Investigating a Sequence of Numbers Essay Example

Investigating a Sequence of Numbers Essay Example Investigating a Sequence of Numbers Essay Investigating a Sequence of Numbers Essay In this Mathematics Portfolio, I am going to investigate a sequence of numbers by mathematical methods which I have learnt in the I.B. Mathematics HL course. Throughout the investigation, I will include all my workings in order to let examiners know exactly how I come up with the answers. A sequence is a set of numbers with a definite order. A series is a sum of a sequence. The sequence of numbers {an}?n =1 is: 1 x 1!, 2 x 2!, 3 x 3!, The two signs outside the bracket of an represent the range of the sequence. The bottom one is where the sequence begins and the one above is where it should end. Since it is stated the sequence starts from n = 1, therefore the first term, a1 = 1 x 1!, the second term, a2 = 2 x 2! and the third term, a3 = 3 x 3! The ! sign after the numbers is called a factorial notation. The notation basically means the product of all the numbers from 1 to the number with the notation. For example: 3! = 1 x 2 x 3 = 6 5! = 1 x 2 x 3 x 4 x 5 = 120 ? n! = 1 x 2 x 3 x 4 x x n 2! x 3 = 1 x 2 x 3 = 3! = 6 ? n! x (n + 1) = (n + 1)! Going back to the investigation, to find the nth term of the sequence, the steps are shown below: a1 = 1 x 1! = 1 a2 = 2 x 2! = 2 x 1 x 2 = 4 a3 = 3 x 3! = 3 x 1 x 2 x 3 = 18 a4 = 4 x 4! = 4 x 1 x 2 x 3 x 4= 96 . . . ? an = n x n! This is because looking at the sequence, I noticed that there is a similarity in each term. For an, when n = 1, the calculation will be 1 x 1!; when n = 2, the calculation will be 2 x 2!. Therefore from this pattern, the formula to find the nth term is: an = n x n! Let Sn = a1 + a2 + a3 + a4 + + an The term Sn means the summation of all the numbers in the sequence from the first term to the nth term. The mathematical explanation is shown above. For example, a sequence of numbers is 1,2,3,4,5,6,. S1 = 1 S2 = 1 + 2 = 3 S6 = 1 + 2 + 3 + 4 + 5 + 6 = 21 In the sequence that I am investigating, I am told to find Sn for different values of n: S1 = a1 = 1 x 1! = 1 S2 = a1 + a2 = 1 x 1! + 2 x 2! = 1 + 4 = 5 S3 = a1 + a2 + a3 = 1 x 1! + 2 x 2! + 3 x 3! = 1 + 4 + 18 = 23 S4 = a1 + a2 + a3 + a4 = 1 x 1! + 2 x 2! + 3 x 3! + 4 x 4! = 1 + 4 + 18 + 96 = 119 S7 = a1 + a2 + a3 + a4 + a5 + a6 + a7 = 1 x 1! + 2 x 2! + 3 x 3! + 4 x 4! + 5 x 5! + 6 x 6! + 7 x 7! = 1 + 4 + 18 + 96 + 600 + 4320 + 35280 = 40319 By using the information above, I will try to conjecture an expression for Sn. I am going to put all the datas on a table to see if there is any significant discovery. n 1 2 3 4 5 6 an 1 4 18 96 600 4320 Sn 1 5 23 119 719 5039 n! 1 2 6 24 120 720 From the table, I noticed that an is always the difference between n! and (n + 1)! The mathematical expression is: an = (n + 1)! n! Since (n + 1)! seems useful for the investigation, I did another table too: n 1 2 3 4 5 6 (n + 1)! 2 6 24 120 720 5040 Sn 1 5 23 119 719 5039 Looking at the row of (n + 1)! and Sn, there is a constant difference of 1 between them. Therefore I added the row of Sn to the second table (in black). When n = 1, (n + 1)! = 2 ; Sn = 1 n = 2, (n + 1)! = 6 ; Sn = 5 . . . n = 6, (n + 1)! = 5040; Sn = 5039 According to what I have discovered, Sn can be express mathematically in this way: Sn = 1 x 1! + 2 x 2! + 3 x 3! + + n x n! = (n + 1)! 