Hasil untuk "eess.SP"

Menampilkan 20 dari ~303567 hasil · dari arXiv, CrossRef

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arXiv Open Access 2025
Frequency-selective Dynamic Scattering Arrays for Over-the-air EM Processing

Davide Dardari

In this paper, we investigate frequency-selective dynamic scattering array (DSA), a versatile antenna structure capable of performing joint wave-based computing and radiation by transitioning signal processing tasks from the digital domain to the electromagnetic (EM) domain. The numerical results demonstrate the potential of DSAs to produce space-frequency superdirective responses with minimal usage of radiofrequency (RF) chains, making it particularly attractive for future holographic multiple-input multiple-output (MIMO) systems.

en eess.SP, cs.IT
arXiv Open Access 2025
Near field transmission using Hermite-Gaussian modes

Chenxi Zhu

RF transmission in line-of-sight near field based on Hermite-Gaussian (HG) modes is developed. Multiple HG modes are transmitted and received using rectangular antenna arrays to form the basic modes and dimensions for MIMO transmission. Beam steering can be achieved by manipulating the antenna arrays with 3D rotation in the desired EM field. The beam parameters are optimized to minimize the size of the antennas. Simulation is performed for a 300GHz system with free space channel model. Spectrum efficiency up to 294.3bps/Hz can be achieved with 36 HG modes and cross-polarization.

en eess.SP
arXiv Open Access 2024
Advanced Receiver Autonomous Integrity Monitoring: Impact of Time-Correlated Pseudorange Measurement Noise

Jindrich Dunik, Martin Orejas

The paper deals with the allocation of the probability of false alert within the advanced receiver integrity monitoring method. Namely, the stress is laid on the correct computation of the probability of false alert per sample under assumption of time-correlated pseudorange noise. Detailed analysis of the dependence of the probability of false alert per sample on the measurement noise time constant is given and a numerical algorithm for the correct computation of the probability is proposed. The algorithm is illustrated using a numerical example.

en eess.SP
arXiv Open Access 2024
Optimum Launch Power in Multiband Systems

Yanchao Jiang, Fabrizio Forghieri, Stefano Piciaccia et al.

We investigate the residual throughput penalty due to ISRS, after power-optimization, in multiband systems. We show it to be mild. We also revisit the launch power optimization 3-dB rule. We find that using it is possible but not advisable due to increased GSNR non-uniformity.

en eess.SP
arXiv Open Access 2024
Fast Signal Interpolation Through Zero-padding and FFT/IFFT

Zijun Gong

Based on the sampling theorem, interpolation should be conducted by employing the sinc functions as the kernels. Inspired by the fact that the discrete Fourier transform (DFT) is sampled from the discrete time Fourier transform, a fast signal interpolation algorithm based on zero-padding and fast Fourier transform (FFT) and inverse FFT (IFFT) is presented. This algorithm gives a good approximate of the ideal interpolation, in spite of the windowing effect. The fundamental difference of this algorithm and the ideal sinc interpolation is unveiled, and shown to be deeply rooted in the connection of the sinc function and the Dirichlet function.

en eess.SP
arXiv Open Access 2024
Perturbation-based Sequence Selection for Probabilistic Amplitude Shaping

Mohammad Taha Askari, Lutz Lampe

We introduce a practical sign-dependent sequence selection metric for probabilistic amplitude shaping and propose a simple method to predict the gains in signal-to-noise ratio (SNR) for sequence selection. The proposed metric provides a $0.5$ dB SNR gain for single-polarized 256-QAM transmission over a long-haul fiber link.

en eess.SP, cs.IT
arXiv Open Access 2024
Non-linear Equalization in 112 Gb/s PONs Using Kolmogorov-Arnold Networks

Rodrigo Fischer, Patrick Matalla, Sebastian Randel et al.

We investigate Kolmogorov-Arnold networks (KANs) for non-linear equalization of 112 Gb/s PAM4 passive optical networks (PONs). Using pruning and extensive hyperparameter search, we outperform linear equalizers and convolutional neural networks at low computational complexity.

en eess.SP, cs.LG
arXiv Open Access 2023
A New Optimal Subpattern Assignment (OSPA) Metric for Multi-target Filtering

Tuyet Vu

This paper proposes and evaluates a new metric. This metric will overcome a limitation of the Optimal Subpattern Assignment (OSPA) metric mentioned by Schuhmacher et al.: the OSPA distance between two sets of points is insensitive to the the case where one is empty. This proposed metric called Complete OSPA (COSPA), retains all the advantages of the OSPA metric for evaluating the performance of multiple target filtering algorithms while also allowing separate control over the threshold of physical distance errors and cardinality errors.

en eess.SP
arXiv Open Access 2023
Efficient Beamforming Designs for IRS-Aided DFRC Systems

Yi-Kai Li, Athina Petropulu

This short tutorial presents several ideas for designing dual function radar communication (DFRC) systems aided by intelligent reflecting surfaces (IRS). These problems are highly nonlinear in the IRS parameter matrix, and further, the IRS parameters are subject to non-convex unit modulus constraints. We present classical semidefinite relaxation based methods, low-complexity minorization based optimization methods, low-complexity Riemannian manifold optimization methods, and near optimal branch and bound based methods.

en eess.SP
arXiv Open Access 2023
Theory of Periodically Time-Variant Linear Systems

Juan I. Bonetti, Agustín C. Galletto, Mario R. Hueda

In this work we provide a mathematical framework to describe the periodically time variant (PTV) linear systems. We study their frequency-domain features to estimate the output bandwidth, a necessary value to obtain a suitable digital representation of such systems. In addition, we derive several interesting properties enabling useful equivalences to represent, simulate and compensate PTVs.

en eess.SP
arXiv Open Access 2022
Rate Adaptive Autoencoder-based Geometric Constellation Shaping

Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros et al.

An autoencoder is used to optimize bit-to-symbol mappings for geometric constellation shaping. The mappings allow for net rate adaptivity without additional hardware complexity, while achieving up to 300km of transmission distance compared to uniform QAM.

en eess.SP
arXiv Open Access 2019
Machine Learning for removing EEG artifacts: Setting the benchmark

Subhrajit Roy

Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the potential to aid the clinical workflow. In this abstract, we share the first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset. We envision that these results will serve as a benchmark for researchers who might work with this dataset in future.

en eess.SP, stat.ML
arXiv Open Access 2019
Mixed-transform based codec for 2D compression of ECG signals

Johan Chagnon, Laura Rebollo-Neira

A method for ECG compression, by imaging the record as a 2D array and implementing a transform lossy compression strategy, is advanced. The particularity of the proposed transformation consists in applying a Discrete Wavelet Transform along one of the dimensions and the Discrete Cosine Transform along the other dimension. The performance of the method is demonstrated on the MIT-BIH Arrhythmia database. Significant improvements upon the 1D version of the codec, and on benchmarks for 2D ECG compression, are achieved.

en eess.SP

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