A Midsummer Night s Dream Opera - 883 Words

Andrew Muckle Professor Boots 15 April 2016 A Midsummer Night’s Dream Opera On Saturday April 16th, I attended A Midsummer Night’s Dream, an opera by Benjamin Britten at Boston University. I have never been to an opera before, so I went in wondering if I would enjoy it as much as I enjoy the orchestra. I read a review by Gillian Daniels who said â€Å"It’s an opera faithful to the spirit of its material† having me go in with somewhat high expectations (Daniels). I walked into a fairly large theater with three friends waiting for the play to begin. We took our seats that were four rows from the front of the balcony I felt that our seats were pretty good since we were more or less close to the stage. The lights finally dimmed and the opera began.†¦show more content†¦Because of this, all vowels sung â€Å"in full voice all sound the same.† (Highfield). The mood created by the back stage and production staff made a tremendous impact on the performance. I found that the lighting was very well done; creating the feel of a gloomy dark forest that would exist in a dream. Some of the sets were basic but very well prepared. I was impressed how their simplicity more than accomplished their purpose, and when the scenes changed their austerity allowed the movement to be very seamless and unobtrusive. I believe Daniels was right in saying that â€Å"This vision is every bit the dream of the title.†(Daniels) The opera A Midsummer Night’s Dream by Benjamin Britten did seem very distinctive in the way it was presented compared to the movie by Michael Hoffman. The characters were dressed, in my opinion, fairly differently from the movie. The scene between Oberon and Titania was largely the same in storyline, but the look, as well as the dialogue didn t seem similar to the movie. Perhaps having to read the dialogue from the teleprompters made a difference versus listening to the dialogue in the film. In the movie, the fairies all seem to be very roman in the way they were dressed. They also had normal human bodies. In the opera however the esthetic seemed somewhat different.

Website Usability Comparison For Immigration Into Australia

Memorandum To: Matthew Harris Project Manager From: Feiyang Tang System Analyst Date: 3 August 2015 Subject: Website usability comparison for Immigration into Australia ______________________________________________________________ An immigration website usability comparison for Immigration into Australia has been completed. Introduction The competitor websites chosen for this research were Immigration New Zealand (www.immigration.govt.nz), Australian Department of Immigration and Border Protection (www.border.gov.au) and U.S. Citizenship and Immigration Services (www.usgis.gov). To find how user-friendly applicants for Australian residence would find that country’s site, the team also surveyed the opinions of different users who used three websites. The following key areas were considered: ï  ¶ Homepage Visual Appeal – Attention attraction, first impression; ï  ¶ Website Performance – Format, design, Ease of access to information; ï  ¶ Navigability – Efficiency, accuracy, usability; ï  ¶ Search Function Effectiveness – Error correction, field size, width of results, type of results; ï  ¶ Data Integrity and Richness Methodology According to the questionnaire, the team conducted that the usability of the three different countries’ immigration websites were collected from seven volunteers, who are from different age groups, genders and ethnicities. The team selected seven volunteers from three age groups: two males (18-30),Show MoreRelatedMedical Tourism22177 Words   |  89 PagesSince this project was awarded as a studentship agreement between Imperial College London and Asian Neuro Cardiac Centre, Malaysia. (ANCC), it is obvious that the market to be researched would be that in Malaysia. An initial survey of medical tourism websites on Malaysia provided very little information about the industry there. Informal talks with some health practitioners and hospital managers in Malaysia revealed that the following facts: ï‚ § The medical tourism industry is fragmented and rather dormantRead MoreManaging Information Technology (7th Edition)239873 Words   |  960 PagesHowever, it may take several ins tructions on one computer model to accomplish what one instruction can do on another computer model. Thus, the use of MIPS ratings has largely gone out of favor because of the â€Å"apples and oranges† nature of the comparisons of MIPS ratings across classes of computers. Another speed rating used is MegaFLOPS or MFLOPS—millions of floating point operations per second. These ratings are derived by running a particular set of programs in a particular language on the

