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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / Constrained codes also known as modulation codes are a key component in the digital magnetic recording systems. The constrained codes forbid particular input data patterns which lead to some of the dominant error events or higher media noise. In data recording systems, a concatenated approach toward the constrained code and error-correcting code (ECC) is typically used and the decoding is done independently. In this paper, we show the improvement in combining the decoding of the constrained code and the ECC using generalized belief propagation (GBP) algorithm. We consider the performance of a combined modulation constraints and the ECC on a binary symmetric channel (BSC). We show that combining demodulation and decoding results in a superior performance compared to concatenated schemes. Furthermore, we compute the capacity of the joint ECC and modulation codes for 1-D and 2-D constraints.

Την τελευταία δεκαετία έχει παρατηρηθεί μια σημαντική διάδοση των συστημάτων πλοήγησης οχημάτων. Τα συστήματα αυτά συνδυάζοντας τις δυνατότητες που προσφέρει η τεχνολογία και χρησιμοποιώντας τη γεωγραφική αναπαράσταση του οδικού δικτύου, την τρέχουσα θέση του οχήματος και συχνά πληροφορίες για την κίνηση προτείνουν στους οδηγούς τη διαδρομή που πρέπει να ακολουθήσουν για να φτάσουν πιο γρήγορα στον προορισμό τους. Οι εφαρμογές πλοήγησης οχημάτων μπορούν να προσφέρουν τη δυνατότητα διαχείρισης της κυκλοφορίας με τέτοιο τρόπο που επιτρέπει την αύξηση της χωρητικότητας του οδικού δικτύου και επομένως τη μείωση της συμφόρησης, χωρίς να είναι απαραίτητη η υψηλού κόστους επέκταση της οδικής υποδομής. Σε αντίθεση με το βέλτιστο για το χρήστη μοντέλο που εφαρμόζεται στα κλασικά συστήματα πλοήγησης και που δεν παρέχει καμία εγγύηση βελτίωσης της κυκλοφοριακής κατάστασης, για το σκοπό αυτό έχει προταθεί το βέλτιστο για το σύστημα μοντέλο. Το μοντέλο προτείνει διαδρομές με στόχο τη βελτίωση της κυκλοφοριακής κατάστασης στο δίκτυο, αλλά η εφαρμογή του είναι μη ρεαλιστική καθώς οι προτεινόμενες διαδρομές μπορεί να είναι πολύ μακρύτερες από το αναμενόμενο. Στην παρούσα διπλωματική εργασία μελετάται μια τρίτη προσέγγιση: ένας βέλτιστος για το σύστημα υπό περιορισμούς αλγόριθμος. Πρόκειται για ένα συνδυασμό των δύο μοντέλων πλοήγησης με σκοπό τη μείωση της συμφόρησης και ταυτόχρονα τη διατήρηση της δικαιοσύνης στην επιλογή των προτεινόμενων διαδρομών για τους οδηγούς. Αφού γίνει θεωρητική μελέτη του προβλήματος παρουσιάζεται η υλοποίηση ενός συστήματος πρότασης διαδρομών που χρησιμοποιεί το βέλτιστο για το σύστημα υπό περιορισμούς αλγόριθμο. / During the last decade, vehicles' route guidance systems have known a significant spread. These systems, taking advantage of the available technological features and by using the geographical representation of the road network, the current position of a vehicle and often traffic data, propose to drivers the route they should follow in order to reach faster their destination. The applications of route guidance systems offer the chance to manage traffic in such a way that allows an increase in road network capacity and therefore a decrease in traffic congestion, without being necessary the high cost expansion of the road infrastructure. In contrast to the user optimal model that is followed by typical route guidance systems and provides no traffic improvement guarantees, the system optimal model has been proposed for this purpose. The model proposes paths with the goal of improving the traffic condition of the network, however its application is unrealistic since the proposed routes may be much longer than expected. In this thesis a third approach is studied: a constrained system optimal algorithm. The algorithm is a combination of the two navigation models with the goal of reducing congestion and at the same time remaining fair for drivers when selecting a route. After the theoretical study of the problem, the implementation of a route recommendation system that incorporates the constrained system optimal algorithm is presented.

