Model-based learning for location-to-channel mapping

Association of estrogen receptor-α gene polymorphisms with cardiac autonomic nervous activity in healthy young Japanese males

Music is known to reduce pain, anxiety and fear in several stressful conditions in both males and females. Further, listening to preferred music enhances the endurance during running performance of women rather than listening to non-preferred music. In recent years Heart Rate Variability (HRV) has been used as an indicator of autonomic nervous activity. This study was aimed to assess the effectiveness of preferential music on HRV after moderate exercise. This was an experimental study done in 30 healthy students aged between 20-25 years, of either sex. HRV was measured at rest, 15 minutes of exercise only and 15 minutes of exercise with listening preferential music in same participants. Data was analysed by One-Way ANOVA and Tukey HSD Post-hoc Test. Statistical significance was taken to be a p-value of less than 0.05. Low frequency and high frequency component was significantly increased followed by only exercise. Music minimized increase in both high and low frequency component followed by exercise. However, only high frequency change was statistically significant. LF/HF ratio was significantly increased followed by only exercise. Music significantly minimized increase in LF/HF ratio. This study provides the preliminary evidence that listening to preferential music could be an effective method of relaxation, as indicated by a shift of the autonomic balance towards the parasympathetic activity among medical students.

Intradialytic hypotension, a major source of morbidity during hemodialysis and ultrafiltration, is often accompanied by paradoxical bradycardia. Relatively little is known about the sequential changes in autonomic nervous system activity up to and during the hypotensive episode. Continuous, beat-to-beat measurements of BP and heart rate were made during hemodialysis in patients prone (n = 8) and not prone (n = 11) to develop intradialytic hypotension. Off-line spectral analysis of heart rate variability (HRV) was performed to assess changes in autonomic nervous system activity during dialysis sessions both with and without hypotension. The low frequency (LF) component of HRV is thought to correlate with sympathetic nervous system activity, the high frequency (HF) component with that of the parasympathetic nervous system. In the sessions not complicated by symptomatic hypotension (n = 26), mean arterial BP (MAP) hardly fell, whereas heart rate increased from 77 +/- 2 to 89 +/- 5 bpm (P < 0.05). The LF component of HRV increased from 45.2 +/- 5.0 normalized units (nu) to 59.9 +/- 4.9 nu (P < 0.05), whereas the HF component fell from 54.8 +/- 5.0 to 40.2 +/- 4.4 nu (P < 0.05). These changes agree with compensatory baroreflex-mediated activation of the sympathetic nervous system (and suppressed parasympathetic activity) during ultrafiltration-induced intravascular volume depletion. In the sessions complicated by severe symptomatic hypotension (n = 22), the changes in heart rate and the results of spectral analysis of HRV were similar to those reported above up to the moment of sudden symptomatic (nausea, vomiting, dizziness, cramps) hypotension, whereas MAP had already fallen gradually from 94 +/- 3 to 85 +/- 3 mmHg (P < 0.05). The sudden further reduction in MAP (to 55 +/- 2 mmHg, P < 0.02) was invariably accompanied by bradycardia (heart rate directly before hypotension 90 +/- 2 bpm, during hypotension 69 +/- 3 bpm, P < 0.002). The LF component of HRV fell from 62.8 +/- 4.6 nu directly before to 40.0 +/- 3.7 nu (P < 0.05) during hypotension, whereas the HF component increased from 37.9 +/- 4.7 to 60.3 +/- 3.7 nu (P < 0.05). These findings agree with activation of the cardiodepressor reflex, involving decreased sympathetic and increased parasympathetic nervous system activity, respectively. These findings indicate that activation of the sympatho-inhibitory cardiodepressor reflex (Bezold-Jarisch reflex), which is a physiologic response to a critical reduction in intravascular volume and cardiac filling, is the cause of sudden intradialytic hypotension.

The aim of this study is classification of correlative data through comparison between ECG-Derived Respiration (EDR) and High Frequency (HF) component obtained frequency analysis of heart rate variability (HRV) from ECG using two of conductive textiles. Generally, individual frequency range of the HF component has been determined only by means of distinctive parameters of respiration such as respiratory rate, range and median value of respiratory rate, etc. However, in many cases Low Frequency (LF) components and High Frequency (HF) components may be superimposed on each other totally or particularly and can not be diagnostically estimated because respiratory rhythms are individually much remarkably differentiated. This study, in consequence, analyze frequency component of EDR derived from ECG and then compare with high frequency components of HRV and finally find out valuable data for further analysis. Hardware constitution is used to bed type ECG measurement equipment based on U-Healthcare, can identify user using Radio Frequency (RF) module as well as manage data each of user. We used Frequency Modulation (FM) method based on Respiration Sinus Arrhythmia (RSA) in order to extract EDR from ECG signal. ECG data and EDR data were processed with Lab-VIEW, then classified through each of frequency analysis.

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.

Hydrocarbon detection using adaptively selected spectrum attenuation

The high prices of pulses continue to be the pain point for both consumers and policymakers. In India, the wholesale price index (WPI) is the main measure of inflation. WPI measures the price of a representative basket of wholesale goods.Therefore, accurate forecasting of WPI is necessary by using some advanced statistical techniques. In the present investigation, Wavelet and artificial neural network (Wavelet-ANN) hybrid models are used for multistep-ahead forecasting of monthly WPI of pulses.The original series is decomposed into the low frequency and high frequency components using Maximal Overlap Discrete Wavelet Transform (MODWT) based on Haar wavelet filter. Subsequently, suitable artificial neural network (ANN) model was fitted to decomposed series before they are combined and predicted using Inverse Wavelet Transform (IWT). A comparative assessment of hybrid models as well as individual counterpart revealed that the hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations.

