. Underwater communication is usually affected by ambient noise, which may be generated by different sources, such as the wind origin sea-surface sources, ships and under water life. The properties of background noise, which are non-stationary in nature, depend on location, sea depth, wind speed and sound propagation conditions in the area. Overall performance of underwater acoustic instruments can be improved by denoising the underwater signals. This paper proposes a novel denoising method using empirical mode decomposition technique. Frequency (...) class='Hi'>domain based thresholding has been used to denoise the signal, which involves three steps: EMD is applied to the noisy signal to decompose the signal into intrinsic mode functions. Thresholding is applied to each IMF in the frequency domain to remove the noise. Thresholded IMFs are added to obtain the denoised signal. Real-time experiments were performed to validate the proposed technique on the basis of the records of the ambient noise data recorded at sea for various wind speed. It was observed that the experimental results are in good agreement with the proposed algorithm under different wind-noise levels. (shrink)

The brain-computer interface of steady-state visual evoked potential is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density, we perform zero-padding in the signal's time domain to improve its performance on the PSD and (...) make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI. (shrink)

The Heisenberg-Gabor uncertainty principle defines the limits of information resolution in both time and frequency domains. The limit of resolution discloses unique properties of a time series by frequency decomposition. However, classical methods such as Fourier analysis are limited by spectral leakage, particularly in longitudinal data with shifting periodicity or unequal intervals. Wavelet transformation provides a workable compromise by decomposing the signal in both time and frequency through translation and scaling of a basis function followed by correlation (...) or convolution with the original signal. This study aimed to compare the accuracy of predictive models in mental arithmetic in time and frequency domains. Analysis of the author's response time at mental arithmetic using a soroban was modeled for two periods, an initial period, and a return period both separated by an interval of 370 days. The median response times in seconds was longer for all tasks during the TI compared to the TR period, for addition [CTAdd 62 vs 50 s] and summation [CTSum 68 vs 57 s]. Response times were longer for errors regardless of the study period or task. There was an increasing phase difference for the addition and summation tasks during the TI period toward the end of the series 49.65o compared to the TR period where the phase difference between the two tasks was only 2.05o, indicating that both tasks are likely demonstrating similar learning rates during the latter study period. A comparison between time and time/frequency domain forecasts for an additional 100 tasks demonstrated higher accuracy of the maximum overlap discrete wavelet transform model, where the mean absolute percentage error ranged between 5.48 and 8.19% and that for the time domain models [autoregressive integrated moving average, generalized autoregressive conditional heteroscedasticity ] was 6.16–10.80%. (shrink)

This study investigated relationships between state anxiety and leisure-domain physical activity levels during Covid-19 pandemic. We used frequency, duration, and intensity as key variables of physical activity. Trait anxiety, state anxiety before pandemic, age, gender, and education level were also included in the analysis. Our general hypothesis was that participants who declared doing more physical activity levels would exhibit lower levels of anxiety during the Covid-19 pandemic. A convenient sample of 571 volunteer adults was drawn mainly from São (...) Paulo State, the epicenter of Covid-19 pandemic in Brazil. To obtain the participants’ levels of anxiety we used a validated short-version of the State and Trait Anxiety Inventory. Levels of physical activity were measured via questions from VIGITEL, a validated questionnaire about the individual’s habits on risk factors. Answers were given regarding the first week of March 2020 and at the very moment the participant was filling in the electronic form. Data analyses were conducted through descriptive and inferential techniques, with the use of non-parametric tests and linear regression models. Overall, participants’ responses indicate that anxiety levels were higher during the pandemic compared to the period that preceded the pandemic, and that frequent and long physical activity in the leisure-domain reduced anxiety, regardless its intensity. The regression models revealed an inverse relationship between physical activity and anxiety and independent of gender, age, education level, trait anxiety, and physical activity before pandemic. (shrink)

Generics and frequency statements are puzzling phenomena: they are lawlike, yet contingent. They may be true even in the absence of any supporting instances, and extending the size of their domain does not change their truth conditions. Generics and frequency statements are parametric on time, but not on possible worlds; they cannot be applied to temporary generalizations, and yet are contingent. These constructions require a regular distribution of events along the time axis. Truth judgments of generics vary (...) considerably across speakers, whereas truth judgments of frequency statements are much more uniform. A generic may be false even if the vast majority of individuals in its domain satisfy the predicated property, whereas a frequency statement using, e.g., usually would be true. This paper argues that all these seemingly unrelated puzzles have a single underlying cause: generics and frequency statements express probability judgments, and these, in turn, are interpreted as statements of hypothetical relative frequency. (shrink)

