We simulate ultrashort pulse propagation through water by numerical methods, which is a kind of optical communication research. Ultrashort pulses have been known to have non Beer-Lambert behavior, whereas continuous waves (CW) obey the Beer-Lambert law. People have expected that the ultrashort pulse loses less intensity for a given distance in water than CW which implies that the pulse can travel over longer distances. In order to understand this characteristic of the pulse, we model numerically its spectral and temporal evolution as a function of traveling distance through water. We achieve the pulse intensity attenuation with traveling distance, obtain the temporal envelope of the pulse and compare them with experimental data. This research proves that the spectral and temporal profile of a pulse can be predicted knowing only the intensity spectrum of the input pulse and the refractive index spectrum of water in the linear regime. The real feasibility and the advantage of using an ultrashort pulse as a communication carrier will also be discussed.

IntelliDrive systems, including inter-vehicle communication and vehicle infrastructure integration, aim to improve safety, mobility, and efficiency of transportation. They build on the wireless ad hoc network technologies, enabling vehicles to communicate with roadside infrastructure and with each other. The process of information propagation in a multi-hop network underlies the system design and efficiency. As of now, the research has been restricted to a single road of traffic. This work expands the study of information propagation to two parallel roads, a step further towards the discrete network case. This thesis presents two methodologies to model the process of information propagation. By identifying an approximate Bernoulli process, we are able to derive the expectation and variance of propagation distance. A road separation distance of square root of 3 over 2 times the transmission range distinguishes two cases for approximating the success probability in the Bernoulli process. In addition, our results take the single road as a special case. The numerical test shows that the developed approximation works well. This work further identities a Markov property for instantaneous information propagation along two parallel roads based on two types of transmission regions. Communication capable vehicles are assumed to follow two homogeneous Poisson processes on both roads. The Markov property enables us to derive exact expectation and variance of the propagation distance and further, obtain a recursive formula for the probability distribution of successful propagation distance. The developed formulas enable numerical calculation of the characteristics of propagation process. We hope this research will shed light on studies of vehicular ad hoc networks on more general discrete roadway networks.

There seems to be more security events happening on the network nowadays, so the administrators have to be able to find the malicious activities in progress as soon as possible in order to launch effective and efficient countermeasures. The Network administrators need to monitor the networks through collecting real time traffic measurement data on their networks, but they might find that the data gathered seems to be too little or too much detail. SNMP-based tools traditionally adopted most often give too little. However, packet sniffing tools investigate too much, so that the performance is sacrificed, especially on a large network with heavy traffic. Flows are defined as a series of packets traveling between the two communicating end hosts. Flow profiling functionality is built into most networking devices today, which efficiently provide the information required to record network and application resource utilization. Flow strikes a balance between detail and summary. NetFlow is the de facto standard in flow profiling. We introduce¡A describe¡Aand investigate its features, advantages, and strengths. Many useful flow-related tools are freely available on the Internet. A mechanism is proposed to make use of the flow logs to monitor the network effectively and efficiently. Through verification, it is believed that using flow logs can benefit the network administrator so much. The administrators can use them for timely monitoring, DoS and worm propagation detection, forensics et al.

In this research, a single PAN-TILT image servo system has been developed with real-time face tracing technology. First, the target face is detected, and then the target template is kept at the image center with the integration of optical flow algorithm and control theory. In motion control, back-propagation neural network is taken to predict and estimate the target position. Experiments are made to analyze the performance of the video servo control system.

The swell waves which are an important component of wind generated waves have significant effects on small craft and fisheries. The swell wave climate has an important role in the design and operation of fishing harbors and harbors for small craft. Despite this fact the swell wave climate is not well known for the Turkish coasts. The purpose of the present study was to identify the swell wave climate along the Aegean and Mediterranean Sea coastline of T&uuml / rkiye. For this purpose wind and swell wave data for a 72 months period is obtained from ECMWF for the analysis. And the data are analyzed for twenty one locations selected along the Turkish coast. For every location the wind and swell wave roses, significant swell wave height versus Mean period of primary swell relations, extreme probability distribution and log-linear cumulative probability distribution are presented. Also some extreme swell events in the Aegean and Mediterranean Sea occurred in the data period are presented for a better understanding of generation and propagation of swell waves. The results showed that the swell wave activity and severity is higher in the Aegean and Mediterranean Sea coastline of T&uuml / rkiye. The investigation of extreme swell events provided that the swell waves occur and diminish in a relatively short duration and the data available from ECMWF which is provided for 12 hour intervals is not sensitive to time enough for the investigation of swell wave occurrence and propagation. The significant swell wave height versus Mean period of primary swell relations and analysis on period of swell waves showed that the swell wave periods could reach up to 12 seconds in the Western and Southern shores of T&uuml / rkiye.

ABSTRACT In this thesis, some aspects of the non-fixed neural network for power load forecasting are discussed. Unlike traditional fixed neural network technique, the structure of neural network is non-fixed during its training and testing phases. Based on the characteristic of the desired forecasting day, the number of input node utilized is changeable. The modified learning algorithms, including fuzzy back-propagation learning algorithm and stochastic back-propagation learning algorithm, will be used in the load forecasters we developed. For precise input selection of the neural network model, the analysis of mutual relationship between load and temperature and gray relational analysis between desired forecasting load and the related previous load are studied. Two types of load forecasting, i.e., peak load forecasting and hourly load forecasting, are investigated. Short term (one-to-several-day-ahead) load forecasting is considered in this research. Hourly loads and relevant temperature data from 1992 to 1998 provided by Taipower Utility and the Central Weather Bureau is implemented for this research. For demonstrating the feasibility and superiority of the forecasters we develop, several forecasting models, including fixed neural network with constant learning rate and momentum, recursive time series model, and artificial neural network short term load forecaster (ANNSTLF) proposed by [Kho.2], are also performed for a comparison. From the results of the simulation, better performances could be obtained by the methods we proposed. Not only the over-training phenomenon is obviously reduced, the forecasting accuracy and the learning speed of the neural model are also effectively improved.

