Components In Frequency Domain Research Articles

An SSVEP-Based BCI System for SMS in a Mobile Phone

In this paper, the purpose is to develop a Short Message Service (SMS) system that can aid the ALS patients to control a mobile phone to send a message by using of the steady state visually evoked potentials (SSVEP) brain computer interface (BCI) to a person who can help him/her. The EEG signals may be detected by electrodes and signal extracting chip. Then these signal can be transmitted to the mobile platforms by using of Bluetooth interface. Finally, the message and phone number can be selected in accordance with the components in frequency domain transferred from time domain. The experimental results had shown that the proposed system can easily send a message from a mobile phone to another.

How Electroencephalography Serves the Anesthesiologist

Major clinical endpoints of general anesthesia, such as the alteration of consciousness, are achieved through effects of anesthetic agents on the central nervous system, and, more precisely, on the brain. Historically, clinicians and researchers have always been interested in quantifying and characterizing those effects through recordings of surface brain electrical activity, namely electroencephalography (EEG). Over decades of research, the complex signal has been dissected to extract its core substance, with significant advances in the interpretation of the information it may contain. Methodological, engineering, statistical, mathematical, and computer progress now furnishes advanced tools that not only allow quantification of the effects of anesthesia, but also shed light on some aspects of anesthetic mechanisms. In this article, we will review how advanced EEG serves the anesthesiologist in that respect, but will not review other intraoperative utilities that have no direct relationship with consciousness, such as monitoring of brain and spinal cord integrity. We will start with a reminder of anesthestic effects on raw EEG and its time and frequency domain components, as well as a summary of the EEG analysis techniques of use for the anesthesiologist. This will introduce the description of the use of EEG to assess the depth of the hypnotic and anti-nociceptive components of anesthesia, and its clinical utility. The last part will describe the use of EEG for the understanding of mechanisms of anesthesia-induced alteration of consciousness. We will see how, eventually in association with transcranial magnetic stimulation, it allows exploring functional cerebral networks during anesthesia. We will also see how EEG recordings during anesthesia, and their sophisticated analysis, may help corroborate current theories of mental content generation.

Kinematic regulation of time and frequency domain components of accelerations measured at the tibia during heel-toe running

PurposeThe transmission of tibial accelerations through the musculoskeletal system may contribute to the aetiology of injuries. Therefore, determining the mechanisms that regulate impact accelerations may have potential clinical significance. This study aimed to determine the influence of lower extremity kinematics on the regulation of both time and frequency domain characteristics of tibial accelerations during running.MethodsForty participants ran at 4.0 m • s<sup>–1</sup> ± 5%. Three-dimensional joint kinematics from the hip, knee and ankle were measured using an eight-camera motion analysis system operating at 250 Hz. Regression analyses treating time and frequency domain tibial acceleration parameters as criterion variables were used to identify lower extremity parameters associated with the passive regulation of impact accelerations.ResultsThe overall regression model yielded an adj. <i>R</i><sup>2</sup> = 0.13, <i>p</i> &#8804 0.01. Knee flexion velocity at footstrike was identified as a significant regulator of tibial accelerations in the time domain. No kinematic variables were identified as significantly related to the frequency domain properties of the signal.ConclusionsThe findings of the current investigation suggest that sagittal plane knee flexion velocity at footstrike can regulate the magnitude of impact loading linked to the development of chronic injuries.

Heart Rate Variability Threshold Values for Early-Warning Nonfunctional Overreaching in Elite Female Wrestlers

Functional overreaching (FOR) represents intense training followed by a brief reduction in performance and then a rapid recovery (<2 weeks) and performance supercompensation. Nonfunctional overreaching (NFOR) occurs when the reduced performance continues ≥3 weeks. Heart rate variability (HRV) is a promising tool for detecting NFOR. In this study, the authors examined HRV thresholds in 34 elite female wrestlers (mean ± SD: age 23 ± 3 years; height 165.6 ± 6 cm, weight 63 ± 8 kg) for FOR/NFOR during training before 11 major competitions. Supine HRV was analyzed weekly at the same time of day using time and frequency domain methods. The authors observed that the time domain index, square root of the mean of the sum of the squares of differences between adjacent R-to-R intervals (rMSSD, milliseconds), denoting parasympathetic tone, showed those responding normally to training (82.76 ms, 95% confidence interval 77.75-87.78) to be significantly different to those showing a decrease (45.97 ms, 95% confidence interval, 30.79-61.14) or hyper-responsiveness (160.44 ms, 95% confidence interval, 142.02-178.85; all, p < 0.001). Similar results were observed for mixed sympathetic and parasympathetic signal standard deviation of the NN intervals (ms): normal (65.39; 95% confidence interval, 62.49-68.29), decrease (40.07; 95% confidence interval, 29-51.14), and hyperresponse (115.00; 95% confidence interval, 105.46-124.54; all, p < 0.001) and synonymous frequency domain components. An examination of the 95% confidence interval shows a narrow band surrounding a normal response compared with broader bands accompanying adverse responses. Thus, severe perturbations both above and below normal responses lasting >2 weeks indicated an athlete's transition to NFOR and, hence, are useful for assessing possible overreaching/training.

