Search

The Oxygen Reserve Index (ORi™) is a dimensionless parameter with a value between 0 and 1. It is related to the real-time oxygenation status in the moderate hyperoxic range. The purpose of this study is to investigate the added warning time provided by different ORi alarm triggers and the continuous trends of ORi, SpO

Taiwan faces challenges in providing long-term family care as its population rapidly ages. This study aimed to construct a health application that could effectively meet family caregivers' needs in providing home care. Mixed methods were adopted to explore their acceptance of the application, as well as to gather their suggestions for improving the application. A nonrandomized controlled group pretest-posttest design was utilized. The participants were family caregivers who were recruited via purposive sampling at a regional hospital in Taipei, Taiwan. The caregivers used the application to monitor and manage the health of those they were caring for. Elderly family caregivers scored 1.57 and 1.16 points higher than their middle-aged counterparts in terms of performance expectancy (0.61-2.53, P = .01) and effort expectancy (0.25-2.06, P = .01), respectively. The caregivers opined that it was convenient to use the application for managing the health data of their patients, and resources and solutions for overcoming problems when using the application were easily within their reach. They also suggested that relevant information for promoting patient mental health could also be included in the application. Thus, the software is acceptable to all the family caregivers regardless of age, indicating that it can assist family caregivers in the future in performing home care management.

BACKGROUND Traditional Chinese medicine (TCM) theories assert that body constitution and meridian energy lay the foundation for disease prevention. TCM-based health concepts have not been incorporated into the mobile health application (mHealth app) for individuals with prediabetes. OBJECTIVE This study aimed to examine the effectiveness of a TCM mHealth app for individuals with prediabetes. METHODS This randomized controlled trial recruited 121 individuals with prediabetes at a teaching hospital in New Taipei City between February 2020 and May 2021. The participants were randomly assigned to the TCM mHealth app (TCMG; n=42), ordinary mHealth app (OMG; n=41), or control groups (CG; n=38). All participants received the usual care that included 15–20 minutes of health education about disease explanation, healthy diet, and exercise encouragement. The ordinary mHealth app included physical activity (PA), diet, disease education, and individual records. The TCM mHealth app additionally included qi and body constitution as well as constitution-based PA and diet advices. The CG received the usual care alone and did not use apps. Data were collected at baseline, the end of the 12-week intervention, and 1 month after the intervention. Body constitution, including yang-deficiency, yin-deficiency, and phlegm-stasis, was measured by the body constitution questionnaire, with higher scores indicating a greater deficiency. Body energy was examined by using the Meridian Energy Analysis Device. The Short-Form 36 was used to evaluate health-related quality of life (HRQOL), which yielded physical component scores (PCS) and mental component scores (MCS), with higher scores indicating better physical and mental aspects of HRQOL, respectively. RESULTS Compared to the CG, the TCMG improved more in hemoglobin A1c (HbA1c), yang-deficiency and phlegm-stasis body constitution, and body mass index (BMI); but no significant differences were found in those outcomes when the TCMG was compared to the OMG. The TCMG had better improvement in body energy and MCS than the OMG. There were no significant differences in fasting plasma glucose, yin-deficiency body constitution, Dietary Approaches to Stop Hypertension dietary behavior, and total PA among the three groups after the intervention. CONCLUSIONS Use of either the ordinary or TCM mHealth app improves HRQOL among individuals with prediabetes. Comparing to controls not using app, use of the TCM mHealth app is effective in improving HbA1c, BMI, yang-deficiency and phlegm-stasis body constitution, and HRQOL. Moreover, using the TCM mHealth app seems to improve the body energy and HRQOL more than using the ordinary mHealth app. Whether the differences favoring the TCM app were clinically meaningful may need further studies with a larger sample size and a longer follow-up period. CLINICALTRIAL ClinicalTrials.gov NCT04096989; https://clinicaltrials.gov/ct2/show/NCT04096989

