Chwan-Lu Tseng

Stability analysis of linear systems with time delay

This paper presents asymptotic stability criteria for linear systems with time delay. The results not only improve previous results, but also provide a bound for the delay time such that if the system is asymptotically stable when the delay does not exist, it retains the asymptotic stability when the delay time is within the bound. >

A NEW APPROACH FOR IDENTIFYING SLEEP APNEA SYNDROME USING WAVELET TRANSFORM AND NEURAL NETWORKS

This paper describes a new technique to classify and analyze the electroencephalogram (EEG) signal and recognize the EEG signal characteristics of Sleep Apnea Syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The EEG signals are separated into Delta, Theta, Alpha, and Beta spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. We treated the wavelet coefficient as the kind of the training input of artificial neural network, might result in 6 groups of wavelet coefficients per second signal by way of characteristic part processing technique of the artificial neural network designed by our group, we carried out the task of training and recognition of SAS symptoms. Then the neural network was configured to give three outputs to signify the SAS situation of the patient. The recognition threshold for all test signals turned out to have a sensitivity level of approximately 69.64% and a specificity value of approximately 44.44%. In neurology clinics, this study offers a clinical reference value for identifying SAS, and could reduce diagnosis time and improve medical service efficiency.

Robust stability analysis for uncertain delay systems with output feedback controller

A systematic approach is given in this paper for analyzing the robust stability of uncertain time-delay systems controlled by output feedback. By checking the eigenvalues of a Hamiltonian matrix, the stability of nominal systems can be examined first. Then, for the nominally stable uncertain systems with multiple time delays, a new method using structured singular value technique is proposed for finding a set of uncertain parameters within which the systems remain stable. Moreover, an illustrative example is given to show the usefulness of the proposed approach.

Design and implementation of wireless multi-channel EEG recording system and study of EEG clustering method

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patients EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

CoCMA: Energy-Efficient Coverage Control in Cluster-Based Wireless Sensor Networks Using a Memetic Algorithm

Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.

Application of a web-based remote agro-ecological monitoring system for observing spatial distribution and dynamics of Bactrocera dorsalis in fruit orchards

Improving fruit farm profitability through integrated pest management (IPM) programs is always an important issue to modern agriculture systems. In order to enhance IPM programs against Bactrocera dorsalis, an automatic infield monitoring system is required to efficiently capture long-term and up-to-the-minute environmental fluctuations in a fruit farm. In this study, a remote agro-ecological monitoring system built upon wireless sensor networks has been developed to provide precision agriculture (PA) services with large-scale, long-distance, long-term, scalable, and real-time infield data collection capabilities. Historical data with spatial information is available through a web-based decision support program built upon a database. Pest population forecast results are also provided so that farmers and government officials would be able to accurately respond to infield variations. Compared with the previous version of the system, various useful functions have been added into the system, and its accuracy has been improved when measuring different parameters in the field. The system could provide a valuable framework for farmers and pest control officials to analyze the relations between population dynamics of the fruit fly and meteorological events. Based on the analysis, a better insect pest risk assessment and accurate decision-making strategy can be made as an aid to PA against B. dorsalis.

A real-time equipment monitoring and fault detection system

In semiconductor fabrication processes, real-time equipment monitoring and fault detection become critical as most problems reveal themselves first on the equipment performance and much later on the wafer quality. The sooner we can detect the problem, the lower the production loss. The goal of this paper is to present an integrated equipment monitoring approach for a PECVD tool. The approach will include: (1) simultaneous monitoring scheme: a dartboard display of real-time data that provides an easy reading of the equipments overall status, (2) system health index: an index that evaluates the equipments overall health, and (3) analysis functions that include various charting functions, real-time SPC, run-to-run SPC, and other advanced SPC functions. The system has been implemented in TSMC FAB IV for testing. The preliminary results show that the proposed system is an effective tool for real-time monitoring and fault detection.

Design and Implementation of a Single-Stage High-Efficacy LED Driver with Dynamic Voltage Regulation

This paper proposes a single-stage high-efficacy fly back power-factor-correction (PFC) converter with optimization efficiency control for driving multi-string light-emitting diodes (LEDs). The LED driver consists of a fly back converter with PFC mechanism and a constant-current drive circuit with functionalities of efficiency optimization and dimming control. The proposed single-stage LED driver topology features benefits of high power factor and low total harmonic distortion (THD) in low-cost outlay. The pulse width modulation (PWM) dimming mechanism cooperating with the constant-current control circuit completes the LED dimming control. In order to reduce the power dissipations of the dimming circuits, a dynamic voltage regulation (DVR) control is presented to regulate the LED supply voltage by means of the sensing of the drain voltage of the MOSFET in the current controller. While maintaining the desired LED brightness, the DVR technique can minimize the voltage drop of the dimming circuit to enhance the LED driver efficiency further. A 30W white LED driver is devised and a corresponding driver prototype is realized. Testing results are shown experimentally to verify the effectiveness and performance improved of the proposed scheme.

The first order load-balanced algorithm with static fixing scheme for centralized WSN system in outdoor environmental monitoring

The power of nodes in the first layer of a wireless sensor network (WSN) cannot last due to their frequent relay tasks. One possible solution to this disadvantage is to adopt the first order load-balanced algorithm (LBA) to redistribute the loading of the first layer node uniformly. The balanced low-latency convergecast tree (BLLCT) algorithm, a LBA, is generally utilized in route planning in ecological surveillance. Given that the balance between layers does not ensure the balance of the whole WSN, we propose a LBA combining with a static fixing scheme to shift the node groups from the subtree with the largest loading to the one with smaller loading. This procedures repeats until the loading of each subtree is approximately identical. Such a modified LBA successfully overcomes many complicated problems when a WSN is implemented, based on the simulation results using grid and random topologies. In addition, the performance of the static fixing on random node distribution is better than that on the grid node distribution. In this regards, the static fixing is an adequate method to apply to non-uniform loading situations.

Evaluation of network robustness for given defense resource allocation strategies

Since the 9/11 terrorist attacks, the effective and efficient protection of critical information infrastructures has become an even more important issue. To enhance network survivability, a network operator needs to invest a fixed amount of budget and distribute it properly. However, a potential attacker always adjust his attack strategies to compromise a network at minimal cost, if he knows the resource allocation strategy of the network operator. In this paper, we first evaluate the survivability of a given network under two different metrics; that is, we assess the minimal attack cost incurred by an attacker. The two survivability metrics are assumed to be the connectivity of at least one given critical origin-destination pair (OD pair) and that of all given critical OD pairs. We then analyze the problem with two optimization-based models, in which the problem structure is, by nature, a mixed integer programming problem.