This work presents an accuracy analysis of a system for Head-Related Impulse Responses interpolation based on artificial neural networks (ANN). The error analysis behavior in the time domain, between the target and the output functions, could lead to mistaken results if the functions were slightly time-shifted. On the other hand, frequency domain errors may be influenced, for instance, by high frequency components, where the resolution is smaller than at lower frequencies and at frequency ranges that does not interfere in the human being perception. The proposed criteria are based on adequating well known room acoustic quality parameters for evaluating the interpolation error. The article presents the ANN architecture and the optimal configuration (in terms of processing speed and accuracy) based on the proposed error criteria. The comparative results were obtained from the receiving area where human auditive capability is most sensible and the interaural differences are most notorious (horizontal plane). The results show that the modified acoustic parameters provide a good estimation of HRIR interpolation comparisons.

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

The hybrid energy storage system (HESS) consists of energy storage and power storage, is an effective solution to smooth the fluctuation of wind power. This paper proposed a means using variational mode decomposition (VMD) and grey relational grades cluster (GRGC) to obtain power and capacity allocation of HESS. First, moving average filtering was used to obtain the grid connected power and the reference power of HESS. The reference power of HESS was decomposed into subsequences by means of VMD, and the GRGC is used to reconstruct the subsequences into low frequency and high frequency components. Then the low frequency and high frequency components are used as power instructions for energy storage and power storage respectively. Considering the state of charge (SOC) and operating life of HESS, establish the annual minimum cost model. The results verified the effectiveness of the proposed method.

Image fusion is a process which combines information from two or more images of the same scene into a single image preserving important features from each. The objective of image fusion is to combine complementary information from multiple images into a single resultant image which is more informative, comprehensive, reliable and precise compared to each source images. Fusion is an effective tool within various fields such as remote sensing, robotics and medical applications. In the proposed method the source images were first decomposed using the shift invariant and directionally selective dual tree complex wavelet transform (DT-CWT) and then fusion rules namely, max and local energy were applied to combine low and high frequency coefficients respectively. The final fused image was obtained by applying inverse DT-CWT to the fused low and high frequency components. The obtained fused images were analyzed qualitatively as well as with various quantitative metrics namely mutual information (MI), structural similarity index (SSIM), entropy, standard deviation (STD) and average gradient (AVG) where both analysis show that the proposed scheme can provide more information and better results compared to the other methods.

HRV and BPV neural network model with wavelet based algorithm calibration

The analysis and interpretation of heart rate variability (HRV) during exercise is challenging not only because of the nonstationary nature of exercise, the time-varying mean heart rate, and the fact that respiratory frequency exceeds 0.4 Hz, but there are also other factors, such as the component centered at the pedaling frequency observed in maximal cycling tests, which may confuse the interpretation of HRV analysis. The objectives of this study are to test the hypothesis that a component centered at the running stride frequency (SF) appears in the HRV of subjects during maximal treadmill exercise testing, and to study its influence in the interpretation of the low-frequency (LF) and high-frequency (HF) components of HRV during exercise. The HRV of 23 subjects during maximal treadmill exercise testing is analyzed. The instantaneous power of different HRV components is computed from the smoothed pseudo-Wigner-Ville distribution of the modulating signal assumed to carry information from the autonomic nervous system, which is estimated based on the time-varying integral pulse frequency modulation model. Besides the LF and HF components, the appearance is revealed of a component centered at the running SF as well as its aliases. The power associated with the SF component and its aliases represents 22±7% (median±median absolute deviation) of the total HRV power in all the subjects. Normalized LF power decreases as the exercise intensity increases, while normalized HF power increases. The power associated with the SF does not change significantly with exercise intensity. Consideration of the running SF component and its aliases is very important in HRV analysis since stride frequency aliases may overlap with LF and HF components.

Semantic communication (SemCom) leveraging advanced deep learning (DL) technologies enhances the efficiency and reliability of information transmission. Emerging stacked intelligent metasurface (SIM) with an electromagnetic neural network (EMNN) architecture enables complex computations at the speed of light. In this letter, we introduce an innovative SIM-aided SemCom system for image recognition tasks, where a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems that transmit modulated signals carrying the image information or compressed semantic information, the carrier EM wave is directly transmitted from the source. The input layer of the SIM performs source encoding, while the remaining multi-layer architecture constitutes an EMNN for semantic encoding, transforming signals into a unique beam towards a receiving antenna corresponding to the image class. Remarkably, both the source and semantic encoding occur naturally as the EM waves propagate through the SIM. At the receiver, the image is recognized by probing the received signal magnitude across the receiving array. To this end, we utilize an efficient mini-batch gradient descent algorithm to train the transmission coefficients of SIM’s meta-atoms to learn the semantic representation of the image. Extensive numerical results verify the effectiveness of utilizing the SIM-based EMNN for image recognition task-oriented SemComs, achieving more than 90% recognition accuracy.

Connected and autonomous vehicle (CAV) vehicle to infrastructure (V2I) scenarios have more stringent requirements on the communication rate, delay, and reliability of the Internet of vehicles (IoV). New radio vehicle to everything (NR-V2X) adopts link adaptation (LA) to improve the efficiency and reliability of road safety information transmission. In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario, resulting in low reliability of information transmission, this paper proposes a deep Q-learning (DQL)-based massive multiple-input multiple-output (MIMO) LA scheduling algorithm for autonomous driving V2I scenario. The algorithm combines deep neural network (DNN) with Q-learning (QL) algorithm, which is used for joint scheduling of modulation and coding scheme (MCS) and space division multiplexing (SDM). The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario, and select the optimal MCS and SDM for the transmission of road safety information, thereby the accuracy of road safety information transmission is improved, collision accidents can be avoided, and bring a good autonomous driving experience.