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Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module.
Sci Rep
June 2024
Department of Mathematics, School of Advanced Sciences, VIT-AP University, Besides AP Secretariate, Amaravati, Andhra Pradesh, 522237, India.
The error correction model's main purpose in heavy hexagonal quantum codes is to improve their reliability for quantum computing applications. Existing challenges include finding the optimal decoder for quantum error correction in heavy hexagonal codes. This research propels the frontier of quantum error correction, with a specific focus on tailoring topological quantum error-correcting codes for the unique challenges posed by superconducting qubits in quantum computers.
View Article and Find Full Text PDFA new chromatographic response function with automatically adapting weight factor for automated method development.
J Chromatogr A
July 2024
Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussel 1050, Belgium. Electronic address:
A critical factor for automated method development in chromatography is the maximization or minimization of an objective function describing the quality (and speed) of the separation. In chromatography, this function is commonly referred to as a chromatographic response function (CRF). Many CRFs have previously been introduced, but many have unfavourable properties such as featuring multiple optima, insufficient discriminatory power, and a too strong dependence on the weight factors needed to balance resolution and time penalty components.
View Article and Find Full Text PDFTransformer guided self-adaptive network for multi-scale skin lesion image segmentation.
Comput Biol Med
February 2024
Ant Group, 310099, China. Electronic address:
Background: In recent years, skin lesion has become a major public health concern, and the diagnosis and management of skin lesions depend heavily on the correct segmentation of the lesions. Traditional convolutional neural networks (CNNs) have demonstrated promising results in skin lesion segmentation, but they are limited in their ability to capture distant connections and intricate features. In addition, current medical image segmentation algorithms rarely consider the distribution of different categories in different regions of the image and do not consider the spatial relationship between pixels.
View Article and Find Full Text PDFRethinking exemplars for continual semantic segmentation in endoscopy scenes: Entropy-based mini-batch pseudo-replay.
Comput Biol Med
October 2023
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China; Department of Biomedical Engineering, National University of Singapore, Singapore; Suzhou Research Institute, National University of Singapore, Suzhou, China; Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong, China; Shenzhen Research Institute, The Chinese University of, Hong Kong, Shenzhen, China. Electronic address:
Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting.
View Article and Find Full Text PDFA Sage-Husa Prediction Algorithm-Based Approach for Correcting the Hall Sensor Position in DC Brushless Motors.
Sensors (Basel)
July 2023
College of Energy and Power Engineering, Shandong University, Jinan 250061, China.
Accurate knowledge of the rotor position is essential for the control of brushless DC motors (BLDCM). Any deviation in this identification can cause fluctuations in motor current and torque, increase noise, and lead to reduced motor efficiency. This paper focused on a BLDCM equipped with a three-phase binary Hall sensor.
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