Aircraft Trajectory Prediction Free Essays

Literature Review Aircraft Trajectory Prediction By Cameron Sheridan I. Abstract The purpose of this review is to identify and analyse work that is currently being done on aircraft trajectory prediction (ATP); particularly the approach of modern day researchers to the problematic issue of the growingly clustered airspace. The benefits of this review include the exploration of several sub-topics of the literature. We will write a custom essay sample on Aircraft Trajectory Prediction or any similar topic only for you Order Now Through examining the current methods towards trajectory modelling validation and the techniques that are now employed to neutralise error sources, it was found that with the modern-day approaches an algorithm and its trajectory prediction (TP) can be assessed and consequently improved upon. A number of systems pertinent to conflict are discussed and results are presented which illustrate and compare the effectiveness of heading and altitudinal resolution manoeuvres. Additionally, a number of recent developments and innovations in the field pertinent to the technologies and techniques used are discussed, thus illustrating a clear indication of research still moving forward in this field. II. Introduction An ATP is a ‘mapping of points over a time interval [a,b] to the space R? ’ (Tastambekova et al. 2010, p. 2). Although this is correct in many senses, this explanation fails to acknowledge the intricacy and designed purpose. More accurately, a TP module has the capacity to calculate the future flight path of an aircraft given that it has been supplied with the required data, i. . the flight intent, an aircraft performance model, and finally, an estimation of the future atmospheric/environmental conditions (Swierstra and Green 2004). An aircraft trajectory is a future path of an aircraft that can be represented visually in three forms: 2D, 3D and 4D (x, y, altitude and time) with 4D the more frequently used nowadays by air traffic con trol (ATC) and air traffic management (ATM) due to its far more realistic representation and ease of interpretation (Vivona et al. 2010; Poretta et al. 010; Paglione and Oaks 2009). The significance of ATP is certainly appreciated. There is support for the importance of TP and the role it plays in advanced ATM operations, especially with a growingly clustered airspace in the next decade (Lee et al. 2010; Porretta et al. 2010 and Denery et al. 2011). The most crucial function of a TP however, as viewed by Lymperopoulos and Lygeros (2010), is to supply advice to ATC. Consequently, they can then make well-informed executive judgments to ensure the safety and effectiveness of our airspace. The purpose of this study is to inform what is happening in this field through examination of both the developments within ATP and the current problems facing researchers: namely, the significant increase in air-traffic by 2025. This will be done through exploring recent literature in this field that pertains to: conflict detection and resolution; the technologies and techniques involved; and, the error sources that are involved with a prediction and their subsequent effect on the uncertainty of a prediction. III. Modelling Validation and Uncertainties Efficiency and accuracy are two central points of this literature, which alone could be considered as the determining factors of a respectable TP model; thus, sufficient research is required to improve both, without the sacrifice of one. How does one validate the performance of an algorithm and whether its TP is ‘accurate’? The common answer it seems (Anonymous 2010 and Paglione and Oaks 2007, pp. 2) is through the degree of conformity between the measured or predicted data and the true data of an aircraft at a given time. A. Uncertainties Figure 1: Paglione and Oaks (2009) Figure 1: Paglione and Oaks (2009) Uncertainties are perhaps the biggest hurdle in further advancements in this field. Obviously, as the prediction increases in time, the uncertainties of the flight begin to take effect – up to a point where the trajectory becomes almost impossible to predict accurately with any degree of assurance. The consequential effect of uncertainties in a prediction may result in: two or more aircrafts losing separation; an aircraft not arriving to schedule; or even, the inability to detect flaws in either the ATP algorithm or the aircraft itself, to name a few. Therefore, there is a need to lessen the ffect of these lingering burdens. In reality this is quite difficult, and as such, requires particular attention of the algorithms used by an aircraft to validate its performance. B. Modelling Validation Performance validation verifies that a TP