Fuzzy logic

Vehicular Ad hoc Networks (VANET) is a wireless ad-hoc network that includes
two types of communications, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure
(V2I). In VANET there are two types of messages. The first type is the event-driven
messages that are only triggered in case of emergency. The second type is the periodical
messages named beacons that are exchanged frequently between vehicles. A
beacon message contains basic information about the sending vehicle such as id, location
and velocity. Beacons are frequently exchanged to increase the cooperative
awareness between vehicles. Increasing beacon frequency helps increasing neighborhood
awareness and improving information accuracy. However, this causes more
congestion in the network, specially when the number of vehicles increases. On the
other hand, reducing beacon frequency alleviates network congestion, but results in
out-dated information.
In this dissertation, we address the aforementioned challenges and propose a
number of smart beaconing protocols and evaluate their performance in di↵erent environments
and network densities. The four adaptive beaconing protocols are designed
to increase the cooperative awareness and information freshness, while alleviating the network congestion. All the proposed protocols take into account the most important
aspects, which are critical to beaconing rate adaptation. These aspects include channel
status, traffic conditions and link quality. The proposed protocols employ fuzzy
logic-based techniques to determine the congestion rank, which is used to adjust beacon
frequency.
The first protocol considers signal to interference-noise ratio (SINR), number
of neighboring nodes and mobility to determine the congestion rank and adjust the
beacon rate accordingly. This protocol works well in sparse conditions and highway
environments. The second protocol works well in sparse conditions and urban environments.
It uses channel busy time (CBT), mobility and packet delivery ratio
(PDR) to determine the congestion rank and adjust the beacon rate. The third protocol
utilizes CBT, SINR, PDR, number of neighbors and mobility as inputs for the
fuzzy logic system to determine the congestion rank and adjust the beacon rate. This
protocol works well in dense conditions in both highway and urban environments.
Through extensive simulation experiments, we established that certain input
parameters are more e↵ective in beacon rate adaptation for certain environments
and conditions. Based on this, we propose a high awareness and channel efficient
scheme that adapts to di↵erent environments and conditions. First, the protocol
estimates the network density using adaptive threshold function. Then, it looks at
the spatial distribution of nodes using the quadrat method to determine whether
the environment is highway or urban. Based on the density conditions and nodes
distribution, the protocol utilizes the appropriate fuzzy input parameters to adapt
the beaconing rate. In addition, the protocol optimizes the performance by adapting
the transmission power based on network density and nodes distribution.
Finally, an investigation of the impact of adaptive beaconing on broadcasting
is conducted. The simulation results confirm that our adaptive beaconing scheme
can improve performance of the broadcast protocols in terms of reachability and bandwidth consumption when compared to a fixed rate scheme.

Vehicular Ad hoc Networks (VANETs) have the potential to enable various
kinds of applications aiming at improving road safety and transportation efficiency.
These applications require uni-cast routing, which remains a significant challenge due
to VANETs characteristics. Given VANET dynamic topology, geographic routing
protocols are considered the most suitable for such network due to their scalability
and low overhead. However, the optimal selection of next-hop nodes in geographic
routing is a challenging problem where the routing performance is highly affected by
the variable link quality and bandwidth availability.
In this dissertation, a number of enhancements to improve geographic routing
reliability in VANETs are proposed. To minimize packet losses, the direction and
link quality of next-hop nodes using the Expected Transmission Count (ETX) are
considered to select links with low loss ratios.
To consider the available bandwidth, a cross-layer enchantment of geographic
routing, which can select more reliable links and quickly react to varying nodes load
and channel conditions, is proposed. We present a novel model of the dynamic behavior of a wireless link. It considers the loss ratio on a link, in addition to transmission
and queuing delays, and it takes into account the physical interference e ect on the
link.
Then, a novel geographic routing protocol based on fuzzy logic systems, which
help in coordinating di erent contradicting metrics, is proposed. Multiple metrics
related to vehicles' position, direction, link quality and achievable throughput are
combined using fuzzy rules in order to select the more reliable next-hop nodes for
packet forwarding.
Finally, we propose a novel link utility aware geographic routing protocol,
which extends the local view of the network topology using two-hop neighbor information.
We present our model of link utility, which measures the usefulness of a
two-hop neighbor link by considering its minimum residual bandwidth and packet
loss rate. The proposed protocol can react appropriately to increased network tra c
and to frequent topology dis-connectivity in VANETs.
To evaluate the performance of the proposed protocols, extensive simulation
experiments are performed using network and urban mobility simulation tools. Results
confirm the advantages of the proposed schemes in increased traffic loads and
network density.

In this dissertation, a fuzzy logic impedance inversion model is developed to classify marine sediments. Expert knowledge and fuzzy decision making constrain the inversion procedures to the resolving ability of the transmitted. The model is validated by comparing the estimated impedance profile with the measured impedance profile. A coherent surface scattering and incoherent volume scattering model are incorporated into a single geoacoustic scattering model that is applied to acoustic subbottom measurements. The reflected signal is modeled as the convolution of the transmitted processed wavelet and the impulse response of the sea bottom. The impedance of the acoustic return is inverted at the layer interfaces and the volume scattering strength is measured between layer interfaces. The model is applied to acoustic subbottom measurements obtained by an X-STAR subbottom profiler sonar system. The inversion techniques are developed for a 2-10 kHz 20 msec swept FM pulse. A fuzzy logic layer tracking procedure identifies the coherent surface scattering layer interfaces in a subbottom profile image. The peak amplitudes and locations are used as fuzzy inputs in the layer tracking rule base. The rule base determines which peak is assigned to the layer when two peaks compete for assignment or which layer is assigned to the peak when two layers compete for assignment. The fuzzy event detection algorithm estimates the impulse response of the acoustic return by complex least squares fitting parts of the transmitted wavelet with sections of the acoustic return. Reflectors are iteratively identified and removed from the return and the residual return is reprocessed. The detection procedure is constrained by the resolving ability of the matching signals and the peak envelope shape of the acoustic return. A genetic algorithm allows up to five low error reflector estimates to be processed until converging on the correct estimated impulse response (the tree branch whose summed error is minimized). The impedance is correlated with sediment bulk density by empirical relation. Experimental results validate that the fuzzy logic impedance inversion model reliably estimates the impedance of the sea bottom. The estimated impedance profiles of fifty acoustic returns are averaged and compared with measured impedance values.