1 To prove that my expression is right, I am using mathematical induction to verify the given result: Pn : 1 x 1! + 2 x 2! + 3 x 3! + + n x n! = (n + 1)! 1 Pk : 1 x 1! + 2 x 2! + 3 x 3! + + k x k! = (k + 1)! 1 If Pk+1 is true the result should be (k + 1 + 1)! 1 = (k + 2)! 1 Pk+1 : 1 x 1! + 2 x 2! + 3 x 3! + + k x k! + (k + 1) x (k + 1)! = (k + 1)! 1 + (k + 1) x (k + 1)! = (k + 1)! [(k + 1) + 1] 1 = (k + 1)! (k + 2) 1 = (k + 2)! 1 ? Pk+1 is true. P1 : LHS = 1 x 1! = 1 RHS = (1 + 1)! 1 = 1 ? P1 is true. ? Pn is true for all positive integers n. Already I have derived the formula an = (n + 1)! n! from the first table. But there is still another way to derive it just from the original formula: an = n x n! = (n + 1 1) x n! = (n + 1) x n! n! = (n + 1)! n! Sn = a1 + a2 + a3 + a4 + a5 + + an = (1 + 1)! 1! + (2 + 1)! 2! + (3 + 1)! 3! + (4 + 1)! 4! + (5 + 1)! 5! + + (n + 1)! n! = 2! 1! + 3! 2! + 4! 3! + 5! 4! + 6! 5! + + (n + 1)! n! At this step, I can see that many numbers cancel out each other except (-1) and (n + 1)! as it goes on to the last term: = -1! + (n + 1)! = (n + 1)! 1 Alternatively, I have used another method to prove of my conjecture for Sn: Sn = 1 x 1! + 2 x 2! + 3 x 3! + + n x n! = (n + 1)! 1 Let cn = an + an+1 According to what I have done before, an = (n + 1)! n! ? cn = (n + 1)! n! + (n + 1 + 1)! (n + 1)! = (n + 1)! n! + (n + 2)! (n + 1)! = (n + 2)! n! To simplify it: cn = (n + 2)! n! = (n + 2) (n + 1) (n!) n! = n! [(n + 2) (n + 1) 1] = n! (n2 + 2n + n + 2 1) = n! (n2 + 3n + 1) Tn = c1 + c2 + c3 + c4 + c5 + + cn To investigate Tn for different values of n, a table is drawn below showing all the values contribute to Tn: n 1 2 3 4 5 6 an : n x n! 1 4 18 96 600 4320 an+1 : (n + 1) x (n + 1)! 4 18 96 600 4320 35280 (n + 1)! 2 6 24 120 720 5040 (n + 2)! 6 24 120 720 5040 40320 n! 1 2 6 24 120 720 cn : (n + 2)! n! 5 22 114 696 4920 39600 T1 = c1 = 1 T2 = c1 + c2 = 5 + 22 = 27 T3 = c1 + c2 + c3 = 5 + 22 + 114 = 141 T4 = c1 + c2 + c3 + c4 = 5 + 22 + 114 + 696 = 837 T5 = c1 + c2 + c3 + c4 + c5 = 5 + 22 + 114 + 696 + 4920 = 5757 T6 = c1 + c2 + c3 + c4 + c5 + c6 = 5 + 22 + 114 + 696 + 4920 + 39600 = 45357 Tn 5 27 141 837 5757 45357 Looking at the column of (n + 1)!, (n + 2)! and Tn, when I add (n + 1)! to (n + 2)!, there is a constant difference of 3 between the sum and Tn. According to what I have found out, Tn can be express mathematically like this: Tn = (1 + 2)! 1! + (2 + 2)! 2! + (3 + 2)! 3! + (n + 2)! n! = (n + 1)! + (n + 2)! 3 Tn = c1 + c2 + c3 + c4 + c5 + + cn-1 + cn ? cn = (n + 2)! n! Tn = (1 + 2)! 1! + (2 + 2)! 2! + (3 + 2)! 3! + (4 + 2)! 4! + (5 + 2)! 5! + + (n 1 + 2)! (n 1)! + (n + 2)! n! = 3! 1! + 4! 2! + 5! 3! + 6! 4! + 7! 5! + + (n + 1)! (n 1)! + (n + 2)! n! At this step, I can see that many numbers cancel out each other except (-1!), (-2!), [(n + 1)!] and [(n + 2)!] as it goes on to the last term: = (n + 1)! + (n + 2)! 1! 2! ? Tn = (1 + 2)! 1! + (2 + 2)! 2! + (3 + 2)! 3! + (n + 2)! n! = (n + 1)! + (n + 2)! 3 In conclusion, throughout the investigation, I have used different methods to find out patterns of the sequences and successfully conjecture expressions for different sequences. Moreover, to prove the conjecture, I used not only by mathematical induciton, but also another method which I carried out for the last part. The 2 main conjectures I have made is: Sn = (n + 1)! 1 Tn = (n + 1)! + (n + 2)! 3 And both of the expressions are true for all positive integers n.

