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Physical fitness indicates favorable statuses of well-being and health and, more specifically, the ability to perform sports and daily activities. In the paper, we studied heart rate variability with electrocardiogram (ECG) signals under levels of exercise intensities for subjects with different grades of cardiopulmonary fitness. Based on the targeted maximum heart rate, three graded upright biking exercise levels were defined for the experiment: light, moderate, and vigorous. ECG signals were accessed under the rest, at three exercise intensity levels, and in their retrieval periods subsequently. The subjects were in the top 20%, middle, and bottom 20% groups of cardiopulmonary fitness norms from university freshman. Firstly, the major of heart rate variability (HRV) indices performed by spectral and time-domain analyses from collected ECG signals were explored. Statistical analysis was then conducted to identify physiological signal indices with significant differences. All appeared significant differences between the three groups in the frequency domain HRV indices, including high-frequency power (HF), total power (TP), normalized high-frequency power (nHF), normalized low-frequency power (nLF), and LF/HF. The time domain HRV parameters, meanwhile, did not exhibit significant differences among the three groups throughout the graded exercises and in the recover periods. A multiple comparison test on the TP indicated significant differences in most between-group comparisons (except for between groups middle and bottom during moderate exercise, top and middle during recovery from moderate exercise, and middle and bottom during recovery from vigorous exercise). Significant differences were found in most between-group comparisons (except for between groups middle and bottom at rest and during recovery from light and medium exercise and between top and middle during recovery from vigorous exercise).

Muscular fitness is not only the ability of the body to adapt to work and the environment but also the operational ability of physical behavior. We speculated whether research could be conducted on the theory of muscular fitness and its qualitative/quantitative relationship based on muscular fitness and exercise physiology from the perspective of muscular endurance and muscular exploration. This study used standing long jumps as a standard metric for physical fitness to identify the bottom 20% groups. The experiment involved eight freshmen from the bottom 20% groups, and the pre-tests of the participants’ electromyography (EMG) signals under different exercise intensities were measured and after performing a set of intensive exercises for post-tests. The signal’ characteristics measured in time and frequency domains were analyzed to find the correlation between them and the participants’ muscular fitness. Weighted squats were chosen as the strength movements, which were separated into an exercise experiment and a force plate experiment. Both experiments included three different exercise intensities: 8 repetition maximum (RM), 18RM, and 28RM. The EMG signals were captured and analyzed in both time and the frequency domains. Finally, paired sample tests were performed to determine the difference of features under different exercise intensities. The comparison of readings before and after intensive exercises shows that, for the exercised experiment, a significant difference in the mean absolute (MAV), the variance of EMG (VAR), the root mean square value (RMS), and the average amplitude of change (AAC) was observed under 8RM. Under 18RM, MAV, VAR, and AAC showed a significant difference. In the force plate experiment, RMS, AAC, mean frequency (MNF), and median frequency (MDF) showed a statistically significant difference under the intensity of 18RM. As for intensity under 28RM, MAV, VAR, RMS, and AAC also showed significant difference.

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Physical fitness refers to the body's ability to adapt to work and environment, as well as the ability to function in physical behavior. This research is based on physical fitness, from the viewpoint of exercise physiology, muscle strength, muscle endurance, and muscle power, to quantitatively study physical fitness. In this study, standing long jump was used as a normal physical fitness test, and the lowest 20% group were screened from university freshmen. After intensive exercise training, the pre-and post-test results were analyzed under different intensities. A correlation between its time and frequency domain characteristics and muscle fitness was found, too. The subjects performed four different exercise intensities (No Load, 8RM, 18RM, 28RM) and plate experiments with weight-bearing squats. Their EMG signals during exercise were recorded for time and frequency domain analysis and analyzed by using the paired sample t-test to find the difference of the characteristic index of the lowest group under different exercise intensities. The research results show that after intensive training, RMS has significant differences in the 8RM exercise experiment, while MAV, VAR, and AAC do in the 18RM exercise experiment. Several time-domain indicators (RMS, AAC) and frequency domain indicators (MNF, MDF) have significant differences in the 18RM exercise experiment. MAV, VAR, RMS, and AAC have significant differences in the 28RM exercise experiment.

Physical fitness is the overall ability of the body to adapt to the working environment and perform sporting and daily activities. The aim of this study was to analyze the correlation between muscle fitness and the electromyography (EMG) signals of lower limbs under varying exercise intensity. The standing long jump was used as a test task for assessing the power of the lower limb muscles. Participants were university freshmen who belonged to the top 20%, middle 20%, and bottom 20% groups in terms of physical fitness norms. The EMG signals of the participants&rsquo, lower limbs while they performed squats were collected under four exercise intensities of repetitions maximum (RM): no load, 8RM, 18RM, and 28RM, the features of the signals were extracted using time-domain and frequency-domain analysis. Statistical analysis was also performed. The top and bottom groups exhibited significant differences time-domain indicators mean absolute value (MAV) and average amplitude change (AAC) in the low-intensity exercise (28RM). The MAV, variance of EMG (VAR), root mean square (RMS), and AAC were significantly different between the top and bottom groups in the three graded intensities (8RM, 18RM, and 28RM). The mean frequency (MNF) and median frequency (MDF), which are frequency-domain indicators, were significantly different between the top and bottom groups in the low-intensity (28RM) and moderate-intensity (18RM) exercises.

