. 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 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)

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both (...) synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques. (shrink)

The inconsistent relation between word frequency and free recall performance and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants’ cognitive system has been shaped by their native language, free recall performance is best understood as the (...) end result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts “what is coming next” so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be “prediction errors” because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf’s law distribution of the words in the language that shaped participants’ brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall. (shrink)

A superluminal signal velocity (i.e. faster than light) is said to violate causality. However, superluminal signal velocities have been measured in tunneling experiments recently. The classical dipole interaction approach by Sommerfeld and Brillouin results in a complex refractive index with a finite real part. For the tunneling process with its purely imaginary refractive index this model obtaines a zero-time traversing of tunneling barriers in agreement with wave meechanics. The information of a signal is proportional to the product (...) of its frequency band width and its time duration. The reasons that superluminal signal velocities do not violate causality are: (i) physical signals are frequency band limited and (ii) signals have a finite time duration. (shrink)

Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can also (...) be improved by optimizing the user’s control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8–13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity. (shrink)

BackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation has proven to be a useful neuroscientific tool. Because of the excessive tACS-caused artifact at the stimulation frequency in electroencephalography signals, tACS + EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate (...) the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection. In the second experiment, we combined tACS and EEG on one human subject to demonstrate the best-performing data-correction approach in a proof of principle.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison With Existing MethodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionOur results demonstrate the feasibility of SSP by applying it to a phantom measurement with pre-recorded signal and one human tACS + EEG dataset. For a full validation of SSP, more data are needed. (shrink)

BackgroundIndividuals who use methamphetamine for a long period of time may experience decreased inhibition and increased impulsivity. In order to reduce impulsivity or improve inhibitory control ability, high-frequency repetitive transcranial magnetic stimulation has attracted much attention of researchers. Recent studies on addiction have shown that rTMS can stimulate different brain regions to produce different therapeutic effects. Recent work also suggests that HF-rTMS over right dorsolateral prefrontal cortex does not affect the impulsivity of patients with alcohol use disorder; while HF-rTMS (...) over left DLPFC could improve the impulsivity of patients with alcohol use disorder and cigarette smokers. However, it should be noted that empirical studies applying HF-rTMS over left DLPFC of patients with MA use disorders are still lacking.MethodsTwenty-nine patients with MAUD underwent five sessions of HF-rTMS on the left DLPFC per week for 4 consecutive weeks. The cue-induced craving and stop-signal and NoGo task were assessed pre-rTMS and post-rTMS. In addition, 29 healthy controls were recruited. There was no rTMS intervention for the controls, the performance of the stop-signal and NoGo task was evaluated on them.ResultsIn total, HF-rTMS of the left DLPFC significantly decreased MA-dependent patients’ cue-induced craving and stop-signal reaction time. For SSRT, the pre-test of experimental group was significantly higher than the score of control group. In the experimental group, the pre-test score was significantly higher than the post-test score. For Go and stop-signal delay, the pre-test scores of the experimental group was significantly lower than the scores of the control group. No significant difference was found between the pre-test and the post-test scores of the experimental group.ConclusionAdd-on HF-rTMS of left DLPFC may be an effective intervention for reducing impulsivity and cue-induced craving of patients with MAUD. Future research with a control group of MAUD that does not undergo the treatment is needed to confirm the effectiveness. (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)

Music reduces pain in fibromyalgia (FM), a chronic pain disease, but the functional neural correlates of music-induced analgesia (MIA) are still largely unknown. We recruited FM patients ( n = 22) who listened to their preferred relaxing music and an auditory control (pink noise) for 5 min without external noise from fMRI image acquisition. Resting state fMRI was then acquired before and after the music and control conditions. A significant increase in the amplitude of low frequency fluctuations of the (...) BOLD signal was evident in the left angular gyrus (lAnG) after listening to music, which in turn, correlated to the analgesia reports. The post-hoc seed-based functional connectivity analysis of the lAnG showed found higher connectivity after listening to music with right dorsolateral prefrontal cortex (rdlPFC), the left caudate (lCau), and decreased connectivity with right anterior cingulate cortex (rACC), right supplementary motor area (rSMA), precuneus and right precentral gyrus (rPreG). Pain intensity (PI) analgesia was correlated ( r = 0.61) to the connectivity of the lAnG with the rPreG. Our results show that MIA in FM is related to top-down regulation of the pain modulatory network by the default mode network (DMN). (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)