A Survey on Fingerprint Mathing Algorithms

A Survey on Fingerprint Mathing Algorithms In this networked world, users store their significant and less significant data over internet (cloud). Once data is ported to public Internet, security issues pop-up. To address the security issues, the present day technologies include traditional user-id and password mechanism and a onetime password (two-factor authentication). In addition to that, using the inexpensive scanners built into smartphones, fingerprint authentication is incorporated for improved security for data communication between the cloud user and the cloud provider. The age old image processing technique is revisited for processing the fingerprint of the user and matching against the stored images with the central cloud server during the initial registration process. In this paper, various fingerprint matching algorithms are studied and analyzed. Two important areas are addressed in fingerprint matching process: fingerprint verification fingerprint identification. The former compares two fingerprint and says they are similar or not; while the latter searches a database to identify the fingerprint image which is fed in by the user. Based on the survey on different matching algorithms, a novel method is proposed. Keywords: image processing, biometrics, fingerprint matching, cloud, security Introduction Automated fingerprint recognition systems have been deployed in a wide variety of application domains ranging from forensics to mobile phones. Designing algorithms for extracting salient features from fingerprints and matching them is still a challenging and important pattern recognition problem. This is due to the large intra-class variability and large inter-class similarity in fingerprint patterns. The factors responsible for intra-class variations are a) displacement or rotation between different acquisitions; b) partial overlap, especially in sensors of small area; c) non linear distortion, due to skin plasticity and differences in pressure against the sensor; d) pressure and skin condition, due to permanent or temporary factors (cuts, dirt, humidity, etc.); e) noise in the sensor (for example, residues from previous acquisitions); f) feature extraction errors. Fingerprint identification system may be either a verification system or an identification system depending on the context of the application. A verification system authenticates a person’s identity by comparing the captured fingerprint with her/his previously enrolled fingerprint reference template. An identification system recognizes an individual by searching the entire enrolment template database for a match. The fingerprint feature extraction and matching algorithms are usually quite similar for both fingerprint verification and identification problems. Fingerprint – Identification and Verification using Minutiae Based Matching Algorithms Fingerprints are commonly used to identify an individual. Research also suggests that fingerprints may provide information about future diseases an individual may be at risk for developing. Fingerprints are graphical flow-like ridges in palm of a human. Fingerprint is captured digitally using a fingerprint scanner. Fingerprints are commonly used to identify an individual. Research also suggests that fingerprints may provide information about future diseases an individual may be at risk for developing. Fingerprints are graphical flow-like ridges in palm of a human, that are unique amongst human beings. The hardware, fingerprint scanners are becoming low cost devices. The two most important ridge characteristics are ridge ending and ridge bifurcation. Automatic fingerprint identification systems (AFIS) have been widely used. An AFIS consists of two phases: offline and online. In the off-line phase, a fingerprint is acquired, enhanced using different algorithms, where features of the fingerprint are extracted and stored in a database as a template. In the on-line phase, a fingerprint is acquired, enhanced and features of the fingerprint are extracted, fed to a matching model and matched against template models in the database as depicted in the figure 1. Among all the biometric techniques, fingerprint-based identification is the most common used method which has been successfully used in numerous applications. Comparing to other biometric techniques, the advantages of fingerprint-based identification are as detailed below: The minutiae details of individual ridges and furrows are permanent and unchanging. The fingerprint is easily captured using low cost fingerprint scanner. Fingerprint is unique for every person. So it can be used to form multiple passwords to improve the security of the systems. Flow of Diagram representing the Fingerprint Identification The above figure clearly explains the simple methodology of fingerprint verification. In off-line process, the fingerprint of all users are captured and stored in a database. Before storing the raw or original image, the image is enhanced. The fingerprint image when captured for the first time may contain unwanted data ie noise. Because our hands being the most used part of our body may contain wetness, dry, oily or grease; and these images may be treated as noise while capturing the original fingerprint. And hence, to remove the noise, image enhancement techniques like adaptive filtering and adaptive thresholding. Original Fingerprint Image. The standard form factor for the image size is 0.5 to 1.25 inches square and 500 dots per inch. In the above original image, the process of adaptive filtering and thresholding are carried out. The redundancy of