model performs correctly, and determines the degree of accuracy of a model’s representation compared to the real system (Vivona et al. 2010 and Garcia et al. 2009). There are further ways to validate predicted data; such methods include those shown by Paglione and Oaks (2007) who looked at the associated accuracy metrics; Poretta et al. 2008) who evaluated a 4D TP model for civil aircraft; and finally, the Plan, Do, Study, Act (PDSA) evaluation process of a TP (see figure 1). This practice and its application have been shown by Paglione and Oaks (2009). Inspired by the relationship of trajectory predictors to higher level applications, the authors stressed the need for improving modelling procedures through an iterative process consisting of four stages. Fredrick et al. (2009) were able to analyse ways to validate a program with their test and evaluation process. Particular focus was on a metrics approach which offers measures on the performance of an aircraft. This method may provide greater effectiveness in programs and is proclaimed to play a â€Å"critical role as a continuum of supporting activities for the TP programs† [Fredrick et al. (2009), pp. 9]. Vivona et al. (2010) also proposed a new methodology in her work which is designed for a similar purpose. The techniques used are titled ‘white box testing’ and ‘test bench testing’. The former involves knowledge of the internal processes that occur within a TP model, and through this information there will be a sequence of tests which accumulate together to validate the entire TP. The latter test is slightly different in that, as opposed to analysing current state data, it requires entering input data into an algorithm’s interface and then assessing the data that was produced as a result. Both are expected to become more commonly used in the approaching years. C. Error Sources and Corrective Measures Jackson (2010) reiterated the ineffectiveness and poor performance of automation systems in the company of errors and uncertainty sources. This suggests, and was considered equally by Paglione and Oaks (2009) and Vivona et al. (2010) that the performance of these systems is dependent on the accuracy of the TP. Consequently, the requirement to minimise all potential error sources has particular precedence in current research. Environmental factors (wind, temperature, air pressure, etc. ), along with human errors and algorithmic/system imperfections are the typical causes for the uncertainty in a prediction. Further error sources such as: the measurement of aircraft state; aircraft performance models; knowledge of aircraft guidance modes and control targets; atmospheric model; and, clearance issues are all predicted to be integral to the improvement of TP modelling accuracy in the near future (Jackson 2010). Alternatively, rather than striving for a flawless system, processes such as the offline smoothing algorithm (Paielli 2011); application of the rapid update cycle (RUC) of the weather (Lee et al. 010); and techniques that take the perspective of the DST user [Interval based sampling technique (IBST)] (Paglione and Oaks 2007) have been established to improve aspects of a prediction model. The first of these has the capacity to improve the accuracy of DR predictions through the smoothing of the radar tracks (shown below). Blue dots Way-points Black full-line Actual path of aircraft Red curve Smoothing of track Blue dots Way-points Black full-line Actual path of aircraft Red curve Smoot hing of track This was demonstrated through application of the technique on past recorded operational error cases. The usage of RUC provides ATC with the benefit of detecting ‘regional variations of uncertainty that are related to actual weather phenomena’ (Lee et al. 2010, pp. 14). The concept behind IBST is that a trajectory provided to a controller may be old and thus filled with errors and uncertainties; so, this two-step process operates by determining the accuracy of the aircraft – through computing spatial errors – after passing through pre-determined waypoints (Paglione and Oaks 2007). Additionally, given the effect of environmental factors on a prediction, there are procedures present to counter the influence of the sources. Russell (2010) presented the ‘consolidated storm prediction for aviation’, which is a prediction on the water content of clouds done through a grid-based prediction which may forecast predictions anywhere up to 8 hours. Results showed that this system was effective up to 2 hours as the predicted data correlated well with the observed weather within a given sector; however, as expected, when the look-ahead time increased the accuracy and reliability steadily decreased. IV. Conflict Detection and Resolution A. Conflict Detection