Background Stress urinary incontinence (SUI) is a major health problem affecting approximately 50% of the female population over 45 years of age. We evaluated the therapeutic effects of a home-based non-invasive wireless sensor pelvic floor muscle training (PFMT) device with assisted Kegel exercise for SUI. Methods We included sixty women aged 40 to 60 years who were diagnosed with urodynamic SUI (mean pad test, 10.52 g). The PFMT device applicator was clamped on the upper inner thigh, and the patients could self-train at home. The signal was recorded and delivered to a 3G/4G smartphone via Bluetooth, which also allows guided feedback via the smartphone's voice. To evaluate the therapeutic effect, all patients completed the following questionnaires: a 3-day bladder diary, the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), the Urogenital Distress Inventory-Short Form (UDI-6-SF), and the Incontinence Impact Questionnaire-7 (IIQ-7). One-hour pad test measurements were performed before the test (M0) and at 1 (M1), 2 months (M2), and 3 months (M3) after the PFMT device-assisted Kegel exercise. Results The one-hour pad test and the scores of the ICIQ-SF, UDI-6, and IIQ-7 questionnaires were improved at M1, M2, and M3, compared with the M0 values. The mean value of the post-voiding residual urine (PVR) significantly decreased at M2 and M3. The subjective and objective improvement rates at M3 were 80% and 72%, respectively. Conclusion The data demonstrated that 3 months of Kegel exercise assisted with a home-based PFMT device improved the number and severity of episodes, post-voiding residual urine, and quality of life (QoL) in patients with SUI, suggesting that this device might serve as an alternative non-invasive therapy for mild and moderate SUI.

Due to the global spreading of the COVID-19 virus, countries all over the world are faced with the need to conduct centralized quarantine or home quarantine for "persons who have been in contact with individuals diagnosed with the COVID-19 virus" and "visitors who have travel histories via COVID-19 hot zones". We have presented the community home quarantine service platform design that was utilized in Nanjing, China when the first wave of citizens returns to work after the Chinese New Year holidays on 10th Feb 2020. The main functions of the home quarantine monitoring system include (1) community grid management,(2) GPS positioning application in home isolation movement management,(3) Bluetooth body temperature patch data transmission integration, (4) health assessment scale (physical and mental health state) and (5) multilingual language options.

BACKGROUND As the population of Taiwan continues to age rapidly, middle-aged and elderly people will increasingly begin to care for their older relatives. Therefore, the responsibility and complexity of care required from family caregivers will increase. The mobile health technology (mHealth) model has been successfully applied to numerous care services. OBJECTIVE To develop a mobile health app that supports family caregivers of various ages by meeting their home care needs, thus improving their quality of life, decreasing their severity of depression, and making them more empowered. METHODS This study applied a nonrandomized control-group pretest–posttest design. Purposive sampling was employed to recruit 30 family caregivers—16 middle-aged and 14 old family caregivers—from the home care cases of a regional hospital in Taipei. The developed home care mHealth app was used for management and monitoring of patient health. The outcomes measured were quality of life, level of depression, and sense of empowerment. RESULTS The results indicated that use of the mHealth app improved the technology acceptance, quality of life, depression level, and empowerment of the family caregivers. The old caregivers obtained quality of life scores that were 2.23 points higher, on average, than those obtained by the middle-aged caregivers P = .01, a significant difference. On the quality of life questionnaire, the old caregivers’ obtained scores for the health and family factors that were 2.75 P = .01 and 2.49 P = .01 points higher, respectively, than those obtained by the middle-aged caregivers, indicating significant differences. Regarding empowerment and depression, the scores obtained at different time points indicated that both groups became more empowered and less depressed over time, but no significant differences were discovered between the two groups. CONCLUSIONS According to our results, using the home care mHealth app had a positive influence the app is concluded to be a favorable tool for care education and management. Old caregivers found the software useful for managing patients’ physiological data, and they wished that other assistive methods were available to help them understand the internal functions. The software contributes to quality of life improvement, a decrease in depression severity, and empowerment enhancement and can be used by family caregivers as a reference for home care management in the future.

This research provided a postoperative system for osteosarcoma which is seperated into two parts, Bone Patient and Bone Care. We focus on using smartphone features and proper interface design to facilitate self-monitoring, health analysis, and to make appointments. It achieves the effects of shared decision making (SDM). During the two weeks of research, we achieved satisfied with 4.03 ± 0.67 TAM score.

