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- Title
- Reliable Vehicle-to-Vehicle Weighted Localization in Vehicular Networks.
- Creator
- Altoaimy, Lina, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Vehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and...
Show moreVehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and improve their driving experience. However, the efficiency of these applications depends on the availability of vehicles real-time location information. A number of methods have been proposed to fulfill this requirement. However, designing a V2V-based localization method is challenged by the high mobility and dynamic topology of VANET and the interference noise due to objects and buildings. Currently, vehicle localization is based on GPS technology, which is not always reliable. Therefore, utilizing V2V communication in VANET can enhance the GPS positioning. With V2V-based localization, vehicles can determine their locations by exchanging mobility data among neighboring vehicles. In this research work, we address the above challenges and design a realistic V2V-based localization method that extends the centroid localization (CL) by assigning a weight value to each neighboring vehicle. This weight value is obtained using a weighting function that utilizes the following factors: 1) link quality distance between the neighboring vehicles 2) heading information and 3) map information. We also use fuzzy logic to model neighboring vehicles' weight values. Due to the sensitivity and importance of the exchanged information, it is very critical to ensure its integrity and reliability. Therefore, in this work, we present the design and the integration of a mobility data verification component into the proposed localization method, so that only verified data from trusted neighboring vehicles are considered. We also use subjective logic to design a trust management system to evaluate the trustworthiness of neighboring vehicles based on the formulated subjective opinions. Extensive experimental work is conducted using simulation programs to evaluate the performance of the proposed methods. The results show improvement on the location accuracy for varying vehicle densities and transmission ranges as well as in the presence of malicious/untrusted neighboring vehicles.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004564, http://purl.flvc.org/fau/fd/FA00004564
- Subject Headings
- Vehicular ad hoc networks (Computer networks)--Mathematical models., Computer communication systems., Wireless communication systems., Routing (Computer network management), Intelligent transportation systems., Intelligent control systems.
- Format
- Document (PDF)
- Title
- DEEP MAXOUT NETWORKS FOR CLASSIFICATION PROBLEMS ACROSS MULTIPLE DOMAINS.
- Creator
- Castaneda, Gabriel, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Machine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its...
Show moreMachine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its computational efficiency, and its ability to train and converge after multiple iterations of training epochs. The selection of an activation function is critical to building and training an effective and efficient neural network. In real-world applications of deep neural networks, the activation function is a hyperparameter. We have observed a lack of consensus on how to select a good activation function for a deep neural network, and that a specific function may not be suitable for all domain-specific applications.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013362
- Subject Headings
- Classification, Machine learning--Technique, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Enhancement of Deep Neural Networks and Their Application to Text Mining.
- Creator
- Prusa, Joseph Daniel, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Many current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level...
Show moreMany current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level concepts from text, such as those found in many applications of text classi- cation, is a challenging task due to the many challenges associated with text mining and classi cation. Recently, classi ers trained using arti cial neural networks have been shown to be e ective for a variety of text mining tasks. Convolutional neural networks have been trained to classify text from character-level input, automatically learn high-level abstract representations and avoiding the need for human engineered features. This dissertation proposes two new techniques for character-level learning, log(m) character embedding and convolutional window classi cation. Log(m) embedding is a new character-vector representation for text data that is more compact and memory e cient than previous embedding vectors. Convolutional window classi cation is a technique for classifying long documents, i.e. documents with lengths exceeding the input dimension of the neural network. Additionally, we investigate the performance of convolutional neural networks combined with long short-term memory networks, explore how document length impacts classi cation performance and compare performance of neural networks against non-neural network-based learners in text classi cation tasks.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00005959
- Subject Headings
- Text Mining, Neural networks (Computer science), Machine learning
- Format
- Document (PDF)
- Title
- Adaptive Routing Protocols for VANET.
- Creator
- Skiles, Joanne, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A Vehicular Ad-hoc Network (VANET) is a wireless ad-hoc network that provides communications among vehicles with on-board units and between vehicles and nearby roadside units. The success of a VANET relies on the ability of a routing protocol to ful ll the throughput and delivery requirements of any applications operating on the network. Currently, most of the proposed VANET routing protocols focus on urban or highway environments. This dissertation addresses the need for an adaptive routing...
