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BCA PROJECT # 1 ONLINE EXAMINATION  SYSTEM 

FRONT END - ASP.NET, C#
BACK END - SQL SEVER 2007 

MCA FINAL YEAR PROJECT Project SYNOPSIS  OF ONLINE EXAMINATION  SYSTEM 

Final Report Of Online Examination System with full Documentation 

BCA PROJECT # 2- Hospital Information System

FRONT END - VB6.0

BACK END - SQL SEVER 2000

 SYNOPSIS of Project Sanatorium (Hospital Information System) 
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BCA Project 3#  ONLINE AUCTION SYSTEM

FRONTEND - C # AND .NET FRAMEWORK 

BACKEND - SQL SERVER 2008

SYNOPSIS OF PROJECT  ONLINE AUCTION SYSTEM

FINAL REPORT WITH FULL DOCUMENTATION and SOURCE CODE  OF PROJECT  ONLINE AUCTION SYSTEM

Active Learning Methods for Interactive Image Retrieval

 

  Scope of the project:

 

The aim is to build a fast and efficient strategy to retrieve the query

Concept in content-based image retrieval

 

Introduction:

 

Human interactive systems have attracted a lot of research interest in recent years, especially for content- based image retrieval systems. Contrary to the early systems, which focused on fully automatic strategies, recent approaches have introduced human-computer interaction. In this paper, we focus on the retrieval of concepts within a large image collection. We assume that a user is looking for a set of images, the query concept, within a database. The aim is to build a fast and efficient strategy to retrieve the query

Concept. In content-based image retrieval (CBIR), the search may be initiated using a query as an example. The top rank similar images are then presented to the user. Then, the interactive process allows the user to refine his request as much as necessary in

a relevance feedback loop. Many kinds of interaction between the user and the system have been proposed, but most of the time, user information consists of binary labels indicating whether or not the image belongs to the desired concept.

 

 

Modules:

 

1)      RGB Projection

2)      Image Utility     

     3)   Comparable Image

     4)   Similarity Images

     5)   Result

 

 

 

 

Module Description:

 

 

1) RGB Projections:

 

               The RGB color model is an additive color model in which red, green, and blue light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three additive primary colors, red, green, and blue. The main purpose of the RGB color model is for the sensing, representation, and display of images in electronic systems, such as conventional photography.

     In this module the RGB Projections is used to find the size of the image vertically and horizontally.  

      2) Image Utility:

 

            Whenever minimizing the error of classification is interesting for CBIR, this criterion does not completely reflect the user satisfaction. Other utility criteria

Closer to this, such as precision, should provide more efficient selections.

 

3)      Comparable Image:

 

              In this module a reselection technique to speed up the selection process, which leads to a computational complexity negligible compared to the size of the database for the whole active learning process. All these components are integrated in our retrieval system, called RETIN and the user gives new labels for images, and they are compared to the current classification. If the user mostly gives relevant labels, the system should propose new images for labeling around a higher rank to get more irrelevant labels.

 

4) Similarity measure:

 

          The results in terms of mean average precision according to the training set size (we omit the KFD which gives results very close to inductive SVMs) for both ANN and Corel databases. One can see that the classification-based methods give the best results, showing the power of statistical methods over geometrical approaches, like the one reported here (similarity refinement method).

 

 

5) Result:

          Finally, the image will take the relevant image what the user search. One can see that we have selected concepts of different levels of complexities. The performances go from few percentages of Mean average precision to 89%. The concepts that are the most difficult to retrieve are very small and/or have a much diversified visual content. The method which aims at minimizing the error of generalization is the less efficient active learning method. The most efficient method is the precision- oriented method.

 

• Graph:

This module is used to determine relationships between the two Images. The precision and recall values are measured by simulating retrieval scenario. For each simulation, an image category is randomly chosen. Next, 100 images are selected using active learning and labeled according to the chosen category. These labeled images are used to train a classifier, which returns a ranking of the database. The average precision is then computed using the ranking. These simulations are repeated 1000 times, and all values are averaged to get the Mean average precision. Next, we repeat ten times these simulations to get the mean and the standard deviation of the MAP

Input/Output:

The image will take the relevant image what the user search. one can see that we have selected concepts of different levels of complexities. The performances go from few percentages of Mean average precision to 89%. The concepts that are the most difficult to retrieve are very small and/or have a very diversified visual content.