parallel ridges is a useful characteristic in image enhancement process. Though there may be discontinuities in a particular ridge, we can determine the flow by applying adaptive, matched filter. This filter is applied to every pixel in the image and the incorrect ridges are removed by applying matched filter. Thereby, the noise is removed and the enhanced image is shown in figure 3. Enhanced Fingerprint Image The enhanced image undergoes feature extraction process wherein: binarization and thinning take place. All fingerprint images do not share same contrast properties as the force applied while pressing may vary for each instance. Hence, the contrast variation is removed by this binarization process using local adaptive thresholding. Thinning is a feature extraction process where the width of the ridges is reduced down to a single pixel. The resultant feature extraction is shown below figure 4. Feature Extraction After Binarization and Thinning The process of minutiae extraction is done as the last step in feature extraction and then the final image is stored in database. Operating upon the thinned image, the minutiae are straightforward to detect and the endings are found at the termination points of thin lines. Bifurcations are found at the junctions of three lines. Feature attributes are determined for each valid minutia found. These consist of: ridge ending, the (x,y) location, and the direction of the ending bifurcation. Although minutia type is usually determined and stored, many fingerprint matching systems do not use this information because discrimination of one from the other is often difficult. The result of the feature extraction stage is what is called a minutia template, as shown in figure 5. This is a list of minutiae with accompanying attribute values. An approximate range on the number of minutiae found at this stage is from 10 to 100. If each minutia is stored with type (1 bit), location (9 bits each for x and y), and direction (8 bits), then each will require 27 bits say 4 bytes and the template will require up to 400 bytes. It is not uncommon to see template lengths of 1024 bytes. Minutiae Template Now, the online process starts. At the verification stage, the template from the claimant fingerprint is compared against that of the enrollee fingerprint. This is done usually by comparing neighborhoods of nearby minutiae for similarity. A single neighborhood may consist of three or more nearby minutiae. Each of these is located at a certain distance and relative orientation from each other. Furthermore, each minutia has its own attributes of type (if it is used) and minutia direction, which are also compared. If comparison indicates only small differences between the neighborhood in the enrollee fingerprint and that in the claimant fingerprint, then these neighborhoods are said to match. This is done exhaustively for all combinations of neighborhoods and if enough similarities are found, then the fingerprints are said to match. Template matching can be visualized as graph matching that is comparing the shapes of graphs joining fingerprint minutiae. A 1:1 matching cannot be carried out and we use a threshold value – termed as match score, usually a number ranging between 0 and 1. Higher the value, higher is the match. Figure 6: Few- Matching in online process Minutiae are extracted from the two fingerprints and stored as sets of points in the two dimensional plane. Minutia-based matching consists of finding the alignment between the template and the input minutiae feature sets, that results in the maximum number of minutiae pairs. 1) Weiguo Sheng et.al In their paper, the authors proposed a memetic fingerprint matching algorithm that aimed to identify optimal global matching between two sets of minutiae. The minutiae local feature representation called the minutiae descriptor that had information about the orientation field sampled in a circular pattern around the minutiae was used by them in the first stage. In the second stage, a genetic algorithm(GA) with a local improvement operator was used to effectively design an efficient algorithm for the minutiae point pattern matching problem. The local improvement operator utilized the nearest neighbor relationship to assign a binary correspondence at each step. Matching function based on the product rule was used for fitness computation. Experimental results over four fingerprint databases confirmed that the memetic fingerprint matching algorithm(MFMA) was reliable. 2) Kai Cao et al A penalized quadratic model to deal with the non-linear distortion in fingerprint matching was presented by the above authors. A fingerprint was represented using minutiae and points sampled at a constant interval on each valid ridge. Similarity between minutiae was estimated by the minutia orientation descriptor based on its neighboring ridge sampling points. Greedy matching algorithm was adopted to establish initial correspondences between minutiae pairs. The proposed algorithm used these correspondences to select landmarks or points to calculate the quadratic model parameters. The input fingerprint is warped according to the quadratic model, and compared with the template to obtain the final similarity score. The algorithm was evaluated on a fingerprint database consisting of 800 fingerprint images. 