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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.

Hollow nanofiber multiple-gas microsensors containing a ceramic package in parallel were developed via doping metal oxides in tin oxide (SnO2) and electrospinning and calcination. Different metal oxides including nickel oxide (NiO), copper oxide (CuO), and indium oxide (In2O3) were doped into SnO2 individually as sensing films for the detection of acetone (CH3COCH), ammonia (NH3), and hydrogen sulfide (H2S) with high selectivity. After heating and calcination, the hollow electrospun nanofibers formed with high specific surface area and porosity resulting in increased sensing area and enhancing the rate of surface chemical reaction and responsivity. The experiments show that the optimal local operation temperature of the activated sensors was 330 °C. High responsivity of the packaged gas sensors was obtained with high accuracy and repeatability. Gas sensitivities were 0.071, 0.075, and 0.09 for sensors of acetone, ammonia, and hydrogen sulfide, respectively, at concentrations of 1 ppm. The response and recovery times of the acetone, ammonia, and hydrogen sulfide gas sensors were 4 and 8 s, 5 and 9 s, and 5 and 12 s, respectively.

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.

To investigate how respiratory resistive loading affects the behavior of optimal chemical–mechanical respiratory control, respiratory signals and breathing pattern were optimized under various dead space loadings (0, 0.4, and 0.8 L) and CO2 inhalation concentrations (0%, 3%, and 5%). The optimal respiratory control model, which has been studied earlier, was characterized to include a description of the neuromuscular drive P(t) as the control output and accurately derive the waveshapes of instantaneous airflow V ˙ ( t ) , lung volume V(t) profiles, and breathing pattern, including total and alveolar ventilation, breathing frequency, tidal volume, inspiratory and expiratory duration, duty cycle, and arterial CO2 pressure. Simulations were performed under various respiratory resistive loads: no load (NL), inspiratory resistive load (IRL), expiratory resistive load (ERL), and continuous resistive load (CRL). The simulation results provide an extended view of breathing pattern and the respiratory signals of neural muscular driving pressure, airflow, and lung volume for rest and various specific cases with external dead space loading and/or CO2 inhalation. The results showed that an IRL generally resulted in a higher amplitude of P(t) and V(t), with more convex upward pressure waveshapes, a prolonged inspiratory duration, and a higher duty cycle (TI/T ≈ 0.55–0.56 at Rest, ≈ 0.54∼0.68 in Case 1, and ≈0.61∼0.69 in Case 3 during resistive loading) for the waveforms. In comparison to the resting state, an abrupt fall occurred at the beginning of the expiratory phases of P(t) and the peak flows ( V ˙ peak ≈ −5, −5.5, and −7 L/min in Cases 1, 2, and 3, correspondingly) in the expiratory phase were significantly boosted with ERL during CO2 inhalation (Case 1_3%, 5%), and EDS loadings (Case 2_EDS = 0.4, 0.8), or a combination of both (Case 3_5% and EDS = 0.4). CRL in all cases exhibited greater rises in amplitude for pressure and lung volume in comparison to IRL and ERL; nevertheless, it also appeared not to have a substantial influence on the waveshapes of airflow, except in the case of a higher concentration of CO2 inhalation.

A brush-distributed metal oxidation sensing nanostructure is developed by growing zinc oxide (ZnO) nanorods on electrospun titanium dioxide (TiO2) nanofibers to improve ions adsorption of lead and copper. A pair of inductive plane coils are designed to measure inductive variation induced by the adsorbed ion concentration. The brush-distributed ZnO–TiO2 nanofibers are fabricated by using electrospinning and hydrothermal methods that are deposited on the PVDF-formed nanofiber membrane to enhance copper and lead ions adsorption efficiency. The sensitivity has been promoted based on the van der Waals force and produced hydroxyl from grown surface nanorods. The TiO2 structure was examined by checking XRD patterns. These diffraction peaks are indexed as the (101), (004), (200), and (105) of anatase TiO2 and are consistent with the standard data (JCPDS No. 78-2486). The circular coils are designed and simulated using the finite element method to sense metal ions by measuring concentration-dependent magnetic flux variation. The developed ZnO–TiO2 ion sensing composites include the following advantages: simple fabrication process, low cost, high selectivity, highly effective adsorption, specific surface area and porosity, rapid response, and good repeatability and reproducibility.