Aiming at the problem of radar target recognition of High-Resolution Range Profile under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network, Short-time Fourier Transform, and Convolutional Neural Network is proposed. Combining the Least-Squares Generative Adversarial Network with the Wasserstein Generative Adversarial Network with Gradient Penalty, the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate (...) feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory network method. (shrink)

There is an urgent need to learn how to appropriately integrate technologies into dementia care. The aims of this Delphi study were to project which technologies will be most prevalent in dementia care in five years, articulate potential benefits and risks, and identify specific options to mitigate risks. Participants were also asked to identify technologies that are most likely to cause value tensions and thus most warrant a conversation with an older person with mild dementia when families are deciding about (...) their use. Twenty-one interdisciplinary domain experts from academia and industry in aging and technology in the U.S. and Canada participated in a two-round online survey using the Delphi approach with an 84% response rate and no attrition between rounds. Rankings were analyzed using frequency counts and written-in responses were thematically analyzed. Twelve technology categories were identified along with a detailed list of risks and benefits for each. Suggestions to mitigate the most commonly raised risks are categorized as follows: intervene during design, make specific technical choices, build in choice and control, require data transparency, place restrictions on data use and ensure security, enable informed consent, and proactively educate users. This study provides information that is needed to navigate person-centered technology use in dementia care. The specific recommendations participants offered are relevant to designers, clinicians, researchers, ethicists, and policy makers and require proactive engagement from design through implementation. (shrink)

Background and purpose: The functional understanding and examination of competitive anxiety responses as temporal events that unfold as time-to-competition moves closer has emerged as a topical research area within the domains of sport psychology. However, little is known from an inclusive and interaction oriented perspective. Using the multidimensional anxiety theory as a framework, the present study examined the temporal patterning of competitive anxiety, focusing on the dimensions of intensity, direction, and frequency of intrusions in athletes across gender and skill (...) level. Methods: Elite and semi-elite Table Tennis athletes from the Ghanaian league (N= 90) completed a modified version of Competitive State Anxiety Inventory-2 (CSAI-2) with the inclusion of the directional and frequency of intrusion scales at three temporal phases (7 days, two days and 1 hour) prior to a competitive fixture. Results: MANOVA repeated measures with follow-up analyses revealed significant interactions for between-subjects factors on all anxiety dimensions (intensity, direction and frequency). Notably, elite (international) female athletes were less cognitively anxious, showed more facilitative interpretation towards somatic anxiety symptoms and experienced less frequency of somatic anxiety symptoms than their male counterparts. However, both elite groups displayed appreciable level of self-confidence. For time-to-event effects, both cognitive and somatic anxiety intensity fluctuated whereas self-confidence showed a steady rise as competition neared. Somatic anxiety debilitative interpretation slightly improved 1 hour before competition whereas cognitive anxiety frequencies also increased progressively during the entire preparatory phase. Conclusions: Findings suggest a more dynamic image of elite athletes’ pre-competitive anxiety responses than suggested by former studies, potentially influenced by cultural differences. The use of psychological skills interventions that require effective structure, content, and timing in a composite manner is suggested. (shrink)

Research into the potential associations between tinnitus and cognition has investigated specific cognitive domains in laboratory settings despite adults with tinnitus reporting broad cognitive difficulties in every-day life. To address this limitation, the present study compared performance and perceived exertion on a visual office-like task in 38 adults with tinnitus and 38 adults without tinnitus matched for age, sex and educational background. All participants were also assessed for hearing, anxiety and depression, and participants with tinnitus were also assessed for tinnitus (...) handicap. No associations were found between presence of tinnitus and cognitive performance and perceived exertion on the visual office-like task when corrected for standard and extended high frequency hearing thresholds, anxiety, and depression. The correction for extended high frequency average hearing threshold was significant for performance but not perceived exertion on the visual office-like task. Overall, the results showed extended high frequency hearing, but not tinnitus, was associated with every-day cognitive performance. This indicates clinical testing of hearing thresholds above 8 kHz could support clinicians’ identification and management of cognitive difficulties. One management method suggested by the current findings would include provision of auditory stimulation at frequencies exceeding the frequency response of many current hearing aids. (shrink)