This dissertation examines a profit-maximizing project selection and scheduling problem. Assume that a set of potentially profitable projects are available, yet limited available resources may not allow all of them to be pursued. Profit profiles for projects are assumed to be non-increasing functions of project completion times, i.e. profit returns are sensitive to time-to-market. Decision needs to be made on which sub-set of projects should be chosen and how resources should be allocated to these projects such that the total profit is maximized.Formal mathematical models are formulated for various versions of the problem, including such ones incorporating a third team formation aspect. Structure of the problem is examined and insights are gained regarding prioritization of project, specifically. Although prioritization is sub-optimal in general, heuristic solution methods based on prioritization are pursued, since the scheduling sub-problem itself is NP-hard.A decomposition heuristic framework is first proposed to obtain good solutions using minimum computational time. Sets of test instances are generated using project network data from well-known source in the literature. Computational runs reveal that three priority rules achieve significantly better profits than the benchmarking random priority rule.Improving upon the prioritization based decomposition heuristic, an implicit enumeration is proposed. This algorithm does not examine all priority sequences, yet guarantees an optimal priority sequence when the computation is completed. Several fathoming rules are proposed to cut back computational time effectively. Comparison to the profits achieved by the best priority rule and the benchmarking random priority rule shows a significant improvement on profits, yet at a cost of reasonable added computation time.Future research areas include identifying general conditions under which prioritization of projects would lead us to an optimal solution. Developing better upper bounds for the implicit enumeration scheme is also of interest. The team formation aspect has yet to be treated computationally. It would also be of interest to consider how synergy deviation information may be fed back to the earlier stages of project selection and scheduling decision. Trade-off between profit and team synergy may also be considered in the future.

Time-optimal solutions provide us with the fastest means to regulate a system in presence of input constraints. This advantage of time-optimal control solutions is offset by the fact that their real-time implementation involves computationally intensive iterative techniques. Moreover, time-optimal controls depend on the initial state and have to be recalculated for even the slightest perturbation. Clearly time-optimal controls are not good candidates for online regulation. Consequently, the search for alternatives to time-optimal solutions is a very active area of research. The work described here is inspired by the simplicity of optimal-aim concept. The "optimal-aim strategies" provide online regulation in presence of bounded inputs with minimal computational effort. These are based purely on state-space geometry of the plant and are inherently adaptive in nature. Optimal-aim techniques involve aiming of trajectory derivative (or the state velocity vector) so as to approach the equilibrium state in the best possible manner. This thesis documents the efforts to develop an online regulation algorithm for systems with input constraints. Through a number of hypotheses focussed on trying to reproduce the exact time-optimal solution, the diffculty associated with this task is demonstrated. A modification of optimal-aim concept is employed to develop a novel regulation algorithm. In this algorithm, aim directions are chosen in a special manner to generate the time-optimal control approximately. The control scheme thus developed is shown to be globally stabilizing for systems having eigenvalues in the CLHP (closed left half-plane). It is expected that this method or its modifications can be extended to higher dimensional systems as a part of future research. An alternative control algorithm involving a simple state-space aiming concept is also developed and discussed.

We present a robust iterative sparseness-constrained interpolation algorithm using 2/3D curvelet frames and Fourier-like transforms that exploits continuity along reflectors in seismic data. By choosing generic transforms, we circumvent the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Simulation and real data examples for data with moderately sized gaps demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior. Our method also shows good results for de-aliasing judged by the behavior of the (f-k)-spectrum before and after regularization.

The 4th generation wireless communication systems aim to provide users with the convenience of seamless roaming among heterogeneous wireless access networks. To achieve this goal, the support of vertical handoff is important in mobility management. This thesis focuses on the vertical handoff decision algorithm, which determines the criteria under which vertical handoff should be performed. The problem is formulated as a constrained Markov decision process. The objective is to maximize the expected total reward of a connection subject to the expected total access cost constraint. In our model, a benefit function is used to assess the quality of the connection, and a penalty function is used to model the signaling incurred and call dropping. The user's velocity and location information are also considered when making the handoff decisions. The policy iteration and Q-learning algorithms are employed to determine the optimal policy. Structural results on the optimal vertical handoff policy are derived by using the concept of supermodularity. We show that the optimal policy is a threshold policy in bandwidth, delay, and velocity. Numerical results show that our proposed vertical handoff decision algorithm outperforms other decision schemes in a wide range of conditions such as variations on connection duration, user's velocity, user's budget, traffic type, signaling cost, and monetary access cost.