Show moreA Vehicular Ad-hoc Network (VANET) is a wireless ad-hoc network that provides communications among vehicles with on-board units and between vehicles and nearby roadside units. The success of a VANET relies on the ability of a routing protocol to ful ll the throughput and delivery requirements of any applications operating on the network. Currently, most of the proposed VANET routing protocols focus on urban or highway environments. This dissertation addresses the need for an adaptive routing protocol in VANETs which is able to tolerate low and high-density network tra c with little throughput and delay variation. This dissertation proposes three Geographic Ad-hoc On-Demand Distance Vector (GEOADV) protocols. These three GEOADV routing protocols are designed to address the lack of exibility and adaptability in current VANET routing protocols. The rst protocol, GEOADV, is a hybrid geographic routing protocol. The second protocol, GEOADV-P, enhances GEOADV by introducing predictive features. The third protocol, GEOADV-PF improves optimal route selection by utilizing fuzzy logic in addition to GEOADV-P's predictive capabilities. To prove that GEOADV and GEOADV-P are adaptive their performance is demonstrated by both urban and highway simulations. When compared to existing routing protocols, GEOADV and GEOADV-P lead to less average delay and a higher average delivery ratio in various scenarios. These advantages allow GEOADV- P to outperform other routing protocols in low-density networks and prove itself to be an adaptive routing protocol in a VANET environment. GEOADV-PF is introduced to improve GEOADV and GEOADV-P performance in sparser networks. The introduction of fuzzy systems can help with the intrinsic demands for exibility and adaptability necessary for VANETs. An investigation into the impact adaptive beaconing has on the GEOADV protocol is conducted. GEOADV enhanced with an adaptive beacon method is compared against GEOADV with three xed beacon rates. Our simulation results show that the adaptive beaconing scheme is able to reduce routing overhead, increase the average delivery ratio, and decrease the average delay.
Show less - Date Issued
- 2017
- PURL
- http://purl.flvc.org/fau/fd/FA00004926, http://purl.flvc.org/fau/fd/FA00004926
- Subject Headings
- Vehicular ad hoc networks (Computer networks)--Design and construction., Routing protocols (Computer network protocols), Wireless sensor networks., Computer algorithms., Mobile computing., Mobile communication systems--Technological innovations., Wireless communication systems--Technological innovations., Intelligent transportation systems--Mathematical models.
- Format
- Document (PDF)
- Title
- Parallel Distributed Deep Learning on Cluster Computers.
- Creator
- Kennedy, Robert Kwan Lee, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Deep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces...
Show moreDeep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces many challenges ranging from distributed optimizers to the large communication overhead speci c to a system with o the shelf networking components. In this thesis, we present a novel synchronous data parallel distributed Deep Learning implementation on HPCC Systems, a cluster computer system. We discuss research that has been conducted on the distribution and parallelization of Deep Learning, as well as the concerns relating to cluster environments. Additionally, we provide case studies that evaluate and validate our implementation.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013080
- Subject Headings
- Deep learning., Neural networks (Computer science)., Artificial intelligence., Machine learning.
- Format
- Document (PDF)
- Title
- Design and implementation of a wireless ad hoc network.
- Creator
- Neelakanta, Mahesh., Florida Atlantic University, Hsu, Sam, Ilyas, Mohammad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This thesis addresses issues faced in the practical implementation of a wireless ad hoc network (WAHN) protocol for data transmission. This study focuses on: (1) Evaluating existing hardware and software options available for the WAHN implementation. (2) Appraising the issues faced while implementing a practical wireless ad hoc protocol. (3) Applying a set of MAC protocol specifications developed for a wireless ad hoc data network to a practical test network. Specific to the above topics of...
Show moreThis thesis addresses issues faced in the practical implementation of a wireless ad hoc network (WAHN) protocol for data transmission. This study focuses on: (1) Evaluating existing hardware and software options available for the WAHN implementation. (2) Appraising the issues faced while implementing a practical wireless ad hoc protocol. (3) Applying a set of MAC protocol specifications developed for a wireless ad hoc data network to a practical test network. Specific to the above topics of interest, the following research tasks are performed: (1) An elaborate survey and relevant discussions on wireless MAC protocols. (2) A comprehensive study comparing various wireless transceivers is performed. Range, data rate, frequency, interfacing method and cost are the factors compared. (3) A simple, low-cost and low baud-rate transceiver is modified with appropriate interface circuits to support wireless communications. A more advanced transceiver is also considered and used for the software foundation of a practical implementation of the ad hoc and MAC protocols. The studies enable assessing the problems faced during the implementation and suggest solutions to resolve these problems. Further areas for study are also discussed.