ObjectiveFreezing of gait is detrimental to patients with idiopathic Parkinson’s disease. Its pathophysiology represents a multilevel failure of motor processing in the cortical, subcortical, and brainstem circuits, ultimately resulting in ineffective motor output of the spinal pattern generator. Electrophysiological studies pointed to abnormalities of oscillatory activity in freezers that covered a broad frequency range including the theta, alpha, and beta bands. We explored muscular frequency domain activity with respect to freezing, and used deep brain stimulation to modulate (...) these rhythms thereby evaluating the supraspinal contributions to spinal motor neuron activity.MethodsWe analyzed 9 PD freezers and 16 healthy controls. We studied the patients after overnight withdrawal of dopaminergic medication with stimulation off, stimulation of the subthalamic nucleus or the substantia nigra pars reticulate, respectively. Patients performed a walking paradigm passing a narrow obstacle. We analyzed the frequency-domain spectra of the tibialis anterior and gastrocnemius muscles in ‘regular gait’ and during the ‘freezing’ episodes.ResultsIn stimulation off, PD freezers showed increased muscle activity of the alpha and low-beta band compared to HC in both TA and GA. This activity increase was present during straight walking and during the freezes to similar extent. STN- but not SNr-DBS decreased this activity and paralleled the clinical improvement of freezing.ConclusionWe found increased muscle activation of the alpha and lower beta band in PD freezers compared to HC, and this was attenuated with STN-DBS. Future studies may use combined recordings of local field potentials, electroencephalography, and electromyography to interrogate the supraspinal circuit mechanisms of the pathological activation pattern of the spinal pattern generator. (shrink)

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency (...) class='Hi'>domain. Secondly, the weighted maximum relevance minimum redundancy algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain. (shrink)

Increasing psychophysical evidence suggests that specific image features - or statistics - can appear unpleasant or induce visual discomfort in humans. Such unpleasantness tends to be particularly profound if the image's amplitude spectrum deviates from the regular 1/f spatial-frequency falloff expected in natural scenes. Here, we show that profound unpleasant impressions also result if the orientation spectrum of the image becomes flatter. Using bandpass noise with variable orientation and spatial-frequency bandwidths, we found that unpleasantness ratings decreased with spatial- (...) frequency bandwidth but increased with orientation bandwidth. Similarly, a subsequent experiment revealed that sinusoidal modulations in the amplitude spectrum of 1/f noise along the spatial frequency increased unpleasantness, but modulations along the orientation decreased it. Given that natural scenes tend to have a linear slope along the spatial frequency but an uneven spectrum along the orientation dimension, our opposing results in the spatial-frequency and orientation domains commonly support the idea that images deviating from the spectral regularity of natural scenes can give rise to unpleasant impressions. (shrink)

The qualitative nature of co-deployed or “associated” metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique “conceptual assemblages” that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study (...) compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the “agentive vs. non-agentive” characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed. (shrink)

Two hundred and sixty-three subjects each gave examples for one of five geographic categories: geographic features, geographic objects, geographic concepts, something geographic, and something that could be portrayed on a map. The frequencies of various responses were significantly different, indicating that the basic ontological terms feature, object, etc., are not interchangeable but carry different meanings when combined with adjectives indicating geographic or mappable. For all of the test phrases involving geographic, responses were predominantly natural features such as mountain, river, lake, (...) ocean, hill. Artificial geographic features such as town and city were listed hardly at all for geographic categories, an outcome that contrasts sharply with the disciplinary self-understanding of academic geography. However, geographic artifacts and fiat objects, such as roads, cities, boundaries, countries, and states, were frequently listed by the subjects responding to the phrase something that could be portrayed on a map. In this paper, we present the results of these experiments in visual form, and provide interpretations and implications for further research. (shrink)

A growing body of research demonstrates the relevance of character strengths for flourishing in general, but also for important outcomes across different life domains. Studies have also shown that there are differences in the extent to which character strengths are applied, that is, perceived as relevant and shown in behavior in a given context, between work and private life, but they have not considered other life domains. This study aims to close this gap by examining the life domains of work, (...) education, leisure, close personal relationships, and romantic relationships. The present study investigates whether strengths-related behavior across different life domains explains additional variance in flourishing beyond the trait level of each respective character strength and studies differences in the relevance of character strengths and strengths-related behavior across different life domains, and examines their relationships with flourishing. A sample of 203 German-speaking adults completed self-reports assessing flourishing and character strengths. They also indicated which of the five life domains were personally relevant to them and reported the character strengths' perceived relevance and the frequency of displaying strengths-related behavior for each of these life domains separately. The results demonstrate that strengths-related behavior averaged across all relevant life domains explained unique variance in flourishing above the trait-level of character strengths in some cases, different life domains were characterized by specific profiles of character strength—regarding both their relevance and strength-related behavior. Moreover, character strengths and strengths-related behavior in different life domains both showed substantial correlations with flourishing. In some cases, these associations were domain-specific. In conclusion, we suggest that examining strengths-related behavior across different life domains represents a worthwhile addition to research on character strengths. (shrink)