Pulmonary embolism is a difficult disease to diagnose in the emergency department. It may be fatal if the diagnosis is missed. Clinical practice guidelines and textbooks publish pretest diagnostic tools and algorithms that facilitate the diagnosis of pulmonary embolism. We developed and administered a questionnaire that determined that such tools can be difficult to remember to use. We also designed an Android application to facilitate diagnosis of pulmonary embolism and used a questionnaire to evaluate the application.

Instant messaging (IM) is one kind of online chat that provides real-time text transmission over the Internet. It becomes one of the popular communication tools. Even it is currnetly an era of smartphones, it still a great challenge to teach and promote the elderly to use smart phone. Besides, the acceptance of the elderly to use IM remains unknown. This study describes the usability and evaluates the acceptance of the IM in the elderly, who use the smartphone for the first time. This study is a quasi-experimental design study. The study period started from October, 2012 to December, 2013. There were totally 41 elderly recruited in the study. All of them were the first time to use LINE app on the smartphones. The usability was evaluated by using the Technology Acceptance Model which consisted of four constructs: cognitive usability, cognitive ease of use, attitude and willingness to use. Overall, the elderly had the best "attitude" for LINE APP communication software, with the highest rating averaging 4.07 points on four constructs, followed by an average of 4 points on "cognitive usefulness". The socres of "cognitive ease of use" and "willingness to use" scores were equal which are an average score of 3.86. It can be interpreted that (1) the elders thought that the LINE APP as an excellent communication tool for them; (2) they found the software is useful (3) it was convenient for them to communicate. However, it was necessary to additionally assist and explain the certain functions such as the options. It would play a great role in the "willingness to use". The positive acceptance of LINE APP in elderly refer to the probable similar acceptance for them to use other communication software. Encouraging the willingness the elderly to explore more technology products and understanding their behavior will be the basic knowledge to develop further software.

High fidelity simulation-based teaching has played an important role in medical education, especially in anesthesiology and emergency. But there is not any currently validated scoring system or prediction model for high fidelity simulation. We will develop a validated prediction model to enhance the efficiency and validation of clinical training with high fidelity simulation.

This study aimed at developing an assessment of smartphone usage competence and constructing a training program for the elderly. A list of smartphone usage competencies with 34 items was defined through expert survey and panel. Based on the competence and previous literature, a training program and learning aids were designed in this study. There were 41 participants in our program. The results of self-administrated smartphone usage ability questionnaire indicated that all competencies were significantly improved after training. However, the results also demonstrated that some items were still difficult for the elderly to comprehend. Overall, this study provided a first exploration of defining smartphone usage competency and built a training program for the elderly. With strong suggestion, future mobile health (mHealth) services can follow this study to insure the smartphone usage ability of the elderly.

Near field communications (NFC) is an emerging technology that may potentialy assist with disaster management. A smartphone-based app was designed to help track patient flow in real time. A table-drill was held as a brief evaluation and it showed significant imporvement in both efficacy and accuracy of patient management. It is feasible to use NFC-embedded smartphones to clarify the ambiguous and chaotic patient flow in a mass casualty incident.

Pregnancy weight gain may be associated with adverse pregnancy outcomes. The article aims to explore the relationship between weight change and pregnancy outcome in the Taiwanese pregnant women. The retrospective cohort study enrolled women with vertex singleton pregnancy at University-associated Hospital between 2011 and 2014. Pregnancy weight change was separated into 3 groups, based on the Institute of Medicine (IOM) guidelines: below (n = 221); within (n = 544); and above (n = 382). Analysis of variance, χ2 tests, generalized linear models, and logistic regression models were used for statistical comparisons. Pregnant women with weight change above IOM guidelines had a significant increase in both maternal and perinatal complications compared with normal controls (odds ratio [OR] 1.65, 95% confidence interval [CI] 1.03–1.98; P = .043; OR 1.45, 95% CI 1.01–1.87; P = .049, respectively). This finding was not found in pregnant women with weight gain below IOM guidelines. Moreover, age (OR 1.08, 95% CI 1.02–1.15; P = .0011), pre-pregnancy weight (OR 1.04, 95% CI 1.01–1.09; P = .0008), pre-pregnancy body mass index (BMI; OR 1.15, 95% CI 1.06–1.30; P