There has been a quantity of research on CDR within this literature, particularly over the last few years (Denery et al. 2011 Erzberger et al. 2009; Tang et al. 2008 and Paielli 2008). In order to overcome the problem of ensuring air safety, technology must exist which prevents a conflict from occurring. A conflict, in an aeronautic context, as described by Paglione and Oaks (2009) is a situation where two or more aircraft exceed the minimum separation distance standards, which can be deduced through a visual TP. The purpose of CDR systems is to alarm ATC well in advance of a predicted collision occurring to allow preventative measures (Erzberger et al. 2009). Paielli (2008) believes that the key challenge in the next decade will be to establish an automated system that is capable of ensuring that the collision probability remains low, even in the face of a number of possible hindrances: i. e. the predicted increase in air traffic in future decades; the (at times) complexity of the system; frequent false alarms; and, the capability of CDR tools to advise the most appropriate manoeuvre. Three of the most highly regarded and reviewed conflict systems amongst ATC (Tang et al. 2008; Paielli 2008; Paglione and Oaks 2009; and Erzberger et al. 2009) are Tactical Separation-Assisted Flight Environment (TSAFE), Conflict Probe (CP), Conflict Alert (CA), and User Request Evaluation Tool (URET). TSAFE has two primary functions 1) conformance monitoring – a process that determines the degree to which an aircraft is meeting its earlier prediction; and 2) trajectory synthesis – the construction of the 4D path. URET was developed to help air traffic controllers by supporting a greater number of user-preferred flight profiles, and increasing both user flexibility and system capacity. ERAM is a Federal Aviation Administration system that has been designed primarily to deal with both route requests and in flight alterations swiftly. Figure 1: Poretta et al. (2010) Figure 1: Poretta et al. (2010) Paglione and Oaks (2009) highlighted the correlation between a TP’s accuracy and a decision supports tool’s (DST) performance. They assessed a number of statistical analysis models including TP metrics (i. . horizontal and vertical) and conflict probe metrics (Along-track; Cross-track; horizontal error; and, altitude). They focus on and use these accuracy metrics to establish a ratio value. Ratio= Horizontal or vertical separationMinimum allowed separation distance (i. e. parameter cut off value) As this ratio increases, the likelihood of producing false and missed conflict alerts increas es– while the probability of producing valid alerts decreases. In Paglione and Oaks (2009) they identified the requirement for a ‘process improvement model’ – i. . Plan-Do-Study-Act (PDSA) – to evaluate and find possible enhancements on a studied TP system to reduce the ratio value. Investigations into false alerts and missed conflict detects have also been conducted recently by Denery et al. (2011) and Poretta et al. (2010). Processes Decisions Data that may be modified Data that may not be modified Algorithm execution flow ——- Data flow Processes Decisions Data that may be modified Data that may not be modified Algorithm execution flow ——- Data flow The latter presented a CDR algorithm (figure 2) which shown by numerical results, is able to produce a conflict-free trajectory whilst also noting the aircrafts capabilities to perform all recommended resolution manoeuvres. Figure 2: Poretta et al. (2010) Figure 2: Poretta et al. (2010) Figure 3: Denery et al. (2011) Figure 3: Denery et al. (2011) Denery et al. (2011) highlighted consequent issues to the above problems – principally, the distraction of controllers and the need to constantly verify whether a concern exists or not. In reply, they proposed a new algorithm, flight-intent (FI) that takes into consideration the present status of the aircraft and all available intent data. Tests were performed with this system in comparison to two other conflict detection algorithms: dual trajectory algorithm (Dual) and dead reckoning (DR). Results (figure 3) illustrate that the FI algorithm yields considerably less false alert rates, especially when the algorithm – already incorporated with area navigation (RNAV) and a noise integrated routing system (NIR) – was paired with the integrated administration and control system (IAC). B. Conflict Resolution Additionally, Anonymous (2010) also noted that two of another CDR systems (conflict probe) faults – including conflict alerts – are that the technology is at times inefficient and will occasionally produce false alerts (or conversely, the lack thereof alerts). The CP’s performance is also compared to URET in tests performed by Santiago