3) Peng Shi et.al In their paper, the authors proposed a novel fingerprint matching algorithm based on minutiae sets combined with the global statistical features. The two global statistical features of fingerprint image used in their algorithm were mean ridge width and the normalized quality estimation of the whole image. The fingerprint image was enhanced based on the orientation field map. The mean ridge width and the quality estimation of the whole image were got during the enhancement process. Minutiae were extracted on the thinned ridge map to form the minutiae set of the input fingerprint. The algorithm used to estimate the mean ridge width of fingerprint, was based on the block-level on non-overlap windows in fingerprint image. Four databases were used to compute the matching performance of the algorithm. 4) Sharat Chikkerur et.al The local neighborhood of each minutiae was defined by a representation called K-plet that is invariant under translation and rotation. The local structural relationship of the K-plet was encoded in the form of a graph wherein each minutiae was represented by a vertex and each neighboring minutiae by a directed graph. Dynamic programming algorithm was used to match the local neighborhood. A Coupled Breadth First Search algorithm was proposed to consolidate all the local matches between the two fingerprints. The performance of the matching algorithm was evaluated on a database consisting of 800 images. 5) Jin Qi and Yang Sheng Wang They proposed a minutiae-based fingerprint matching method. They defined a novel minutiae feature vector that integrated the minutiae details of the fingerprint with the orientation field information that was invariant to rotation and translation. It captured information on ridge-flow pattern. A triangular match method that was robust to non-linear deformation was used. The orientation field and minutiae were combined to determine the matching score. They evaluated the performance of their algorithm on a public domain collection of 800 fingerprint images. 6) Atanu Chatterjee et.al Another method for fingerprint identification and verification by minutiae feature extraction was proposed by the above authors. Minutiae were extracted from the thinned ridges from the fingerprint images and these feature matrices were applied as input data set to the Artificial Neural Network. Post processing was done to remove false minutia. Back propagation algorithm was used to train the network. Extracted features of the input fingerprint were verified with stored trained weights and threshold values. Experiments were conducted on 160 fingerprint images and the proposed system exhibited an accuracy of 95%. 7) Tsai Yang Jea et.al A flow network-based fingerprint matching technique for partial fingerprints was introduced by. For each minutiae along with its two nearest neighbors, a feature vector was generated which was used for the matching process. Minimum cost flow (MCF) problem algorithm was used to find the one-to-one correspondence between the feature vectors and the list of possibly matched features was obtained. A two hidden layer fully connected Neural Network was proposed to calculate the similarity score. Their experiments on two fingerprint databases showed that using neural networks for generating similarity scores improved accuracy. 8) Marius Tico et.al They have proposed a method of fingerprint matching based on a novel representation for the minutiae. The proposed minutiae representation incorporated ridge orientation information in a circular region, describing the appearance of the fingerprint pattern around the minutiae. Average Fingerprint Ridge period was evaluated to select the sampling points around the minutiae. Matching algorithm was based on point pattern matching. To recover the geometric transformation between the two fingerprint impressions, a registration stage was included. The Greedy algorithm was used to construct a set of corresponding minutiae. Experiments were conducted on two public domain collections of fingerprint images and were found to achieve good performance. 9) Asker M.Bazen et. al A minutiae matching method using a local and global matching stage was presented by Asker M. Bazen et. Al. Their elastic matching algorithm estimated the non-linear transformation model in two stages. The local matching algorithm compared each minutia neighborhood in the test fingerprint to each minutia neighborhood in the template fingerprints. Least square algorithm was used to align the two structures to obtain a list of corresponding minutia pairs. Global transformation was done to optimally register the two fingerprints that represented the elastic deformations by a thin-plate spline (TPS) model. The TPS model describes the transformed coordinates independently as a function of the original coordinates. Local and global alignments were used to determine the matching score. Conclusion This paper, we presented Fingerprint identification and verification based on minutiae based matching. The original fingerprint captures is pre-processed and the pattern is stored in the database for verification and identification. The pre-processing of the original fingerprint involves image binarization, ridge thinning, and noise removal. Fingerprint Recognition using Minutiae Score Matching method is used for matching the minutiae points. Usually a technique called minutiae matching is used to be able to handle automatic fingerprint recognition with a computer system. In this literature review, nine papers are explored and an insight is obtained regarding different methods. References: [1] Weiguo Sheng, Gareth Howells, Michael Fairhurst, and Farzin Deravi,(2007), â€Å"A Memetic Fingerprint Matching Algorithm†, IEEE Transactions On Information Forensics And Security. [2] Aparecido Nilceu Marana and Anil K. Jain, (2005), â€Å"Ridge-Based Fingerprint Matching Using Hough Transform†, IEEE Computer Graphics and Image