Ratio Analysis Essay Example | Topics and Well Written Essays - 1500 words

Ratio Analysis - Essay Example The paper uses Profitability, efficiency, liquidity and shareholder ratios as the basic tools. The more complex tools like IRR, WACC etc are ignored to keep the analysis simple and meaningful. In addition, the paper identifies the Key Performance indicators (KPIs) of the company and highlights the basic steps taken by the company to achieve the KPI targets. The company’s current year’s results with respect to the KPI targets are also discussed and highlighted. To:  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Board of Directors of Go Ahead group Plc. From:  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Robert Frost, Accountant Re:  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Ratio Analysis and KPI discussion Date:  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  February 25, 2013 With regard to your concerning the analysis of organizational financial performance and position, I would like to present this report which summarized, analyzes and properly interprets the basic financial ratios of the co mpany. I hope it will be of great help for you to understand and target the areas where improvement is required and further take strategic actions to improve and sustain the strong growth areas of our business. Introduction Go Ahead Group Plc is a leading company in the public transport industry. The company has a high regard in the industry for having social, financial and environmental aims working together at its core strategic plans. The company aims at providing transportation in the urban and other areas with less delays, high environmental targets and social responsibility. The company has captured the highest share of the transportation industry by focusing on the KPIs it has set. The company has also improved its efficiency and effectiveness in operations by reducing waiting times, increasing punctuality, spending more on security and comfort of the people and availability of their services with proper schedules and plans. The company’s financial analysis and discuss ion on its core competences is given below. Ratio Analysis Current & Previous Year The Return on Net Assets ratio reveals that the company is using the net assets very efficiently in carrying out its operations. The company is employing and making higher than expected profits by properly allocating the assets. The Return on Shareholders’ Funds in 2011 and 2012 shows significant returns to meet the company’s profit demands. Moreover, it completely satisfies the shareholders on the use and allocation of their shareholdings as per the returns generated in the two years under consideration. The operating profit margin, although slightly lower than previous year, is satisfactory. Moreover, if the one-off benefit is eliminated, the company shows an increase of ?8.1m in operating profit. Hence, the actual operating profit margin is higher than the previous year further showing a strengthened financial position of the company. The ROCE of 17.6% and 19.2% is satisfactory in 201 2 and 2011 respectively (Appendix 1). The slight reduction is not worrying, yet it should be stopped from recurrence to maintain the position that the company holds. The net asset

Belief and Knowledge Essay -- essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  There are many contentions our present world has faced that require a thorough thought process in order to represent a side of the argument. We see that there are many different authorities that tell us we should be thinking in certain directions. However, most people need to realize that influence from these different sources such as academics, politicians, companies, global organizations, media, and others in this nebulous category, don’t always steer us in the write direction. Maybe they can provide us with knowledge about a certain problem, or information regarding each side, but when it comes down to the bottom, belief and knowledge seems to be what most people turn to. We see many people opposing social issues because of what their families have taught them, we see many people opposing scientific technology because of what their religion says. We see many people then opposing the â€Å"religious fanatics† because science is â€Å"the key to th e future.† And lastly we see many people opposing things just to cause trouble, and those are the types of protestors, I really cannot stand. But that’s beside the point. Reason and emotion are reflected in the way one uses them to distinguish between their faith and belief, and knowledge and solid fact. This can be shown through the abortion debate, stem cell research, and of course, the hot topic of 2004 and the near future- gay marriage.   Ã‚  Ã‚  Ã‚  Ã‚  First, abortion has really taken center stage in our society. Both sides of the argument have been well thought out and make good sense; it is up to you to decide which one you feel more strongly represents your views. Or, you must interpret it and make your own opinion. Those who oppose abortion are called the â€Å"pro-life† group. These people believe that the fetus is a living thing, and that it should not be killed for it has yet to develop fully into a human being. By taking the life of a fetus, one is taking the life of a potential human being, and an innocent child. The opposing view is usually referred to as â€Å"pro- choice.† This argument is strong in the sense that people believe they should be able to exercise their rights as a free human being, and if they choose to abort their child, it is the potential parent’s choice. You will find that many people that belong to the â€Å"pro- life† side are religious. Most who are on the... ...Scotts Valley High. It took a long while for me to develop my full thoughts, and although some teachers at my school may choose a different lifestyle than me, it does not make them a â€Å"bad† person, or one that has â€Å"sinned.† As far as I am concerned, God will send those who discriminate and harass and who are hateful all down to hell before any homosexual who has lived their life virtuously. However, I do not support gay marriage because I believe that marriage is sacred and that it is something that should stay between a man and a woman.   Ã‚  Ã‚  Ã‚  Ã‚  Through this class, I have learned so much about the world, different cultures, perception, how we know things, what influences us, but most of all, I have learned the most about myself. I have learned to work thorough all factors that may intimidate me into forming my own opinion about things, and I have found that things I really rely strongly on, are driven not by fact or knowledge, but by what I believe and what is in my heart. Being able to distinguish belief and knowledge comes down to the individual and whether they feel that they can be more persuaded through solid fact, or what they believe and have faith in.