A previous study demonstrated that consonant recognition improved significantly in normal hearing listeners when useful frequency and time ranges were intensified by 6 dB. The goal of this study was to determine whether bilateral cochlear implant and bilateral hearing aid users experienced similar enhancement on consonant recognition with these intensified spectral and temporal cues in noise. In total, 10 BCI and 10 BHA users participated in a recognition test using 14 consonants. For each consonant, we used the frequency (...) and time ranges that are critical for its recognition, identified from normal hearing listeners. Then, a signal processing tool called the articulation-index gram was utilized to add a 6 dB gain to target frequency and time ranges. Consonant recognition was monaurally and binaurally measured under two signal processing conditions, unprocessed and intensified target frequency and time ranges at +5 and +10 dB signal-to-noise ratio and in quiet conditions. We focused on three comparisons between the BCI and BHA groups: AI-Gram benefits, enhancement in binaural benefits via the AI-Gram processing, and reduction in binaural interferences via the AI-Gram processing. The results showed that the mean AI-Gram benefit was significantly improved for the BCI and BHA groups. However, the mean binaural benefit was not improved after AI-Gram processing. Individual data showed wide ranges of the AI-Gram benefit and binaural benefit for both groups. Individual data also showed a decrease in binaural interference in both groups after AI-Gram processing. These results suggest that the frequency and time ranges, intensified by the AI-Gram processing, contribute to consonant enhancement for monaural and binaural listening and both BCI and BHA technologies. The intensified frequency and time ranges helped to reduce binaural interference but contributed less to the synergistic binaural benefit in consonant recognition for both groups. (shrink)

Common spatial pattern is an effective algorithm for extracting electroencephalogram features of motor imagery ; however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels, wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass (...) filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II, which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels. (shrink)

The transition of gene switch induced by external noises and external signals is investigated in the genetic regulatory system. Results show that the state-to-state transition of gene switch as well as resonant behaviors, such as the explicit coherence resonance, implicit coherence resonance and control parameter coherence biresonance, can appear when noises are injected into the genetic regulatory system. The ECR is increased with the increase of the control parameter value when starting from the supercritical Hopf bifurcation parameter point, and there (...) exists a critical control parameter value for the occurrence of ECR. However, the ICR is decreased as the control parameter value is increased when starting from the subcritical Hopf bifurcation point. In particular, the coherence of ECR is higher and more sensitive to noise than that of ICR. When an external signal is introduced into the system, the enhancement or suppression of the CPCBR and the number of peaks strongly depend on the frequency and amplitude of the external signal. Furthermore, the gene regulation system can selectively enhance or decrease the noise-induced oscillation signals at preferred frequency and amplitude of an external signal. (shrink)

Sexually signalling animals must trade off the benefits of attracting mates with the consequences of attracting predators. For male guppies, predation risk depends on their behaviour, colouration, environmental conditions and changing intensity of predation throughout the day. Theoretically, this drives diel patterns of display behaviour in native Trinidadian populations, where males display more under low-light conditions when their most dangerous predator is less active. Here, we observed Australian guppies in a laboratory setting to investigate their diel display pattern, and if (...) this pattern is controlled by ambient light intensity. We also quantified individual variation in both the daily frequency and diel pattern of displays, and if such variation relates to body size, colouration and a non-sexual behaviour. Under a typical daily light regime, male guppies displayed mostly in the first hour of observation. Extending the duration of dawn-like lighting, however, resulted in an extended period of high display, demonstrating that light intensity per se is an important cue for this behaviour. These findings mirror those obtained for Trinidadian populations, suggesting that male courtship timing is likely shaped by broad, potentially generalizable features of guppy ecology. The effect of acclimation to captive conditions on male behaviour is also discussed. Whereas the temporal pattern of display appeared consistent, individuals varied in their daily display frequency, and this was correlated with variation in colour phenotype and a measure of non-sexual risk acceptance behaviour. Such relationships pose promising avenues for integrating behavioural and sensory ecology with contemporary work on behavioural syndromes and animal personality. (shrink)

The goal of this study was to implement a Riemannian geometry -based algorithm to detect high mental workload and mental fatigue using task-induced electroencephalogram signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and (...) MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings and behavioral measures were also considered. (shrink)

In this paper, an active frequency control strategy of wind turbines based on model predictive control is proposed by using the power margin of wind turbines operating in load shedding mode. The frequency response model of the microgrid system with the load shedding of the wind turbines is used to predict the output power and system frequency deviation of the wind turbine. According to the prediction information, the output power control signal of the model predictive controller (...) in the wind turbine can be optimized. On this basis, a wind turbine active participation frequency control strategy based on model predictive control is designed by rolling prediction and optimization. The wind turbine power control signal after the strategy is used to adjust the output power of the wind turbine and balance the change of the active power of the system to reduce the frequency deviation. (shrink)

