Results 81 to 90 of about 449,358 (260)
Deep Convolutional Neural Networks as Generic Feature Extractors [PDF]
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this ...
Barth, Erhardt +3 more
core
A Scalable Perovskite Platform With Multi‐State Photoresponsivity for In‐Sensor Saliency Detection
A scalable in‐sensor computing platform (32 × 32 array) with ultra‐low variability is developed by incorporating ferroelectric copolymers into halide perovskite thin films. These devices achieve 1000 programmable photoresponsivity states and high thermal reliability.
Xuechao Xing +10 more
wiley +1 more source
Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue +8 more
wiley +1 more source
Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using Convolutional Neural Networks
Background: Alzheimer’s disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020.
Sara Ghasemi Dakdareh, Karim Abbasian
doaj +1 more source
An Embedded Inference Framework for Convolutional Neural Network Applications
With the rapid development of deep convolutional neural networks, more and more computer vision tasks have been well resolved. These convolutional neural network solutions rely heavily on the performance of the hardware. However, due to privacy issues or
Sheng Bi +3 more
doaj +1 more source
Convolutional Neural Networks In Convolution
Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
openaire +2 more sources
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane +4 more
wiley +1 more source
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 +8 more
wiley +1 more source
Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook
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 +12 more
wiley +1 more source
ABSTRACT Quantifying oral polymorphonuclear neutrophils (oPMNs) is a clinically validated approach for assessing periodontal inflammation. However, current methods, such as manual hemocytometry and flow cytometry, are time‐consuming (>3 h), require invasive sampling, and depend on staining and complex instrumentation, making them unsuitable for point ...
Mohsen Hassani +9 more
wiley +1 more source

