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Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.

Macrostate and microstate characteristics of interregional nonlinear interdependence of brain dynamics are investigated for Zen-meditation and normal resting EEG. Evaluation of nonlinear interdependence based on nonlinear dynamic theory and phase space reconstruction is employed in the 30-channel electroencephalographic (EEG) signals to characterize the functioning interactions among different local neuronal networks. This paper presents a new scheme for exploring the microstate and macrostate of interregional brain neural network interactivity. Nonlinear interdependence quantified by similarity index is applied to the phase trajectory reconstructed from multi-channel EEG. The microstate similarity-index matrix (miSIM) is evaluated every 5 millisecond. The miSIMs are classified by K-means clustering. The cluster center corresponds to the macrostate SIM (maSIM) evaluated by conventional scheme. Zen-meditation EEG exhibits rather stationary and stronger interconnectivity among frontal midline regional neural oscillators, whereas resting EEG appears to drift away more often from the midline and extend to the inferior brain regions.

Addicted drugs like nicotine affect autonomic nervous system that results in arrhythmia and other cardiovascular diseases. Notable effects of Zen meditation on autonomic nervous system have been reported during the past decade. Holistic Detox Association (HDA) in Taiwan offered Zen-meditation program to drug addicts as the core scheme among a variety of drug addiction treatments. This paper reports the results of quantifying the cardiorespiratory interactions and autonomic nervous system function to evaluate the on-site effect of Zen meditation on drug rehab. Methods and schemes for quantifying time-domain heart rate variability were employed to electrocardiograph and respiratory signals. Peak-valley method was developed to quantify the respiratory sinus arrhythmia (RSA) behavior. Poincaré Plot Analysis was adopted to evaluate the cardiorespiratory functioning. Among 18 voluntary drug addicts during the 10-minute Zen meditation session, about two-third subjects have significant improvement in autonomic nervous system function characterized by heart rate variability (SDNN, RMSSD and pNN50). Group average of RSA increases from 33.43 ms(Rest) to 69.14 ms(AR Zen meditation). Poincaré-plot analysis reveals the improvement of SD1, SD2 and SD2/SD1 by respectively 14.7%, 19.8% and 8.8%. The group averages of all the parameters exhibit significantly positive changes in the 10-minute session of abdominal-respiration Zen meditation. Even the subject with heart transplant showed the improvement of all the quantitative indicators during the AR Zen meditation. Keywords: Cardiorespiratory interaction, Drug addiction, Heart rate variability, Respiratory sinus arrhythmia, Zen meditation

(1) Background: This research is aimed to develop new methods to investigate the cardiorespiratory interaction of Zen-meditation practitioners (Zen-meditation group) and healthy ordinary young people (control group) by quantitatively evaluating the respiratory sinus arrhythmia (RSA) behavior. (2) Methods: Twenty-five voluntary controls and seven Zen-meditation practitioners were recruited. Experimental protocol involves five sessions of different mental-stress levels (control group) and five sessions of Zen-meditation practice (experimental group). Forty-minute (thirty-minute) ECG and respiratory signals were recorded for each control (experimental) subject. By detecting R peaks, heart-rate sequence is constructed. Two methods are proposed to evaluate RSA behavior based on cycle-to-cycle synchronization between heart-rate and respiratory sequence. (3) Results: Based on the proposed method of computing RSA coefficient, control group average for each session is 0.70 (Rest I), 0.65 (CAT I), 0.75 (BC), 0.63 (CAT II) and 0.70 (Rest II); whereas Zen-meditation group average is 0.81 (S1), 0.84 (S2), 0.84 (S3), 0.86 (S4) and 0.81 (S5), all superior to which of the control group. Average RSA normal rate in Zen-meditation group (91.03%) remarkably surpasses which in the control group (80.48%) in the Rest sessions. (4) Conclusions: Using the time-domain HRV (heart-rate variability) as a reference, our methods, RSA coefficient and RSA normal rate, provide more reliable and direct estimate of RSA behavior than conventional peak-valley method computing the inspiratory–expiratory difference in R-to-R interval of ECG. Moreover, results of the proposed methods are confined in a specified range (RSA coefficient between 0 and 1, RSA normal rate between 0% and 100%) facilitates the interpretation of the quantitative RSA behavior. In the RSA analysis, breathing-control session using anthropic interventions induces more prominent RSA activity in control group. Nevertheless, HHIS Zen meditation, with excellent qi circulation activated by ten-mailuns system, naturally (in the way of do-nothing) elicits superior RSA performance better than sophisticated way of breathing control.

Innovatively new behaviors of heart rate variability caused by special heart-transition process were observed in the long-term, well-experienced Zen practitioners while practicing the heart-to-heart imprint sealing (HHIS) Zen meditation. HHIS Zen practice involves specific neurocardiac-cardiorespiratory interaction while on the way of realizing the heart-dominant, detached brain. Results of analyzing the electrocardiogram and respiratory signals of 10 experienced practitioners reveal several distinctive characteristics: (1) remarkably linear correlation between standard deviation of the normal R-to-R intervals, SDNN, and total power in very-low-frequency (VLF, 0.0033–0.04[Formula: see text]Hz) band of power spectrum of the heart-rate sequence, (2) time-varying VLF power dominating over the low-frequency and high-frequency power in heart rate variability (HRV) variations, (3) intermittent transition into slowly, deeply abdominal respiration inducing a boost of heart rates, (4) heart-rate baseline slowly fluctuating at 0.005–0.0067[Formula: see text]Hz, about 1.5–2 cycles in 5-min period, and (5) remarkable respiratory sinus arrhythmia (RSA) synchrony between heart rate and respiration rhythm. This paper proposes a rational scientific hypothesis for the neurocardiac-cardiorespiratory mechanism. The unique scheme of HHIS Zen meditation involves the spiritual-qi concentration and refinement for pinpointing into the particular energy centers, mailuns. Ignition by a subtle, deepest abdominal respiration, electrical impulses rapidly transmit from solar plexus to branchial plexuses to activate unique heart-transition process. Simultaneously, another branch streams upward the spinal cord to cervical plexus and brainstem that effectively harmonizes neurocardiac interactions. To investigate the underlying behaviors, time-domain and frequency-domain HRV based on continuous wavelet transform were employed.

The connotation and extension of furniture design, which aims to enhance the quality of people's lives, is constantly expanding and improving. The innovation of furniture design also demands us to take the users' prospective, focus on people's lifestyle, seek the designing elements and seize the designing inspiration so as to complete the design.

Micro mobility gives a basic the support of intra-domain roaming. Most recent micro-mobility researches have focused on providing routing service without considering security. In this paper, we employ the hop-by-hop routing authenticated protocol for authentication verification . We create a lightweight cryptography via one-way hash chains. Furthermore, we analyze the security properties.

With the development of wireless sensor networks (WSNs), many applications require for the privacy preserving. Providing an efficient privacy-preserving data aggregation in WSNs is becoming more and more important. The secure data aggregation in WSNs has been studied in recent years, but most of the algorithms are suffering from the high computation overhead and high communication overhead, and some of the algorithms do not supply the data integrity checking. To resolve these problems, a modified private-preserving data aggregation based on complex number algorithm for WSN is proposed in this paper. An efficient key distribution scheme is used in the algorithm to improve the security of the key. The complex number is used in the algorithm to protect the privacy and check the data integrity. According to the analysis in the paper, the algorithm is efficient in terms of communication, computation and integrity checking.