et al. (2010). Deductions that were made from this report included the possible benefits of increasing both the look-ahead time of a prediction to 25min, and the minimum horizontal parameters. Further investigation (Paielli 2008; Paielli et al. 009; and Denery et al. 2011) with TSAFE has been ongoing with the aim to develop an algorithm to perform at least as effectively as URET. Ryan et al. (2008) also looked at achieving this goal. They analysed and compared an emerging conflict resolution algorithm, ERAM, against URET in a quantity of tests and comparisons that were designed to evaluate the precision of th e technology. ERAM’s accuracy and strategic conflict notification capabilities were belittled in comparison to the URET system, where ERAM only managed to obtain the minimum standard in two of the seven test categories. TSAFE is often used as a back-up strategic system that computes simple resolution manoeuvres to resolve potential conflicts that are expected to occur within two minutes (Denery et al. , 2011; Paielli et al. 2009; Alonso-Ayuso et al. 2011). TSAFE and its application during en route is the primary focus of Paielli (2011). Examined in his work was the heading-trials algorithm that he developed. This system produces a number of possible manoeuvre resolutions that change the heading of the involved aircraft in  ±10? increments up to  ±90? f the original direction of travel. The best of these manoeuvres – in terms of cost and applicability – is then measured against the best altitude manoeuvre by means of a separation ratio (see pp. 4). His experimentation was on 100 past operational error cases where a conflict had occurred. His results (shown on table 1) illustrate the effectiveness of each manoeuvre in each particular situation. Consequently, he was able to deduce tha t altitudinal amendments were far more advantageous than his proposed heading algorithm. For e. g. the right most column indicates that when the separation ratio was ? 1. 2, 95% of the altitudinal amendments resulted in a successful avoidance of conflict, whilst the heading algorithm only resolved a comparably low 62% For e. g. the right most column indicates that when the separation ratio was ? 1. 2, 95% of the altitudinal amendments resulted in a successful avoidance of conflict, whilst the heading algorithm only resolved a comparably low 62% Separation ratio (? ) %| | 0. 2| 0. 4| 0. 6| 0. | 1. 0| 1. 2| No resolution| 98| 92| 74| 25| 0| 0| Heading only| 99| 95| 91| 77| 71| 62| Altitude only| 100| 100| 100| 100| 99| 95| Heading + altitude| 100| 100| 100| 100| 100| 98| Table 1: Paielli (2011) Table 1: Paielli (2011) Similarly, Paielli (2008) performed a comparable experiment with a restricted focus on altitude manoeuvres. His results further validated the success of such resolution procedures, particularly when augmented altitude amendments were supplemented to the input data (see table 2). The purpose of adding these amendments in his experiment was to compensate for the controllers negligence or inability to do so at the time of the conflict occurring. Note: Other tests and procedures that were tested in (Paeilli 2008) are not shown, i. e. altitude rejections; temporary altitudes; step altitudes; and, critical level-offs. Note: Other tests and procedures that were tested in (Paeilli 2008) are not shown, i. e. altitude rejections; temporary altitudes; step altitudes; and, critical level-offs. | Separation ratio (? ) %| | 0. | 0. 4| 0. 6| 0. 8| 1. 0| 1. 2| No resolution| 99| 94| 75| 29| 0| 0| Augmented altitude amendments| 100| 99| 99| 97| 94| 90| Table 2: Paeilli (2008) Table 2: Paeilli (2008) Note was made in both reports that operational error cases are by no means a precise representation of the computer-generated routine operation that occurred. Given the importance of conflict detection and resolution it is important that ample research continues in this field to ensure the safety and welfare of all air traffic. V. Techniques and Technologies A. Technologies CDR could not be possible if there wasn’t the appropriate equipment present today to compute the complex algorithms that are used. A 4D TP is established upon no easy means. Cate et al. (2008) articulate that it not only requires (at times) convoluted formulas, but also the technology and methodologies to then dissect and string together the state and intent data of the aircraft. The techniques and technologies currently utilised are crucial in this field. Already discussed above are a number of systems which are integral to the concept of trajectory prediction as they all serve a specific purpose. This is exemplified when looking at the conflict detection and resolution component of this literature, where there are often four stages to the process: 1) Traffic collision avoidance system (TCAS) which focuses on the immediate future (1-2min) (Paielli 2011); 2) Short term conflict alert (STCA) which operates anywhere between 2-5min (Shakarian and Haraldsdottir 2001); 3) Tactical controller tools (TCT) which concentrates on up to 8-10min (Leone 2009 and Graffica 2009); and finally, 4) Mid-term conflict detection (MTCD) which will look ahead anywhere up to 20-30min (Graffica 2009 and Lymperopoulos et al. 010). Systems that look any further in advance typically become ineffective due to the dependence of their predictions accuracy on look-ahead time (Russell 2010). Prediction accuracy ? 