A Modified 2-D Logarithmic Search Technique for Video Coding

A Modified 2-D Logarithmic Search Technique for Video Coding With Reduced Search Points Tahmina Akhtar† , Rahima Akter† , Chhalma Sultana Chhaya † , Ashfaqur Rahman †¡ †  Military Institute of Science and Technology/Dept of CSE, Dhaka, Bangladesh, †¡ Central Queensland University/Centre for Intelligent and Networked Systems, QLD, Australia [email  protected] com, [email  protected] com, [email  protected] com, a. [email  protected] edu. au Abstract Video coding is a process for representing video sequences in a compact manner.A significant step in video coding is searching for similar segments in previous frames and use only the difference information for reconstruction thus reducing space requirement. Different search techniques including Full search and 2-D logarithmic search etc. are used in the current literature. Full search restricts its application because of its computational load. 2D logarithmic search is computationally less expensive a lthough there are some spaces for improvement. In this paper we propose a new search technique by modifying the 2-D logarithmic search that requires less search points with insignificant loss in visual quality.Experimental results demonstrate the effectiveness of the proposed technique. Keywords: video coding, 2-D logarithmic search. i. INTRODUCTION Video is a sequence of still images representing scenes in motion. A video is created by capturing a numbers of still images in a short time interval. When these still images are displayed very quickly, it represents the motion of the object in the images. Video represent the huge amount of data. In order to transfer video data from one place to another efficiently it is required to compress the size of video data.One way to compress the size of video data is video coding [ [1] ] [ [2] ]. The principal goal in the design of a video-coding system is to reduce the transmission rate subject to some picture quality constraint. In transmissio n side, the first frame (normally called the reference frame) is transmitted as it is and the remaining frames are sent as a function of the reference frame. The frame to be sent is divided into a number of blocks and the best match for the block is looked for in the search window of the reference frame. This processing is called the search technique in video coding literature.There exist a number of video coding techniques including MPEG-1/2/4 [ [2] ] [ [7] ], H. 26X [ [8] ] etc. uses search techniques like Full search [ [1] ], 2-D logarithmic search [ [3] ], Coarse-Fine-Three-Step search [ [4] ], Conjugate Direction search [ [5] ], and Pyramid search [ [6] ]. Each of these search techniques has merits and demerits in their favor. Full search finds the best match for a block as it searches all the candidate positions in the search window. Full search however is computationally expensive and renders difficulty for real time implementation.Some variants exist that applies some heuris tics to reduce the candidate search points and reduce the computational complexity although compromising the image quality a bit. 2-D logarithmic search is one such search technique that reduces the search points to a subset of the search window (to be detailed in literature review) and finds the near-optimal best match with reduced computational complexity. Although computationally inexpensive it contains some redundancy in the search space. We aim to reduce this redundancy and aim to find a modified 2-D logarithmic search technique with even reduced computational load.Experimental results demonstrate that the proposed technique reduces the number of search points and thus reduces search time with insignificant sacrifice of image quality. The paper is organized as follows. In Section II we elaborate some related works. In Section III we present our proposed search approach. Some experimental results to demonstrate the effective of the proposed approach is presented in Section IV. F inally Section V concludes the paper. II. Related works In this section we present full search technique and the logarithmic search technique.In both cases the frame to be coded is divided into a number of non-overlapping equal size blocks of size p? q. The best match is looked for in a search window of size (2d+1)? (2d+1) in the reference frame . Fig 1: Block matching process in video coding that