Postural orthostatic tachycardia syndrome (POTS) typically occurs in youths, and early accurate POTS diagnosis is challenging. A recent hypothesis suggests that upright cognitive impairment in POTS occurs because reduced cerebral blood flow velocity (CBFV) and cerebrovascular response to carbon dioxide (CO2) are nonlinear during transient changes in end-tidal CO2 (PETCO2). This novel study aimed to reveal the interaction between cerebral autoregulation and ventilatory control in POTS patients by using tilt table and hyperventilation to alter the CO2 tension between 10 and 30 mmHg. The cerebral blood flow velocity (CBFV), partial pressure of end-tidal carbon dioxide (PETCO2), and other cardiopulmonary signals were recorded for POTS patients and two healthy groups including those aged &gt, 45 years (Healthy-Elder) and aged &lt, 45 years (Healthy-Youth) throughout the experiment. Two nonlinear regression functions, Models I and II, were applied to evaluate their CBFV-PETCO2 relationship and cerebral vasomotor reactivity (CVMR). Among the estimated parameters, the curve-fitting Model I for CBFV and CVMR responses to CO2 for POTS patients demonstrated an observable dissimilarity in CBFVmax (p = 0.011), mid-PETCO2 (p = 0.013), and PETCO2 range (p = 0.023) compared with those of Healthy-Youth and in CBFVmax (p = 0.015) and CVMRmax compared with those of Healthy-Elder. With curve-fitting Model II for POTS patients, the fit parameters of curvilinear (p = 0.036) and PETCO2 level (p = 0.033) displayed significant difference in comparison with Healthy-Youth parameters, range of change (p = 0.042), PETCO2 level, and CBFVmax also displayed a significant difference in comparison with Healthy-Elder parameters. The results of this study contribute toward developing an early accurate diagnosis of impaired CBFV responses to CO2 for POTS patients.

To study the impaired cerebral autoregulation mechanism of postural orthostatic tachycardia syndrome (POTS) patients under carbon dioxide, a nonlinear regression model was applied to evaluate their cerebrovascular responses to carbon dioxide (CO 2 ). The cerebral blood flow (CBFV), end-tidal carbon dioxide (${{\text{P}}_{{\text{ETC}}{{\text{O}}_2}}}$), and other cardiopulmonary signals were processed for each subject with a tilt table experiment and hyperventilation. The sigmoidal model for cerebrovascular responses to CO 2 during the hyperventilation phase for POTS patients demonstrated an observable dissimilarity in comparison with control groups of healthy elders and healthy youths, especially in its range of change in CBFV.

Wishbone flower (Torenia fournieri L.) is a common ornamental plant for flower bed in Taiwan. In August 2018, root and stem rot of wishbone flower occurred on the flower bed in the campus of Nation...

In this paper, with the use of noninvasive TCD, Finapres, and Capnography, we investigated the cerebrovascular response to CO 2 in PD patients and explored the interaction between cerebral autoregulation and ventilatory control by using nonlinear regression model. The results of these patients were compared with those of healthy subjects by examining their CBFV, CVMR (cerebrovascular Vasomotor Reactivity), and CVC i (cerebrovascular conductance index) responses to CO 2 . Statistical analysis of significance values between PD patients and healthy groups was evaluated. The results showed that the PD patients demonstrated a significantly lower level of CBFV max (%) than the healthy elders with nonlinear regression model.

An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each person's cardiac signal possesses unique characteristics. Thus, researchers have attempted to use ECG signals for personal identification. However, most studies verify results using ECG signals taken from databases which are obtained from subjects under the condition of rest. Therefore, the extraction and analysis of a subject's ECG typically occurs in the resting state. This study presents experiments that involve recording ECG information after the heart rate of the subjects was increased through exercise. This study adopts the root mean square value, nonlinear Lyapunov exponent, and correlation dimension to analyse ECG data, and uses a support vector machine (SVM) to classify and identify the best combination and the most appropriate kernel function of a SVM. Results show that the successful recognition rate exceeds 80% when using the nonlinear SVM with a polynomial kernel function. This study confirms the existence of unique ECG features in each person. Even in the condition of exercise, chaotic theory can be used to extract specific biological characteristics, confirming the feasibility of using ECG signals for biometric verification.

Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control. Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc. Still, there are novel identification technologies based on the individual's biometric features under development [1-4].

This study presents a method for reconstructing respiration signals from ECG tracings based on average electrical-cardiac-axis theory with heart rate variability. The algorithm is simplified to use QRS characteristic points and subsequent spectrum analysis. The experiments were conducted on eight healthy patients during rest with spontaneous (voluntarily) and paced breathings. Five QRS characteristic series are constructed, and the corresponding electrocardiography (ECG)-derived respiration (EDR) is compared with measured data. The experiment results show the R-Q-S series-derived respiration exhibited superior correlation with the original respiration from the perspective of breathing frequency.

In this study, we proposed an innovative design for mechanical ventilation by applying the previously implemented optimal respiratory control simulator as the controller of an experimental ventilation device. Instead of providing a fixed airflow pattern, an optimal spontaneous flow pattern was optimized by the simulator based on patient’s estimated respiratory mechanics and was applied to drive a ventilation device. We also implemented an experimental ventilation control system, including the simulator, a ventilation device, an artificial lung, and a feedback control mechanism to attain minimum work of breathing during mechanical ventilation. The experiments were elaborated to verify that once patient’s respiratory physiology was significant changed, the breathing signals and measured respiratory mechanics were instantaneously monitored, and the optimization process was renewed by the simulator under the feedback control strategy.

In the present study, hybrid manganese oxide films with additions of multiwalled carbon nanotubes (MWCNTs) were prepared by sol–gel process. Manganese acetate was used as the precursors and MWCNTs were added during the process. The effects of MWCNT addition and post heat treatment on the material characteristics and pseudocapacitive performance of the hybrid MWCNT/MnO x films were investigated. Experimental results showed that manganese oxide was composed of Mn 3 O 4 (minor) and Mn 2 O 3 (major) phases after heat treatment. MWCNTs served as the template for the growth of manganese oxide films. Among the hybrid films prepared in the present study, manganese oxide films with 0.05 wt.% MWCNT addition heat treated at 350 °C exhibited the best electrochemical performance. The maximum specific capacitance was 340.3 F/g and retained 280.8 F/g (82.5%) after 1000 CV tests. With the addition of MWCNTs, not only the specific capacitance increased but also the reliability improved.

The analytical understanding and applications of human postural balance could possibly be extended to understand the postural sway. On the basis of a human body model with nine segments and articulations at the neck, waist, hip, knee, and ankle, a two-stage feedback control law to remain the human body’s upright posture subjected to the subjective verticals is developed. The movement equations for a 24 DOFs body model are determined first. A hybrid control law is then developed for balancing. For the controller, the primary part is designed to track the desired reference trajectory, and the secondary part is developed to compensate for the influences due to modeling uncertainties and external disturbances. The results developed herein are to be served as a basis for stance posture control of bionic robots or motion-aid robots.

A highly efficient blue-light emitter, 2-tert-butyl-9,10-bis[4'-(diphenyl-phosphoryl)phenyl]anthracene (POAn) is synthesized, and comprises electron-deficient triphenylphosphine oxide side groups appended to the 9- and 10-positions of a 2-tert-butylanthracene core. This sophisticated anthracene compound possesses a non-coplanar configuration that results in a decreased tendency to crystallize and weaker intermolecular interactions in the solid state, leading to its pronounced morphological stability and high quantum efficiency. In addition to serving as an electron-transporting blue-light-emitting material, POAn also facilitates electron injection from the Al cathode to itself. Consequently, simple double-layer devices incorporating POAn as the emitting, electron-transporting, and -injecting material produce bright deep-blue lights having Commission Internationale de L'Eclairage coordinates of (0.15,0.07). The peak electroluminescence performance was 4.3% (2.9 cd A -1 ). For a device lacking an electron-transport layer or alkali fluoride, this device displays the best performance of any such the deep-blue Organic light-emitting diodes reported to date.

Human suffering from vertiginous might experience their body tilting and may eventually fall over. The analytical understanding and applications of human posture control could possibly be extended to understand the postural sway. On the basis of a simplified human body model with two stiff segments and articulation at the hip and ankle, this paper presents a two stage feedback control law for assisting balance in the human’s standing posture under interference of subjective verticals. A robust control law is developed from a control-theoretical point of view to guarantee posture stability under disturbances caused by vertigo. In understanding the task of posture control feedback, the control theory based on the human model could be a source of inspiration to physiologists for physiological modeling and understanding of complex mechanical and neurological interactions. The results developed are to be used as a basis for stance posture control of bionic robots and motion-aid robots.