This study performed a comparison based on electrocardiogram (ECG) signals for participants with distinct levels of cardiopulmonary fitness at graded exercise intensities. The participants were university freshmen with cardiopulmonary fitness norms in the bottom 20%, middle, and top groups. Cardiopulmonary signals were acquired under the rest condition, at three graded exercise levels, and in subsequent recovery periods. Time domain and spectral analyses were performed to study the principal components of heart rate variability (HRV) from recorded ECG signals. The results showed that the frequency domain measures TP, HF, nLF, nHF, and LF/HF of the HRV indices all exhibit significant differences between the three groups.

In this study, an automatic microfluidic fluorescence-array measurement system is developed to detect the concentration of organic phosphate based on the luminol-hydrogen peroxide catalytic fluorescent mechanism. Not only sample quantity and cost can be reduced, but also detection time, accuracy and precision can be improved in the system. The system is composed of a CCD image module, a stepper motor with driver, a microfluidic fluorescence array, a background light elimination module, and a dynamic image-analyzed interface. The pesticides of chlorpyrifos and fenitrothion of organic phosphate are chosen as experimental samples. Only a 2.5 μ l quantity of sample is required to have a fast response time of 1.4 second. Experimental results show that the sensitivities of chlorpyrifos and fenitrothion are 1.88 V/ppm in the range of 0.166 ∼ 10 ppm with averaged error of 1.66% and 0.32 V/ppm in the range of 0.03 ∼ 10 ppm with averaged error of 1.68% respectively. The organophosphorus effective detection range of the developed system covers the legal prescription for pesticide residues.

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.

In this study, the optimal chemical-mechanical respiratory control model was modified to include nonlinear respiratory mechanics with a lumped viscous resistance of the flow through the entire respiratory system, and a flow resistance that is proportional to the power of the flow rate. To evaluate the optimality of the system, a quadratic rising neuromuscular drive was applied to a neuro-mechanical effector and the respiratory signals were optimized under hypercapnia and eucapnia conditions. A continuous resistive load was imposed to compare the model behavior of respiratory mechanical loading with normal load. The optimized respiratory signals were demonstrated and the ventilatory responses with the optimized breathing patterns were examined. Our results showed that the nonlinear model acquired the intended level of ventilation with higher tidal volume VT and lower breathing frequency F during CO2 inhalation and lower VT and higher F during exercise.

The problem concerning the nature and the function of the dead space is of basic importance for the full comprehension of the respiratory physiology and pathophysiology. To study the effect of an imposed external dead space on the optimal respiratory control system, we simulated the optimal neuro-muscular drive and respiratory signals, including instantaneous airflow and lung volume profiles, with dead space loading under hypercapnia. The dead space measurement model by Gray was employed and the human respiratory control simulator based on an optimality hypothesis was implemented. The ventilatory control simulations were performed with external dead space loading of 0, 0.4 and 0.8 liters under rest condition (PICO2=0%) and CO2 inhalation of 3% to 7%. The optimization of the respiratory signals and model behavior of the optimal respiratory control under dead space loading and hypercapnia were verified and found to be in general agreement with experimental findings.

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.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

This paper develops an inertial-sensing-based wearable human activity classification system and its associated human activity classification algorithm for accurately recognizing human daily activity. The proposed system used two inertial sensing modules, which are worn on subjects' wrist and ankle, to collect motion signals of human activities, and utilized the nonparametric weighted feature extraction (NWFE) algorithm to reduce the feature dimensions and improve the classification rate simultaneously. First, we integrated a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and a RF wireless transmission module into the inertial sensing module. Next, the two inertial sensing modules are worn on subjects' wrist and ankle to form a wearable human activity classification system, and collect motion signals generated by daily activities. Subsequently, we developed an inertial-sensing-based human activity classification algorithm composed of signal preprocessing, human motion detection, windowing, feature extraction, feature reduction, and activity classification to recognize the types of human activities. In addition, we also utilized the NWFE algorithm to reduce the feature dimensions and computational complexity, and improve the classification rate. Finally, the experimental results show that the wearable human activity classification system and its human activity classification algorithm can recognize 10 daily activities with the classification rate of 90.5%.

In this paper, we perform the comparison study of ECG signals for subjects at distinct ends of cardiopulmonary fitness. The cardiopulmonary signals were acquired under rest condition, three graded exercise intensities, and recovery periods. The participants were recruited from university freshmen with cardiopulmonary fitness norm in top, middle, and bottom groups. Spectral analysis was performed to study the hear rate variability (HRV) from recorded ECG signals. From the view of biomedical signal and statistical analysis, results of current study are expected to provide valuable scientific basis for further evaluation of physical fitness assessment and exercise reinforcement.

