7 citations · 29 across the 22 of their papers we have counts for
22 papers
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network
Xin Zhu, Hongyi Pan, Ahmet Enis Cetin
The large volume of electroencephalograph (EEG) data produced by brain-computer interface (BCI) systems presents challenges for rapid transmission over bandwidth-limited channels i…
Sparse Mamba: Introducing Controllability, Observability, And Stability To Structural State Space Models
Emadeldeen Hamdan, Hongyi Pan, Ahmet Enis Cetin
Structured state space models' (SSMs) development in recent studies, such as Mamba and Mamba2, outperformed and solved the computational inefficiency of transformers and large lang…
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Xin Zhu, Hongyi Pan, Yury Velichko +5
Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in th…
A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression
Xin Zhu, Daoguang Yang, Hongyi Pan +3
The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present…
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer
Xin Zhu, Hongyi Pan, Shuaiang Rong +1
Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetric…
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
Nastaran Darabi, Maeesha Binte Hashem, Hongyi Pan +3
The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize lat…
Domain Generalization with Fourier Transform and Soft Thresholding
Hongyi Pan, Bin Wang, Zheyuan Zhang +5
Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-t…
Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC
Xin Zhu, Hongyi Pan, Salih Atici +1
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC). We present a novel preamble detect…
Wildfire Detection Via Transfer Learning: A Survey
Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao +3
This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookou…
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Bin Wang, Hongyi Pan, Armstrong Aboah +7
Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as…
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
Hongyi Pan, Xin Zhu, Salih Atici +1
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the H…
Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets
Hongyi Pan, Emadeldeen Hamdan, Xin Zhu +2
In this paper, we propose a set of transform-based neural network layers as an alternative to the Conv2D layers in Convolutional Neural Networks (CNNs). The proposed lay…
Input Normalized Stochastic Gradient Descent Training of Deep Neural Networks
Salih Atici, Hongyi Pan, Ahmet Enis Cetin
In this paper, we propose a novel optimization algorithm for training machine learning models called Input Normalized Stochastic Gradient Descent (INSGD), inspired by the Normalize…
DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer
Hongyi Pan, Xin Zhu, Salih Atici +1
In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the Conv2D layers in the Residual ne…
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
Salih Atici, Hongyi Pan, Mohammed H. Elnagar +4
We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network co…
Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer
Hongyi Pan, Xin Zhu, Zhilu Ye +2
In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the su…
Multipod Convolutional Network
Hongyi Pan, Salih Atici, Ahmet Enis Cetin
In this paper, we introduce a convolutional network which we call MultiPodNet consisting of a combination of two or more convolutional networks which process the input image in par…
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Badawi, Ahmet Enis Cetin
Convolution has been the core operation of modern deep neural networks. It is well-known that convolutions can be implemented in the Fourier Transform domain. In this paper, we pro…
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
Hongyi Pan, Diaa Badawi, Ishaan Bassi +2
We propose a kernel-PCA based method to detect anomaly in chemical sensors. We use temporal signals produced by chemical sensors to form vectors to perform the Principal Component…
Multiplication-Avoiding Variant of Power Iteration with Applications
Hongyi Pan, Diaa Badawi, Runxuan Miao +2
Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking alg…
Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm
Hongyi Pan, Diaa Badawi, Erdem Koyuncu +1
We consider a family of vector dot products that can be implemented using sign changes and addition operations only. The dot products are energy-efficient as they avoid the multipl…
Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Dabawi, Ahmet Enis Cetin
In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace convolution layers in deep neural networks. In t…