Orthogonal Mode Decomposition: A Precise and Efficient Approach for Analyzing Finite Discrete Signals
The orthogonal mode decomposition method provides a precise and efficient approach to decompose finite length real signals into a unique and orthogonal set of modes, where each mode is a narrowband function with a clear center frequency and bandwidth.
Efficient Time-Frequency Domain Audio Inpainting Using Autoregressive Modeling
The paper proposes a novel time-frequency domain audio inpainting method, Janssen-TF, which outperforms the recent deep learning-based approach in both objective and subjective evaluations.
Enhancing In-Ear Electrocardiogram Signals Using Denoising Convolutional Autoencoders for Improved Cardiovascular Monitoring
A denoising convolutional autoencoder model can effectively enhance in-ear electrocardiogram signals, improving signal-to-noise ratio, heart rate estimation accuracy, and R-peak detection precision compared to the original noisy in-ear ECG recordings.
Efficient Reconstruction of Periodic Band-Limited Signals from Multi-Input Multi-Output Sampled Data
This paper introduces an efficient FFT-based algorithm to perfectly reconstruct a set of periodic band-limited signals from the samples of the output signals of a multi-input multi-output (MIMO) system.
Zak-OTFS: Pulse Shaping and Its Impact on Predictability and Performance
Gaussian pulse shaping filters in the delay-Doppler domain improve the predictability and performance of Zak-OTFS modulation compared to sinc and root raised cosine filters.
Optimal Signal Detection Using Correlation and Energy Metrics
The optimal detector for distinguishing between a null hypothesis (no signal) and an alternative hypothesis (signal corrupted by noise) is derived by jointly optimizing the transmitted signal and the detector parameters. The detector is based on a linear combination of the correlation and energy of the received signal.
Quantization Analysis and Reconstruction Error Bounds for Bandlimited and Finite-Rate-of-Innovation Signals using Integrate-and-Fire Time Encoding Machines
The quantization step size of the integrate-and-fire time encoding machine (IF-TEM) sampler can be reduced when the maximum frequency of a bandlimited signal or the number of pulses of a finite-rate-of-innovation (FRI) signal is increased. This allows the IF-TEM sampler to achieve a mean squared error (MSE) bound that is roughly 8 dB lower than that of a classical analog-to-digital converter (ADC) with the same number of bits, under specific parameter settings.
Optimal Filter Design for Gear Fault Detection under Time-Varying Speed Conditions using Generalised Envelope Spectrum-Based Signal-to-Noise Objectives
A generalised envelope spectrum-based signal-to-noise objective is proposed to derive optimal filter coefficients that enhance gear fault signatures and attenuate extraneous components under time-varying speed conditions.
Sparse Signal Recovery Guarantees and Cramér-Rao Bounds in the Fractional Fourier Domain
A novel time-domain sparse recovery method that avoids the limitations of transform domain approaches, along with Cramér-Rao Bounds for the sparse parameter estimation problem in the fractional Fourier domain.
Efficient Characteristics-Based Design of Tunable Bandpass Filters
This paper presents a direct, non-iterative method to design bandpass filters by specifying desired frequency-domain characteristics, enabling fine control over filter behavior.
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