This study focused on developing a hydrogen sulfide (H 2 S) gas microsensor using nanometal oxides of copper oxide-doped tin dioxide. Electrospinning and high-temperature calcination were adopted to fabricate a high-surface area and porous hollow nanofibrous gas sensing film. The film that served as a sensing mechanism on the heterojunction of the microsensor exhibited a high resistance change rate; therefore, the gas-sensing sensitivity was effectively improved. The ceramic ring-based sensor was coated with a nanosensing film, on which gold sensing electrodes were formed through sputtering by using a shadow mask. Thus, a sensing mechanism was completed that allowed the absorption of the gases to be tested. A heating wire was designed in the chamber of the ceramic ring to control the local temperature of the sensing structure. The response level and repeatability of the device to the gases were effectively enhanced. Results indicated that the microsensor could sense H 2 S at a concentration of 1–100 ppm, had a sensitivity of 0.39 MΩ/ppm, and possessed response and restoration times of 5 ± 0.12 s and 12 ± 0.24 s, respectively.

The objective of this study is to develop a multi-sensor data fusion technology based smart home system and its intelligent monitoring interface. The functions of this system include: 1) smart entertainment and household appliances remote control; 2) indoor positioning and intelligent energy management; and 3) home fire prevention. In addition, an intelligent monitoring interface is developed for the smart home system to provide indoor environmental temperature, CO concentration, communicative environmental alarm, household appliances situation, human motion signals, and the results of gesture classification and indoor positioning in real-time. Finally, the abovementioned functions and intelligent monitoring interface are implemented in an experimental model for validating the effectiveness of the smart home system. We have executed the following three research subjects to accomplish the aforementioned objectives: 1) Develop a wearable sensing device placed on hand and its 3D gesture recognition algorithm to implement a real-time, convenient, and low-cost household appliances remote control system. 2) Develop a wearable sensing device placed on foot and its indoor pedestrian positioning algorithm to realize an effective pedestrian navigation system for smart energy management. 3) Develop a multi-sensor circuit and its intelligent fire prevention and alarm algorithm to realize an intelligent fire prevention and alarm system for home fire prevention. The experimental results have successfully validated the effectiveness of the proposed smart home system and its intelligent monitoring interface.

The modified nanoporous membranes on centrifugal microfluidic platforms are developed for detecting heavy metal ions with high selectivity. The nanomaterials of polyrhodanine, activated alumina and activated carbon are deposited on nanoporous anodic aluminium oxide (AAO) membranes for enhancing filtration to remove ions of lead (Pb(II)), arsenic (As(III)) and cadmium (Cd(II)) from aqueous solution. By detecting chemiluminescence (CL) reaction, the concentrations of heavy metal ions can be obtained. A pneumatic-and-centrifugal microfluidic detection system was used to perform microfluid separation and CL detection for measuring selected ions concentration. The removal efficiency of 92, 82 and 78% were observed for Pb(II), As(III) and Cd(II) ions, respectively. The removal rate is performed up to 70% for mixed ions filtration by a sequence of multi-modified AAO membranes. The modified nanofiltration films on microfluid platform have potential applications in removing heavy metal ions from water, soi...

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].

In this paper, we simulate the effect of imposed external dead space by examining the optimized neural muscular drive and respiratory signals, including, airflow and lung volume profiles. To study the effect of external dead space loading, the measurement model by Gray is used and human respiratory control simulator based on an optimal respiratory control mechanism is implemented. The respiratory control simulations are performed with external dead space loading (0, 0.4 and 0.8 l) under rest and CO2inhalation of 3% to 7%. The waveshaping of both the imposed external dead space and CO2inhalation on the respiratory waveforms are studied.

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.

An optical real-time pneumatic-and-centrifugal controlled microfluidic detection system for dynamic information acquisition is developed based on the quasi-stationary imaging technique. The programmable airflow applied on the centrifugal microstructures for improving efficiency in samples separation. The dynamic characteristic of a loaded disc is stable with vibrating under 0.3 mm at a speed of 1000 rpm by applying 3 bar-induced pneumatic forces on a 12 cm-diameter disc. A conversion model for converting RGB images into CIEL*a*b*color space have been used to enhance the inspection images. A linear relationship between threshold frequency and sample density is 167 rpm/g/cm3. The pressures between 0.1 and 0.5 bars are applied to bias microflow from 15° to 80°. The conduction angles between 30° and 90° have better pneumatic control. The control efficiency observed up to 89% and the largest microflow biased angle reached 80°. The pneumatic force dominates microfluidic behaviors when the force is greater than 10 times the centrifugal force. A sequential of triple-reservoir tests has been verified by analyzing enhanced optical images in separation using arranged acid-base indicators for pH reactions.