Processing, 18th Brazilian Symposium pp. 112-119. [3] Koichi Ito, Ayumi Morita, Takafumi Aoki, Tatsuo Higuchi, Hiroshi Nakajima, and Koji Kobayashi, (2005), â€Å"A Fingerprint Recognition Algorithm using Phase-Based Image Matching for low quality fingerprints†, IEEE International Conference on Image Processing, Vol. 2, pp. 33-36. [4] Kai Cao, Yang, X., Tao, X., Zhang, Y., Tian, J. ,(2009), â€Å"A novel matching algorithm for distorted fingerprints based on penalized quadratic model†, IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1-5. [5] Anil K. Jain and Jianjiang Feng, (2011), â€Å"Latent Fingerprint Matching†, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 33, No. 1, pp. 88-100. [6] Unsang Park, Sharath Pankanti, A. K. Jain, (2008), â€Å"Fingerprint Verification Using SIFT Features†, SPIE Defense and Security Symposium, Orlando, Florida, pp. 69440K-69440K. [7] Anil Jain, Yi Chen, and Meltem Demirkus, (2007), â€Å"Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features†, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 29, No.1, pp. 15-27. [8] Mayank Vatsa, Richa Singh, Afzel Noore, Max M. Houck, (2008), â€Å"Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory†, Sciencedirect International Journal of Approximate Reasoning 50, no. 1, pp. 51–61. [9] Haiyun Xu, Raymond N. J. Veldhuis, Asker M. Bazen, Tom A. M. Kevenaar, Ton A. H. M. Akkermans and Berk Gokberk ,(2009), â€Å"Fingerprint Verification Using Spectral Minutiae Representations†,IEEE Transactions On Information Forensics And Security, Vol. 4, No. 3,pp. 397-409. [10] Mayank Vatsa, Richa Singh, Afzel Noore and Sanjay K. Singh ,(2009),â€Å"Combining Pores and Ridges with Minutiae for Improved Fingerprint Verification†, Elsevier, Signal Processing 89, pp.2676–2685. [11] Jiang Li, Sergey Tulyakov and Venu Govindaraju, (2007), â€Å"Verifying Fingerprint Match by Local Correlation Methods†, First IEEE International Conference on Biometrics: Theory, Applications,and Systems, pp.1-5. [12] Xinjian Chen, Jie Tian, Xin Yang, and Yangyang Zhang, (2006), â€Å"An Algorithm for Distorted Fingerprint Matching Based on Local Triangle Feature Set†, IEEE Transactions On Information Forensics And Security, Vol. 1, No. 2, pp. 169-177. [13] Peng Shi, Jie Tian, Qi Su, and Xin Yang, (2007), â€Å"A Novel Fingerprint Matching Algorithm Based on Minutiae and Global Statistical Features†, First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1-6. [14] Qijun Zhao, David Zhang, Lei Zhang and Nan Luo, (2010), â€Å"High resolution partial fingerprint alignment using pore–valley descriptors†, Pattern Recognition, Volume 43 Issue 3, pp. 1050- 1061. [15] Liu Wei-Chao and Guo Hong-tao ,(2014), † Occluded Fingerprint Recognition Algorithm Based on Multi Association Features Match â€Å", Journal Of Multimedia, Vol. 9, No. 7, pp. 910—917 [16] Asker M. Bazen, Gerben T.B. Verwaaijen, Sabih H. Gerez, Leo P.J. Veelenturf and Berend Jan van der Zwaag, (2000), A correlation-based fingerprint verification system , ProRISC 2000 Workshop

Multistep reserach Research Paper Example | Topics and Well Written Essays - 1500 words

Multistep reserach - Research Paper Example At the same time, burning it pollutes the environment as well. The objective of this paper is to discuss various forms of transport and their impacts to the society. Transport is a primary contributor to the process of industrialization. It facilitates the movement of raw materials to manufacturers, and processed products to potential buyers. It also assists in the creation of employment opportunities, for instance, it employs drivers and pilots (Organisation for Economic Co-operation and Development 90). Thus, enhances standards of living. Transport networks and mechanisms assist greatly during emergencies and natural calamities. They provide a means through which people and property can be moved from volatile to safer areas. Air transport involves the use of planes, choppers, and air balloons. It is the most efficient means of transport when it comes to business connectivity and efficiency. It is also reliable for leisure since it offers an aerial view for diverse views and is quite fast. Reliable air transportation facilitates international tourism and faster transportation of goods, particularly perishable goods, more than any other means. Thus, it is an important instrument for economic growth. It is a fundamental instrument of globalization. This is because, it has a high capacity for enhancing integration of political, economic, social and cultural activities at an international level. It is the most expensive mode of transport, and therefore, often reserved for affluent travelers (Daley 1). In the occurrence of natural calamities air transport is usually the most convenient mode of movement of goods and people. Air transport is often affected by adverse weather conditions and is quite uneconomical for short distance movement. In terms of security, air transport accidents are usually the harshest as it normally leads to massive damage of goods and loss of life.

Do police reduce crime(Know how to use stata) Research Paper