Show less - Date Issued
- 1998
- PURL
- http://purl.flvc.org/fcla/dt/15615
- Subject Headings
- Wireless communication systems, Data transmission systems, Computer networks
- Format
- Document (PDF)
- Title
- Design of a signaling gateway to interoperate H.323 and SIP protocols.
- Creator
- Akbarian, Hamid., Florida Atlantic University, Hsu, Sam
- Abstract/Description
-
This thesis proposes a design for a signaling gateway to address the issues of interoperability between H.323 and SIP. The two IP Telephony standards currently compete for the dominance of IP telephony protocols. We investigate and study these two protocols in terms of interoperability. The question central to this thesis is the issue of interoperability between H.323 and SIP. A signaling gateway model is proposed and designed to address and solve this issue. The proposed model includes Call...
Show moreThis thesis proposes a design for a signaling gateway to address the issues of interoperability between H.323 and SIP. The two IP Telephony standards currently compete for the dominance of IP telephony protocols. We investigate and study these two protocols in terms of interoperability. The question central to this thesis is the issue of interoperability between H.323 and SIP. A signaling gateway model is proposed and designed to address and solve this issue. The proposed model includes Call Initialization, Call Setup and Control, Capability Exchange and Call Termination, which are the four fundamental features supported by H.323 and SIP for establishing calls. Furthermore, we design the four internal components, which are common to the four main functions mentioned above. These four components are type checking, decomposition, conversion and reformatting. In addition, we illustrate in six different cases the functionality of the proposed signaling gateway for establishing calls between H.323 and SIP. These six cases also demonstrate the conversion capability of the proposed signaling gateway during a call between H.323 and SIP endpoints.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12784
- Subject Headings
- Internet telephony, Computer network protocols, Packet switching (Data transmission)
- Format
- Document (PDF)
- Title
- Neural network system for operations management.
- Creator
- Ezziane, Zoheir Hocine., Florida Atlantic University, Mazouz, Abdel Kader, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
Distribution centers and warehouses are becoming more and more dependent on advanced computer technologies to establish and maintain competitiveness in a global economy. Neural network represent a new technology with a wide scope of potential warehouses applications, ranging from planning and forecasting to overall performance. In this dissertation, numerous results are showing increases in warehouse performance, when using neural network technology. The neural network system is used as a...
Show moreDistribution centers and warehouses are becoming more and more dependent on advanced computer technologies to establish and maintain competitiveness in a global economy. Neural network represent a new technology with a wide scope of potential warehouses applications, ranging from planning and forecasting to overall performance. In this dissertation, numerous results are showing increases in warehouse performance, when using neural network technology. The neural network system is used as a forecasting tool. It is then compared to time series forecasting analysis. The comparison process is designed to increase the warehouse performance understudy. At the end of this process, the results are forecasting variables needed to eventually increase warehouse performance and efficiency. Initially, neural networks along with time series are used to make the forecast on inventory control. Then the following step is to let different neural network modules perform the forecasting analysis on other management operations like inventory adjustments, accuracy and turnover, customer complaints and labor productivity for any distribution center or warehouse. The concept of benchmarking is also used, in order to provide tools which will help warehouse management determining performance levels for each subcomponent of the warehouse operations, and consequently the overall performance of the warehouse or distributor center taken into consideration after feeding in the appropriate data to the system.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12369
- Subject Headings
- Operations research, Warehouses, Neural networks (Computer science)--Industrial applications
- Format
- Document (PDF)
- Title
- PRGMDH algorithm for neural network development and its applications.
- Creator
- Tangadpelli, Chetan., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm...