A low cost novel integrated portable elasticity measurement system is developed by using strain gauges and photoconductive sensors on a coaxial movement mechanism for processing signal of force and displacement synchronously. Real-time acquisition data are averaged to reduce transmission delay for assuring signal taken exactly and synchronously. Switching circuit modules connected to the multiplexer are designed for processing nonlinear sensing data using a piecewise linear technology. An experimental study of force, displacement and elasticity is undertaken to illustrate the adjustment operation of tapping keys in alto saxophones. Experimental results show that the resolution and operation range of displacement and force are 0.1 mm in the range of 0 to 40 mm and 10 g in the range of 0 to 1000 g, respectively. The average error of the elasticity in real-time measurement is 2.07 %. The application of tapping keys adjustment using the system are outlined and discussed in three-zone elasticity models.

The research presents a novel automatic anemometer based on the technology of automatic multi-dimensional thermoelectric sensor array. The multi-dimensional thermoelectric sensor array was designed to form a double-ring structure using commercial thermocouples to detect real-time airflow temperature distribution, velocity and direction. The sensing arrays and a controllable heater and a human-machine interface that is calibrated by a hot-wire anemometer. The anemometer has advantages of low cost, real-time detection with fast response. Related air flow information has measured accurately and repeatability.

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.

Current study aims to investigate how the respiratory resistive loading affects the behaviour of the optimal chemical-mechanical respiratory control model, the respiratory signals and breathing pattern are optimized under external dead space loading and CO2 breathing. The respiratory control was modelled to include a neuro-muscular drive as the control output to derive the waveshapes of instantaneous airflow, lung volume profiles, and breathing pattern, including total/alveolar ventilation, breathing frequency, tidal volume, inspiratory/expiratory duration, duty cycle, and arterial CO2 pressure. The simulations were performed under various respiratory resistive loads, including no load, inspiratory resistive load, expiratory resistive load, and continuous resistive load. The dead space measurement was described with Gray’s derivation, and simulation results were studied and compared with experimental findings.

The development of efficient data encryption to ensure high security of information transmission has long been a popular research subject. Because electrocardiogram (ECG) signals vary from person to person, and can be used as a new tool for biometric recognition. This study introduces an individual feature of ECG with chaotic Henon and logistic maps for personalized cryptography. This study also develops an encryption algorithm based on the chaos theory to generate initial keys for chaotic logistic and Henon maps. The proposed personalized encryption system uses a convenient handheld device to collect ECG signals from the user. 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 reported in this study demonstrate the use of this approach in encrypting texts and images, and applied of the proposed approach to secure communications.

Current paper focus on Parkinson's patients with autonomic dysfunction and how their interactions between cerebral autoregulation and ventilatory control are affected. The experimental data of dynamic CA assessment from the ANS Laboratory of CCGH was accessed for further processing and analysis. The subjects were classified into the groups of healthy and with Parkinson's disease. Based on the accessed ventilation and CBF data, the percentage changes in ventilation and CBF responses to PETCO2 were examined. To minimize effects of changes in ABP on cerebral vasomotor reactivity (CVMR) estimation, cerebrovascular conductance index (CVCi) was calculated, and CBFV-PETCO2 and CVCi-PETCO2 relationships were quantified by nonlinear logistic regression. The interaction between ventilation responses and CBF autoregulation will be modeled and parameters will be validated.

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.

This research aims to use personal digital assistant (PDA) to monitor falls of stroke patients and the elderly. With the combination of micro sensor, digital data access application, and wireless transmission, this research has developed this PDA based wearable system for real-time monitoring of human falls. The sensor of unique monitoring system equipped with real-time automatic responsive function can be loaded in user's clothing and wearable. When the user falls down, the system will detect the position of falls and automatically sends the warning signal out. In the near end, detected signals are sent to PDA for analysis via wireless Bluetooth (BT) technology and at the same time, signals for help stored in the speech database inside PDA are also initiated to inform the nearby personnel of the emergency. The user is able to send user's information to the computer In the far end monitoring station and when the possible fall occurs, the system automatically informs designated subject via MSM for proper crisis management. Through this system for real-time monitoring of human falls, home helpers and families are able to save some time and energy for inspection and have a good command of safety information of users.