Micro ring-cavity, like the Fabry-Perot cavity, is an optical device that resonates at certain frequencies. It is used as a phase compensator, and filter. Easily fabricated, the micro ring-cavity can be mass-produced, the ring-cavity is becoming evermore important as integrated opto-electronic technology advances. In this thesis, we begin with a novel one-dimensional theory that considers bi-directional traffic in the micro-ring cavity. By separating the device into easily manageable regions, and employing only fundamental modes in each of the sections, we obtain a closed-form formula for the transmission and reflection coefficient of this device. Under certain circumstances, when the directional coupler length is short but its coupling strength is strong, we observed a significant amount of reflection of optical energy at some frequencies. This phenomena is currently unknown to the opto-electronic industry. To further study this, we developed a more rigorous multi-mode propagation method for two-dimensional bi-directional ring cavities. The problem at hand is first being sliced into regions of multi-layered sections. Within each section, we can express the fields in terms of the underlying waveguide modes of the structure. At the interfaces of these sections, we construct coupled integral equations, which are derived from the continuity requirement of the tangential fields. We have complete formulations for both TE and TM cases, down to the coupled matrix equation for the unknown modal coefficients at each junction.

This objective of thesis is to derive the adaptive load shedding by artificial neural network (ANN) so that the amount of load shedding can be minimized. An actual industrial customer and Taipower system are selected for computer simulation to fit the ANN model. The mathematical models of generation, exciters, governors and loads are used in the simulator program. The back propagation neural method is considered for the neural network training of load shedding.To create the training data set for ANN models, the transient stability analysis is performed to fit the load shedding under different operation and fault condition. The back propagation method and L-M learning process are then used to fit the minimum load shedding without causing system stability problem. To verify the effectiveness of the proposed methodology for adaptive load shedding, three fault contingencies for both the industrial cogeneration system and Taipower system have been simulated. By compare to the conventional load shedding, it is found that the amount of load shedding can be minimized and adjusted according to the real time operation conditions of power systems.

Inspired by promotion of commercial applications, support of location-based services to mobile terminals through their current location has been receiving a lot of attention in recent years even though emergency communications is the primary motivation for development of wireless location. A major challenge to wireless location technique is how to balance the implementation complexity and required accuracy. In the first part of this dissertation, we address one of the fundamental problems in wireless location when using the ToA measurements and develop a simple model to estimate the mobile terminal location with low complexity and promising accuracy. The model employs the geometrical transformation method with single propagation delay measurement. The contribution is that the use of geometrical transformation allows us to overcome the location handover problem, i.e., a forcing handover in a GSM (global system for mobile) network or a three-way soft handover in a UMTS (universal mobile telecommunications system) network. By using the proposed location model, the impact on network performance is kept at the minimum level and the complexity and requirements for hardware and software changes are reduced. In the second part of this dissertation, we address one of the fundamental problems in wireless location when using the SS (signal strength) measurements. The first contribution is to develop a novel wireless location technique based on a ¡§differ- encing¡¨ way, called the SSSD (stationary signal-strength-difference), to remove the uncertainty propagation parameters when merging environment-dependent signal propagation model into the location estimation. This is due to the uncertainty in propagation parameters causes a propagation model error that enlarges error in the distance estimation. The performance gained from the preliminary analysis of SSSD location technique, however, is degraded as a result of the large bias error in the estimated distance and distance difference. To achieve the performance enhancement, the second contribution is to correct the bias error in the estimated distance difference by using a correction method based on a geometric constraint condition. With the corrected distance difference, the final contribution is that we generalized the work on correction method and provide a new framework to correct the error in the estimated distance. As the corrected distance and distance difference is derived by LS (least square) computation, respectively, low computation burden and non-iterative solutions were achieved. To the best of our knowledge thus far, this is first such proposal for a correction to the SS-based location technique. It is demonstrated that the proposed error correction method is shown to perform well when encountering the large error in the estimated distance and distance difference, and prove that the location accuracy can be improved considerably.

In the study of sound identification, land animals such as birds and bats have been well investigated, and so are their habitats. On the other hand, sound making creatures in the ocean are much less researched. In this research, the stress calls of three Holocentridaes, Neoniphon sammara, Myripristis murdjan, and Sargocentron spinosissimum, who are commonly found in coral reefs, were recorded in water tank for analysis of sound characteristics. The averaged characteristic parameters of single pulse among three is around 410 Hz for the peak frequency, 100 Hz for the bandwidth, 0.07 dB/Hz for the slope, and duration of 0.05 s. As for the impulse train, averaged peak frequency is 415 Hz, 55 Hz for the bandwidth, 0.07 dB/Hz for the slope, and duration of 0.5 s. These parameters were first checked by the Kolmogorov-Smirnov Test to identify if each parameter follows normal distribution; the slopes of ascending and descending frequency and the total duration are not in normal distribution. The three parameters were later transferred so as to concentrate variances. Next, analysis of variance was applied on all characteristics to extract the significant parameters (including non transferred and transferred data), which were then tested by Stepwise Discriminat and Back-propagation Network. The identification rate of for single pulse with and without data transfer is 63% and 82% while pulse train is 57% and 73%. Both identification rates were raised up approximately 20% due to the data transfer. Both methods provide an reliable tool for marine sound identification, and the whole process of the study may be applied to another biological identification.