Artificial Immune Systems (AIS) are intelligent algorithms derived on the principles inspired by human immune system. In this research work, electroencephalography (EEG) signals for four distinct motor movement of human limbs are detected and classified using Negative Selection Classification Algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV - Graz dataset 2a, comprising 9 subjects) has been used. Mel Frequency Cepstral Coefficients (MFCCs) are extracted as selected feature from recorded EEG signals. Dimensionality (...) reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic Algorithm (GA) optimized detectors (Artificial Lymphocytes) are trained using Negative Selection Algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with optimized radius of detector as a non-self detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over 9 subjects with a maximum individual subject classification accuracy of 97.5 % for subject number eight. (shrink)

Speech comprehension across languages depends on encoding the pitch variations in frequency-modulated sweeps at different timescales and frequency ranges. While timescale and spectral contour of FM sweeps play important roles in differentiating acoustic speech units, relatively little work has been done to understand the interaction between the two acoustic dimensions at early cortical processing. An auditory oddball paradigm was employed to examine the interaction of timescale and pitch contour at pre-attentive processing of FM sweeps. Event-related potentials to (...) class='Hi'>frequency sweeps that vary in linguistically relevant pitch contour and timescale in Mandarin Chinese were recorded. Mismatch negativities were elicited by all types of sweep deviants. For local timescale, FM sweeps with F0 contours yielded larger MMN amplitudes than F1 contours. A reversed MMN amplitude pattern was obtained with respect to F0/F1 contours for global timescale stimuli. An interhemispheric asymmetry of MMN topography was observed corresponding to local and global-timescale contours. Falling but not rising frequency difference waveforms sweep contours elicited right hemispheric dominance. Results showed that timescale and pitch contour interacts with each other in pre-attentive auditory processing of FM sweeps. Findings suggest that FM sweeps, a type of non-speech signal, is processed at an early stage with reference to its linguistic function. That the dynamic interaction between timescale and spectral pattern is processed during early cortical processing of non-speech frequency sweep signal may be critical to facilitate speech encoding at a later stage. (shrink)

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform, which is often used in the field of (...) signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT. (shrink)

Background High-frequency oscillation (HFO) represents a promising biomarker of epileptogenicity. However, the significant interindividual differences among patients limit its application in clinical practice. Here, we applied and evaluated an individualized, frequency-based approach of HFO analysis in stereoelectroencephalography (SEEG) data for localizing the epileptogenic zones (EZs). Methods Clinical and SEEG data of 19 patients with drug-resistant focal epilepsy were retrospectively analyzed. The individualized spectral power of all signals recorded by electrode array, i.e., the relative strength of HFO, was computed (...) with a wavelet method for each patient. Subsequently, the clinical value of the relative strength of HFO for identifying the EZ was evaluated. Results Focal increase in the relative strength of HFO in SEEG recordings were identified in all 19 patients. HFOs identified inside the clinically identified seizure onset zone had more spectral power than those identified outside ( p p Conclusion The relative strength of HFO, especially fast ripples, is a promising effective biomarker for identifying the EZ and can lead to a favorable seizure outcome if used to guide epilepsy surgery. (shrink)

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about$750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective (...) attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences. (shrink)

Based on increased user experience during stimulation, frequency-modulated steady-state visual evoked potentials have been suggested as an improved stimulation method for brain-computer interfaces. Adapting such a novel stimulation paradigm requires in-depth analyses of all different stimulation parameters and their influence on brain responses as well as the user experience during the stimulation. In the current manuscript, we assess the influence of different values for the modulation index, which determine the spectral distribution in the stimulation signal on FM-SSVEPs. We (...) visually stimulated 14 participants at different target frequencies with four different values for the modulation index. Our results reveal that changing the modulation index in a way that elevates the stimulation power in the targeted sideband leads to increased FM-SSVEP responses. There is, however, a tradeoff with user experience as increased modulation indices also lead to increased perceived flicker intensity as well as decreased stimulation comfort in our participants. Our results can guide the choice of parameters in future FM-SSVEP implementations. (shrink)

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by (...) selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method. (shrink)

In this paper, a three-dimensional chaotic system with a line equilibrium is studied, in which a single nonbifurcation parameter is used to control the amplitude and frequency. A variety of chaotic signals can be modified using the amplitude-frequency control switch. The realization of circuit simulation based on multisim further verifies the theoretical analysis. Finally, the method for encrypting color images is tested, and the process performance is valued. It shows that the novel chaotic oscillation has a promising application (...) prospect in image encryption. (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)