Highway accidents significantly impact normal traffic flow. Consequently, automatic detection of abnormal traffic events has gradually attracted the attention of researchers interested in intelligent transportation system. This work presents a vision-based approach for automatic traffic congestion and incident detection. The proposed approach involves extracting entropy-based features to create a grid model that simulates dynamic traffic flow behavior. When an unusual event occurs in the lane of the vehicle employing the system, the system can immediately detect it and issue signals to approaching vehicles to prevent accidents. Experiments conducted using various simulation results clearly demonstrate the validity and effectiveness of the proposed approach for managing traffic congestion and detecting incidents.

We propose a novel adaptive fast learning (AFL) algorithm for two-dimensional principal component analysis (2DPCA) in this paper. As opposite to conventional PCA which is based on 1D data vectors, 2DPCA is based on 2D image matrices and thus has higher accuracy than conventional PCA when applied to applications such as face recognition, facial expression recognition, palmprint recognition, etc. Our proposed AFL algorithm simultaneously estimates both eigenvectors and corresponding eigenvalues, and then adaptively sets the learning rate parameters of neurons to ensure all neurons learning with almost the same fast speed. Requiring no image covariance matrix evaluation, the desired multiple eigenvectors of 2DPCA can thus be learned effectively in the form of weight vectors of neurons. The proposed AFL algorithm can also be applied to learning for T-2DPCA. Simulation experiments performed on face database such as the YaleB database clearly demonstrate that the proposed AFL algorithm performs very well and thus is a very effective computational tool for both 2DPCA and T-2DPCA.

Inexhaustible solar power is booming for carbon reduction in recent years. The current efficiency of solar power generation is still low. Therefore, improvement of the power generation efficiency will be the research objective to scientists all over the world. The maximum power point tracking (MPPT) is an effective way to improve efficiency. Existing maximum power point tracking methods are incremental conductance method, perturbation and observation method, voltage feedback method and so on. Perturbation and observation method has the simple, easy to understand and the most common structure. In this paper, the nonlinear phenomena of buck-boost DC-DC converting solar cell system with perturbation and observation method for MPPT, considering converter operation in continuous conduction mode (CCM) with inductor current by varying load resistance is studied. The system is from periodic steady-state into period-doubling bifurcation to the chaos of nonlinear phenomena. Firstly the solar cell maximum power tracking system and converter working principle are explained. Secondly, the construction of solar maximum power tracking with perturbation and observation method and buck-boost DC - DC converter equivalent circuit model, then mathematical model is derived, and simulated by MATLAB / SIMULINK software package. Through the time-domain waveforms, phase plane and bifurcation diagrams, to explore the changing of load resistance system from a periodic steady-state into the chaos of nonlinear phenomena, chaos evolution mechanism of the system is analyzed.

Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D)2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.

The back propagation (BP) algorithm for function approximation is multilayer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter is used to partition the nonlinear system's domain into several piecewise linear subspaces to be represented by neural networks. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm is proposed to greatly mitigate the influence of data noise and outliers. (2) A fuzzy-based data sifter is proposed to locate good turning-points to partition a given nonlinear data domain into piecewise clusters so that a neural network can be constructed with fewer rules. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.

This paper studies the nonlinear behaviors of the half-bridge buck converter when the load resistance changes. The characteristics of the study are considered as the inductor current in the continuous conduction mode. Firstly, the converter is equivalent to two switching states and is derived into the equivalent mathematical model. According to this model, using MATLAB/ SIMULINK tool to build a half-bridge buck converter block diagram for simulation. When the load resistance changes as the varying parameter in the converter, this paper observes the time-domain waveforms and phase portraits of steady-state to period-doubling bifurcation into chaos. The nonlinear phenomena of converter are verified through simulation results.