In this paper a polar coordinate mapping method for an improved infrared eye-tracking system is described. In the proposed control system for an eye-tracking device, users do not need to wear any equipment. Rather they just ware an infrared light source and an infrared CCD camera to extract the eye images for the computer to analyze, record the information of the moving traces and pupil diameters, control the mouse's cursor and operate many kinds of applied programs. The advantage of this system lies in solving non-contact requirement problems for which the measured system would require whole eye monitoring and a quick response. We also improve the feasibility and the safety of this eye-tracking device by using infrared rays and new coordinate mapping method.

Two methods that have been studied for solving large, sparse sets of algebraic equations connected with power system problems, the multiple factoring method and the W-matrix method, are shown to be two independent methods of explaining equivalent computational procedures. The forward and backward substitution part of these methods are investigated using parallel processing techniques on commercially available computers. The results are presented from testing the proposed methods on two local memory machines, the Intel iPSC/1 and iPSC/860 hypercubes, and a shared memory machine, the Sequent SymmetryS81. With the iPSC/1, which is characterised by its slow communication rate and high communication overhead for a short message, the best speedup obtained is less than 2.5, and that was with only 8 of the 16 available processors in use. The iPSC/860, a more advanced model of the iPSC family, is even worse as far as these parallel methods are concerned. Much better results were obtained on the Sequent Symmetry where a speedup of 7.48 was obtained with 16 processors. >

To develop a simulation and control mechanism for mechanical ventilator of respiratory therapy is aimed as the continuous mandatory ventilation mode is also objected for study. Based on the optimal chemical-mechanical respiratory control model, sinusoidal waveform which is commonly utilized in clinical applications of volume-targeted approach is modeled and optimized. To meet the needs for ventilation by considering both the chemical and mechanical cost, the effort of breathing for simulated patient under mechanical ventilation is minimized through the optimization tool. The experiments are performed and the resultant waveforms are examine under various simulations of muscular exercise, CO2 inhalation, and resistive and elastic loading for simulate patient. The system is found to perform properly as the resultant ventilatory responses and optimized waveforms are examined through an implemented MATLAB® Graphic User Interface.

A previous optimal chemical-mechanical model (C.-S. Poon. J. Appl. Physiol. 62: 2447–2459, 1987) suggested that the normal ventilatory responses to CO2 and exercise inputs and mechanical loading can be predicted by the minimization of a controller objective function consisting of the total chemical and mechanical costs of breathing. In this study the model was generalized to include a description of the inspiratory neuromuscular drive as the control output. With a mechanical work rate index for both inspiration and expiration, the general optimization model accurately reproduced the observed responses in the waveshape of inspiratory drive, breathing pattern, and total ventilation under differing conditions of CO2 inhalation, exercise, and inspiratory/expiratory mechanical loads. The simulation results are in general agreement with a wide range of respiratory phenomena, including exercise hyperpnea, CO2 chemoreflex, and post-inspiratory (postinflow) inspiratory activity, as well as respiratory neural compensations for mechanical loading, respiratory muscle fatigue, and muscle weakness.

The establishment of fuzzy control rules has long been an important issue in fuzzy controller design. Several ways have been developed to construct fuzzy control rules, which may be based on the operator's control action, expert's experience and control engineering knowledge. However, trial-and-error efforts are inevitable in most methods and, consequently, make the design procedure a time-consuming task. Hence, efficient construction of fuzzy control rules become an active topic in fuzzy controller design. Among the most recent studies, the use of fuzzy identification to build a fuzzy model of a system was proposed by Takagi and Sugeno (1985). In this paper, a procedure is proposed to construct fuzzy control rules and the pseudoinverse method is employed to modify the fuzzy identification. It is found that the trial-and-error effort can be effectively reduced. The procedure has been applied to the synthesis of a fuzzy controller for a hexapod robot control model, and the method was examined by comparing the results with those of a traditional PID controller.

This study investigated an optomechatronic array-integrated signal processing module and a human–machine interface based on fiber Bragg grating sensing elements embedded in an elastic support matrix that involves using a self-located electromagnetic mechanism for curvature sensing and solid contour reconstruction. Using bilinear interpolation and average calculation methods, the smooth and accurate surface contours of convex and concave lenses are reconstructed in real-time. The elastic supporting optical sensing array is self-balanced to reduce operational errors. Compared with our previous single-head sensor, the sensitivity of the proposed array is improved by more than 15%. In the curvature range from −20.15 to +27.09 m−1, the sensitivities are 3.53 pm m for the convex measurement and 2.15 pm m for the concave measurement with an error rate below 8.89%. The curvature resolutions are 0.283 and 0.465 m−1 for convex and concave lenses, respectively. This array could be applied in the curvature measurement of solar collectors to monitor energy conversion efficiency or could be used to monitor the wafer-level thin-film fabrication process.