A real-time data acquisition and control of gradient coil noise for fMRI identification of hearing disorder in children with history of ear infection.

Early ear infection and trauma, from birth to age 12 are known to have a significant effect on sensory and cognitive development. This effect can be demonstrated through the fMRI study of children who have a history of ear infection compared to a control group. A second research question is the extent to which brain plasticity at an early age can reduce the impact of infection on hearing and cognitive development. Functional Magnetic Resonance Imaging (fMRI) provides a mapping of brain activity in cognitive and sensory regions by recording the oxygenation state of the local cerebral blood flow. The gradient coils of fMRI scanners generate intense acoustic noise (GCN) - to which the subject is in close proximity - in the range of 90 to 140 db SPL during the imaging process. Clearly this noise will impress its signature on low level brain response patterns. An Active Noise Canceller (ANC) system can suppress the effect of GCN on the subject's perception of a phonetic stimulus at the phoneme, word or phrase level. Due to a superimposition of the frequency and time domain components of the test signal and GCN for MR test, the ANC filtering system performs its function in real time - we must capture the brain's response to the test signal AFTER the noise has been removed. This goal is achieved through the application of field programmable gate array (FPGA) technology of NI LabVIEW. The presentation (in the noisy fMRI environment) of test words and phrases to hearing impaired children can identify sources of distortion to their perceptual processes associated with GCN. Once this distortion has been identified, learning strategies may be introduced to replace the hearing function distorted by early infection as well as the short term effect of GCN. The study of speech cognition without the confounding effect of GCN and with the varying level of GCN for a repeated test signal at later age can be allowed to a measure of recovery through brain plasticity.

Traffic-related air pollution exposures and changes in heart rate variability in Mexico City: a panel study.

BackgroundWhile air pollution exposures have been linked to cardiovascular outcomes, the contribution from acute gas and particle traffic-related pollutants remains unclear. Using a panel study design with repeated measures, we examined associations between personal exposures to traffic-related air pollutants in Mexico City and changes in heart rate variability (HRV) in a population of researchers aged 22 to 56 years.MethodsParticipants were monitored for approximately 9.5 hours for eight days while operating a mobile laboratory van designed to characterize traffic pollutants while driving in traffic and “chasing” diesel buses. We examined the association between HRV parameters (standard deviation of normal-to-normal intervals (SDNN), power in high frequency (HF) and low frequency (LF), and the LF/HF ratio) and the 5-minute maximum (or average in the case of PM2.5) and 30-, 60-, and 90-minute moving averages of air pollutants (PM2.5, O3, CO, CO2, NO2, NOx, and formaldehyde) using single- and two-pollutant linear mixed-effects models.ResultsShort-term exposure to traffic-related emissions was associated with statistically significant acute changes in HRV. Gaseous pollutants – particularly ozone – were associated with reductions in time and frequency domain components (α = 0.05), while significant positive associations were observed between PM2.5 and SDNN, HF, and LF. For ozone and formaldehyde, negative associations typically increased in magnitude and significance with increasing averaging periods. The associations for CO, CO2, NO2, and NOx were similar with statistically significant associations observed for SDNN, but not HF or LF. In contrast, PM2.5 increased these HRV parameters.ConclusionsResults revealed an association between traffic-related PM exposures and acute changes in HRV in a middle-aged population when PM exposures were relatively low (14 μg/m3) and demonstrate heterogeneity in the effects of different pollutants, with declines in HRV – especially HF – with ozone and formaldehyde exposures, and increases in HRV with PM2.5 exposure. Given that exposure to traffic-related emissions is associated with increased risk of cardiovascular morbidity and mortality, understanding the mechanisms by which traffic-related emissions can cause cardiovascular disease has significant public health relevance.