uses search techniques. * A. Full Search In Full search [ [1] ] finds the best match by inspecting all the (2d+1)? (2d+1) candidate positions within the search window. Full search procedure is brute force in nature. The advantage of Full Search is that it delivers good accuracy in searching for the best match.The disadvantage is that it involves a large amount of computation. * B. 2-D Logarithmic Search Jain and Jain [ [3] ] developed a 2-D logarithmic search technique that successively reduces the search area, thus reducing the computational burden. The first step computes the similarity for five points in the search window. These five points are as follows: the central point of the search window and the four points surrounding it, with each being a midpoint between the central point and one of the four boundaries of the window. Among these five points, the one corresponding to the minimum dissimilarity is picked as the winner.In the next step, surrounding this winner, another set of five points are selected in a similar fashion to that in the first step, with the distances between the five points remaining unchanged. The exception takes place when either a central point of a set of five points or a boundary point of the search window gives a minimum dissimilarity. In these circumstances, the distances between the five points need to be reduced. The procedure continues until the final step, in which a set of candidate points are located in a 3Ãâ€"3 2-D grid.The steps in a 2-D logarithmic search technique are presented in Fig 2. Fig 2: The 2-D logarithmic search tec hnique. The circle numbered n is searched at the n-th step. The arrows indicate the points selected as the center of the search for the next pass. The 2-D logarithmic search hits a maximum of 18 points and a minimum of 13 search points. The advantage of this technique is that it successively reduces the search area, thus reducing the computational burden. One of the disadvantages is that some points are searched more than once thus leave some space for improvement.Moreover, it follows a greedy approach by selecting the minimum dissimilar point at each step thus posing a threat to follow a local minimum trend. Considering these facts we propose to modify the 2-D logarithmic search to overcome the local minimum problem and also eliminate the redundant computing as described in the following section. iii. proposed search technique We mainly modified the 2-D logarithmic search technique to eliminate the redundancy and local minimum problem associated with it. The search technique is ela borated next under the light of 2-D logarithmic search technique.Our proposed search technique starts with the five points in the search window where the one is at the center and other four surrounds center point (Fig 3(a)). Unlike 2-D logarithmic search, our proposed technique selects two points min1 and min2 (Fig 3(b)) that has dissimilarity scores lower than the other three points. We then select a point as the center of search for the next pass that lies on the line in between min1 and min2. This selection reduces the local minimum effect as it simply does not follow the minimum point.Moreover, the five points selected in the next pass does not match with any of the previous points thus eliminates the redundancy that exists in 2-D logarithmic search. Centered at the point selected at the next pass the search continues (Fig 3(d)-Fig 3(f)). The steps of the search are portrayed in Fig 3. Following are some of the merits of our proposed technique: * Successively reduces the search area with no point searched twice * Maximum search points are 12 and minimum search points are 5 – an improvement over 2-D logarithmic search. iv. Results and DiscussionWe have conducted a comparative analysis of Full Search, 2-D logarithmic Search and our proposed search technique as presented next. All the experiments were conducted on MPEG sequences using MATLAB. We used sequences like garden, Akiyo, Table Tennis, Car, and coastguard. Full search, 2-D logarithmic search and our proposed technique applied in these standard MPEG file and we computed the ASNR (Average Signal to Noise Ratio) and Computational load (i. e. number of search points). The results on different sequences are presented next. Akiyo Sequence: Each frame of the Akiyo sequence is of 352? 