In this article, a new adaptive data-driven strategy for voice activity detection using empirical mode decomposition is proposed. Speech data are decomposed using an a posteriori, adaptive, data-driven EMD in the time domain to yield a set of physically meaningful intrinsic mode functions. Each IMF preserves the nonlinear and nonstationary property of the speech utterance. Among a set of IMFs, the IMF that contains source information dominantly called characteristic IMF can be identified and extracted by designing a zero-frequency (...) filter-assisted peaking resonator. The detected CIMF is used to compute energy using short-term processing. Choosing proper threshold, voiced regions in speech utterances are detected using frame energy. The proposed framework has been studied on both clean speech utterance and noisy speech utterance. The proposed method is used for voice activity detection in the presence of white noise and shows encouraging result in the presence of white noise up to 0 dB. (shrink)

Professor Franklin is correct to say that there are significant areas of agreement between his account of formal science (Franklin, 1994) and my critique of his account. We both agree that the domain-independence exhibited by the formal sciences is ontologically and epistemically interesting, and that the concept of ‘structure’ must be central in any analysis of domain-independence. We also agree that knowledge of the structural, relational properties of physical systems should count as empirical knowledge, and that it makes (...) sense to talk about an empirical ‘science’ of structure. Where we disagree is over the frequency of occasions where ‘practical certainty’ is actually attained: Franklin argues that practical certainty is not uncommon in the formal sciences, while I argue that there are barriers to practical certainty that Franklin fails to appreciate. In Franklin’s response, he briefly presents and offers criticisms of my two main arguments against his central thesis. Franklin asserts that the flaw in my first argument is that it does not appreciate that modern mathematical models exhibit structural stability, and thus that many of their predictions are robust under small variations in input or parameter values (and hence, insensitive to the inevitable gaps between estimates and actual values). Nevertheless, what I had in mind in using the term ‘realistic modelling situations’ were models of fairly complex natural systems, such as the dripping faucet, spruce budworm and ecosystem ecology examples. My objection is not that mathematical models of such systems cannot legitimately and accurately describe structural properties that are genuinely predicable of real-world systems, but simply that for.. (shrink)

Laboratory-based studies of problem solving suggest that transfer of solution principles from an analogue to a target arises only minimally without the presence of directive hints. Recently, however, real-world studies indicate that experts frequently and spontaneously use analogies in domain-based problem solving. There is also some evidence that in certain circumstances domain novices can draw analogies designed to illustrate arguments. It is less clear, however, whether domain novices can invoke analogies in the sophisticated manner of experts to (...) enable them to progress problem solving. In the current study groups of novices and experts tackled large-scale management problems. Spontaneous analogising was observed in both conditions, with no marked differences between expertise levels in the frequency, structure, or function of analogising. On average four analogies were generated by groups per hour, with significantly more relational mappings between analogue and target being produced than superficial object-and-attribute mappings. Analogising served two different purposes: problem solving (dominated by relational mappings), and illustration (which for novices was dominated by object-and-attribute mappings). Overall, our novices showed a sophistication in domain-based analogical reasoning that is usually only observed with experts, in addition to a sensitivity to the pragmatics of analogy use. (shrink)

Adolescence is a time of transition from childhood to adulthood during which significant changes occur across multiple domains, including emotional experience. This article reviews the relevant literature on adolescents’ experience of four specific dimensions of emotion: emotional frequency, intensity, instability, and clarity. In an effort to examine how emotional experiences change as individuals approach adulthood, we examine these dimensions across ages 10 to 19, and review how the emotional functioning of adolescents compares to that of adults. In addition, we (...) explore whether and how gender and puberty explain age differences in emotional experience. Finally, we discuss how these findings could inform future research on both the typical trajectory of emotional experience and the development of psychopathology in adolescence. (shrink)

