Simultaneous blood perfusion and oxygenation monitoring is crucial for patients undergoing a transplant procedure. This becomes of great importance during the surgical recovery period of a transplant procedure when uncorrected loss of perfusion or reduction in oxygen saturation can result in patient death. Pulse oximeters are standard monitoring devices which are used to obtain the perfusion level and oxygen saturation using the optical absorption properties of hemoglobin. However, in cases of varying perfusion due to hemorrhage, blood clot or acute blockage, the oxygenation results obtained from traditional pulse oximeters are erroneous due to a sudden drop in signal strength. The long term goal of the project is to devise an implantable optical sensor which is able to perform better than the traditional pulse oximeters with changing perfusion and function as a local warning for sudden blood perfusion and oxygenation loss. In this work, an optical sensor based on a pulse oximeter with an additional source at 810nm wavelength has been developed for in situ monitoring of transplant organs. An algorithm has been designed to separate perfusion and oxygenation signals from the composite signal obtained from the three source pulse oximetry-based sensor. The algorithm uses 810nm reference signals and an adaptive filtering routine to separate the two signals which occur at the same frequency. The algorithm is initially applied to model data and its effectiveness is further tested using in vitro and in vivo data sets to quantify its ability to separate the signals of interest. The entire process is done in real time in conjunction with the autocorrelation-based time domain technique. This time domain technique uses digital filtering and autocorrelation to extract peak height information and generate an amplitude measurement and has shown to perform better than the traditional fast Fourier transform (FFT) for semi-periodic signals, such as those derived from heart monitoring. In particular, in this paper it is shown that the two approaches produce comparable results for periodic in vitro perfusion signals. However, when used on semi periodic, simulated, perfusion signals and in vivo data generated from an optical perfusion sensor the autocorrelation approach clearly (Standard Error, SE = 0.03) outperforms the FFT-based analysis (Standard Error, SE = 0.62).

With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to acquire desired information from the Web environment due to the information overload problem. To overcome this difficulty, two major methods, information retrieval and information filtering, arise. Recommender systems that employ information filtering techniques also emerge when the users¡¦ requirements are too vague in mind to express explicitly as keywords. Collaborative filtering (CF) refers to compare novel information with common interests shared by a group of people for recommendation purpose. But CF has major problem: sparsity. This problem refers to the situation that the coverage of ratings appears very sparse. With few data available, the user similarity employed in CF becomes unstable and thus unreliable in the recommendation process. Recently, several collaborative filtering variations arise to tackle the sparsity problem. One of them refers to the item-based CF as opposed to the traditional user-based CF. This approach focuses on the correlations of items based on users¡¦ co-rating. Another popular variation is the trust-based CF. In such an approach, a second component, trust, is taken into account and employed in the recommendation process. The objective of this research is thus to propose a hybrid approach that takes both advantages into account for better performance. We propose the item-level trust-based collaborative filtering (ITBCF) approach to alleviate the sparsity problem. We observe that ITBCF outperforms TBCF in every situation we consider. It therefore confirms our conjecture that the item-level trusts that consider neighbors can stabilize derived trust values, and thus improve the performance.

¡@¡@Recommendation techniques are developed to uncover users¡¥ real needs among large volume of information. Recommender systems help us filter information and present those similar to our tastes. As wireless technology develops and mobile devices become more and more powerful, new recommender systems appear to adapt to new implementation environment. We focus on travel recommender systems implemented in a mobile P2P environment using collaborative filtering recommendation algorithms which intend to provide real-time suggestions to travelers when they are on the move. Using the concept of incorporating other travelers¡¥ suggestions to the next attraction, we let users exchange their ratings toward visited attractions and use these ratings as a basis of recommendation. ¡@¡@We proposed six data exchange algorithms for travelers to exchange their ratings. The proposed methods were experimented in the homogeneous and heterogeneous environment. The experimental results show that the proposed data exchange methods have better recommendation hit ratio than content-based recommendation methods and better performance compared with other methods only using ratings of users when they meet face-to-face. Finally, all methods are compared and evaluated. An optimal method should be able to strike a balance between algorithm performance and the amount of data communication.

Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods.

There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications. LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory. The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang¡¦s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.

The field of computer vision focuses on the goal of developing techniques to exploit and extract information from underlying data that may represent images or other multidimensional data. In particular, two well-studied problems in computer vision are the fundamental tasks of 2D image segmentation and 3D pose estimation from a 2D scene. In this thesis, we first introduce two novel methodologies that attempt to independently solve 2D image segmentation and 3D pose estimation separately. Then, by leveraging the advantages of certain techniques from each problem, we couple both tasks in a variational and non-rigid manner through a single energy functional. Thus, the three theoretical components and contributions of this thesis are as follows: Firstly, a new distribution metric for 2D image segmentation is introduced. This is employed within the geometric active contour (GAC) framework. Secondly, a novel particle filtering approach is proposed for the problem of estimating the pose of two point sets that differ by a rigid body transformation. Thirdly, the two techniques of image segmentation and pose estimation are coupled in a single energy functional for a class of 3D rigid objects. After laying the groundwork and presenting these contributions, we then turn to their applicability to real world problems such as visual tracking. In particular, we present an example where we develop a novel tracking scheme for 3-D Laser RADAR imagery. However, we should mention that the proposed contributions are solutions for general imaging problems and therefore can be applied to medical imaging problems such as extracting the prostate from MRI imagery

Recent theoretical and experimental studies have demonstrated that an estimate of the Green's function between two hydrophones can be extracted passively from the cross‐correlation of ambient noise recorded at these two points. Hence monitoring the temporal evolution of these estimated Green's functions can provide a means for noise‐based acoustic tomography using a distributed sensor network. However, obtaining unbiased Green's function estimate requires a sufficiently spatially and temporally diffuse ambient noise field. Broadband ambient noise ([200 Hz-20 kHz]) was recorded continuously for 2 days during the SWAMSI09 experiment (next to Panama City, FL) using two moored vertical line arrays (VLAs) spanning 7.5m of the 20‐m water column and separated by 150 m. The feasibility of noise‐based acoustic tomography ([300-1000 Hz]) was assessed in this dynamic coastal environment over the whole recording period. Furthermore, coherent array processing of the computed ocean noise cross‐correlations between all pairwise combinations of hydrophones was used to separate acoustic variations between the VLAs caused by genuine environmental fluctuations-such as internal waves-from the apparent variations in the same coherent arrivals caused when the ambient noise field becomes strongly directional, e.g., due to an isolated ship passing in the vicinity of the VLAs.

Spam is an increasing menace in email: 90% of email is spam, and over 90% of spam is sent by botnets---networks of compromised computers under the control of miscreants. In this dissertation, we introduce email spam filtering using network-level features of spammers. Network-level features are based on lightweight measurements that can be made in the network, often without processing or storing a message. These features stay relevant for longer periods, are harder for criminals to alter at will (e.g., a bot cannot act independently of other bots in the botnet), and afford the unique opportunity to observe the coordinated behavior of spammers. We find that widely-used IP address-based reputation systems (e.g., IP blacklists) cannot keep up with the threats of spam from previously unseen IP addresses, and from new and stealthy attacks---to thwart IP-based reputation systems, spammers are reconnoitering IP Blacklists and sending spam from hijacked IP address space. Finally, spammers are "gaming" collaborative filtering by users in Web-based email by casting fraudulent "Not Spam" votes on spam email. We present three systems that detect each attack that uses spammer behavior rather than their IP address. First, we present IP blacklist counter-intelligence, a system that can passively enumerate spammers performing IP blacklist reconnaissance. Second, we present SpamTracker, a system that distinguishes spammers from legitimate senders by applying clustering on the set of domains to which email is sent. Third, we analyze vote-gaming attacks in large Web-based email systems that pollutes user feedback on spam emails, and present an efficient clustering-based method to mitigate such attacks.