88 pixels, recorded at 25 frames per second and there are a total of 398 video frames. Fig 4 shows the reconstructed 20th frame of Akiyo sequence coded using Full search, 2D-logarithmic search and proposed search techniqu e. In this video only face portion is moving. Search point comparison for these three search techniques is presented in Fig 5 and ASNR is reported in Fig 6. ASNR achieved using the proposed search technique is almost equal 2D logarithmic search but at reduced number of search points (Fig 5). Number of search points remains almost similar over the different frames.ASNR value shown in Table 1. (a)| (b)| (c)| (d)| (e)| (f)| Fig 3: The different steps of our proposed 2-D logarithmic search technique. (a) five points of search window, (b) the direction of the search in between the direction offered by the two points min1 and min2. (c) Search at step 2, (d) min1 and min2 at step 2, (e) Search points at step 3, and (f) Search ends at the blue point. (a)| (b)| (c)| Fig 4: Reconstructed 20th frame of the Akiyo sequence using (a) Full search, (b) 2-D logarithmic search, and (c) Our proposed search technique.Fig 5: Comparison of # of search points for Akiyo sequence. Fig 6: Comparison of ASNR for Akiyo sequence. Table 1: ASNR value of different search for Akiyo sequence Frame No| Full Search| 2D logarithmic Search| Proposed Search| 1st| 25. 86188| 25. 55678| 25. 46245375| 5th| 24. 84504| 23. 77938883| 23. 57562323| 10th| 24. 37532| 23. 01043038| 22. 67351877| 15th| 24. 38495| 22. 98908004| 22. 5831958| 20th| 24. 4424| 22. 90227928| 22. 56886825| 25th| 24. 44956| 23. 03416597| 22. 51615637| Car Sequence: Each frame of the Car sequence is of 320? 240 pixels and ecorded at 25 frames per second and there are a total of 398 video frames. The reconstructed 20th frame of Car sequence using the three search techniques is presented in Fig 7. In this video sequence the car moves but background is still. Here each repeated two times. Average no of search point is almost 10. 46 for repeated frames and 11. 50 for new frames. Here number of search points vary significantly compared to Akiyo sequence. Overall the proposed technique has reduced search points (Fig 8) although the ASNR is bit low (Fig 9). ASNR value of some frames shown in Table 2. a)| (b)| (c)| Fig 7: Reconstructed 20th frame of the Car sequence using (a) Full search, (b) 2-D logarithmic search, and (c) Our proposed search technique. Fig 8: Comparison of # of search points for Car sequence. Fig 9: Comparison of ASNR for Car sequence. Table 2: ASNR value of different search for Car sequence Frame No| Full Search| 2D logarithmic Search| Proposed Search| 1st| 27. 13312| 26. 5682| 26. 08265| 5th| 26. 68718| 25. 75123| 25. 16904| 10th| 26. 10589| 25. 12647| 24. 27394| 15th| 26. 31185| 25. 16266| 24. 54981| 20th| 26. 28613| 25. 1915| 24. 61234| 25th| 25. 86261| 25. 02255| 24. 12599| Garden Sequence: Each frame of the Garden sequence is of 352? 240 pixels and recorded at 30 frames per second and there are a total of 59 video frames. Fig 10 represents the reconstructed 20th frame of this sequence coded using the three search techniques. In this video the motion is due to camera movement. Fig 11 and Fig 12 reveals that the new search technique reduces the number of search points with minor loss in ASNR. ASNR value of some frames shown in Table 3. Here Average no of search point for each frames required almost same.In frame 20th average no of search point is 11. 6053 and ASNR is 18. 22931. (a)| (b)| (c)| Fig 10: Reconstructed 20th frame of the Garden sequence using (a) Full search, (b) 2-D logarithmic search, and (c) Our proposed search technique. Fig 11: Comparison of # of search points for Garden sequence. Fig 12: Comparison of ASNR for Garden sequence. Table 3: ASNR value of different search for Garden sequence Frame No| Full Search| 2Dlogarithmic Search| Proposed Search| 1st| 24. 27663| 24. 27663| 23. 5971| 5th| 21. 6078| 21. 6078| 20. 49847| 0th| 20. 71779| 20. 71779| 19. 34323| 15th| 19. 9641| 19. 9641| 18. 69269| 20th| 19. 6754| 19. 6754| 18. 22931| 25th| 19. 39791| 19. 39791| 18. 05226| Coastguard Sequence: Each frame of the Coastguard sequence is of 320? 