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pro vyhledávání: '"Khan, Adil"'
Autor:
Ahmad, Muhammad, Distifano, Salvatore, Khan, Adil Mehmood, Mazzara, Manuel, Li, Chenyu, Yao, Jing, Li, Hao, Aryal, Jagannath, Vivone, Gemine, Hong, Danfeng
Hyperspectral Image Classification (HSC) is a challenging task due to the high dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine Learning approaches while effective, face challenges in real-world data due to varying op
Externí odkaz:
http://arxiv.org/abs/2404.14955
Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for
Externí odkaz:
http://arxiv.org/abs/2308.13883
Deep neural networks (DNNs) have gained prominence in various applications, such as classification, recognition, and prediction, prompting increased scrutiny of their properties. A fundamental attribute of traditional DNNs is their vulnerability to m
Externí odkaz:
http://arxiv.org/abs/2308.06467
Recent breakthroughs in the transport spectroscopy of 2-D material quantum-dot platforms have engendered a fervent interest in spin-valley qubits. In this context, Pauli blockades in double quantum dot structures form an important basis for multi-qub
Externí odkaz:
http://arxiv.org/abs/2308.04937
Autor:
Cao, Faxian, Cheng, Yongqiang, Khan, Adil Mehmood, Yang, Zhijing, Chang, S. M. Ahsan Kazmiand Yingxiu
Uncertainty in timing information pertaining to the start time of microphone recordings and sources' emission time pose significant challenges in various applications, such as joint microphones and sources localization. Traditional optimization metho
Externí odkaz:
http://arxiv.org/abs/2307.07096
Controlled data generation with GANs is desirable but challenging due to the nonlinearity and high dimensionality of their latent spaces. In this work, we explore image manipulations learned by GANSpace, a state-of-the-art method based on PCA. Throug
Externí odkaz:
http://arxiv.org/abs/2305.14551
In an era where asynchronous environments pose challenges to traditional self-positioning methods, we propose a new transformation to the existing paradigm. Traditionally, time of arrival (TOA) measurements require both microphone and source signals,
Externí odkaz:
http://arxiv.org/abs/2305.11397
This study comes as a timely response to mounting criticism of the information bottleneck (IB) theory, injecting fresh perspectives to rectify misconceptions and reaffirm its validity. Firstly, we introduce an auxiliary function to reinterpret the ma
Externí odkaz:
http://arxiv.org/abs/2305.11387
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques ar
Externí odkaz:
http://arxiv.org/abs/2211.03148