Results 121 to 130 of about 367,129 (284)

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

Chemically Doped Conductive Polymers for Wearable Health Monitoring

Advanced Materials Technologies, EarlyView.
Among conductive polymers, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) are the most studied and applied. Chemical doping significantly boosts intrinsic conductivity and mechanical robustness.
Mengdi Zuo, Jian Song, Hong Hu, Le Zheng, Lei Zhang, Charles H. Lawrie   +5 more
wiley   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges

Applied Sciences
Multimodal interaction technology has become a key aspect of remote education by enriching student engagement and learning results as it utilizes the speech, gesture, and visual feedback as various sensory channels.
Yangmei Xie, Liuyi Yang, Miao Zhang, Sinan Chen, Jialong Li   +4 more
doaj   +1 more source

Multimodal Machine Learning for Automated ICD Coding

, 2019
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured ...
Band, Charlotte, Gao, Xin, Lam, Mike, MD, Ashish K. Khanna, MD, Frank Papay, MD, Jacek B. Cywinski, MD, Kamal Maheshwari, MD, Piyush Mathur, Pang, Jingzhi, Xie, Pengtao, Xing, Eric, Xu, Keyang   +11 more
core  

Multimodal Data Efficient Learning

Recently, unimodal models have attained good performance in many tasks. However, using one modality may not provide sufficient information in complex situations. Humans use multimodal input, such as vision and hearing, to act in the real world. Similarly, this thesis proposes systems that use multimodal input for video classification and visual ...
openaire   +2 more sources

Multimodal Emotion Recognition Using Multimodal Deep Learning

, 2016
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models from multiple physiological signals. For unimodal enhancement task, we indicate that the best recognition accuracy of 82.11% on SEED dataset is ...
Liu, Wei, Zheng, Wei-Long, Lu, Bao-Liang
openaire   +2 more sources

Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook

Advanced Materials Technologies, EarlyView.
A smart closed‐loop e‐textile integrates multimodal sensing, onboard processing, wireless communication, and wearable power to enable real‐time physiological/biochemical monitoring and feedback‐controlled therapy. ABSTRACT Smart textiles represent a revolutionary frontier in healthcare, seamlessly blending fabric and advanced technologies to create ...
Safoora Khosravi, Sang Won Lee, Mohammad Abrar Uddin, Sina Hassanpoor, Neha Suvindran, Katherine Le, Harun Arkaz, Harishkumar Narayana, Saeid Soltanian, Danial Khorsandi, Negar Hosseinzadeh Kouchehbaghi, Taeil Kim, Yangzhi Zhu   +12 more
wiley   +1 more source

Modeling a Multimodal Learning Analysis in the Context of Smart Classrooms

Jiaoshi jiaoyu xuebao
In contemporary education, the prevalence of the test-oriented education has led to a single, rigid learning assessment, as well as the neglecting of the personalized learning needs of learners.
TANG Qianwen, ZHANG Hao, WU Yian
doaj   +1 more source