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Found 6 papers, 0 papers with code
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
We propose a kernel-PCA based method to detect anomaly in chemical sensors.
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks
In both 1-D and 2-D layers, we compute the binary WHT of the input feature map and denoise the WHT domain coefficients using a nonlinearity which is obtained by combining soft-thresholding with the tanh function.
Multiplication-Avoiding Variant of Power Iteration with Applications
In this paper, we introduce multiplication-avoiding power iteration (MAPI), which replaces the standard $\ell_2$-inner products that appear at the regular power iteration (RPI) with multiplication-free vector products which are Mercer-type kernel operations related with the $\ell_1$ norm.
Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm
We consider a family of vector dot products that can be implemented using sign changes and addition operations only.
Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors
Our results show that we can have an accurate and smooth drift estimate even when the observed sensor signal is very noisy.
Detecting Gas Vapor Leaks Using Uncalibrated Sensors
Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors.