1Look ahead time Huang and Chung (2011) presented a TP model through their Heirachical timed coloured Petri net (HTCPN) models that is composed of seven different stages during the aircrafts trajector y. This model differs from those that have been presented by (Cate et al. 2008 and Paglione and Oaks 2009) in that 1) their models consist of a three-stage process; and 2) far more input information is required to produce a trajectory. Different still, a TP model has been shown to be represented by an algorithm with many procedures covering difficult mathematical equations of motion (Lymperopolous and Lygeros 2010). As would be expected, with such an intricate form of technology comes a quantity of DSTs, modelling techniques and algorithms to go with it. We require a large range of prediction technologies to both further aid the pilot and ATCs and to meet the particular needs of the interested automation concept (Cate et al. 2008). Tests and comparisons on differing DSTs are continuing (Denery et al. 2011; Vivona et al. 010; and Pagilone and Oaks 2007) and they have confirmed that with the inclusion of particular technologies, a TP system can have an improvement in accuracy without sacrificing either functionality or efficiency. The use of mathematics and observations are verified for two important TP filtering methods: Kalman Filtering (KF), and Particle Filtering (PF) [Lymperopolous and Lygeros (2010); Lymperopol ous et al. (2010) and Delahaye and Puechmorel (2009)]. There are two differing key issues between the two filters – i. e. the inability to handle either high dimensional states or non-linear dynamics. Consequently, a new filtering algorithm, sequential conditional particle filter, was proposed to resolve this problem. Pan and Schonfeld (2009) also explored this technology, demonstrating through ‘splitting a graph with cycles into several directed cycle-free subgraphs’ its enhanced performance compared to other current methods of particle filtering. B. Techniques Data shown by Denery et al. (2011) illustrate the considerable improvements of accuracy and efficiency between their tested algorithms (Dual, DR and FI) with the inclusion of systems such as IAC, NIR, and RNAV (see pp. ). The concept of TP interoperability between systems has also been considered as a potential point of source for advancement in this field (McNally et al. 2010 and Cate et al. 2008). In short, this concept consists of two (or more) unlike automation systems which may compare their disagreeing trajectories and data to result in a similar prediction. This would prove particularly beneficial for a ir-to-ground base control and traffic flight management due to correlation between the two systems. Mujumdar and Padhi (2011) provided an overview of seven encouraging online path-planning methods tested by unmanned aerial vehicles: graph search, potential field method, vision-based neutral networks and minimum effort guidance to list a few. Two common advantages amongst these methods were the ability to compute promptly without the expense of accuracy and the capability to account for environmental information through regular trajectory updates. These benefits correlate well with the necessities of a sought-after algorithm. Modern research is making serious headway towards establishing sound modelling procedures. This is critical as without the appropriate technologies and methods to compute the mathematical representations and data, innovations in this field would not be conceivable. VI. Critical Assessment It rapidly became apparent the quantity of research that has already been done (especially in the past 2-3 years) and that research is still ongoing. The universal desire to constantly improve on each individual part of a TP modelling process is confirmation of its vital importance in not only contemporary society, but our future airspace. The range of exploration and advancements in this literature is both comprehensive and widespread. Every few years, it seems each component of a TP model is enhanced to accommodate for the rapid innovations in modern