Shyan-Lung Lin

Parallel solution of sparse algebraic equations

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. >

Personalized information encryption using ECG signals with chaotic functions

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.

A Chaotic Theorectical Approach to ECG-Based Identity Recognition [Application Notes]

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 individuals biometric features under development [1-4].

Individual identification based on chaotic electrocardiogram signals during muscular exercise

An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each persons 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 subjects 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.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

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.

Polar coordinate mapping method for an improved infrared eye-tracking system

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 mouses 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.

A PDA based Wearable System for Real-Time Monitoring of Human Falls

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 users 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 users 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.

OPTIMAL RESPIRATORY CONTROL SIMULATION AND COMPARATIVE STUDY OF HYPERCAPNIC VENTILATORY RESPONSES TO EXTERNAL DEAD SPACE LOADING

There has been considerable research effort regarding ventilatory responses to breathing with an imposed external dead space, and inhalation of fixed levels of CO2 by human subjects. A human respiratory control model incorporating the optimality hypothesis can successfully demonstrate ventilatory responses to both chemical stimuli and muscular exercise. In this study, to verify the model behavior of the optimal chemical–mechanical respiratory control model, we simulated the ventilatory control under dead space loading and CO2 inhalation. The simulation was provided by a LabVIEW® based human respiratory control simulator and signal monitoring system. The dead space measurement was described with two distinct models, derived from Gray and Coon, and predicted behaviors with corresponding ventilatory responses were investigated and compared with experimental findings. While both dead space models produced satisfactory predictions on simulated optimal versus PaCO2, versus PaCO2, F versus PICO2, VT versus PICO2, VD-total versus VT, VD-total/VT versus VT, versus VT and versus VT relationships, Grays model provided better correlation and more consistent results throughout most of the ventilatory responses. The study of relative behavior of respiratory signals and comparative relationship of the ventilator responses between dead space loading during rest and CO2 inhalation will certainly provide valuable understanding of increases in central respiratory motor command output of human respiratory control, which is also associated with Dyspnea on exertion, and give potential clinical perspective to realize the impaired ability to excrete CO2 in patients diagnosed with acute respiratory distress syndrome.

Optimization of the Neural Muscular Drive and Respiratory Signals under Dead Space Loading and CO2 Inhalation

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 CO2 inhalation of 3% to 7%. The waveshaping of both the imposed external dead space and CO2 inhalation on the respiratory waveforms are studied.

The analysis of cardio-respiratory signals and cerebral autoregulation based on CO2 reactivity with healthy subjects and Parkinson's patients

Current paper focus on Parkinsons 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 Parkinsons 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.