Patients fit with cochlear implants commonly indicate at the time of device fitting and for some time after, that the speech signal sounds abnormal. A high pitch or timbre is one component of the abnormal percept. In this project, our aim was to determine whether a number of years of CI use reduced perceived upshifts in frequency spectrum and/or voice fundamental frequency. The participants were five individuals who were deaf in one ear and who had normal hearing (...) in the other ear. The deafened ears had been implanted with a 18.5 mm electrode array which resulted in signal input frequencies being directed to locations in the spiral ganglion that were between one and two octaves higher than the input frequencies. The patients judged the similarity of a clean signal presented to their implanted ear and candidate, implant-like, signals presented to their normal-hearing ear. Matches to implant sound quality were obtained, on average, at 8 months after device activation and at 35 months after activation. At Time 1, the matches to CI sound quality were characterized, most generally, by upshifts in the frequency spectrum and in voice pitch. At Time 2, for four of the five patients, frequency spectrum values remained elevated. For all five patients F0 values remained elevated. Overall, the data offer little support for the proposition that, for patients fit with shorter electrode arrays, cortical plasticity nudges the cortical representation of the CI voice toward more normal, or less upshifted, frequency values between 8 and 35 months after device activation. Cortical plasticity may be limited when there are large differences between frequencies in the input signal and the locations in the SG stimulated by those frequencies. (shrink)

An ongoing assumption made by sleep researchers is that since dreams are more often recalled on awakening from rapid eye movement sleep, dreams must occur more often during this stage of sleep. An alternative hypothesis is that cognition occurs throughout sleep, but the recall of this mentation differs on awakening. When a dream is not reported on awakening, there is no method of establishing whether it did not happen or was forgotten. The aim of the present study was to investigate (...) this issue using an eye movement signal verification technique. Participants were instructed to produce an EM signal whenever they heard a tone. Tones were presented at increasing volume during Stage 2 and REM sleep until EM signal verification was observed. Ninety seconds after signal verification, participants were awakened and asked if they remembered hearing the tone or responding with the EM signal. Such recollection of signal verified tone presentations was significantly less after Stage 2 sleep compared to REM sleep presentations. Furthermore, SVT recall was significantly correlated with reported dream recall frequency, suggesting the same processes involved in recalling SVTs might also underlie dream recall. (shrink)

This paper explores the potential for systematic scrutiny of the language of top management to reveal signals of possible deceptive conduct. The language used in letters signed by Ramalinga Raju, Chair of the Indian multi-national company Satyam, are analysed using a multi-method quantitative approach. We explore the language in Raju’s annual report letters from 2002–2003 to 2007–2008; and in his letter of January 7, 2009 in which he confessed to deceptive conduct. We analyse the frequency of personal pronouns, the (...) tone of positivity or negativity, the frequency of words of extreme emotion and aspects of the characteristics of language use indicated by the DICTION 5.0 text analysis master variable, CERTAINTY. The text of Raju’s letters appears to have been influenced by his self-confessed deceptive behaviour. Raju’s word choice changed noticeably in his five annual report letters prior to the collapse of Satyam as the scale and impact of his financial misstatements increased. The methods presented should be considered for adoption by auditors and regulators—as a way of assessing whether signals emerging from language use warrant closer (including audit) scrutiny of a firm’s financial reporting and governance. They should also be considered for use by monitors of entity risk, such as credit rating agencies and financial analysts. (shrink)

We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning, developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies ridges of the overlapped multicomponent from a time-frequency representation. The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs (...) first extraction is decided using the framework of the intrinsic mode polynomial chirp transform, which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications. (shrink)

. Epochs present in the voiced speech are defined as time instants of significant excitation of the vocal tract system during the production of speech. Nonstationary nature of excitation source and vocal tract system makes accurate identification of epochs a difficult task. Most of the existing methods for epoch detection require prior knowledge of voiced regions and a rough estimation of pitch frequency. In this paper, we propose a novel method that relies on time-order representation based on short-time Fourier–Bessel (...) series expansion which can be employed on entire speech signal to detect epochs without any prior information. The proposed method automatically detects voiced regions in the speech signal by computing the marginal energy density with respect to time in the low frequency range from the energy distribution in the time-frequency plane. An estimate of pitch frequency for each detected voiced region is then obtained by computing the marginal energy density with respect to frequency in the LFR from the energy distribution in the time-frequency plane. Epochs are located for each detected voiced region as peaks in the derivative of the low pass filtered signal corresponding to falling edges of peak negative cycles in the LPF signal synthesized from TOR coefficients corresponding to LFR. Experimental results obtained by the proposed method on speech signals taken from the CMU-Arctic database are found to be promising. The proposed method detects epochs with high accuracy and reliability. (shrink)