Show moreThe existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm provides visualization of the network displaying all the neurons in the network. The algorithm is general enough that it will accept any number of inputs and any sized training set. To show the flexibility of the Pruning based Regenerated Network, this algorithm is used to analyze different combinations of drugs and determine which pathways in these networks interact and determine the combination of drugs that take advantage of these interactions to maximize a desired effect on genes.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13397
- Subject Headings
- Neural networks (Computer science), GMDH algorithms, Pattern recognition systems
- Format
- Document (PDF)
- Title
- Performance analysis of back propagation algorithm using artificial neural networks.
- Creator
- Malladi, Sasikanth., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Backpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and...
Show moreBackpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and discusses the results obtained by other researchers. A series of test cases are then developed and run to perform the performance analysis of the backpropagation algorithm. As the performance of the networks depends strongly on the inputs, the effect of variation of the design parameters for the networks are evaluated and discussed.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12961
- Subject Headings
- Back propagation (Artificial intelligence), Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Performance evaluation of Bluetooth(TM) network in the presence of self-interference.
- Creator
- Gandhi, Viraf Rusi., Florida Atlantic University, Pandya, Abhijit S.
- Abstract/Description
-
Wireless personal area networks (WPAN) are becoming more and more popular for use by mobile professionals in areas like airports, hotels, or convention centers. The demand for wireless networks is expected to undergo an explosive growth as Bluetooth(TM) capable devices become more and more popular. In such a scenario, it is imperative that designer are aware of the performance characteristics of several Bluetooth(TM) networks operating within the same area. There are several issues that need...
Show moreWireless personal area networks (WPAN) are becoming more and more popular for use by mobile professionals in areas like airports, hotels, or convention centers. The demand for wireless networks is expected to undergo an explosive growth as Bluetooth(TM) capable devices become more and more popular. In such a scenario, it is imperative that designer are aware of the performance characteristics of several Bluetooth(TM) networks operating within the same area. There are several issues that need consideration like security, self-interference and adjacent network interference. The objective of this research is to evaluate the performance of a Bluetooth(TM) network in the presence of self-interference which included adjacent and co-channel interference from neighboring Bluetooth(TM) networks. Specific to the above topics of interest, the following research tasks are performed: (1) The magnitude of self-interference problem in Bluetooth(TM) networks. (2) The system throughput is evaluated by varying duty cycles of the various networks. (3) The pathloss difference is measured between the desired and the interfering device.
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12679
- Subject Headings
- Bluetooth technology, Telecommunication--Equipment and supplies, Computer network protocols
- Format
- Document (PDF)
- Title
- Path selection through a three-stage switching network using neural networks.
- Creator
- Keskiner, Haluk., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Several neural network applications solving practical problems in communications are presented. A neural network algorithm to select paths through a three stage switching network is developed. An analysis of the dynamics of the neural network and a convergence proof are provided. With the help of computer simulations, a four dimensional region for the valid combinations of the neural network parameters was discovered. An analysis is performed to determine the characteristics of this region....
Show moreSeveral neural network applications solving practical problems in communications are presented. A neural network algorithm to select paths through a three stage switching network is developed. An analysis of the dynamics of the neural network and a convergence proof are provided. With the help of computer simulations, a four dimensional region for the valid combinations of the neural network parameters was discovered. An analysis is performed to determine the characteristics of this region. The behavior of the neural network algorithm for different switching network configurations and varying traffic patterns were investigated. The effect of initial state of the neural network and heuristic improvements to the algorithm is provided. A comparative analysis of the neural network path selection algorithm against a sequential search method is also given.
Show less - Date Issued
- 1991
- PURL
- http://purl.flvc.org/fcla/dt/14741
- Subject Headings
- Neural networks (Computer science), Packet switching (Data transmission)
- Format
- Document (PDF)
- Title
- Obstacle avoidance for AUVs.
- Creator
- Gan, (Linda) Huilin., Florida Atlantic University, Ganesan, Krishnamurthy
- Abstract/Description
-
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...
Show moreThis 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.
Show less - Date Issued
- 1997
- PURL
- http://purl.flvc.org/fcla/dt/15451
- Subject Headings
- Submersibles--Automatic control, Fuzzy logic, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Mutual interference in a Bluetooth Scatternet under different environmental conditions.
