20 papers · ranked by Valyu relevance
Sebastian, Rinku, O'Keefe Simon, Trefzer Martin
—Extracting features from the speech is the most critical process in Speech signal processing. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used features in the majority of the speaker and speech recognition applications as the filtering in this feature is similar to the filtering taking place in…
Helena A. Merker, Isabella Dalla Betta, Matthew A. Wilson, Francisco J. Flores + 1 more
Rapid and accurate detection of electrographic seizures is critical for both clinical diagnosis and neuroscience research. Although seizure identification is commonly performed in the time domain, analysis in the time-frequency domain provides a more comprehensive representation of seizure characteristics. In this…
Türker Tuğrul, Sertaç Oruç, Jessica Louise Hall, Ali Ulvi Galip Şenocak + 1 more
Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices…
Doaa Sami Khafaga, El-Sayed M. El-kenawy, Nima Khodadadi, Marwa M. Eid + 1 more
Image watermarking is an important extension of intellectual property protection that facilitates the identification and authentication of multimedia content. This paper aims to improve and optimize image watermarking techniques to ensure effective image protection regardless of image size or format. The proposed…
Meng Ling Ming, Qi Wei Min, Dong Yi Fan, Zheng Yu Ning + 1 more
Multivariate time series analysis and prediction are of great significance in traffic management, weather forecasting and other practical applications. However, most of the existing research focuses on using the traditional transformer model as the framework to predict short series or predict with time domain features…
R. S. Soundariya, P. Thangaraj
Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human-computer interaction, especially in adaptive systems. This paper proposes a novel emotion recognition system that improves classification accuracy through advanced signal processing, adaptive channel…
Hao Zhang, Yujun Qi, Lili Wang
Nucleic acid sequence analysis constitutes a core research area in biomedical and health informatics, playing a critical role in infectious disease surveillance, epigenetic regulation, and genomic biomarker discovery. However, most existing sequence encoding methods rely on discrete representations, which are…
Peng Wang, Yanjun Feng, Xiaodong Sun, Xing Cheng + 1 more
Accurate differentiation between microseismic signals induced by hydraulic fracturing and those from roof fracturing is vital for optimizing fracturing efficiency, assessing roof stability, and mitigating mining-induced hazards in coal mining operations. We propose an automatic identification method for microseismic…
P. Argoul, J. Taillard, F. Argoul
This paper revisits the continuous wavelet transform framework by establishing a rigorous physical and dimensional formulation of the Cauchy-Paul mother wavelet, tailored specifically for intermittent, non-sinusoidal electrophysiological oscillations. Departing from conventional, purely mathematical definitions, we…
Randall K. Julian, Brian A. Rappold, Stephen R. Master
Detection and quantification of low-level analytes in complex chromatographic-mass spectrometric data ultimately depend on objective criteria for deciding when an apparent peak is distinguishable from background. Conventional signal-to-noise and LOD/LOQ rules are typically justified under simple, constant-variance…
Anupinder Singh, Vinay Arora, Mandeep Singh, Oskars Kalejs
Background/Objectives: Cardiac auscultation is an important method for identifying cardiovascular abnormalities, but conventional methods are limited by examiner-dependent variability and sensitivity. The automatic classification of phonocardiogram (PCG) has the potential to be applied for standardized cardiac…
Katsunori Yoshimatsu, Zetao Lin, Hideaki Miura, Kai Schneider
We examine anisotropy and spatial intermittency at small scales in drift-wave turbulence with zonal flows. We use a two-dimensional directional continuous wavelet transform, which allows simultaneous localization in scale, position, and direction. This wavelet analysis is applied to vorticity fields obtained from…
Jeongho Chae, Benjamin McMichael, Terrence S. Furey
Bulk tissue-based accessible chromatin studies provide summary annotations across all cell types within the tissue. These annotations can be skewed by varying proportions of individual cell types, especially in the context of disease studies. Estimated sample specific cell-type proportions can be used to mitigate…
Aaron Johnson, Katerina Chatziioannou, Jake Summers
For slowly-varying noise, time-frequency methods offer a natural middle ground between the efficiency of the frequency domain and the generality of the more expensive time domain. Despite growing adoption, such methods remain less well documented and less familiar in the gravitational-wave literature, compared to the…
Lina Chato, Alex Kagozi
Accurate diagnosis of cardiac abnormalities from electrocardiogram signals remains a central challenge in automated cardiovascular assessment. This study investigates the efficiency of time–frequency representations and deep learning architectures in classifying 12-lead ECGs into five diagnostic super-classes using the…
Armin Hakkak Moghadam Torbati, Narges Davoudi, Giuseppe Longo
Beta bursts are brief, transient increases in beta-band (13–30 Hz) EEG activity that play a key role in motor control, particularly in processes like movement initiation and inhibition. While most existing methods detect these bursts using simple amplitude thresholds, they often ignore variability in burst duration and…
Rajesh Debnath, Amitabha Majumder, Arvind Kumar Jain, Bishwajit Dey
Sustaining power quality is an utmost priority for energy distributors in a modern power system integrated with distributed generation and nonlinear loads. The power quality disturbances (PQDs) comprising of multiple PQD events creates complexities in accurate detection and classification. Therefore, this study…
Asiryan, V., Volchkov, V. + 2 more
—This paper is devoted to the development and research of a new compression technology based on Weyl-Heisenberg bases (WH-technology) for modifying the JPEG compression standard and improving its characteristics. For this purpose, the paper analyzes the main stages of the JPEG compression algorithm, notes its key…
Zhicheng Pan, El Mehdi Zahraoui, Patricio Maturana-Russel, Guillermo Cabrera-Guerrero + 2 more
Core-collapse supernovae (CCSNe) remain a critical focus in the search for gravitational waves in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates the use of convolutional neural networks (CNN) to enhance the…
Rafael Abreu, S. Durand, Jochen Kamm, Christine Thomas + 1 more
- 1 Institut de Physique du Globe de Paris, CNRS, Université de Paris, 75005 Paris, France - 2 Univ Lyon, UCBL, ENSL, UJM, CNRS, LGL-TPE, F-69622, Villeurbanne, France - 3 Geological Survey of Finland, Espoo, Findland - 4 Institut für Geophysik, Westfälische Wilhelms-Universität Münster, Münster, Germany - 5 Louisiana…