The relationship between physical activity and heart rate variability in orthotopic heart transplant recipients

To investigate the relationship between physical activity and heart rate variability in orthotopic heart transplant recipients, to compare the difference in heart rate variability between patients one year after orthotopic heart transplant and healthy adults matched to the heart transplant recipients in terms of age, gender and physical activity levels. Although physical activity affects the heart rate variability in patients with heart disease, there is a paucity of literature discussing the correlation between physical activity and heart rate variability among heart transplant recipients. This was a descriptive and cross-sectional study. A total of 120 eligible subjects were divided into the orthotopic heart transplant recipient group (n = 60) and the healthy adult group (n = 60). The Seven-day Physical Activity Recall questionnaire was used to record the subjects' amount of physical activity per week. Heart rate variety parameters were determined by separate frequency domain components. Results indicated heart transplant recipients' heart rate variety was significantly lower than that of healthy adults in terms of mean, sdr, total power (ms(2)), low frequency (ms(2)), low frequency (nu), high frequency (ms(2)) and low frequency/high frequency. Heart transplant recipients' heart rate variety including total power (ms(2)), low frequency (ms(2)) and high frequency (ms(2)) was 18·2, 2 and 7·2% of healthy controls, respectively; the amount of absolutely and relatively moderate physical activity was positively related to high frequency (ms(2)) and high frequency (nu), but was negatively related to low frequency/high frequency. High frequency (nu) increases while the total amount of weekly physical activity increases. Results confirmed that the more the moderate physical activity performed, the better the patient's heart rate variability. We suggest that clinical care providers have to encourage heart transplant recipients to engage in moderate physical activity.

Near-infrared spectral tomography integrated with digital breast tomosynthesis: effects of tissue scattering on optical data acquisition design.

Design optimization and phantom validation of an integrated digital breast tomosynthesis (DBT) and near-infrared spectral tomography (NIRST) system targeting improvement in sensitivity and specificity of breast cancer detection is presented. Factors affecting instrumentation design include minimization of cost, complexity, and examination time while maintaining high fidelity NIRST measurements with sufficient information to recover accurate optical property maps. Reconstructed DBT slices from eight patients with abnormal mammograms provided anatomical information for the NIRST simulations. A limited frequency domain (FD) and extensive continuous wave (CW) NIRST system was modeled. The FD components provided tissue scattering estimations used in the reconstruction of the CW data. Scattering estimates were perturbed to study the effects on hemoglobin recovery. Breast mimicking agar phantoms with inclusions were imaged using the combined DBT∕NIRST system for comparison with simulation results. Patient simulations derived from DBT images show successful reconstruction of both normal and malignant lesions in the breast. They also demonstrate the importance of accurately quantifying tissue scattering. Specifically, 20% errors in optical scattering resulted in 22.6% or 35.1% error in quantification of total hemoglobin concentrations, depending on whether scattering was over- or underestimated, respectively. Limited frequency-domain optical signal sampling provided two regions scattering estimates (for fat and fibroglandular tissues) that led to hemoglobin concentrations that reduced the error in the tumor region by 31% relative to when a single estimate of optical scattering was used throughout the breast volume of interest. Acquiring frequency-domain data with six wavelengths instead of three did not significantly improve the hemoglobin concentration estimates. Simulation results were confirmed through experiments in two-region breast mimicking gelatin phantoms. Accurate characterization of scattering is necessary for quantification of hemoglobin. Based on this study, a system design is described to optimally combine breast tomosynthesis with NIRST.

Design of Digital Filters for Si Wafer Surface Profile Measurement - Denoising by Total Variation

In the semiconductor industry, high resolution and high accuracy measurement is needed for the geometric evaluation of Si wafers. The flatness parameters are important to evaluate the wafer profile and are required to be the same level as the design rule of IC, and the tolerance for flatness is very tight. According to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness will be 22 nanometres by the year 2016. However, to obtain a higher resolution for sensors, the uncertainty becomes very large compared to the resolution and influences the measured data when the noise is increased. High resolution instruments always incorporate a certain degree of noise. In the presence of noise, form parameters are normally biased. Correction and compensation need a large population of measurements to analytically estimate both bias and uncertainty. The estimation is still far from perfect because of the nature of noise. Another approach is to extract a true profile by filtering noise from the measured data. For the purpose of noise reduction, low-pass filters by Gaussian smoothing and Fourier transform are often used. The noise is normally considered to be a component of small deviation (amplitude) with high frequency which also takes a normal distribution around zero. However these conventional filters can remove the noise in the spatial frequency domain only. So, it is essential to design a filter capable of removing the noise both in the spatial frequency domain and the amplitude component. Thus, we have designed and developed new type of digital filter for denoising. We introduce two new digital filters. One is wavelet transform capable of denoising in the spatial frequency domain and amplitude component, and the other is total variation that can be applied to discontinuous signals without introducing artificial Gibbs Effects.