With the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic Controllers (FLCs) is followed, which is then realized in a PC-based environment in the design. An essential issue in this thesis study is the auto tuning of the Fuzzy Logic Controller. An efficient tuning method, mu-law functions, which can adjust both the shape and scaling gain of fuzzy controller's decision table is adopted. Also a search process called Downhill Simplex Search is chosen. Combining these two methods, a Simplex-mu-law auto-tuning algorithm that fits our application is applied to tune the FLC for the laser tracking system. Another issue covered in this research is to modify the Fuzzy Logic Controller structure by changing the distribution of the membership functions. Based on the analysis of the real time error histogram of the system, a novel method is proposed in the thesis for the modification of the membership functions To assess the effectiveness of the methods proposed in this thesis, a prototype laser tracking system is constructed at the FAU Robotics Center. The control strategy proposed in this thesis is tested extensively by simulations and experimentations on the prototype system.

This thesis describes a general three-dimensional Obstacle Avoidance approach for the Autonomous Underwater Vehicle (AUV) using a forward-looking high-frequency active sonar system. This approach takes into account obstacle distance and AUV speed to determine the vehicle's heading, depth and speed. Fuzzy logic has been used to avoid the abrupt turn of the AUV in the presence of obstacles so that the vehicle can maneuver smoothly in the underwater environment. This approach has been implemented as an important part of the overall AUV software system. Using this approach, multiple objects could be differentiated automatically by the program through analyzing the sonar returns. The current vehicle state and the path of navigation of the AUV are self-adjusted depending on the location of the obstacles that are detected. A minimum safety distance is always maintained between the AUV and any object. Extensive testing of the program has been performed using several simulated AUV on-board systems undergoing different types of missions.

In this thesis work, hierarchical control techniques will be used for controlling a robotic manipulator. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control will consist on solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Micro-robot with three degrees of freedom will be used to evaluate this methodology. A decentralized fuzzy controller will be used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint will generate the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller will be compared to a conventional PD controller.

This project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets based membership evaluation rules. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results form the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.

An analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a closed form, suitable for stochastic analysis when the input is an earthquake excitation. A space-time earthquake ground motion model that accounts for both coherent decay and seismic wave propagation is used to specify motions at supports. The proposed technique can be used to obtain lifeline structural response accurately and determine the correlation between any two locations in an effective manner. The computational aspects of its implementation are also discussed. Numerical examples are presented to illustrate the application and efficiency of the proposed analytical scheme.

Response time to a threat or incident for coastline security is an area needing improvement. Currently, the U.S. Coast Guard is tasked with monitoring and responding to threats in coastal and port environments using boats or planes, and SCUBA divers. This can significantly hinder the response time to an incident. A solution to this problem is to use autonomous underwater vehicles (AUVs) to continuously monitor a port. The AUV must be able to navigate the environment without colliding into objects for it to operate effectively. Therefore, an obstacle avoidance system (OAS) is essential to the activity of the AUV. This thesis describes a systematic approach to characterize the OAS performance in terms of environments, obstacles, SONAR configuration and signal processing methods via modeling and simulation. A fuzzy logic based OAS is created using the simulation. Subsequent testing of the OAS demonstrates its effectiveness in unknown environments.

In the last 10 years, due to the rapid developments in computers and Internet, the
Electronic Commerce has advanced significantly. More and more companies have
shifted their businesses activities to the Internet. However, the popular use of ecommerce
has also raised serious security problems.
Therefore developing security schemes has become a key issue both in the academic
as well as industrial research. Since the Internet is open to the public, the associated
security issue is challenging. A good security strategy should not only protect the
vendors' interest, but also enhance the mutual trust between vendors and customers.
As a result, the people will feel more confident in conducting e-commerce.
This thesis is dedicated to develop a fuzzy-logic based trust model. In general, the ecommerce
transactions need costly verification and authentication process. In some
cases, it is not cost effective to verify and authenticate each transaction, especially for transactions involving only small amount of money and for customers having an
excellent transaction history.
In view of this, in this research a model that distinguishes potentially safe transactions
from unsafe transactions is developed. Only those potentially unsafe transactions need
to be verified and authenticated.
The model takes a number of fuzzy variables as inputs. However, this poses problems
in constructing the trust table since the number of fuzzy rules will increase
exponentially as the number of fuzzy variables increase. To make the problem more
trackable, the variables are divided into several groups, two for each table. Each table
will produce a decision on trust. The final decision is made based on the
"intersection" of all these outputs.
Simulation studies have been conducted to validate the effectiveness of the proposed
trust model. Therefore simulations, however, need to be tested in a real business
environment using real data. Relevant limitations on the proposed model are hence
discussed and future research direction is indicated.