Results 231 to 240 of about 96,682 (288)

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

Enhancing Atomic‐Resolution in Electron Microscopy: A Deep Learning Denoiser Operating in the Frequency Domain

Advanced Intelligent Systems, EarlyView.
The newly developed AI‐automated Fast Fourier Transform denoising algorithm surpasses conventional real‐space methods by revealing even light atoms otherwise hidden in noisy backgrounds. Atomic resolution electron microscopy has become an essential tool for many scientific fields, when direct visualization of atomic arrangements and defects is needed ...
Ivan Pinto‐Huguet, Marc Botifoll, Xuli Chen, Martin Børstad Eriksen, Jing Yu, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Jordi Arbiol   +8 more
wiley   +1 more source

EMG‐Driven Telemetry and Inference System for Fish: Pose Reconstruction and Flow Sensing

Advanced Intelligent Systems, EarlyView.
This work introduces an electromyography (EMG)‐driven telemetry framework that reconstructs body pose and infers hydrodynamic conditions in freely swimming fish. A custom 16‐channel archival system records intramuscular EMG, enabling deep‐learning models to decode joint kinematics, classify flow regimes, and reveal channel‐efficient sensing strategies.
Rahdar Hussain Afridi, Waqar Hussain Afridi, Muhammad Hamza, Ahsan Tanveer, Mingxin Wu, Xingwen Zheng, Liang Li, Guangming Xie   +7 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Discrete wavelet transforms - biomedical applications

, 2012
openaire   +1 more source

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Discrete Lattice Wavelet Transform

IEEE Transactions on Circuits and Systems II: Express Briefs, 2007
The discrete wavelet transform (DWT) has gained a wide acceptance in denoising and compression coding of images and signals. In this work we introduce a discrete lattice wavelet transform (DLWT). In the analysis part, the lattice structure contains two parallel transmission channels, which exchange information via two crossed lattice filters.
Olkkonen, H., Olkkonen, Juuso
openaire   +3 more sources

Discrete Wavelets and Fast Wavelet Transform

1991
The wavelet analysis, introduced by J. MORLET and Y. MEYER in the middle of the eighties, is a processus of time-frequency (or time-scale) analysis which consists of decomposing a signal into a basis of functions (o jk ) called wavelets. These wavelets are in turn deduced from the analyzing wavelet o by dilatations and translations. More precisely:
Bonnet, Pierre, Rémond, Didier
openaire   +2 more sources

Hardware implementation of Discrete Wavelet Transform and Inverse Discrete Wavelet Transform on FPGA

2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010
In this paper, hardware implementation of the Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based on FPGA is explained. DWT and IDWT algorithms are implemented on the Altera Cyclone-II FPGA. Filtering processes of rows and columns are seriatim applied as in level-by-level architecture. But both addressing for read/write
Çavuşlu, Mehmet Ali, Karakaya, Fuat
openaire   +2 more sources

Discrete Wavelet Transform

2010
According to the definition of the continuous wavelet transform (CWT) given in (3.7), Chap. 3, the scale parameter s and translation parameter \(\tau\) can be varied continuously. As a result, performing the CWT on a signal will lead to the generation of redundant information.
Robert X. Gao, Ruqiang Yan
openaire   +1 more source

Discrete Wavelet Transforms using Daubechies Wavelet

IETE Journal of Research, 2001
The Wavelet co-efficients have to be calculated using sampled version of basis functions. As an attempt to compute the Wavelet co-efficients and to find the mother function co-efficients from discrete Wavelet Transform an 8 bit data vector has been used and also the input 8 bit data vector has been derived from only the 4 bit data which is the result ...
openaire   +1 more source