In this paper, we propose a system, which uses Harris Corner Detector , a mathematical operator that finds features in an image. It is simple to compute, and is fast enough to work on computers. Also, it is popular because it is rotation, scale and illumination variation independent. Harris Detector and SIFT based strategies have been successfully used to detect the features of the images. KNN addresses the pattern recognition problems and also the best choices for addressing some of the classification related tasks. The simple version of the K-Nearest Neighbor classifier algorithms is to predict the target label by finding the nearest Neighbor class. The closest class will be identified using the distance measures like Euclidean distance. In the proposed system, the K-Nearest Neighbor technique will be trained, which has the values of ingredients. When a userselects an ingredient, feature values of ingredient should match with the nearest value of the KNN classifier.
This article focuses on pipe cutting as an illustration of the generic approach, since this is a common repetitive task in nuclear decommissioning. The userselects the object to be cut from an on-screen image with a mouse click, whilst the computer control system determines the required position and orientation of the manipulators in 3D space, and calculates the necessary joint angles i.e. for one manipulator to grasp and stabilise the selected object and the other to position for a cutting operation. The approach is similar to Kent et al. , who use a single manipulator with two 3D cameras. By contrast, we have dual manipulators on a mobile platform, and use a single camera. Marturi et al.  discuss some of the challenges involved in a nuclear context, and the results of a related pilot study. In all these cases, it is clear that, to improve task execution speed and accuracy, high performance control of nonlinear manipulator dynamics is required.
The pattern-based layout approach allows for further user support: When the userselects a set of diagram components and, therefore, defines a sub-diagram, the layout engine can suggest those patterns from the set of all available patterns that may be applied to the sub-diagram. Computing these layout suggestions has been sketched in [MM12] already and is straight-forward: The set of all layout suggestions initially consists of the set of all available layout patterns. First, each pattern is removed from the set of suggestions that cannot be applied to the selected sub- diagram because the pattern either does not fit the chosen sub-diagram or is inconsistent with the currently active pattern instances. Next, each of the remaining patterns is “tried” to be applied to the selected sub-diagram, i.e., the layout engine computes the layout modifications that would be necessary if the patterns were applied. Only those patterns are kept in the set of suggestions that would require a small layout modification, the others are removed from the set. We use a rather simple metrics for deciding whether a layout modification is small as described in the following. Note that layout suggestions do not change the diagram layout but just suggest those patterns that are appropriate for the selected sub-diagram.
Abstract— Now a day’s user authentication is one of the most important topic in information security. In this project, we propose a new graphical password scheme for authentication. Here userselects number of images as a password and while login user needs to enter the random codes generated below each image, which has been set as a password. The GUA (Graphical User Authentication) or simply Graphical based Password on the fact that humans tend to remember images better. This type of interface provides an easy to create and remember passwords for the users. This Scheme provides a way of making more user-friendly passwords. Here the security of the system is very high and every time user needs to enter different set of random codes for authentication i.e. every time new password gets generated for every login attempt. Dictionary attacks, Brute Force attack, shoulder surfing attack and spyware attack are infeasible on this password scheme.
This work builds upon previous movement matching selec- tion techniques. The principle behind such techniques has been referred to by several names including rhythmic path mimicry , periodical motion coincidence , and feedback- based active selection , but is essentially the same across them: each selectable target on the interface presents a distinct movement and the userselects the desired target by matching (e.g. PathSync ) or counteracting (e.g. Eggheads ) the corresponding movement. Advantages of this principle in- clude multiple users having the ability to interact with a shared display without a cursor [22, 3], enabling interaction with feed- back modalities not suited for pointing [22, 19], no need to split the user’s attention between the target and the cursor , the possibility of fitting many targets in a small space due to target size independence [20, 5], and the high discoverability of the available gestures as they are continuously displayed [6,
The custom search would ensure quick searching for a given parking location input by the user. As soon as the userselects the parking area, the app connects to the cloud and fetches the latest information about the availability of slots in that parking and presents it to the user as shown in Fig.6. After the userselects a slot, a timer starts to run and the user is expected to reach the slot before the timer runs out (as in Fig.-7) and scan the QR code (as in Fig.-8) available adjacent to every slot.This ensures that the user parks the vehicle in the slot that was chosen while booking. If the QR code scanning fails then the user needs to book the slot again and so the user is taken back to the main window to reselect the slot.
Upon entering the nominal dimension, the userselects the tolerance classes for holes and shafts, then the application provides, the complete information regarding the chosen fit, all relevant values including, type of fit, basis system, if it is a recommended or preferable fit and a graphical representation of the tolerance intervals.
Android clients will register themselves to the system (application) .Then client authentication is performed by administrator after successful registration. If they are already registered then they will just login by using the UID and password. When the client logs in to the system ,the client will view three options.The first option will have the list of the different tourist locations and places,the second option will be the augmented view option which will have the augmented(360 degree panaromic view) of the different locations and the third option will be the GPS option. Whichever option the userselects, then that information is sent to the server.The server then requests the system and provides the result to the android client. If the userselects first option then from the list of different locations displayed the user will select anyone tourist location which he finds interesting and after selecting that location the user will be able to view the positive and negative comments given by different people who have already visited the location,rating of the location and some information about the location.If the user does not finds the location interesting then the user can directly move back and view the next location or tourist spot among the spots displayed and if the user finds the location interesting and wants to view that location then the user is supposed to go back to the window that displays the Three options and select the augmented view option.By selecting that option the user will be able to view the 360 degree panaromic view of the location which will give the user a feel that he is already present in that location.Apart from all these things a tourists also requires where exactly that location is and the nearby hotels , ATMs , bus stations etc ,So for this purpose the third option that is the GPS option will allow the user to have an idea regarding all these things.
