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Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8 % – 86.2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

AIM: Size is one of the most important and obvious traits of an organism. Both small and large sizes have adaptive advantages and disadvantages. Body size–frequency distributions of most large clades are unimodal and right skewed. Species larger than the mean or range midpoint of body sizes are relatively scarce. Theoretical models suggest evolutionary rates are higher in small organisms with short generation times. Therefore diversification rates are usually thought to be maximal at relatively small body sizes. Empirical studies of the rates of molecular evolution and clade diversification, however, have usually indicated that both are unrelated to body size. Furthermore, it has been claimed that because snakes are longer than lizards, the size–frequency distribution of all squamate species is bimodal overall. We examined the shape of the size–frequency distribution of nearly all Squamata and Rhynchocephalia species, and investigated how size affected diversification rates. LOCATION: Global. METHODS: We collected data on maximum body length for 9805 lepidosaur (squamates and the tuatara) species (99.7% of all species) and converted them to mass using clade‐specific allometric equations. Using methods that test for relationships between continuous traits and speciation and extinction rates on a large, dated phylogeny (4155 species), we investigated the relationship between diversification rates and body size. RESULTS: Living squamates span six orders of magnitude in body size, eight when giant extinct snakes and mosasaurs are included. The body size–frequency distributions of snakes and lizards separately, and of all lepidosaur species combined, are unimodal and right skewed. Nonetheless, we find neither linear nor hump‐shaped relationships between size and diversification rates, except in snakes, where speciation and diversification are hump shaped. MAIN CONCLUSIONS : Despite a clear modality and skew in the body sizes of lepidosaurs, we find little evidence for faster diversification of modal‐sized taxa, perhaps implying that larger‐sized clades are relatively young.

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.

Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.

At present, the micro-Doppler effects of underwater targets is a challenging new research problem. This paper studies the micro-Doppler effect of underwater targets, analyzes the moving characteristics of underwater micro-motion components, establishes echo models of harmonic vibration points and plane and rotating propellers, and reveals the complex modulation laws of the micro-Doppler effect. In addition, since an echo is a multi-component signal superposed by multiple modulated signals, this paper provides a sparse reconstruction method combined with time–frequency distributions and realizes signal separation and time–frequency analysis. A MicroDopplerlet time–frequency atomic dictionary, matching the complex modulated form of echoes, is designed, which effectively realizes the concise representation of echoes and a micro-Doppler effect analysis. Meanwhile, the needed micro-motion parameter information for underwater signal detection and recognition is extracted.

Crater is a geologic structure in solid bodies (including the terrestrial planets, moons, and asteroids) formed by hyper-speed impact, and the impact process is extremely important to the formation and evolution of these celestial bodies. This paper presents a review of the studies on remote sensing observation, formation mechanism, and scientific application of craters. On the remote sensing study of craters, the topographic characteristics of the micro-craters, simple craters, complex craters, and impact basins are described; the related parameters in the morphological studies of craters are subsequently introduced, and the distribution characteristics of the minerals and rock types during the impact excavation process are analyzed; the methods of crater identification and the crater databases on the Moon, Mars, Ceres, and Vesta are summarized. On the studies of crater formation mechanism, the general formation process of the craters is firstly described, and then the most frequently used methods are presented, and the importance of the empirical equations is also elucidated. On the scientific applications of the craters, the principle and currently utilization of the planetary surface dating method with crater size-frequency distribution are firstly presented, and the applications, including modeling the lunar regolith formation and thickness derivation of both the regolith and basalt, are reviewed. Finally, the future prospects of the formation mechanism study of the craters are discussed.

