT is provided. 24 06 2025 29 08 2025 16 09 2025 18 09 2025 Electrical impedance tomography (EIT) Deep learning Identification Tumor location © 2025 Emerald Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only Figure 2...

... attention module. Meanwhile, to handle non-continuous information, the authors introduced the ICB module. The authors reconstructed a loss function, which takes into account both the incremental directions of each small range and the global incremental errors. MEMS Attitude estimation Deep learning...

... and buzzer). The Raspberry Pi runs a YOLOv9 deep learning model locally on a camera feed for real-time fire detection. A mobile application provides user oversight and manual control. The system’s performance was validated through experimental testing, measuring detection accuracy, response times and IAQ...

... methods to deep learning architectures. Selection followed explicit inclusion criteria (time-/batch-split evaluations, MOX arrays, quantitative outcomes), with emphasis on 2023–2025 studies comparing one- and two-dimensional convolutional neural networks pipelines and recent drift-adaptation methods...

... Robustness Deep learning Stochastic configuration network Consistency regularization Adaptive learning Fault classification Shenzhen Science and Technology Program No. JCYJ20230807154300002 Graduate Innovation Program of China University of Mining and Technology KYCX25_3052...

... and above) and the elderly, for 14 different walking activities from different directions and magnitudes. Research limitations/implications To automate identification, a hybrid deep learning model based on convolutional neural networks and long short-term memory (CNN-LSTM) is used to predict fall...

... Publishing Limited Licensed re-use rights only Vibration measurement Rotating machinery Non-contact sensing Stereo vision Deep learning The structural characteristics of the generator set were identified as the primary focus of the study. To this end, a vibration detection system...

... for cardiac testing. Computer-aided systems can capture important information that may have been overlooked by a physician’s subjective judgment, laying the foundation for the development of precision medicine and personalized treatment strategies (Sun and Liu, 2024). In recent years, deep learning techniques...

...Zengkun Liu; Justine Hui Purpose This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure...

... objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points...

... to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics. Design/methodology/approach The deep learning method is used to establish the model of multipath linear velocity...

...Siyuan Huang; Limin Liu; Xiongjun Fu; Jian Dong; Fuyu Huang; Ping Lang Purpose The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding...

... of vehicle parts (Liu, 2019 ; Showinshu et al., 2015). For the increasing demands on the safety, smoothness and comfort of highway transportation facilities, it is very important to study the smoothness of pavement and classify the pavement grade. Deep learning Sensors Neural networks...

... different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based...