- Creator
- Godani, Amee Mahendrabhai., Florida Atlantic University, Mahgoub, Imad
- Abstract/Description
-
Wireless Personal Area Networks (WPANs) are becoming popular with mobile professionals. Bluetooth is a Wireless Protocol proposed by the Bluetooth SIG (Special Interest Group). Bluetooth allows a user to create a WPAN just by inserting a chip into a mobile device. Such devices are called Bluetooth enabled devices. The market for Bluetooth enabled devices is going to boom in the coming few years. For this reason, it is important to analyze the performance of a Bluetooth network in proximity to...
Show moreWireless Personal Area Networks (WPANs) are becoming popular with mobile professionals. Bluetooth is a Wireless Protocol proposed by the Bluetooth SIG (Special Interest Group). Bluetooth allows a user to create a WPAN just by inserting a chip into a mobile device. Such devices are called Bluetooth enabled devices. The market for Bluetooth enabled devices is going to boom in the coming few years. For this reason, it is important to analyze the performance of a Bluetooth network in proximity to other Bluetooth networks. Important issues addressed by such analysis include data security and interference caused by other Bluetooth devices and non-Bluetooth devices. The objective of this research is to evaluate the performance of a Bluetooth network in the presence of interference caused by neighboring Bluetooth networks. This is called mutual interference. The simulation is done with consideration of different packet lengths, where packet lengths are distributed geometrically. The research also considers the near-far effect on the throughput of a Bluetooth unit. The effect of environmental conditions on the performance of Bluetooth Scatternet is also examined.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12744
- Subject Headings
- Bluetooth technology, Wireless Application Protocol (Computer network protocol)
- Format
- Document (PDF)
- Title
- Security in voice over IP networks.
- Creator
- Pelaez, Juan C., Florida Atlantic University, Fernandez, Eduardo B., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Voice over IP (VoIP) is revolutionizing the global communications system by allowing human voice and fax information to travel over existing packet data networks along with traditional data packets. The convergence of voice and data in one simplified network brings both benefits and constraints to users. Among the several issues that need to be addressed when deploying this technology, security is one of the most critical. This thesis will present a combination of security patterns based on...
Show moreVoice over IP (VoIP) is revolutionizing the global communications system by allowing human voice and fax information to travel over existing packet data networks along with traditional data packets. The convergence of voice and data in one simplified network brings both benefits and constraints to users. Among the several issues that need to be addressed when deploying this technology, security is one of the most critical. This thesis will present a combination of security patterns based on the systematic analysis of attacks against a VoIP network and the existing techniques to mitigate these attacks, providing good practices for all IP telephony systems. The VoIP Security Patterns which are based on object-oriented modeling, will help network designers to improve the level of security not only in voice but also in data, video, and fax over IP networks.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13190
- Subject Headings
- Internet telephony--Security measures, Computer network protocols, Multimedia systems
- Format
- Document (PDF)
- Title
- Sediment layer tracking using neural networks.
- Creator
- Freyermuth, Vincent Nicolas., Florida Atlantic University, Schock, Steven G.
- Abstract/Description
-
The detection of sediment layer interfaces in normal incidence acoustic reflection data is a requirement for automatic classification and geologic mapping of subsurface layers. The detection is difficult because of the constructive and destructive interference caused by the impedance changes in the sediment column and high scattering noise levels. The purpose of this work is to implement a procedure using neural networks that automatically detects the sediment layers from the envelope of...
Show moreThe detection of sediment layer interfaces in normal incidence acoustic reflection data is a requirement for automatic classification and geologic mapping of subsurface layers. The detection is difficult because of the constructive and destructive interference caused by the impedance changes in the sediment column and high scattering noise levels. The purpose of this work is to implement a procedure using neural networks that automatically detects the sediment layers from the envelope of acoustic reflections. The data was collected using a sub-bottom profiler that transmits a 2 to 10 kHz FM pulse. The detection procedure is a three step method: a first neural network removes most of the reflections due to random scatterers, a second neural network tracks the layers and a third algorithm recognizes the segments of detected layers corresponding to the same sediment interface Applied on different sub-bottom images, the procedure detects more than 80% of the layers correctly.