technology; consequently, this then allows for additional research and analysis in search of further improvements. There were often limitations in the tests that were performed, as often only one (sometimes two) type of aircraft were used during experimentation. The data also, is often decorated by removing plenty of outliers from the tight parameters set. If current research is to progress any further, we must divert away from the existing trend of producing monotonous findings with similar solutions in reports and journals, i. e. modifications to current methodologies; more convoluted analysis; or simply, the suggestion that ‘further work needs to be completed in this area of study’. VII. Conclusion and Open Questions Current research into the TP algorithms that are used specifically for CDR are continuing due to its significance in this field; though, there is a lot of further study required to effectively manage the increasing number of challenges facing ATM in the foreseeable future. For this to occur, the modern techniques and prediction models that are employed must be innovated and evolved even further. The present technologies utilised in constructing a trajectory prediction appear to be effective. Similarly, the techniques which are employed to improve any imperfections or inaccuracies during the modelling process are very specifically designed to serve their own designed purposes. Even still however, the greatest obstacle that needs to be overcome – and surely, where further research must be applied – is reducing the overall effect of error sources and subsequently reducing the uncertainties in the predictions. Given the recent growth of advancements in this field, one may expect that both researchers and developers will be able to competently confront any arising issues in this field of aeronautic technology. In conclusion, a few unanswered questions are immediately brought to mind: * To what extent will the role humans play as decision support systems in response to any complications following any possible system malfunctions or incompetencies. * Does there exist a minimum set of constraints for a given prediction, such that an algorithm is simply unable to compute with any assuring accuracy? If so, how could this be dealt with. Such queries may have place in further study as they are outside the scope of this review. VIII. References 1. Denery, D. , Robinson, J. , Tang, H (2011). Tactical Conflict Detection in Terminal Airspace. Journal of Guidance, Control, and Dynamics. 34 (2), pp. 403-413. 2. Alonso-Ayuso, A; Escudero, L; Martin-Campo, F. (2011). Collision avoidance in air traffic management: A mixed-integer linear optimization approach. IEEE Transactions on Intelligent Transportation Systems. 12 (1), pp. 47-57. 3. Mujumdar, A; Padhi, R. (2011). Evolving philosophies on autonomous obstacle/collision avoidance of unmanned aerial vehicles. Journal of Aerospace Computing, Information and Communication. 8 (2), pp. 17-41. 4. Paielli, R. (2011). Evaluation of tactical conflict resolution algorithms for enroute airspace. Journal of Aircraft. 48 (1), pp. 324-330. 5. Huang, Y; Chung, T. (2011). Modelling and analysis of air traffic control systems using hierarchical timed coloured Petri nets. Transactions of the Institute of Measurement and Control. 33 (1), pp. 30-49. 6. Author not given. (2010). ERAM Habituation. Available: http://atcfreqs. om/wpblog/? p=4181. Last accessed 18th April 2010. 7. McNally, D. , Mueller, E. , Thipphavong, D. , Paielli, R. , Cheng, J. , Lee, C. , Sahlman, S. , Walton, J. (2010). A near-term concept for trajectory-based Operations with Air/Ground Data Link Communication. Internation Congress of the Aeronautical Sciences. 27 pp. 1-24. 8. Lee, A. , Weygandt, S. , Schwartz, B. , Murphy, J. (2010). Performance of Trajectory Models with Wind Uncertainty. American Institute of Aeronautics and Astronautics. pp. 1-16. 9. Jackson, M. (2010). Role of Avionics in Trajectory-Based Operations. IEEE ae systems magazine. 5 (7), pp. 12-19. 10. Anonymous. (2010). Human Factors; Data from E. Rovira and colleagues advance knowledge in human factors. Transportation Business Journal. pp. 633. 11. Vivona, R. , Paglione, M. , Cate, K. , Enea, G. (2010). Definition and Demonstration of a Methodology for Validating Aircraft Trajectory Predictors. American Institute of Aeronautics and Astronautics. pp. 1-22. 12. Poretta, M. , Schuster, W. , Majumdar, A. , Ochieng, W. (2010). Strategic Conflict Detection and Resolution Using Aircraft Intent Information. The Journal of Navigation. 63 pp. 61-88. 13. Lymperopoulos, I. nd Lygeros, J. (2010). Improved Multi-Aircraft Ground Trajectory Prediction for Air Traffic Control. Journal of Guidance, Control and Dynamics. 33 (2), pp. 348-362. 