240 pixels and recorde d at 25 frames per second and there are a total of 378 video frames. Here the boat and the camera are moving. Fig 13 represents a reconstructed frame of this sequence coded using the three search techniques. Fig 14 represents the search point required by the three techniques. Our proposed technique shows periodic nature in terms of search points.This is due to the repetitive nature of motion in the video. Fig 15 represents a comparison of ASNR obtained using different techniques. Table 4 shown ASNR of some frames. (a)| (b)| (c)| Fig 13: Reconstructed frame of the Coastguard sequence using (a) Full search, (b) 2-D logarithmic search, and (c) Our proposed search technique. Fig 14: Comparison of # of search points for Coastguard seq. Fig 15: Comparison of ASNR for Coastguard sequence. Table 4: ASNR value of different search for Coastguard seq. Frame No| Full Search| 2D logarithmic Search| Proposed Search| 1st| 24. 8771| 24. 33338| 23. 61801| 5th| 24. 31753| 23. 35416| 22. 54516| 10th| 23. 90367| 23. 03317| 22. 07546| 15th| 24. 36529| 23. 44171| 22. 66604| 20th| 24. 38658| 23. 26823| 22. 50994| 25th| 24. 54524| 23. 91583| 22. 91885| Table tennis Sequence: Each frame of the Table tennis sequence is of 352? 240 pixels and recorded at 30 frames per second and there are a total of 9 video frames. Here ball is moving fast. The reconstructed frames, number of search points, and ASNR of the three search techniques are presented in Fie 16, Fig 17, and Fig 18. Some ASNR of Table tennis sequence shown in table 5. a)| (b)| (c)| Fig 16: Reconstructed frame of the Table tennis sequence using (a) Full search, (b) 2-D logarithmic search, and (c) Our proposed search technique. Fig 17: Comparison of # of search points for Table tennis sequence. Overall the result of ASNR for Full Search is best in all cases but number of search point is so high. The result of ASNR for 2-D logarithmic and our proposed search is almost same but the number of search point of our proposed search is sm aller than the 2-D logarithmic search and thus an improvement over the existing technique.Fig 18: Comparison of ASNR for Table tennis sequence. Table 5: ASNR value of different search for Table tennis seq Frame No| Full Search| 2D logarithmicSearch| ProposedSearch| 1st| 25. 2698| 24. 56416| 23. 90544| 3rd| 23. 60795| 22. 69326| 21. 81273| 5th| 23. 43996| 22. 35007| 21. 29301| 7th| 23. 71878| 22. 71607| 21. 58383| v. Conclusion In this paper we have presented a new search technique for video coding that is a modification of the existing 2-D logarithmic search. The proposed technique reduces the search time of 2-D logarithmic search by reducing the redundant search points.Although ASNR is sacrificed to some extent it had insignificant visual impact as observed from the experimental results. References [1] Shi and H. Sun, â€Å"Image and Video Compression for Multimedia Engineering†, Fundamentals, Algorithms and Standards, 2nd Edition. [2] P. N. Tudor, â€Å"MPEG-2 Video Compre ssion†, IEEE J Langham Thomson Prize, Electronics and Communication Engineering journal, December 1995. [3] J. R. Jain and A. K. Jain, â€Å"Displacement Measurement and Its Application in Interframe Image Coding†, IEEE Transactions on Communications, vol. com-29, no. 12, December 1981. [4] T. Koga, K. Linuma, A. Hirano, Y. Iijima, and T.Ishiguro, â€Å"Motion-compensated interframe coding for video conferencing,† Proc. NTC’81, G5. 3. 1-G5. 3. 5, New Orleans, LA, Dec. 1981. [5] R. Srinivasan and K. R. Rao, â€Å"Predictive coding based on efficient motion estimation,† Proc. of ICC, 521-526, May 1984. [6] D. Tzovaras, M. G. Strintzis, and H. Sahinolou, â€Å"Evaluation of multiresolution block matching techniques for motion and disparity estimation,† Signal Process. Image Commun. , 6, 56-67, 1994. [7] MPEG-4, http://en. wikipedia. org/wiki/MPEG-4, last accessed in December 2008. [8] H. 264, http://en. wikipedia. org/wiki/H. 264, last accessed in December 2008. *

Using Shaping to Mold Child Behavior

Shaping (also known as successive approximation) is a teaching technique that involves a teacher rewarding a child as she or he successfully improves the acquisition of a target skill. Shaping is considered an essential process in teaching because behavior cannot be rewarded unless it first occurs. Shaping is intended to lead children in the direction of appropriate complex behavior, and then reward them as they complete each successive step. Best Practices for Behavior Shaping First, a teacher needs to identify the students strengths and weaknesses around a specific skill, and then break the skill into a series of steps that lead a child toward that target. If the targeted skill is being