Screening of Cardiovascular Autonomic Neuropathy in Patients with Diabetes by Quick and Simple Assessment of Sudomotor Function

Aims: Cardiovascular Autonomic Neuropathy (CAN) is a common but overlooked complication of diabetes. Sympathetic C-fibers innervating sweat glands can be impaired early in patients with diabetes. SUDOSCAN, a new and non-invasive device that assesses sudomotor function, was compared to Heart Rate Variability (HRV) and to Ewing tests, known to be reliable methods for the investigation of CAN. Methods: 232 patients with diabetes were measured for HRV at rest and during moderate activity (stair climbing). Time and frequency domain analysis techniques, including measurement of Standard Deviation of the average NN intervals over 5 minutes (SDNN), High Frequency domain component (HF) and Low Frequency domain component (LF), were assessed during HRV testing. Heart rate variations during deep breathing and heart rate and blood pressure responses while standing, as described by Ewing were also assessed. Electrochemical Sweat Conductance (ESC) was measured on the hands and feet, and a risk score was calculated. Patients were classified according to their risk score. The classifications were as follow: no sweat dysfunction, moderate sweat dysfunction and high sweat dysfunction. All results are means ± SD. Results: The highest correlation was observed between the risk score based on sudomotor function and the LF component during moderate activity (r = 0.47, p 405 ms 2 (3 rd quartile) (46 ± 13 vs 30 ± 13, p < 0.001). When taking the LF power component during moderate activity < 90 ms 2 as a reference value, the AUC of the ROC curve for the SUDOSCAN risk score was 0.77. Conclusions: SUDOSCAN allowing quick and quantitative assessment of sudomotor function can be used for the early screening of cardiovascular autonomic neuropathy in daily clinical practice before more sophisticated, specific, and time-consuming tests.

Near Shannon Limit and Low Peak to Average Power Ratio Turbo Block Coded OFDM

In this paper, we present an advanced solution for the long standing problem of large peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. Although the design of low PAPR codewords has been extensively studied and the existence of asymptotically good codes with low PAPR is also proven, still no code has been constructed to satisfy all requirements. The main goal of the paper is to develop a coding scheme that not only generates low PAPR codewords, but it also performs relatively close to the Shannon limit. We achieve this goal by implementing a time-frequency turbo block coded OFDM. In this scheme, we design the frequency domain component to have a tightly bounded PAPR. The time domain component code is designed to obtain good performance while the decoding algorithm has reasonable complexity. Through comparative performance evaluation we show that utilizing the proposed method, we achieve considerable improvement in terms of PAPR while we slightly loose the performance compared to capacity achieving codes with similar overall block length.

Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

Kalman filtering based approach for light flicker evaluation of power systems

In this study, a Kalman filtering based approach is proposed to measure the light flicker of the electricity transmission systems. Analytical expressions of the instantaneous light flicker sensation are used to obtain a flicker estimation method based on the International Electrotechnical Commission (IEC) flickermeter. In the proposed method, the frequency domain components of the voltage waveform are obtained by Kalman filtering and these components are used to obtain the light flicker. The method is tested on both simulated data and field data obtained from three different electrical arc furnace (EAF) plants, where the flicker level is considerably high compared to other types of loads. The comparison of the results with the digital realisation of the IEC flickermeter shows that the method gives satisfactory estimations with low computational complexity. The method is especially useful for conditions such as disturbances and subsequent system transients where the system frequency deviates significantly. Since the frequency decomposition is obtained by using Kalman filtering, no leakage effect of the fast Fourier transform (FFT) method is involved in case of frequency deviations.

