A hybrid convolutional neural network with fusion of handcrafted and deep features for FHSS signals classification
Shared spectrum utilization is unavoidable because of the continuous rise of wireless usage
and bandwidth needs. Effective spectrum sharing can be done by spectrum monitoring that …
and bandwidth needs. Effective spectrum sharing can be done by spectrum monitoring that …
Experimental Demonstration of Reservoir Computing with Self‐Assembled Percolating Networks of Nanoparticles
JB Mallinson, JK Steel, ZE Heywood… - Advanced …, 2024 - Wiley Online Library
The complex self‐assembled network of neurons and synapses that comprises the
biological brain enables natural information processing with remarkable efficiency …
biological brain enables natural information processing with remarkable efficiency …
[PDF][PDF] Spoken english alphabet recognition with mel frequency cepstral coefficients and back propagation neural networks
Spoken alphabet recognition as one of the subsets of speechrecognition and pattern
recognition has many applications. Unfortunately, spoken alphabet recognition might not be …
recognition has many applications. Unfortunately, spoken alphabet recognition might not be …
Wavelet cesptral coefficients for isolated speech recognition
The study proposes an improved feature extraction method that is called Wavelet Cepstral
Coefficients (WCC). In traditional cepstral analysis, the cepstrums are calculated with the …
Coefficients (WCC). In traditional cepstral analysis, the cepstrums are calculated with the …
Signsworld; deeping into the silence world and hearing its signs (state of the art)
Automatic speech processing systems are employed more and more often in real
environments. Although the underlying speech technology is mostly language independent …
environments. Although the underlying speech technology is mostly language independent …
Wavelet based Cepstral Coefficients for neural network speech recognition
Traditional cepstral analysis methods are often used as part of feature extraction process in
speech recognition. However the cepstral analysis method uses the Discrete Fourier …
speech recognition. However the cepstral analysis method uses the Discrete Fourier …
Classification of FHSS signals in a multi-signal environment by artificial neural network
MT Khan, AZ Sha'ameri… - International Journal of …, 2021 - journal.uob.edu.bh
Frequency-hopping spread spectrum (FHSS) spreads the signal over a wide bandwidth
where the carrier frequencies change rapidly according to a pseudorandom number making …
where the carrier frequencies change rapidly according to a pseudorandom number making …
[PDF][PDF] Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition
R Haldar, PK Mishra - Int. Res. J. Eng. Technol, 2016 - academia.edu
Speech is the most popular and efficient way to communicate and interact with each other. It
is also a useful medium to connect with the machine. This paper presents a technique for …
is also a useful medium to connect with the machine. This paper presents a technique for …
Power spectral analysis and Neural network for feature extraction and recognition of speech
MSA Chowdhury, MF Khan - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
The research is about the feature extraction and recognition of Bangla (Bengali) phoneme,
word and command. Power spectral analysis as a feature extraction technique used. Each …
word and command. Power spectral analysis as a feature extraction technique used. Each …
[PDF][PDF] MACHINE LEARNING CLASSIFICATION OF FREQUENCY-HOPPING SPREAD SPECTRUM SIGNALS IN A MULTI-SIGNAL ENVIRONMENT
MT KHAN - 2023 - researchgate.net
Frequency-hopping spread spectrum (FHSS) spreads the signal over a wide bandwidth,
where the carrier frequencies change rapidly according to a pseudorandom number making …
where the carrier frequencies change rapidly according to a pseudorandom number making …