This paper investigates the nonlinear phenomena in a current-mode controlled buck-boost DC-DC converter with solar cells. The converter is operated in continuous conduction mode (CCM) with inductor current. It is also controlled by varying reference current to study the occurrence of nonlinear phenomena from the initial periodic steady-state to period-doubling bifurcation into the last chaos condition. Firstly, this paper introduces the basic principles of solar cells and current-mode controlled buck-boost converter. Secondly, the equivalent circuit model of solar cells and converter is constructed and the mathematical model is derived. Then, this study adopts MATLAB / SIMULINK for modeling and simulation. The converter nonlinear phenomena are verified through time-domain waveforms, phase portraits and bifurcation diagrams.

This paper investigates the nonlinear behaviors when the switching frequency changed in the UC3842 current-mode controlled flyback converter. First, State equations of the flyback converter are derived. The converter in continuous conduction mode (CCM) is then considered. Second, MATLAB/SIMULINK is used to construct simulation of the converter whose operation under varying switching frequency parameters. The output voltage, inductor current waveforms and phase-plane portraits are observed. It can be seen that the system exhibits nonlinear dynamics from period-one operation through period-doubling to chaos phenomena as the switching frequency are changed. From the simulation results, it is clearly evident that SIMULINK provide consistent results and verify the mathematical models and able to really understand the nonlinear behavior of the flyback converter.

The subtractive clustering algorithm (SC) is a popular method for data clustering. But the radius of each cluster is an important factor which affects the performances of clustering results. This paper proposes an objective function for the genetic algorithm to estimate the optimal value of this parameter. Two experiments show that the proposed method can automatically obtain this parameter for the subtractive clustering algorithm.

The feasibility of a real-time electrocardiogram (ECG) transmission via satellite phone from Mount Everest to determine a climber’s suitability for continued ascent was examined. Four Taiwanese climbers were enrolled in the 2009 Mount Everest summit program. Physiological measurements were taken at base camp (5300 m), camp 2 (6400 m), camp 3 (7100 m), and camp 4 (7950 m) 1 hour after arrival and following a 10 minute rest period. A total of 3 out of 4 climbers were able to summit Mount Everest successfully. Overall, ECG and global positioning system (GPS) coordinates of climbers were transmitted in real-time via satellite phone successfully from base camp, camp 2, camp 3, and camp 4. At each camp, Resting Heart Rate (RHR) was transmitted and recorded: base camp (54–113 bpm), camp 2 (94–130 bpm), camp 3 (98–115 bpm), and camp 4 (93–111 bpm). Real-time ECG and GPS coordinate transmission via satellite phone is feasible for climbers on Mount Everest. Real-time RHR data can be used to evaluate a climber’s physiological capacity to continue an ascent and to summit.

Objective To evaluate the risk and protective factors for functional decline of veteran home residents in Taiwan to develop disability prevention programs in long term care settings. Design A prospective cohort study. Setting Two veteran homes in Taiwan. Participants A total of 1045 residents of veteran homes. Intervention None. Measurements Minimum data set (MDS), resident assessment protocols (RAP), MDS-COGS, Resource Utilization Group-III Activities of Daily Living scale, MDS Social engagement scale, MDS Pain scale. Results A total of 1045 (83.0 ± 5.1 years, all men) residents completed the study, and 11.3% of them developed significant functional decline during the study period. Participants with long-term institutionalization history (odds ratio [OR] 2.966, confidence interval [CI] 1.270–6.927), underlying cerebrovascular disease (OR 4.432, CI 1.994–9.852) and dementia (OR 2.150, CI 1.029–4.504), and higher sum of RAP triggers (OR 1.366, CI 1.230–1.517) were more likely to develop functional decline, whereas those who had better social engagement were significantly protective from functional decline (OR 0.528, CI 0.399–0.697). Conclusions Cerebrovascular disease, dementia, higher sum of RAP triggers, and long-term institutionalization were independent risk factors for functional decline, whereas better social engagement had a protective effect on functional dependence. Further study is needed to develop an integrated disability prevention program based on the discoveries of this study.