Heart rate variability (HRV), the change in the time intervals between adjacent heartbeats, is an emergent property of interdependent regulatory systems that operate on different time scales to adapt to challenges and achieve optimal performance. This article briefly reviews neural regulation of the heart, and its basic anatomy, the cardiac cycle, and the sinoatrial and atrioventricular pacemakers. The cardiovascular regulation center in the medulla integrates sensory information and input from higher brain centers, and afferent cardiovascular system inputs to adjust heart (...) rate and blood pressure via sympathetic and parasympathetic efferent pathways. This article reviews sympathetic and parasympathetic influences on the heart, and examines the interpretation of HRV and the association between reduced HRV, risk of disease and mortality, and the loss of regulatory capacity. This article also discusses the intrinsic cardiac nervous system and the heart-brain connection, through which afferent information can influence activity in the subcortical and frontocortical areas, and motor cortex. It also considers new perspectives on the putative underlying physiological mechanisms and properties of the ultra-low-frequency (ULF), very-low-frequency (VLF), low-frequency (LF), and high-frequency (HF) bands. Additionally, it reviews the most common time and frequency domain measurements as well as standardized data collection protocols. In its final section, this article integrates Porges' polyvagal theory, Thayer and colleagues' neurovisceral integration model, Lehrer et al.'s resonance frequency model, and the Institute of HeartMath's coherence model. The authors conclude that a coherent heart is not a metronome because its rhythms are characterized by both complexity and stability over longer time scales. Future research should expand understanding of how the heart and its intrinsic nervous system influence the brain. (shrink)

Research on electroencephalogram (EEG) characteristics associated with major depressive disorder (MDD) has accumulated diverse neurophysiologic findings related to the content, topography, neurochemistry, and functions of EEG oscillations. Significant progress has been made since the first landmark EEG study on affective disorders by Davidson 35 years ago. A systematic account of these data is important and necessary for building a consistent neuropsychophysiologic model of MDD and other affective disorders. Given the extensive data on frequency-dependent functional significance of EEG oscillations, a (...) frequency domain approach may reveal the types of brain functions involved and disturbed in MDD. In this review, we systematize and integrate diverse and often unconnected observations on the content, topography, neurochemistry, and functions of EEG oscillations involved in MDD within the general concept of an EEG oscillatory pattern. (shrink)

We revisit the flight-to-quality and flight-from-quality occurrences vis-à-vis the stock-bond nexus across differing investment time scales in the COVID-19 era, using a novel technique hinged on a denoised frequency-domain transfer entropy. Our findings divulge that flights, both FTQ and FFQ, could be attained during stress periods. Generally, in the intermediate term of the COVID-19 pandemic, both Islamic and conventional bonds could act as safe havens, diversifiers, and hedges for international equities, and the same could be observed for international (...) equities. We reiterate empirically that flights may improve the financial system’s stability and robustness by allowing diversity to be effective when it is most required. The findings have financial and portfolio implications for investors considering how to deploy their investments in the COVID-19 era. Our findings may impact policymakers’ responses to changes in various asset classes, allowing them to better monitor financial markets and adjust macroeconomic policies. (shrink)

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data. We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: single- versus multiple-person data; univariate versus multivariate models; stationary versus nonstationary models; linear versus nonlinear models; discrete time versus continuous time models; discrete versus continuous variables; time versus frequency (...) domain; and modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics. (shrink)

The paper reports our research aiming at utilization of human interactive communication modeling principles in application to a novel interaction paradigm designed for brain–computer/machine-interfacing (BCI/BMI) technologies as well as for socially aware intelligent environments or communication support systems. Automatic procedures for human affective responses or emotional states estimation are still a hot topic of contemporary research. We propose to utilize human brain and bodily physiological responses for affective/emotional as well as communicative interactivity estimation, which potentially could be used in the (...) future for human–machine/environment interaction design. As a test platform for such an intelligent human–machine communication application, an emotional stimuli paradigm was chosen to evaluate brain responses to various affective stimuli in an emotional empathy mode. Videos with moving faces expressing various emotional displays as well as speech stimuli with similarly emotionally articulated sentences are presented to the subjects in order to further analyze different affective responses. From information processing point of view, several challenges with multimodal signal conditioning and stimuli dynamic response extraction in time frequency domain are addressed. Emotions play an important role in human daily life and human-to-human communication. This is why involvement of affective stimuli principles to human–machine communication or machine-mediated communication with utilization of multichannel neurophysiological and periphery physiological signals monitoring techniques, allowing real-time subjective brain responses evaluation, is discussed. We present our preliminary results and discuss potential applications of brain/body affective responses estimation for future interactive/smart environments. (shrink)

In this article, a new adaptive fuzzy wavelet neural network model is proposed for nonlinear function approximation problems. The AFWNN model is based on the traditional Takagi-Sugeno-Kang fuzzy system. Specifically, this model replaces the membership functions of fuzzy rules with wavelet basis functions, which are known to have time and frequency localization properties, i.e., they can approximate patterns both in the time and frequency domains. The structure of the AFWNN model is derived from that of the adaptive neuro-fuzzy (...) inference system. However, the AFWNN improves over the ANFIS by replacing Gaussian functions in the hidden layer with wavelet basis functions. The AFWNN model is trained using a gradient-based optimization algorithm. The AFWNN is then tested on three function approximation problems of time series prediction. For certain types of nonlinear time series, for instance fractal processes, the AFWNN is found to be substantially more accurate than alternative methods. (shrink)

Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification (...) about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed. (shrink)

Sensory dysfunction has been shown to be a part of the pathophysiology of schizophrenia. Nowadays we have an objective, non-invasive tool with which to measure neural manifestations of sensory dysfunction. Defined as time-locked changes to external stimuli in the EEG, event-related potentials (ERPs) provide an objective index of information processing in the human brain. Importantly, ERPs may be analyzed through a variety of approaches such as conventional ERP analysis, analysis in the time-frequency domain, microstate segmentation and topographical analysis, (...) as well as source localisation analysis. Each of the methods gives distinct information; they also supplement each other. Here, an attempt is made to verify the validity of combining different approaches to study sensory dysfunction in neuropsychiatric disorders. For example, the data from a schizophrenic patient and an age- and sex-matched healthy subject generate a picture of the events which emerges after visual, proprioceptive and simultaneous presentation of stimuli in both modalities. This approach, though time-consuming, allows the visualisation of changes appearing in the malfunctioning brain as compared to the healthy brain. These methods could ultimately lead to a better establishment of one or more endophenotypes for the schizophrenic disorders. They might also serve as a way to track changes in response to various medications and therapies. (shrink)

Metaverse is to build a virtual world that is both mapped and independent of the real world in cyberspace by using the improvement in the maturity of various digital technologies, such as virtual reality, augmented reality, big data, and 5G, which is important for the future development of a wide variety of professions, including education. The metaverse represents the latest stage of the development of visual immersion technology. Its essence is an online digital space parallel to the real world, which (...) is becoming a practical field for the innovation and development of human society. The most prominent advantage of the English-teaching metaverse is that it can provide an immersive and interactive teaching field for teachers and students, simultaneously meeting the teaching and learning needs of teachers and students in both the physical world and virtual world. This study constructs experiential situational English-teaching scenario and convolutional neural networks –recurrent neural networks fusion models are proposed to recognize students’ emotion electroencephalogram in experiential English teaching during the feature space of time domain, frequency domain, and spatial domain. Analyzing EEG data collected by OpenBCI EEG Electrode Cap Kit from students, experiential English-teaching scenario is designed into three types: sequential guidance, comprehensive exploration, and crowd-creation construction. Experimental data analysis of the three kinds of learning activities shows that metaverse-powered experiential situational English teaching can promote the improvement of students’ sense of interactivity, immersion, and cognition, and the accuracy and analysis time of CNN–RNN fusion model is much higher than that of baselines. This study can provide a nice reference for the emotion recognition of students under COVID-19. (shrink)

Optimization for vertical vibration performance of a rail-train coupling system is investigated in this paper with the introduction of inerters for both primary and secondary suspensions. A model of a typical Chinese passenger train that travels on a traditional rail with track, sleepers, and ballast is simulated. The goal is to improve the ride quality for the train and vibration attenuation for the rail system in response to track irregularities. Optimizations for only inertance and all suspension parameters are carried out (...) by the particle swarm algorithm. Performance benefits for both the train and the rail system are demonstrated and suspension layouts with inerters connected in parallel and series are compared with the traditional one in both time domain and frequency domain. (shrink)

Pantographs are important devices on high-speed trains. When a train runs at a high speed, concave and convex parts of the train cause serious airflow disturbances and result in flow separation, eddy shedding, and breakdown. A strong fluctuation pressure field will be caused and transformed into aerodynamic noises. When high-speed trains reach 300 km/h, aerodynamic noises become the main noise source. Aerodynamic noises of pantographs occupy a large proportion in far-field aerodynamic noises of the whole train. Therefore, the problem of (...) aerodynamic noises for pantographs is outstanding among many aerodynamics problems. This paper applies Detached Eddy Simulation to conducting numerical simulations of flow fields around pantographs of high-speed trains which run in the open air. Time-domain characteristics, frequency-domain characteristics, and unsteady flow fields of aerodynamic noises for pantographs are obtained. The acoustic boundary element method is used to study noise radiation characteristics of pantographs. Results indicate that eddies with different rotation directions and different scales are in regions such as pantograph heads, hinge joints, bottom frames, and insulators, while larger eddies are on pantograph heads and bottom frames. These eddies affect fluctuation pressures of pantographs to form aerodynamic noise sources. Slide plates, pantograph heads, balance rods, insulators, bottom frames, and push rods are the main aerodynamic noise source of pantographs. Radiated energies of pantographs are mainly in mid-frequency and high-frequency bands. In high-frequency bands, the far-field aerodynamic noise of pantographs is mainly contributed by the pantograph head. Single-frequency noises are in the far-field aerodynamic noise of pantographs, where main frequencies are 293 Hz, 586 Hz, 880 Hz, and 1173 Hz. The farther the observed point is from the noise source, the faster the sound pressure attenuation will be. When the distance of two adjacent observed points is increased by double, the attenuation amplitude of sound pressure levels for pantographs is around 6.6 dB. (shrink)

