A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences

Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequence

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks

Detection of pulsations and a spectral feature in the X-ray emission of the isolated neutron star 1RXS J214303.7+065419/RBS 1774

We report on the results of a deep XMM-Newton observation of RBS 1774, the most recent dim isolated neutron star candidate found in the ROSAT archive data. Spectral and timing analysis of the high-quality PN and MOS data confirm the association of this source with an isolated neutron star. The spectrum is thermal and blackbody-like, and there is evidence at a significance level > 4sigma that the source is an X-ray pulsar, with spin period of 9.437 s. Spectral fitting reveils the presence of an absorption feature at ~0.7 keV, but at this level data do not have enough resolution to allow us to discriminate between an absorption line or an edge. We compare the newly measured properties of RBS 1774 with those of other known dim isolated neutron stars, and discuss possible interpretations for the absorption feature.Comment: 21 pages, 5 figures, ApJ accepte