This paper proposes related research of the nonlinear dynamic behaviors in voltage-mode controlled single-phase half-bridge inverters. The system exhibits nonlinear dynamic behaviors from period-l operation through period-doubling bifurcations to chaos state using the proportional gain changeable, variable DC input voltage and load resistance. First, a mathematical model of single-phase half-bridge inverter is derived. Then, SIMULINK software tools are used to construct models, simulate results and verify the nonlinear dynamic behaviors in this inverter.

With wireless communication network and GPS positioning technology, most of current existing systems using IP protocol to provide various information services such as traffic and navigation. In this paper, we propose a location-based system with WAVE/DSRC present a low delay and high mobility system architecture constructed on the road. It provides vehicles with more intelligent real time information such as news and location-based contents. We also implement the system, which shows location-based content in the service area.

A novel low-complexity scalable architecture for dual basis multiplications over GF(2m) is proposed in this paper. This multiplier architecture is derived from the Hankel matrix-vector representation of dual basis multiplication, and is feasible for the finite fields generated by irreducible trinomials. By appropriately selecting its digit size, the proposed scalable architecture can achieve a satisfactory trade-off between hardware complexity and throughput performance for implementing ECC cryptosystems such as ECDSA in resource-constrained environments such as embedded systems and smart cards. Analytical results exhibit that the space complexity of the proposed multiplier architecture is substantially lower than that of the non-scalable architectures. Besides, owing to its features of regularity, modularity and concurrency, the proposed architecture is highly feasible for VLSI implementations.

Based on the characteristics of electric vehicles (EV), this paper proposes a conceptual framework utilizing the Controller Area Network (CAN) bus technology, OBD-II connector and the existing telematics service systems to construct a novel telematics gateway for EV. Vehicle gateway interface has been applied to telematics service systems for many years but not to EV. The proposed framework of telematics gateway establishes a three layers' architecture, namely, the physical layer, data link layer, and application layer. The telematics gateway needs to resolve two issues: One is to simplify the framework of EV gateway interface and the other is to ensure the application scenarios for power saving and location based service (LBS). Using the framework of telematics gateway, the entire service network can meet different application scenarios for EV.

This paper proposes study of the nonlinear dynamic behaviors in voltage-mode controlled single-phase half-bridge inverters. The system exhibits nonlinear dynamic behaviors from period-1 operation through period-doubling bifurcations to chaos state using the proportional gain changeable. First, a mathematical model of single-phase half-bridge inverter is derived. Then, SIMULINK and IsSpice software tools are used to construct models, simulate results and verify the nonlinear dynamic behaviors in this inverter

A Novel architecture of low-complexity digit serial GF(2m) multiplier using dual basis representation is proposed in this paper. The architecture of digit serial multiplier is suitable for large word lengths such as those found in cryptographic applications and error correction codes. Digit serial multipliers can give a better trade-off between area and latency in comparison with bit-parallel realization which is costly, and bit-serial realization which is slower. The proposed multiplier is based on an irreducible trinomial and a look-ahead technique that performs the algorithm to calculate the extra elements of the operand represented in the dual basis multiplication process, and is formed by only one cell of tree structure in the MSD (most significant digit) first scheme. Compare to existing architectures, the results reveal that the new multiplier evidently have lower complexity of area and latency.

This paper proposes a robust validity index for Fuzzy c-Means (FCM) algorithm. The Fuzzy c-Means algorithm has become of most widely used method in fuzzy clustering. After clustering, it is often necessary to evaluate its results. Such assessment techniques are called cluster validity. The disadvantage of FCM is that the number of clusters must be predetermined. Even if the number of clusters is given, the clustering results of these algorithms are influenced by the choice of initial cluster centers. In this paper, a new cluster validity index is proposed to evaluate the fitness of clusters obtained by FCM. The example shows the result of proposed index have good performances than other cluster validities.