14. Tastambekova, K. , Puechmorel a, S. , Delahayea, D. , Rabutb, C. (2010). Trajectory Prediction by Functional Regression in Sobelev Space. Manuscrit auteur. 1 (1), pp. 1-5. 15. Lymperopoulos, I. , Chaloulos, G. , Lygeros, J. (2010). An advanced particle filtering algorithm for improving conflict detection in Air Traffic Control. Preprint 4th International Conference on Research in Air Transportation, June 2010, pp. 1-8. 16. Russell, C. (2010). Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation. American Institute of Aeronautics and Astronautics. 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, pp. 11. 17. Santiago, C; Lehman, J; Crowell, A. (2010). Accuracy Comparison of an Operational and Experimental Strategic Conflict Probe. AIAA Guidance, Navigation, and Control Conference 2 – 5 August 2010, Toronto, Ontario Canada. ( ), pp. 1-14. 18. Leone, M. (2009). 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International Journal of Microwave and Wireless Technologies. 1 (3), pp. 1-8. 24. Delahaye, D. and Puechmorel, S. (2009). TAS and wind estimation from radar data. Digital Avionics Systems Conference, 2009. DASC ’09. IEEE/AIAA 28th. pp. 2. B. 1-2. B. 16. 25. Fredrick, J; Jaggard, C; Paglione, M; and, Baldwin, C. 2009). Verification and Validation Standards to Test and Evaluate New Complex Systems for the National Airspace System. International Test and Evaluation Association. 30 (2), pp. 1-11. 26. Tang, H. , Den ery, D. , Erzberger, H. , Paielli, R.. (2008). Tactical Separation Algorithms and their Interaction with Collision Avoidance Systems. AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, HI, AIAA paper 2008-6973. , pp. 1-18. 27. Paielli, R. (2008). Tactical Conflict Resolution Using Vertical Manoeuvres in En Route Airspace. Journal of Aircraft. 45 (6), pp. 111-2119. 28. Ryan, H. , Chandler, G. , Santiago, C. , Paglione, M. , Liu, S. (2008). 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Chances by Five For Fighting free essay sample

I tend to stray on the unrealistic side of things. I’m a dreamer, what can I say? When I fall for someone, I fall hard and immediately start thinking about what the future could hold with them. I realize this is not the healthiest perspective to always hold, but at times I can’t help it. Once you become a â€Å"hopeless romantic† it is hard to change that. However in eleventh grade I started to realize it might save me from getting as hurt if I started to look at things more realistically. I was in my first real relationship and I could tell it was coming to an end as much as I didn’t want it to. It was around this time I rediscovered an old song I used to listen to on repeat while I was in middle school. Back then, I didn’t listen as much to the lyrics but listening to it again I realized the message it was sending and it was just what I needed to hear at the moment. We will write a custom essay sample on Chances by Five For Fighting or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The song was â€Å"Chances† by Five For Fighting, and unlike myself, it took the more realistic side of relationships. It basically had the attitude that I needed to have and still need to have, but still find it hard to. In this song the guy is involved with a girl and they both are very into each other and obviously want what they have to work out. However, the guy seems to realize he can’t get too ahead of himself and get his hopes too high because eventually they will likely grow apart from each other and go their separate ways. This was basically what was happening to me with my ex, and this song helped me to realize that no matter how much I can dream about what I want to happen, I need to focus on what is happening and let it go if it needs to go. In fact, one of my favorite lyrics from this song is â€Å"nothing lasts forever no matter how it feels today†. I really don’t think that line could be more true; in the moment with someone you may be abl e to see a whole future with them, but it is likely that down the road something will change and that â€Å"forever† that you dreamed about will come crashing down with reality. However, the last part of this song focuses on those rare occasions when this person you’re with now does turn out to be your future and all you dreamed of. There are those instances when that happens, and it happens because you took a chance on it. Whether or not you are a dreamer or a realist, you need to take chances in life to find love. It does help to keep things in perspective so it hurts less if it doesn’t work out, but there is always the possibility it will work out. As the song ends, â€Å"chances are waiting to be taken†¦Ã¢â‚¬  and those chances waiting for us could turn out to be what we’ve always dreamed of.