The relationship between a species' growth rate and its size—its growth function—represents essential biological information for supporting sustainable fisheries and wildlife management. Yet, growth functions are known for only a fraction of species. Progress is especially limited in marine invertebrates, including shellfish, due to challenges rearing early life stages in the lab and identifying statolith ring patterns indicative of individual age. We overcome these challenges by deriving a species' growth function using multi‐year size‐frequency population survey data collected from 71 subtidal sites over 35 years. We fit Gaussian mixture models to the data at each survey site and year to identify cohorts, then tracked cohorts between survey years to estimate cohort growth over time. We then used the estimates of growth to parameterize growth functions containing initial and asymptotic size constraints based on the survey data. We demonstrated our method with the kelp forest gastropod and commercial fisheries species, Kellet's whelk (Kelletia kelletii). The assembled survey data included 28,816 whelks, 9–180 mm in shell length. Through cohort tracking, we generated 297 estimates of cohort growth. We fit seven growth functions to the growth estimates and used information criterion and least squares to select the best‐fit model; in this case the Richards, characterized by maximum initial growth at small size that initially declines exponentially and then linearly with size, reaching asymptotic growth by approximately 40 years of age. We also analyzed and compared select portions of the population survey data to test for biogeographic and fisheries management effects on growth. The method we developed can support research on species with size‐frequency population survey data, and the function we derived for Kellet's whelk can inform research on its population biology and sustainable fisheries management.

Anthropocene coral reefs are faced with increasingly severe marine heatwaves and mass coral bleaching mortality events. The ensuing demographic changes to coral assemblages can have long-term impacts on reef community organisation. Thus, understanding the dynamics of subtropical scleractinian coral populations is essential to predict their recovery or extinction post-disturbance. Here we present a 10-yr demographic assessment of a subtropical endemic coral, Pocillopora aliciae (Schmidt-Roach et al. in Zootaxa 3626:576–582, 2013) from the Solitary Islands Marine Park, eastern Australia, paired with long-term temperature records. These coral populations are regularly affected by storms, undergo seasonal thermal variability, and are increasingly impacted by severe marine heatwaves. We examined the demographic processes governing the persistence of these populations using inference from size-frequency distributions based on log-transformed planar area measurements of 7196 coral colonies. Specifically, the size-frequency distribution mean, coefficient of variation, skewness, kurtosis, and coral density were applied to describe population dynamics. Generalised Linear Mixed Effects Models were used to determine temporal trends and test demographic responses to heat stress. Temporal variation in size-frequency distributions revealed various population processes, from recruitment pulses and cohort growth, to bleaching impacts and temperature dependencies. Sporadic recruitment pulses likely support population persistence, illustrated in 2010 by strong positively skewed size-frequency distributions and the highest density of juvenile corals measured during the study. Increasing mean colony size over the following 6 yr indicates further cohort growth of these recruits. Severe heat stress in 2016 resulted in mass bleaching mortality and a 51% decline in coral density. Moderate heat stress in the following years was associated with suppressed P. aliciae recruitment and a lack of early recovery, marked by an exponential decrease of juvenile density (i.e. recruitment) with increasing heat stress. Here, population reliance on sporadic recruitment and susceptibility to heat stress underpin the vulnerability of subtropical coral assemblages to climate change.

A variety of subaerial and submarine events, including mass‐wasting and volcanism, can generate sediment gravity flows and fallout deposits that are preserved in deep‐water stratigraphic records. This study examines whether event beds with differing depositional and transport histories exhibit distinct thickness‐frequency distributions. Analyzing over 4,500 event beds from seven drilling sites near Montserrat, the Izu Arc, the Kyushu‐Palau Ridge, and Gran Canaria, the analyses explore variations in event‐bed characteristics across different climatic periods, volcanic stages, and geomorphological settings. Statistical methods include characterizing thickness‐frequency distributions and assessing subset similarity using t‐tests and smoothed distribution patterns. The data‐driven results indicate discernible differences where dominant geological processes vary. For example, volcanic growth stages at the Kyushu–Palau Ridge produced thicker, coarser, and more frequent event beds compared with quiescent stages. Similarly, beds from the north slope of Gran Canaria—where submarine canyons enhanced sediment delivery—were nearly twice as thick as those from the south. In contrast, indistinguishable characteristics between the rear and frontal Izu Arc subsets after 3 Ma are attributed to the development of an extensional zone supplying material to both arc sides. Comparable distributions were also observed within intervals with minimal geological differences. The reliability of this analytical approach depends on high‐quality sediment recovery, as drilling‐related disturbances may obscure primary depositional signals. Beyond stratigraphic characterization, the method shows broader potential for identifying the provenance of volcanic glass shards through geochemical comparisons and for evaluating the statistical compatibility of data sets from neighboring sites, ensuring sufficient sample size for robust integrated analyses.

This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88 . 8 % and 90 . 2 % , respectively, for the subject-dependent training procedure, and 80 . 8 % and 87 . 8 % , respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.