The theory of regions has been recognized as the best control policy for obtaining a maximally permissive controller in existing literature. All legal and live maximal behavior of the Petri nets model can be held by using the marking/transition-separation instance (MTSI) method. It has been established that the requirement of all sets of MTSIs is a problem in conventional studies stems from time consuming. However, the new crucial marking/transition-separation instance (CMTSIs) allows designers to employ few MTSIs to deal with system deadlocks. The advantage of the proposed policy is that the computational cost can be reduced due to few MTSIs involved in. The paper unveils a maximally permissive controller with the efficient computation can be obtained by our control policy. Experimental results, by varying the markings of given net structures, indicate that the CMTSI method is the most efficient policy among the close related approaches in existing literature.

Power circuit is a voltage-mode controlled single-phase half-bridge inverter. This work investigates nonlinear dynamic behaviors with a valid and novel approach. Firstly, the operational principle of a single-phase half-bridge inverter is examined and mapping state-space equations are generated. Additionally, the SIMULINK tool for mathematical simulation and IsSpice for approximately physical simulation are adopted. The input DC voltage is changed to analyze the nonlinear dynamic behaviors waveforms of the inductor current and output voltage. Simulation results clearly demonstrate that SIMULINK and IsSpice provide consistent results and validate the mathematical models.

Two-dimensional principal component analysis (2D-PCA) which is based on 2D image matrices as opposed to the standard PCA, which is based on 1D vectors, has been first successfully applied to face recognition and has higher accuracy than the latter. It was also successfully applied to other problems such as facial expression recognition, object recognition, etc. later. However, there exists still no neural network learning algorithm for 2D-PCA like those for PCA yet. In this paper, we propose a learning algorithm for 2D-PCA. Requiring no image covariance matrix evaluation and just repeatedly presenting training image samples to the single layer neural network, the desired multiple eigenvectors for 2D-PCA can be learned in the form of weight vectors of generalized linear neurons. It also profit from the parallel architecture of neural network. Simulation experiments are performed on YaleB face database, and the experimental results show that the proposed learning algorithm performs well as expected.

Efficient architecture of low-complexity digit serial GF(2m) multiplier using dual basis representation is proposed in this paper. The architecture of digit serial multiplier is suitable for large word lengths such as those found in cryptographic applications and error correction codes. Digit serial multipliers can give a better trade-off between area and latency in comparison with bit-parallel realization which is costly, and bit-serial realization which is slower. The proposed multiplier is based on an irreducible trinomial and a look-ahead technique that performs the algorithm to calculate the extra elements of the operand represented in the dual basis multiplication process, and is formed by only one cell of tree structure in the MSD (most significant digit) first scheme. Compare to existing architectures, the results reveal that the new multiplier evidently have lower complexity of area and latency.

This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal number of clusters.

In recent years, the increasing demand for security in home care services has led to further improvements in surveillance activities. This paper proposes an efficient approach for motion detecting and tracking in a surveillance system based on the cellular model. The system can monitor human activities and recognize the abnormal events of an elderly man in an indoor environment. The experimental room should be divided into several unit areas. Each unit area can be considered as a simple cell in the 2D cellular model. The state of each cell can be represented by the changeable number of active pixels within corresponding unit area. The approach groups those cells with nonzero status value to represent the target. Then, the centroid of a moving target is computed by its zeroth and first moments. The variation of the centroid can be tracked to recognize abnormal events, such as the fall of an old man or children. The results of the experiments conducted in this study clearly demonstrate that the proposed approach provides a more efficient and effective detecting mechanism.

This paper presents a novel algorithm for type-t Gaussian normal basis (GNB) binary finite field multiplication using Toeplitz matrix-vector representation. It is shown that the GNB multiplication can be realized through block Toeplitz matrix-vector-products. A digit-serial systolic GNB multiplier is proposed where each processing element is comprised of a Toeplitz multiplier and three registers. Analytical results indicate that our proposed architecture has significantly lower area complexity than existing digit-serial multipliers.

People have the need to use a novel consumer clock device for home automation, personal security and convenience. This paper presents the design and implementation of the innovative system. The system architecture which is controlled by Micro-Controller Unit (MCU) includes main alarm clock module, lucky module and security module. In the main alarm clock module, we create a novel hourglass clock model and intelligent alarm clock features based on grey relational analysis. In the lucky module, we design a unique structure of live round according to the medical theory of psychology and hashing function for randomization. In the live round, it includes the fortune of love, career, family, and health. It adopts funny and encouraged voice to transmit messages. The system also provides the function of security using infrared sensors and third-generation (3G) mobile phone unit. The system has been successful and gets the award in Taiwan.