impact on the DAS password space -- The size of DAS password space decreases significantly with fewer strokes for a fixed password length. The length of a DAS password also has a significant impact but the impact is not as strong as the stroke- count. To improve the security, Thorpe and van Oorschot proposed a “Grid Selection” technique. The selection grid is an initially large, fine grained grid from which the userselects a drawing grid, a rectangular region to zoom in on, in which they may enter their password (figure7). This would significantly increase the DAS password space. Goldberg et al.  did a user study in which they used a technique called “Passdoodle”. This is a graphical password comprised of handwritten designs or text, usually drawn with a stylus onto a touch sensitive screen. Their study concluded that users were able to remember complete doodle images as accurately as alphanumeric passwords. The user studies also showed that people are less likely to recall the order in which they drew a DAS password. However, since the user study was done using a paper prototype instead of computer programs, with verifications done by a human rather than computer, the accuracy of this study is still uncertain.
Further user, selects the grid size in which the image gets segmented into blocks and small segments. These segments are further stored in the database which are send to the server side and it gets shuffled accordingly. The shuffled fragmented images are presented to the user on which userselects the patterns depending on his will, he/she can either select some or all the segmented images.
The PassMatrix prototype is built with Android SDK 2.2.3 since it was the mainstream version of the distribution in 2012. After connecting to the Internet, users can Sign-Up an account, log in a few times in practice mode, and then log in for the experiment with a client’s device in the client side of our prototype, we used XML to build the user interface and used JAVA and Android API to implement functions, including username checking, pass-images listing, image is in grid, pass-squares selection, login indicator delivery, and the horizontal and vertical bars circulation. In the server side of our implementation, we used JAVA web server and MySQL to store and fetch registered accounts to/from the database to handle the password verification. Although in our proposed system we mentioned that users can import their own images, or display image which stored in local storage of sever side. Each image size is not greater than 20 Mb and is grid into 4x4 matrix format. Thus, users have 4x4 squares of pass-image. After a userselects any number of pass-square of image sequence, the password will be stored as a list of coordinates in a database table (i.e., the locations of those selected pass-squares in the 4x4 grid as show in fig.7.
IDF is zero) and TF-IDF in the documents (TF-IDF is weight of a term - the product of its TF weight and its IDF weight). Domain specific documents contain many significant compound words. Therefore, accurate extraction of compound words is necessary to construct domain ontologies. At this step, while considering part of speech (POS), TF, and so on, the userselects input terms which are significant terms for the domain. For certain domains, important terms do not occur in the documents. In such case, the Input Term Selection Module has a function, allowing the manual addition of important terms as input terms by the user. In order to prevent the leakage of the selection of input terms from the documents, the Input Term Selection Module maintains the relationships between the extracted terms and the terms in the documents as shown in Fig. 3.
We have not implemented this module yet. However we have conceptualised it as follows. A user interface is planned, to model this part, through which the speaker selects the proper lexical terms as well as declares his intention selecting the syntactico-semantic relations and the voice. The gender associated with the nominal stem is provided by the interface, and the user does not have to bother about it. The user only provides the nominal stem, chooses the number and its role with respect to the verb. In the case of verbs, the userselects the verb based on its meaning, and the information of pada and gaṇa is automatically picked by the interface, coding this information in the form of a subscript. User also chooses appropriate relations between the words. The user interface takes care of exceptional cases hiding the language specific information from the user. The output of this module is, for the example sentence under discussion, is as shown in the Table 1.
In given input image the userselects a target region Ω manually to be removed and filled. The source region, Φ may be defined as the total image minus the target region Φ= I- Ω. Next the size of window Ψ must be specified. Criminisi has stated it to be 9X9. After these parameters are defined, the region filling algorithm proceeds automatically. In this algorithm, each pixel maintains a colourvalue and a confidencevalue, which reflects confidence in the pixel value, and which is frozen once a pixel has been filled. During the course of algorithm, patches along the fill front are also given a temporary priorityvalue, which determines the order in which they are filled. Then this algorithm iterates the following three steps until all pixels have been filled
In Automation system is been developed to control the electrical appliances and the notice board digitally, so an android application is been developed to control the hardware system by which the android application which has different modules. The first module is the mode selector which the userselects the manual mode or automatic mode, if the user enables the automatic mode it redirects to either GSM control or Bluetooth control and if the user clicks the GSM mode in which the outdoor control of appliances can be done and also the control of digital notice board can be done by using the GSM mode and if the user enables the Bluetooth mode the indoor control of home appliances can be done respectively. The devices can also be controlled through any mode even it is turned on or off using another mode. The digital notice which has the typing screen for the display of messages this can be done by using GSM mode by which communication can be done easily and quickly. The automatic control of electronic devices can reduce the human work and time consumption. It also minimizes the unwanted power consumption. Typical functions are:
From photos to emails to credit card numbers, our mobile apps hold invaluable data that is often left unprotected, especially given that some of the most commonly used apps on the different platform don’t necessarily require a log-in each time they are launched. So if some other person get the mobile phone, he may misuse it. To address the problem, we are suggesting a mobile application that provides access control on the personal data in mobile of the user.