This paper conducts an evaluative study on the rehabilitation of limb motor function by using a microsensor information flow gain algorithm and investigates the surface electromyography signals of the upper limb during rehabilitation training. The surface EMG signals contain a large amount of limb movement information. By analysing and processing the surface EMG signals, we can grasp the human muscle movement state and identify the human upper limb movement intention. The EMG signals were processed by the trap and filter combination (...) denoising method and wavelet denoising method, respectively, the signal-to-noise ratio was used to evaluate the noise reduction effect, and finally, the wavelet denoising method with a better noise reduction effect was selected to process all the EMG signals. After the noise is removed, the signal is extracted in the time domain and frequency domain, and the root mean square, absolute mean, median frequency in the time domain, and average power frequency in the frequency domain are selected and input to the classifier for pattern recognition. The support vector machine is used to classify the myoelectric signals and optimize the parameters in the support vector machine using the grid search method and particle swarm optimization algorithm and classify the test samples using the trained support vector machine. Compared with the classification results of the grid search optimized support vector machine, the optimized vector machine has a 7% higher recognition rate, reaching 85%. The action recognition classification method of myoelectric signals is combined with an upper limb rehabilitation training platform to verify the feasibility of using myoelectric signals for rehabilitation training. After the classifier recognizes the upper limb movements, the upper computer sends movement commands to the controller to make the rehabilitation platform move according to the recognition results, and finally, the movement execution accuracy of the rehabilitation platform reaches 80% on average. (shrink)

The view from inside improves our understanding on market failure and regulation failure in financial market. The EMH fails to understand the causes of financial bubbles and crashes. Behavioral finance introduces insight from psychology. The heuristic and biases approach studied behavioral asymmetry in static environment that leads to market irrationality and information distortion. The fast and frugal thinking in decision-making further explore more complex situation under changing environment. They argue that soft-paternalistic regulation is needed under information overload. The most critical (...) issue is information uncertainty and complexity. Lacking information in frequency domain is the main barrier in managing business cycles. Data form inside reveals current limitations of financial data mainly in the short-term price changes. Microstructure studies show that pricing process is shaped by trading rules. Quantitative analysis reveals severe instability in high frequency trading and derivative market. Feasible regulation should aim to encourage new technology and sustainable growth, rather than protect obsolete technology and short-term speculation. The most fundamental challenge to sustainable economic order is the excessive size of the derivative markets that crowding out investment in real economy. This is a more severe issue than the climate change. (shrink)

Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample (...) identification information is processed with the method of watermaking on the hiding environment created by using a chaos-based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in-line holographic microscopy setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in the laboratory environment, were obtained. The standard USAF 1951 target was used to evaluate the resolution of our imaging setup. Various QR codes were generated for medical sample identification, and the captured medical data were processed by watermarking it with chaos-based random number generators. A new method using chaos-based discrete wavelet transform and singular value decomposition has been developed and applied to high-resolution data to eliminate the problem of encrypted data being directly targeted by third-party attacks. The performance of the proposed new watermarking method has been demonstrated by various robustness and invisibility tests. Experimental results showed that the proposed scheme reached an average PSNR value of 564588 dB and SSIM value of 0.9972 against several geometric and destructive attacks, which means that the proposed method does not affect the image quality and also ensures the security of the watermarking information. The results of the proposed method have shown that it can be used efficiently in various fields. (shrink)