Do police reduce crime(Know how to use stata) - Research Paper Example After a terrorist attack that took place in July 1994, Argentina, the main Jewish center in Buenos Aires, it led to all Jewish institutions receiving police protection. Hence, this hideous event initiated a police force geographical allocation that can be presumed exogenous in a crime regression. Using data on the car thefts after and before the attack, the study found out a significant effect of police on crime. The impact was observed to be local, with no impact outside the small area where the police were deployed. Introduction In the study a different approach has been presented to estimate the police on crime causal effect. Terrorists exploded a bomb on July 18, 1994 that brought down the Asociacion Mutual Israelita Argentina (A.M.I.A.), which is the key Jewish center in Argentina. This saw 85 persons dyeing and at least 300 were injured in the attack. The federal government had to assign police one week later in order to protect every Muslim and Jewish building in the country. These were done because the institutions’ geographical distribution was presumed to be ‘exogenous in a crime regression,’ this hideous event is composed of a natural experiment where the simultaneous determination of police presence and crime might be broken. The motor vehicle thefts number per block information was collected in 3 neighborhoods in Buenos Aires after and before the terrorist attack. The information includes a period of 9 month starting April 1 to December 31, 1994. Information on each Jewish institution location in these neighborhoods was also collected. There was then an estimation of the police presence effect on car theft. The estimates indicated that blocks which received police protection had substantially fewer car thefts as compared to the rest of the neighborhoods. There was no evidence that the presence of police in a certain block reduced car theft one or two blocks that was away from the buildings which were protected. There has been a major interest to identify the mechanisms where presence of police reduces crime. Is it that the presence of police results to criminal activity to be less attractive or is it that police men arrested criminals and few of them were left around to com- on car thefts? The total number of car thefts per block was used as the dependent Variable; this gave a panel with nine observations for every given block. This data on blocks without and with protected institutions enabled us to define a control and treatment group. Month fixed effects were included that controlled any aggregate shocks in the crime evolution. The main procedure utilized in this study was regression analysis. It was utilized to explain the total variation of the dependent variable, Car theft. The dependent variable was accompanied by 9 variables, which were tested against the dependent variable to determine how much of the total variation is explained. The analyses also discussed the comparison of the different regress ion models, and determine which model is the most effective. In regards to the regression analysis results, it is clearly evident that model 2 is the strongest. The independent variables including street, dummy Jews institution one block away, block distance to closest institution and dummy gas station were omitted and not included in the second model because there P value was greater than 0.05 implying the data collected was either not valid or there was no enough evidence to make

Muted Women in Virginia Woolf’s A Room of One’s Own and Elizabeth Barre

Muted Women in Virginia Woolf’s A Room of One’s Own and Elizabeth Barrett Browning’s Aurora Leigh In the predominantly male worlds of Virginia Woolf’s A Room of One’s Own and Elizabeth Barrett Browning’s â€Å"Aurora Leigh (Book I)†, the women’s voices are muted. Female characters are confined to the domestic spheres of their homes, and they are excluded from the elite literary world. They are expected to function as foils to the male figures in their lives. These women are â€Å"trained† to remain silent and passive not only by the males around them, but also by their parents, their relatives, and their peers. Willingly or grudgingly, the women in Woolf and Browning’s works are regulated to the domestic circle, discouraged from the literary world, and are expected to act as foils to their male counterparts. Without the means of securing financial independence, women are confined to the world of domestic duties. In Woolf’s A Room of One’s Own, Mary Seton’s â€Å"homely† mother is neither a businesswoman nor a magnate on the Stock Exchange. She cannot afford to provide formal education for her daughters or for herself. Without money, the women must toil day and night at home, with no time for learned conversations about â€Å"archaeology, botany, anthropology, physics, the nature of the atom, mathematics, astronomy, relativity, geography† – the subjects of the men’s conversations (26). As Woolf notes, if Mary’s mother had gone into business, there would have been no Mary. Children are financial burdens and they make heavy demands on a mother’s time. It is impossible that a mother could feed and play with their children while making money, because women are expected to raise large families; they are the ones who ca rry o... ...n. And muted the women are, in A Room of One’s Own and â€Å"Aurora Leigh†. They cannot vocalize their opinions, wants, and needs when they are confined to their homes and discouraged from joining the predominantly male literary circles. Moreover, females are expected to act as foils to the males so that the patriarchal societies may flourish. Coleridge once said that a great mind is androgynous (Woolf, 106). When the men and women can cooperate and unite their minds and bodies, Shakespeare’s gifted sister will be able to re-emerge, freeing the muted voices of these oppressed women. WORKS CITED Woolf, Virginia. A Room of One’s Own. London: Flamingo, 1994. Browning, Elizabeth Barrett. â€Å"Aurora Leigh†. 1856. Correspondence Course Notes: ENGL 205*S Selected Women Writers I, Spring-Summer 2003, pp. 26, 27. Kingston, ON: Queen’s University, 2003.