Future spacecraft missions are trending towards the use of distributed systems or fractionated spacecraft. Initiatives such as DARPA's System F6 are encouraging the satellite community to explore the realm of collaborative spacecraft teams in order to achieve lower cost, lower risk, and greater data value over the conventional monoliths in LEO today. Extensive research has been and is being conducted indicating the advantages of distributed spacecraft systems in terms of both capability and cost. Enabling collaborative behaviors among teams or formations of pico-satellites requires technology development in several subsystem areas including attitude determination and control subsystems, orbit determination and maintenance capabilities, as well as a means to maintain accurate knowledge of team members' position and attitude. All of these technology developments desire improvements (more specifically, decreases) in mass and power requirements in order to fit on pico-satellite platforms such as the CubeSat. In this thesis a solution for the last technology development area aforementioned is presented. Accurate knowledge of each spacecraft's state in a formation, beyond improving collision avoidance, provides a means to best schedule sensor data gathering, thereby increasing power budget efficiency. Our solution is composed of multiple software and hardware components. First, finely-tuned flight system software for the maintaining of state knowledge through equations of motion propagation is developed. Additional software, including an extended Kalman filter implementation, and commercially available hardware components provide a means for on-board determination of both orbit and attitude. Lastly, an inter-satellite communication message structure and protocol enable the updating of position and attitude, as required, among team members. This messaging structure additionally provides a means for payload sensor and telemetry data sharing. In order to satisfy the needs of many different missions, the software has the flexibility to vary the limits of accuracy on the knowledge of team member position, velocity, and attitude. Such flexibility provides power savings for simpler applications while still enabling missions with the need of finer accuracy knowledge of the distributed team's state. Simulation results are presented indicating the accuracy and efficiency of formation structure knowledge through incorporation of the described solution. More importantly, results indicate the collaborative module's ability to maintain formation knowledge within bounds prescribed by a user. Simulation has included hardware-in-the-loop setups utilizing an S-band transceiver. Two "satellites" (computers setup with S-band transceivers and running the software components of the collaborative module) are provided GPS inputs comparable to the outputs provided from commercial hardware; this partial hardware-in-the-loop setup demonstrates the overall capabilities of the collaborative module. Details on each component of the module are provided. Although the module is designed with the 3U CubeSat framework as the initial demonstration platform, it is easily extendable onto other small satellite platforms. By using this collaborative module as a base, future work can build upon it with attitude control, orbit or formation control, and additional capabilities with the end goal of achieving autonomous clusters of small spacecraft.

Structural health monitoring (SHM) is an active research area devoted to the assessment of the structural integrity of critical components of aerospace, civil and mechanical systems. Guided wave methods have been proposed for SHM of plate-like structures using permanently attached piezoelectric transducers, which generate and sense waves to evaluate the presence of damage. Effective interrogation of structural health is often facilitated by sensors and actuators with the ability to perform directional scanning. In this research, the novel class of Frequency Steerable Acoustic Transducers (FSATs) is proposed for directional generation/sensing of guided waves. The FSATs are characterized by a spatial arrangement of the piezoelectric material which leads to frequency-dependent directionality. The resulting FSATs can be employed both for directional sensing and generation of guided waves, without relying on phasing and control of a large number of channels. Because there is no need for individual control of transducer elements, hardware and power requirements are drastically reduced so that cost and hardware limitations of traditional phased arrays can be partially overcome. The FSATs can be also good candidates for remote sensing and actuation applications, due to their hardware simplicity and robustness. Validation of the proposed concepts first employs numerical methods. Next, the prototyping of the FSATs allows an experimental investigation confirming the analytical and numerical predictions. Imaging algorithm based on frequency warping is also proposed to enhance results representation.