Speech is intelligible even when the temporal envelope of speech is distorted. The current study investigates how native and non-native speakers perceptually restore temporally distorted speech. Participants were native English speakers (NS), and native Japanese speakers who spoke English as a second language (NNS). In Experiment 1, participants listened to “locally time-reversed speech” where every x-ms of speech signal was reversed on the temporal axis. Here, the local time reversal shifted the constituents of the speech signal forward or backward from (...) the original position, and the amplitude envelope of speech was altered as a function of reversed segment length. In Experiment 2, participants listened to “modulation-filtered speech” where the modulation frequency components of speech were low-pass filtered at a particular cut-off frequency. Here, the temporal envelope of speech was altered as a function of cut-off frequency. The results suggest that speech becomes gradually unintelligible as the length of reversed segments increases (Experiment 1), and as a lower cut-off frequency is imposed (Experiment 2). Both experiments exhibit the equivalent level of speech intelligibility across six levels of degradation for native and non-native speakers respectively, which poses a question whether the regular occurrence of local time reversal can be discussed in the modulation frequency domain, by simply converting the length of reversed segments (ms) into frequency (Hz). (shrink)

Consensus of fractional-order multiagent systems with single integral has been wildly studied. However, the dynamics with multiple integral also exist in FOMASs, and they are rarely studied at present. In this paper, consensus problems for multi-integral fractional-order multiagent systems with nonuniform time-delays are addressed. The consensus conditions for MIFOMASs are obtained by a novel frequency-domain method which properly eliminates consensus problems of the systems associated with nonuniform time-delays. Besides, the method revealed in this paper is applicable to classical (...) high-order multiagent systems which is a special case of MIFOMASs. Finally, several numerical simulations with different parameters are performed to validate the correctness of the results. (shrink)

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level (...) semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application. (shrink)

Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. (...) The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements. (shrink)

Consensus of fractional-order multiagent systems with single integral has been wildly studied. However, the dynamics with multiple integral also exist in FOMASs, and they are rarely studied at present. In this paper, consensus problems for multi-integral fractional-order multiagent systems with nonuniform time-delays are addressed. The consensus conditions for MIFOMASs are obtained by a novel frequency-domain method which properly eliminates consensus problems of the systems associated with nonuniform time-delays. Besides, the method revealed in this paper is applicable to classical (...) high-order multiagent systems which is a special case of MIFOMASs. Finally, several numerical simulations with different parameters are performed to validate the correctness of the results. (shrink)

A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults (...) are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments. (shrink)

In this study, experiments were conducted on 30 subjects by means of electrocardiogram and electroencephalogram methodologies as well as a money game to examine the effects of stress on creativity in business problem-solving. The study explained the relationship between creativity and human physiological response using the biopsychosocial model of challenge and threat. The subjects were asked to perform a cognitive mapping task. Based on the brain wave theory, we identified the types of brain waves and locations of brain activities that (...) occurred during the creative problem-solving process in a business environment and studied the effects of stress on creativity. The results of the experiments showed significant differences in creativity in business problem-solving depending on whether or not stress was triggered. Differences were found in the time domain and frequency domain of heart rates, a physiological stress indicator, between the stress group and the no-stress group. A brain wave analysis confirmed that alpha waves increased in the frontal lobe of the brain during creative business problem-solving but decreased when the subjects were under stress, during which beta waves in the brain increased. This study seeks to examine creativity in business problem-solving by studying the effects of stress on human physiological response and cognitive functions in the hope of providing a new and objective interpretation of existing research results. (shrink)

This is a further development of the author's paper “A Unified Theory of Biology and Physics.” It is found that male and female in biology, as well as particle and antiparticle in physics, are analogues of symmetrical sideband pairs in communication theory. This gives a new point of view from which to investigate the significance and characteristics of these different paired entities.These findings are intimately related to the fact that there are two transform domains of representation of entities in all (...) the cases involved. They are the somatic and the genetic domains in biology, the configuration domain and the domain of conserved observables in physics, and the time and frequency domains in communication. (shrink)

Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency (...) domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models. (shrink)

A new medical image enhancement algorithm based on spatial frequency domain is presented in this article. The medical image is first divided into several sub-images based on dyadic wavelet scale analysis. At each level, different directional sub-band images can reflect the different characteristics of the image. A low-frequency sub-band image maintains the original image content information, and high-frequency sub-band images represent image details such as edges and regional boundaries. The corresponding sub-band images are then enhanced by (...) different Butterworth homomorphic filtering functions, which can attenuate the low frequencies and amplify the high frequencies. A linear adjustment is carried out on the low frequency of the highest level. Then, the wavelet reconstruction course is used to obtain the final enhanced image. Experiments on magnetic resonance images of temporomandibular joint soft tissues have shown that the proposed method can effectively eliminate the non-uniform luminance distribution of medical images. Its performance is much better than traditional Butterworth homomorphic filtering algorithm whether in subjective vision quality or objective evaluations such as detailed information entropy and average gradient. (shrink)