Accurate and rapid defect identification based on pulsed eddy current testing plays an important role in the structural integrity and health monitoring of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network, such as large memory and high computational cost, an intelligent defect recognition pipeline based (...) on the general Warblet transform method and optimized two-dimensional DCNN is proposed. The GWT method is used to convert the one-dimensional PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer after every convolutional layer and fully connection layer, simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too. (shrink)

Alzheimer’s Disease (AD) and Autism Spectrum Disorder (ASD) represent two distinct but equally impactful challenges in the field of neurology and cognitive health. AD is a degenerative neurological condition characterized by its progressive nature, typically affecting individuals in later stages of life. The hallmark features include cognitive impairment, mem- ory deterioration, and alterations in behavior. In contrast, ASD is a developmental disorder typically diagnosed in childhood, marked by difficulties in social interaction, communication, and repetitive behaviors. This paper explores the potential (...) of the time-frequency feature extraction model known as the Left-Right Fast Fourier Transform (LR-FFT) in the context of these two disorders. While AD and ASD differ significantly in their onset, presentation, and demographic affected, both necessitate early and accurate diagnosis to enable timely intervention and tailored treatment strategies. Our research yields promising results, with classification accuracies reaching 88.26% for AD and 99.86% for ASD, demonstrating the LR-FFT’s potential to enhance diagnostic accuracy. By contributing to improved differentiation between these complex neurological conditions, this work aims to advance our understanding and management of AD and ASD, ultimately benefiting patients, their families, and healthcare practitioners. (shrink)

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression accepts input from a hybrid (...) of technical indicators and reconstructed variational mode functions. The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns. (shrink)

The paper focuses on automation of signal processing, which also considers analysis of biomedical data, such as EEG. The results of the study prove that this enables a better understanding of signal changes and makes it possible to address some specific disturbances. It also makes it possible to describe the relevant changes in signals mathematically and helps to create markers of various brain disorders. This paper presents the study at the initial stage and focuses on the mathematical markers (...) of concentration disorders associated with Theta waves. The presented markers presented are based on Welch’s periodograms. The obtained results are very promising and further studies aimed at refining them are currently being carried out. (shrink)

This article deals with the reframing of the concept of universal mediated communication on a global scale. Subjects include the following: the universe has a conscious force field at all its scales, requiring continuous inter-scale communication of information; the field exhibits distinct electromagnetic frequencies associated with the building blocks of life; and advances in the technology of sound production with electronic synthesizers can be applied to study mechanisms of such universal communication. The question being addressed is how electronic synthesizers can (...) be used to create sonic xenolinguistic expressions when composing with sonified frequencies obtained from bio-organisms, human protein structures, phonemes and various types of interstellar frequencies that include, but are not limited to, gamma bursts, pulsars and neutron stars. The objective is to construct sonic xenolingusitic expressions that can act like feedback loops within the field of consciousness. These sonic expressions can include anomalous cognitive, emotive or spiritual experiences that may be emerging out of the universal field of consciousness in real time. Xenolinguistics is a hypothetical language used to communicate with other sentient beings in the universe. The rationale for composing sonic xenolingustic expressions is the belief that the universe is considered conscious and that there is a field of consciousness existing throughout the universe that in principle is also instrumental in how communication can evolve with extraterrestrials. Outlines of other attempts at extraterrestrial communication by other researchers will be offered based on current literature. The probability that other life forms have established some mode of information exchange that is detectable and that, in return, may be able to create a form of communication to respond will be covered. It is postulated that various energy fields existing before and after the creation of our universe interacted with one another becoming abiotic, and then over time, formed even more complex structures. It is these complex structures that were then able to communicate essential information for maintaining their stability and survivability by using specific signalling frequencies. It is hypothesized that first life in the universe emerged within an already existing field of information and that our consciousness is thereby able to establish communication with other forms of consciousness in the universe. It is on this basis that a novel scientific and philosophical paradigm is considered in which the universe is conceived of as an active information matrix that can entertain potential extraterrestrial communication. In this framework, the present article will offer a new approach to orchestrated electronic sounds as a means to such communication. By integrating quantum physical properties into that sonic framework, a new study emerged on how sonic xenolinguistic elements can be incorporated into electronic composition techniques. This new framework may produce new algorithms for musical expression that not only represent an innovative future of electronic music but may also be crucial in the formulation and induction of new states of consciousness that will enable humankind to directly participate in the further becoming of the universe as a result of the evolution of this universal field of consciousness. (shrink)

This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals (...) and forget old ones. Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy. (shrink)

Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (< 0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged (...) stroke survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP + PT) within 14-51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06 Hz – 0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. (shrink)