ECG signal vary from person to person, making it difficult to be imitated and duplicated. Biometric identification based on ECG is therefore a useful application based on this feature. Synchronization of chaotic systems provides a rich mechanism which is noise-like and virtually impossible to guess or predict. This study intends to combine our previously proposed information encryption/decryption system with chaotic synchronization circuits to create private key masking. To implement the proposed secure communication system, a pair of Lorenz-based synchronized circuits is developed by using operational amplifiers, resistors, capacitors and multipliers. The verification presented involves numerical simulation and hardware implementation to demonstrate feasibility of the proposed method. High quality randomness in ECG signals results in a widely expanded key space, making it an ideal key generator for personalized data encryption. The experiments demonstrate the use of this approach in encrypting texts and images via secure communications.

In the measurement of physiological signals, the electro-cardiogram (ECG) is essential to most medical diagnoses related to the heart behavior, which is closely associated with human's health. In this study, we have developed a health care platform (HCP) which is able to solve the baseline drift problem, detect the heart-rate status, and record the ECG signals. The HCP provides a convenient handheld device to collect ECG signals in moving state from only two conductive electrodes. The device transmits the data to computers through wireless transmission and shows real-time user's status including spectrum analysis and Fourier transform. In this paper, the HRV analysis is divided into two parts, one is time-domain analysis in HCP and frequency-domain analysis in PC. The experimental results analyze the life-behavior patterns of tested subjects that may affect HRV parameters (HF, LF and HF/LF) so that one may achieve the task of disease prevention and health management.

This paper presents a new vision-based vehicle detection method for Forward Collision Warning System (FCWS) at nighttime. Also, lane detection is performed for assistance. To effectively extract the bright objects of interest, an essential image preprocessing including the tone mapping, contrast enhancement and adaptive binaryzation is applied in the nighttime road scenes. The characteristics of taillights in gray-level image are extracted by night vehicle detection method, and the resulted taillight candidates are verified by their corresponding red-component which results from R and B color channels. The taillight candidates to be performed with pairing algorithm are filtered by our proposed adaptive lane boundaries on the basis of Inverse Perspective Mapping (IPM). In addition, we proposed a new detecting scheme which performs the detecting algorithm on two Region of Interest (ROI) defined by different size each time. The computing burden is then reduced because vehicle detection does not have to be performed on the entire image. Finally, relative distance and Time To Collision (TTC) are estimated to warn the inappropriate driving behavior of the driver. The proposed night vehicle detection which integrates lane detection has successfully implemented in ADI-BF561 600MHz dual-core DSP.

Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the human's heart over time captured and is highly irregular, random, and variable from person to person. Recently, the literature has revealed that this kind of signal is, in fact, chaotic. Because of people's ECGs are extremely hard to be artificially duplicated, this paper intends to investigate the way of extracting the ECG signals' biometric features for the possibility of biometric recognition. The ECG signal is converted into the phase plane by using phase space reconstruction. Then, chaos extractor is applied to capture the representative indices of chaotic ECG signals i.e., correlation dimension and Lyapunov exponents spectrum. The root mean square, Lyapunov exponent and correlation dimension are used as the key variables in neural network training and utilized in the identification scheme.

Security of information has become a popular subject during the last decades. Due to electrocardiogram (ECG) signals varying from person to person, it could be applied as a new tool for biometric recognition. This paper introduces an individual feature of ECG with Logistic map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signal from encryption person, then using an intelligent algorithm based on the Chaos theory to generate initial keys for Logistic map, so that the user needn't to set up the initial values of the chaotic function. Simulation results show that the proposed system is capable of encrypting text information for secure communication efficiently and securely.

This paper studied the point stabilization problem for a constrained autonomous lawnmower. A constrained kinetic model is established first. The authors proposed a backstepping adaptive controller to solve the stabilizing control design problem. The proposed approach has been numerically verified.

Electrocardiogram (ECG) signals varying from person to person, it could possibly be applied as a tool for biometric recognition. This paper attempts to introduce an individual feature of ECG with a chaotic Henon map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signals from the encrypted person and applies an intelligent algorithm based on Chaos theory to generate initial keys for chaotic Henon map. High quality randomness of ECG signals results in a widely expanded key space which would be an ideal key generator for data encryption.

In this paper, we shall introduce the concept of k-ary multiple key hashing (MKH) files and the disk modulo (DM) allocation method. It is pointed out that k-ary MKH files exhibit the property of facilitating partial match retrieval. We then derive a close-form performance formula of the DM allocation method for k-ary MKH files on multidisk systems. Further more, we show that the DM allocation method is optimal for allocating k-ary MKH files on a k-disk system.

[reaction: see text] A series of carbazole/fluorene (CBZm-Fn) hybrids were effectively synthesized through Friedel-Crafts-type substitution of the carbazole rings. These compounds were thermally and morphologically stable host materials for OLED applications. Efficient blue phosphorescent OLEDs were obtained when employing CBZ1-F2 as the host and FIrpic as the guest.