Show less - Date Issued
- 1998
- PURL
- http://purl.flvc.org/fcla/dt/15561
- Subject Headings
- Neural networks (Computer science), Marine sediments--Acoustic properties
- Format
- Document (PDF)
- Title
- A case study: Performance enhancement of nonlinear combinational optimization problem by neural networks.
- Creator
- Soni, Saurabh., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Artificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution....
Show moreArtificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution. However there are certain factors which result in instability and local optimization of Hopfield Networks. In such cases the solutions obtained may not be optimal and feasible. In this thesis, the application of the K-Means algorithm is combined with the Hopfield Networks to generate more stable and optimum solutions to traveling salesperson problem.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13108
- Subject Headings
- Neural networks (Computer science), Traveling-salesman problem
- Format
- Document (PDF)
- Title
- The human face recognition problem: A solution based on third-order synthetic neural networks and isodensity analysis.
- Creator
- Uwechue, Okechukwu A., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Third-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the...
Show moreThird-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the input pixels. Pattern recognition requires the nonlinear subdivision of the pattern space into subsets representing the objects to be identified. Single-layer neural networks can only perform linear discrimination. However, multilayer first-order networks and high-order neural networks can both achieve this. The most significant advantage of a higher-order net over a traditional multilayer perceptron is that invariances to 2-dimensional geometric transformations can be incorporated into the network and need not be learned through prolonged training with an extensive family of exemplars. It is shown that a third-order network can be used to achieve translation-, scale-, and rotation-invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. A model based on an enhanced version of the Widrow-Hoff training algorithm and a new momentum paradigm are introduced and applied to the complex problem of human face recognition under varying facial expressions. Arguments for the use of isodensity information in the recognition algorithm are put forth and it is shown how the technique of coarse-coding is applied to reduce the memory required for computer simulations. The combination of isodensity information and neural networks for image recognition is described and its merits over other image recognition methods are explained. It is shown that isodensity information coupled with the use of an "adaptive threshold strategy" (ATS) yields a system that is relatively impervious to image contrast noise. The new momentum paradigm produces much faster convergence rates than ordinary momentum and renders the network behaviour independent of its training parameters over a broad range of parameter values.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/12464
- Subject Headings
- Image processing, Face perception, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Virtual leaky bucket for congestion control in high-speed ATM networks.
- Creator
- AlFadhel, Fahad A., Florida Atlantic University, Ilyas, Mohammad
- Abstract/Description
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High speed ATM networks support a variety of communication services, that have different traffic characteristics, which causes the network to be congested quickly. An ATM network with different communication services, data, voice and video, is simulated to study the effect of congestion on network operation. A modified leaky bucket mechanism is used to shape the traffic entering the network, which improved the performance in terms of cell losses and cell delay. The original leaky bucket...
Show moreHigh speed ATM networks support a variety of communication services, that have different traffic characteristics, which causes the network to be congested quickly. An ATM network with different communication services, data, voice and video, is simulated to study the effect of congestion on network operation. A modified leaky bucket mechanism is used to shape the traffic entering the network, which improved the performance in terms of cell losses and cell delay. The original leaky bucket mechanism is so conservative, that it drops a large number of ATM cells. Another scheme called virtual leaky bucket is proposed in this thesis. In this scheme violating cells are marked and then allowed to enter the network. The scheme is simulated and its performance is compared to the leaky bucket mechanism. Shaped virtual leaky bucket with marking is shown to have much better performance as long as the minimum requirements of non-violating cells are guaranteed.
Show less - Date Issued
- 1999
- PURL
- http://purl.flvc.org/fcla/dt/15644
- Subject Headings
- Asynchronous transfer mode, Telecommunication--Traffic, Computer networks
- Format
- Document (PDF)
- Title
- An intelligent system for predicting bridge condition rating.
- Creator
- Thiruppathi, Arulseelan., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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A neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and...
Show moreA neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and the super-structure. Fuzzy logic is used to evaluate the overall condition rating of the bridge using the condition rating of the components. To demonstrate the superiority of the neural network model over the traditional models, the history of the deterioration rates for the components were also considered in the prediction of their future condition. The proposed system is versatile and can be easily extended to include other parameters and updated from time to time without much effort.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/15096
- Subject Headings
- Bridges--Maintenance and repair, Neural networks (Computer science)
- Format
- Document (PDF)