The main circuit is a voltage-controlled single-phase inverter in this paper. The work investigates on nonlinear behaviors of fast-scale instability phenomena with this inverter. First, the operating principle of a single-phase full-bridge inverter is examined. Hence, relational mapping state-space equations are then derived. Additionally, we present the MATLAB/SIMULINK tool for simulation and IsSpice is adopted for approximately physical simulation. The variable load resistance is selected to analyze the fast-scale instability waveforms of the inductor current and output voltage. Simulation results clearly indicate that MATLAB/SIMULINK and IsSpice can provide consistent results and validate the mathematical models of single-phase full-bridge inverter.

This work investigates fast-scale instability phenomena and chaotic control of voltage-controlled single-phase full-bridge inverter via varying load resistance. First, the operational principles of a voltage-controlled single-phase full-bridge inverter are examined and the mapping state-space equations are generated. Additionally, the MATLAB/SIMULINK tool is adopted for experimental simulation in this paper. The load resistance is changed to analyze the fast-scale instability waveforms of the inductor current and output voltage, and we propose the method of time-delay feedback for chaotic control to stable state of period-1. Experimental simulation clearly indicates that MATLAB/SIMULINK can provide available results and validate the mathematical models.

It is evident that effective control over chaos in the forward converter is very important for engineering applications. In this paper, a mathematical model of the forward converter is firstly derived. Secondly, the nonlinear phenomenon in the forward converter is discussed, and then we apply the method of time-delay feedback control over chaos in the forward converter. The validity of this method is confirmed by simulation results.

In this paper, we propose a novel GreyCMAC with robust FCM (RFCM) method for function approximation. The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating. In order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method (RFCM) is proposed to effectively mitigate the influence of noise and outliers and then a GreyCMAC model is used to learn the nonlinear system's features for function approximation. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm (RFCM) is proposed to greatly mitigate the influence of data noise and outliers; and (2) A Grey-based CMAC (GreyCMAC) is proposed to locate a given fine piecewise linear data domain by RFCM so that a neural network can be constructed for function approximation. The conducted experimental results clearly indicate that the proposed approach provides good performance.

This study investigates the instability phenomena of an active power factor corrector (PFC) circuit based on a buck-boost converter. First, the circuit is analyzed, and the state space mathematical model of the converter is derived. Second, MATLAB/SIMULINK and IsSpice are used to construct and simulate the voltage-mode control model of the buck-boost power factor corrector. The intermediate-scale instability phenomena of inductance current waveforms, control voltage, output voltage waveforms and phase-plane portraits are observed by varying the load resistance. These waveforms exhibit intermediate-scale instability phenomena in specific circuit parameters. Furthermore, the two software simulation results are compared, confirming the validity of this mathematical model.

The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating, simple architecture, easily processing and hardware implementation. In the training phase, the disadvantage of some CMAC models with a larger fixed learning rate is the unstable phenomenon. The smaller learning rate would cause slower convergence speed. In the aspect, we propose grey learning rate for training phase. We incorporate the grey relational analysis with the number of training iterations to get an adequate learning rate for better convergence performance. In addition, a serious problem of learning interference reduces learning speed and accuracy. The idea is that the error correcting must be proportional to the inverse of learning times, trained input area and grey relational grade for the addressed hyper cube. A credit apportionment adopts the idea to provide fast and accurate learning effects. This paper proposes a novel learning framework of CMAC for better performance and real-time applications. From the simulation results, it is evident that the proposed algorithm provides more accurate and fast convergence in the early cycles of training phase and also becomes significant in the follow-up cycles.

The back propagation (BP) algorithm for function approximation is multi-layer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter (FDS) is used to partition the nonlinear systempsilas domain into several piecewise linear subspaces to be represented by neural networks. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.