Temporal response patterns determined by the GMM clustering algorithm across varying primary stimulus parameters (amplitude or onset ramp velocity) of all test stimuli. by Dinh K. Tang (20469082)

Source Code and Dataset by Swathi Ganesan (14394924)

Comparison of power consumption in optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

Subjects:

Comparison of results based on CycleGAN method. by Xiaojun Wang (50332)

Subjects:

Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

Subjects:

Processing results and code. by Lei Wang (6656)

Dispersion signal in principal and residual space in PCA and KPCA algorithm. by Emmanuel Ndongue Esseme (22615091)

Accuracy of different electrical and optical neural network on Caltech and ETH-80 data sets. by Reyhane Ahmadi (14857363)

Subjects:

The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

Subjects:

CY45-0.5/1#9 Code file for data processing and prediction results. by Yanhua Xian (21417128)

Dispersion signal in principal and residual space in PCA-FFT and KPCA-FFT algorithm. by Emmanuel Ndongue Esseme (22615091)

GA pseudo-code. by Jianpeng Zhang (528185)

The specific architecture of the FcaNet-cycleGan style transfer method. by Xiaojun Wang (50332)

Subjects:

Coprime array with interpolated array elements. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. by Tomokaze Shiratori (9635271)

Subjects:

A comparison of the absorption coefficients obtained from the current simulation method (FEM) with those obtained by Sharma et al. [13]. by Xiaogang Li (299205)

Subjects:

Comparison of grayscale conversion algorithms. by Tianke Fang (22648035)

Comparison of noise reduction algorithms. by Tianke Fang (22648035)

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. …”

Code and data for the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms" by Noelle Madyson Daigle (22120114)

“…<p dir="ltr">Code and data for analysis detailed in the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms." …”