able to write with a pencil, a child might have difficulty holding a pencil. An appropriate assistive step-wise strategy might start with the teacher placing his or her hand over the childs hand, demonstrating to the child the correct pencil grasp. Once the child achieves this step, they are rewarded and the next step is undertaken. The first step for another student who is uninterested in writing but does like to paint might be providing the student with a paint brush  and rewarding the painting of a letter. In each case, you are helping a child approximate the topography of the behavior you want so that you can reinforce that behavior as the child grows and develops. Shaping may require a teacher to create a task analysis of the skill in order to create a roadmap for shaping the behavior or meeting the final skill goal. In that case, it is also critical for the teacher to model the shaping protocol for classroom para-professionals (teachers aides) so that they know what approximations are successful and which approximations need to be cleared and retaught. Although this may seem like a painstaking and slow process, the step and reward process deeply embeds the behavior in the students memory, so that he or she will be likely to repeat it. History Shaping is a technique that arose from behaviorism, a field of psychology established by B.F. Skinner and based on the relationship between behaviors and their reinforcement. Skinner believed that behaviors need to be reinforced by specific preferred items or food, but can be also paired with social reinforcement like praise. Behaviorism and behavioral theories are the foundations of applied behavior analysis  (ABA), which is used successfully with children who fall somewhere on the autistic spectrum. Although often considered mechanistic, ABA has the advantage of allowing the therapist, teacher, or parent to take a dispassionate look at the specific behavior, rather than focus on a moral aspect of the behavior (as in Robert should know that its wrong!). Shaping is not restricted to teaching techniques with autistic children. Skinner himself used it to teach animals to perform tasks, and marketing professionals have used shaping to establish preferences in a customers shopping behaviors. Examples Maria used shaping to help Angelica learn to feed herself independently, by helping Angelica use the spoon hand over hand — moving to touch Angelicas wrist until Angelica was finally able to pick up her spoon and eat from her bowl independently.While teaching Robert to use the toilet independently to urinate, his mother, Susan, saw that he had difficulty pulling up his pants. She decided to shape this step in her task analysis by praising and reinforcing his ability to pull his pants up to his knees, then stretching out the elastic waist to finish the step, and then helping Robert by using hand over hand to complete the pulling up pants step.One shaping experiment that Skinner conducted was when he and his associates decided to teach a pigeon to bowl. The target task was to get the bird to send a wooden ball down a miniature alley toward a set of toy pins, by swiping the ball with a sideward movement of its beak. The researchers first reinforced any swipe that looked like what they had in mind, then reinforced any that approximated what they wanted, and within a few minutes, they had succeeded.One way modern marketers use shaping is to provide a free sample of a product and include a coupon for the large discount on the purchase price. In the first purchase, the consumer would find a coupon for a smaller discount, and so forth, until the consumer no longer needs the incentives and has established the desired behavior. Sources Koegel, Robert L. Assessing and Training Teachers in the Generalized Use of Behavior Modification with Autistic Children, Dennis C. Russo, Arnold Rincover, Journal of Applied Behavior Analysis, Wiley Online Library, 1977. Peterson, Gail B. A Day of Great Illumination: B. F. Skinners Discovery of Shaping. Journal of the Experimental Analysis of Behavior, 10.1901/jeab.2004.82-317, National Center for Biotechnology Information, U.S. National Library of Medicine, November 2004, Bethesda, MD. Rothschild, Michael L. Behavioral Learning Theory: Its Relevance to Marketing and Promotions. Journal of Marketing, William C. Gaidis, Vol. 45, No. 2, Sage Publications, Inc., JSTOR, Spring 1981.