Damage monitoring of reinforced concrete frames under seismic loading using cement-based piezoelectric sensor

Damage process monitoring of concrete structures using acoustic emission (AE) technology has been drawn more and more attention due to its powerful capability. In this paper, a brand new cement-based piezoelectric composite sensor was introduced with improved performance, particularly concerning its AE signals detection capacity in the high frequency domain. Such sensors were embedded into the foundation of reinforced concrete (RC) frames during construction, and appointed to monitor concrete damage due to shake table excitation. A standard ground motion record of 1999 Taiwan earthquake with 840 gal and 1300 gal peak ground accelerations were employed in the tests as the excitation inputs. The signals detected by the sensors were stored and analyzed by commercial available data acquisition devices. Advanced stochastic signal analysis methods were adopted to effectively interpret the frequency domain components and identify the useful information representing the damage processes of the RC frames. The results were compared with the eye observations of structural damage and corresponding cyclic loading tests results. It is shown that the health monitoring method using cement-based piezoelectric composite sensors and advanced stochastic signal analysis are capable of detecting and evaluating the damage process of RC frames due to seismic loading. An effective damage indicator of the RC frames is possible to be evaluated from extracted AE information.

Interharmonics analysis of power signals with fundamental frequency deviation using Kalman filtering

In this paper a spectral decomposition-based method for interharmonic computation is proposed for power systems where the fundamental frequency fluctuates significantly. In the proposed method, the frequency domain components of the voltage waveform are obtained by Kalman filtering. Both the system fundamental frequency and the correct spectrum of the voltage waveform, and hence the exact interharmonics are obtained. The proposed method is tested with both simulated and field data obtained from different electric arc furnace (EAF) plants, where the system frequency deviates continuously due to the fluctuating load demands. Since the interharmonic frequencies are obtained by using Kalman filtering, no leakage effect of the DFT-based methods is involved in case of frequency deviations, which is an important advantage of the proposed method.

Time and frequency domain event-related electrical activity associated with response control in schizophrenia

To confirm previously reported abnormalities in time domain EEG components during a go/no-go task in schizophrenia, and to test the hypothesis that patients exhibit abnormalities in frequency domain components reflecting indices of behavioural impairment. EEG data were recorded from 17 male schizophrenia patients in a stable phase of illness and 17 healthy controls. As compared with controls, patients displayed smaller N200 amplitudes and less evoked theta for correct hit trials; and smaller N200 and P300 amplitudes and less evoked delta and theta for correct reject trials. Effect sizes were largest for evoked delta. Source localisation revealed reduced activation in schizophrenia patients during the N200 and P300 time windows in anterior and posterior cingulate, medial frontal gyrus and precuneus. Evoked delta and theta oscillations were significantly correlated with the variability of reaction times and the performance level statistic d-prime. The results demonstrate impairment of frontal and parietal brain areas involved in response control in schizophrenia. They also suggest that the timing of oscillations in patients is less precise leading to smaller evoked amplitudes and more variable reaction times. These findings add to the evidence that abnormal EEG oscillations contribute to impaired behavioural control in schizophrenia.

Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area

Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean land-covers from the perspective of vegetation phenology and its relation with climate. A time series from 2001 to 2007 of the MODIS Enhanced Vegetation Index 16-day composite (MOD13Q1) was analyzed to extract anomalies (by calculating z-scores) and frequency domain components (by the Fourier Transform). Vegetation phenology analyses were developed for diverse land-covers for an area in south Alicante (Spain) providing a useful way to analyze and understand the phenology associated to those land-covers. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response.

Identification of characteristic components in frequency domain from signal singularities

In rotating machinery condition monitoring, identification of characteristic components is fundamental in many engineering applications so as to obtain fault sensitive features for fault detection and diagnosis. This paper proposed a novel method for the identification of characteristic components in frequency domain based on singularity analysis. In this process, Lipschitz exponent function is constructed from the signal through wavelet-based singularity analysis. In order to highlight the periodic phenomena, autocorrelation transform is employed to extract the periodic exponents and Fourier transform is used to map the time-domain information into frequency domain. Case study with rolling element bearing vibration data shows that the proposed has very excellent capability for the identification of characteristic components compared with traditional methods.

An application of digital signals in an atom Chip

Digital signals （square wave） are superpositions of many frequency components in frequency domain. We analyze and calculate the coupling potentials between the atom and the frequency components respectively, and demonstrate that the atomic trapping adiabatic potential can be generated by taking into account the total coupling potential. An application scheme of the digital signal in an atom chip is proposed.

Cognitive processing in sleep as revealed by time-domain and frequency-domain components

Cognitive processing in sleep as revealed by time-domain and frequency-domain components