Anna Avalon Character Sketch Essay

Anna Avalon, the adventurous and admirable main character of â€Å"The Leap’ written by Louise Erdich has many traits that prove her to be a very admirable woman. Her daughter is very grateful to have her as a mother. Throughout this short story, it is quite easy to see that Anna Avalon is talented, careful and brave. Considered to be â€Å"The surviving half of a blindfold trapeze act† (Pg. 190) Anna Avalon is very talented. She had previously been a performer. â€Å"Anna of the Flying Avalon’s† She had been involved in many performances and was definitely a crowd favourite, Anna had performed many â€Å"Double somersaults and heart-stopping catches† (Pg. 90) However, one day there had been a tragic accident. While seven months pregnant, lightning struck a pole resulting in three deaths. â€Å" Lightning struck the main pole and sizzles down the guy wires, filling the air with a blue radiance† (Pg. 192) Anna was the only one who survived this tragic accident, this showing her talent. Along with talented, Anna Avalon is very careful. She is an elderly lady living in New Hampshire, with sightless eyes. Although blind, â€Å"she has never upset an object or as much as brushed a magazine onto the floor. She has never lost her balance or bumped into a closet door left carelessly open. The â€Å"catlike precision of her movements’ (Pg. 190) is probably due to her early training. When caught in a house fire, Anna was willing to risk her own life in order to save her daughter. This shows that she’s a brave and courageous woman and would do anything for her child. Several years ago, Anna Avalon’s house caught on fire, when her daughter was just seven years old. The staircase to her upstairs room had been cut off by flames so everyone was outside thinking there was no rescue. â€Å"Outside, my mother stood below my dark window and saw clearly that there was no rescue. (Pg. 195) However, Anna Avalon did everything in her power to assure her daughter would be safe. â€Å"Standing there, beside Father, who was preparing to rush back around to the front of the house, my mother asked him to unzip her dress. When he wouldn’t be bothered, she made him understand. He couldn’t make his hands work, so she finally tore it off and stood there in her pearls and stockings. She directed one of the men to lean the broken half of the extension ladder up against the trunk of the tree. † (Pg. 195) Anna had well thought out a way to save her seven year old. She leaped through the icy-air and â€Å"was hanging by the backs of her heels from the new gutter†. (Pg. 195) She then tapped on the window to let her daughter know she came to rescue her. Although she was only in her underclothing, she had bigger things to worry about, such as saving her daughter. She successfully saved her daughter, showing her true heroism and bravery. Throughout â€Å"The leap’ it’s clear that Anna is very admirable. She has done many things in her life to help others and we see that she is a wonderful person. From her actions, Anna Avalon is talented, careful and brave.

Coffee Consumption The Health Benefits - 1831 Words

Coffee Consumption: The Health Benefits By: Kate Spinosa â€Å"Americans run on Dunkin†, this is a phrase that almost everyone knows or has heard in their lifetime. Over 500 billion cups of coffee are consumed annually worldwide [1]. There are on average 70 countries that grow the coffee plant for coffee production, and coffee trade is exceeding 10 billion dollars worldwide [1]. While water is the most consumed drink globally, coffee is the actually the next most consumed beverage [1]. I really did not have much background information prior on coffee, just that I need three cups a day to even function. This is because coffee is the richest source of caffeine for Americans, accounting for 71% of caffeine intake [2]. Eight ounces†¦show more content†¦Cancer unfortunately is such a common tragedy that many people encounter personally or through a family/friends lifetime. I wanted to know the specific components of coffee that decreased risks of developing cancer and how effective this was. I found that as of 2010 there were ne arly 400 published studies of epidemiologic research on the topic of coffee and cancer [4]. This shows that there is a large amount of interest in this topic and much research dedicated to discovering findings. Coffee consumption has claims to most typically help lessen the risk of developing liver, renal, skin, ovarian, colorectal, esophagus, and pharyngeal cancer [1]. The rich phytochemisty/antioxidant component of coffee is what lowers the risk of developing cancer. One study taken showed results of a 36% decrease risk for nonmelanoma skin cancer from six or more cups of caffeinated coffee daily [1]. Malignant hepatoma (HCC) is a common form of liver cancer which can be prevented more so with coffee consumption [1]. Liver cancer is the 5th most common cancer in males and 8th in females [5]. Three separate studies showed the results of a high consumption of coffee (6+ cups) compared to a almost never consumption (1 cup) which gave a 50%, 55% and 43% reduction rate for liver cancer in subjects [5]. Between 1990 and 2003 among 147,227 subjects, 3 cohorts, and 15 case-control studies, and 11 countries, the results showed a 24% lower risk of colon cancer per cup consumed fromShow MoreRelatedHealth Benefits And Side Effects On The Consumption Of Coffee2973 Words   |  12 PagesIntroduction The consumption of coffee is an essential staple to start an early morning in the United States and worldwide (Freedman et al. 2012). Like coffee, caffeine is a component that also can be present in energy drinks, tea, and pre-workout supplements. But to be specific, coffee, according to statistics has over 150 million people who drink an average of 3.2 cups of coffee daily, which means that about 400 million cups of coffee every day is consumed. (Patil et al. 2011) For some peopleRead MoreTopic On Benefits Of Drinking Coffee960 Words   |  4 PagesOutline Topic: Benefits of drinking coffee. General Purpose: To inform Specific Purpose: To inform the class of some of the many ways in which coffee can have a positive impact on your health. Thesis Statement: Drinking coffee can benefit your health in a number of ways, including lowering your risk of developing (1) cardiovascular problems, (2) neurological diseases, and (3) certain kinds of cancer. Introduction I. [Attention Getter] By show of hands, how many of you drink coffee on a daily orRead MoreShould Coffee Be Benefit Or Harm? Health?1447 Words   |  6 PagesxStatement of Position Coffee is one of the most popular beverages all over the world. Many people love the smell and taste of coffee, and rely on it to help them invigorate their brains and keep refreshed. 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