Vibration control is inevitable in many fields, including mechanical and civil engineering. This matter becomes more crucial for lightweight systems, like those made of magnesium. One of the most commonly practiced methods in vibration control is to apply constrained layer damping (CLD) patches to the surface of a structure. In order to consider the weight efficiency of the structure, the best shape and locations of the patches should be determined to achieve the optimum vibration suppression with the lowest amount of damping patch. In most research work done so far, the shape of patches are assumed to be known and only their optimum locations are found. However, the shape of the patches plays an important role in vibration suppression that should be included in the overall optimization procedure. In this research, a novel topology optimization approach is proposed. This approach is capable of finding the optimum shape and locations of the patches simultaneously for a given surface area. In other words, the damping optimization will be formulated in the context of the level set technique, which is a numerical method used to track shapes and locations concurrently. Although level set technique offers several key benefits, its application especially in time-varying problems is somewhat cumbersome. To overcome this issue, a unique programming technique is suggested that utilizes MATLAB© and COMSOL© simultaneously. Different 2D structures will be considered and CLD patches will be optimally located on them to achieve the highest modal loss factor. Optimization will be performed while having different amount of damping patches to check the effectiveness of the technique. In all cases, certain constraints are imposed in order to make sure that the amount of damping material remains constant and equal to the starting value. Furthermore, different natural frequencies will be targeted in the damping optimization, and their effects will also be explained. The level set optimization technique will then be expanded to 3D structures, and a novel approach will be presented for defining an efficient 4D level set function to initialize the optimization process. Vibrations of a satellite dish will be optimally suppressed using CLD patches. Dependency of the optimum shape and location of patches to different parameters of the models such as natural frequencies and initial starting point will be examined. In another practical example, excessive vibrations of an automotive dash panel will be minimized by adding damping materials and their optimal distribution will be found. Finally, the accuracy of the proposed method will be experimentally confirmed through lab tests on a rectangular plate with nonsymmetrical boundary conditions. Different damping configurations, including the optimum one, will be tested. It will be shown that the optimum damping configuration found via level set technique possesses the highest loss factor and reveals the best vibration attenuation. The proposed level set topology optimization method shows high capability of determining the optimum damping set in structures. The effective coding method presented in this research will make it possible to easily extend this method to other physical problems such as image processing, heat transfer, magnetic fields, etc. Being interconnected, the physical part will be modeled in a finite element package like COMSOL and the optimization advances by means of Hamilton-Jacobi partial differential equation. Thus, the application of the proposed method is not confined to damping optimization and can be expanded to many engineering problems. In summary, this research: - offers general solution to 2D and 3D CLD applications and simultaneously finds the best shape and location of the patches for a given surface area (damping material); - extends the level set technique to concurrent shape and location optimization; - proposes a new numerical implementation to handle level set optimization problems in any complicated structure; - makes it possible to perform level set optimization in time dependent problems; - extends level set approach to higher order problems.

The aim of this study was to determine the concurrent validity, test-retest intra-rater reliability, and test-retest inter-rater reliability of photographic measures of anterior pelvic tilt range of motion (APT ROM) and hip extension ROM range of motion (HE ROM) during a constrained forward lunge test (CFLT) in healthy adult runners. Measurements of start, end, and range of motion (ROM) variables for APT and HE motion were taken from an Optorak kinematic measurement system and from printed photographs extracted from digital video footage using a protractor. A total of 13 healthy adult male and female recreational runners participated in the study. Measures of APT ROM and HE ROM were found to be valid compared to Optorak measures, with intraclass correlation coefficients (ICC) of 0.94 and 0.99 respectively, and limits of agreement of -1.42 ± 1.99 degrees and 0.41 ± 2.13 degrees respectively. APT ROM and HE ROM demonstrated high between-day intra-rater reliability with ICCs ranging from 0.75 to 0.91 and within-day inter-rater reliability with ICCs ranging from 0.86-0.90. For between day intra-rater measurements smallest detectable differences (SDDs) ranged from 5.59 to 4.12 for APT ROM and from 9.08 to 11.08 for HE ROM. The present study suggests that photographic measurements of APT ROM and HE ROM during a CFLT are valid and reliable in healthy adult runners; however, these measurements display a low sensitivity with respect to detecting changes between trials.

This dissertation explores methods for finding irreducible infeasible subsystems (IISs) of systems of inequalities with binary decision variables and for solving probabilistically constrained stochastic integer programs (SIP-C). Finding IISs for binary systems is useful in decomposition methods for SIP-C. SIP-C has many important applications including modeling of strategic decision-making problems in wildfire initial response planning. New theoretical results and two new algorithms to find IISs for systems of inequalities with binary variables are developed. The first algorithm uses the new theory and the method of the alternative polyhedron within a branch-and-bound (BAB) approach. The second algorithm applies the new theory and the method of the alternative polyhedron to a system in which zero/one box constraints are appended. Decomposition schemes using IISs for binary systems can be used to solve SIP-C. SIP-C is challenging to solve due to the generally non-convex feasible region. In addition, very weak lower (upper) bounds on the objective function are obtained from the linear programming (LP) relaxation of the deterministic equivalent problem (DEP) to SIP-C. This work develops a branch-and-cut (BAC) method based on IIS inequalities to solve SIP-C with random technology matrix and random righthand- side vector. Computational results show that the LP relaxation of the DEP to SIP-C can be strengthened by the IIS inequalities. SIP-C modeling can be applied to wildfire initial response planning. A new methodology for wildfire initial response that includes a fire behavior simulation model, a wildfire risk model, and SIP-C is developed and tested. The new method- ology assumes a known standard response needed to contain a fire of given size. Likewise, this methodology is used to evaluate deployment decisions in terms of the number of firefighting resources positioned at each base, the expected number of escaped and contained fires, as well as the wildfire risk associated with fires not receiving a standard response. A study based on the Texas district 12 (TX12) that is one of the Texas A&M Forest Service (TFS) fire planning units in east Texas demonstrates the effectiveness of the new methodology towards making strategic deployment decisions for wildfire initial response planning.