Literature review:

There are alternative ways to avoid the scheduling latency issue described above.

 

 The main options are:

1) Bring the scheduler closer to the adapters;

2) Use provisioning (circuit switching);

 3) Use a buffered switch core;

 4) Eliminate the scheduler altogether.

Although one can attempt to locate the scheduler as close to the adapters as possible, a certain distance determined by the system packaging limitations and requirements will remain. Although the RTT can be minimized, the fundamental problem of non-negligible RTTs remains valid. One can also do without cell-level allocation and rely on provisioning to resolve contention. Of course, this approach has several well-known drawbacks, such as a lack of flexibility, inefficient use of resources, and long set-up times when a new connection is needed, which make this approach unattractive for Parallel computer interconnects. An alternative approach is to provide buffers in the switch core and employ some form of link-level flow control (e.g.,credits) to manage them. As long as an adapter has credits, it can send immediately without having to go through a centralized scheduling process. However, as optical buffering technology is currently neither practically nor economically feasible and the key objective of OSMOSIS is to demonstrate the use of optics, this is not an option.

 

The last alternative is the load-balanced Birkhoff–von-Neumann switch, which eliminates the scheduler entirely. It consists of a distribution and a routing stage, with a set of buffers at the inputs of the second stage. Both stages are reconfigured periodically according to a sequence of permutation matrices.The first stage uniformizes the traffic regardless of destination, and the second stage performs the actual switching. Its main advantage is that, despite being crossbar-based, no centralized scheduler is required. Although this architecture has been shown to have 100% throughput under a technical condition on the traffic, it incurs a worst-case latency penalty of time slots: if a cell arrives at an empty VOQ just after the VOQ had a service opportunity, it has to wait for exactly time slots for the next opportunity. The mean latency penalty is time slots plus a minimum transit latency intrinsically added by the second stage. Moreover, missequencing can occur. This approach results in overall lower latency if the total architecture-induced latency penalty can be expected to be less than the control-path

latency In a traditional IQ switch. In the OSMOSIS system this is not the case, hence we choose the centrally-scheduled architecture.

 

SPECULATIVE TRANSMISSION:

 

Our objective is to eliminate the control-path latency in the absence of contention. To this end, we introduce a speculative transmission (STX) scheme. The principle behind STX is related to that of the original ALOHA and Ethernet protocols: Senders compete for a resource without prior scheduling. If there is a collision, the losing sender(s) must retry their data transmissions in a different time slot. However, the efficiency of ALOHA-like protocols is very poor (18.4% for pure ALOHA and 36.8% for slotted ALOHA)  because under heavy load many collisions occur, reducing the effective throughput. Therefore, we propose a novel method to combine scheduled and speculative (non-scheduled) transmissions in a crossbar switch. The objective is to achieve reduced latency at low utilization owing to The speculative mode of operation and achieve high maximum Throughput owing to the scheduled mode of operation.

 

We consider the presence of multiple receivers per output port, allowing up to cells to arrive simultaneously. Although in OSMOSIS , we are interested in the general case with here. We exploit this feature to improve the STX success rate. The first receiver is for either a scheduled or a speculative cell. The extra receivers can accommodate additional speculative cells. Correspondingly, the STX arbitration can acknowledge multiple STX requests per output per time slot. The following rules govern the design of the STX scheme: Upon cell arrival, a request for scheduling (REQ) is issued to the central scheduler. This request is processed by a bipartite graph matching algorithm, and will eventually result in a  corresponding scheduled grant (GRT). An adapter is eligible to perform an STX in a given time slot if it has no grant for a scheduled transmission in that time slot. Performing an STX involves selecting a cell, sending it on the data path, and issuing a corresponding speculative request (SRQ) on the control path. When multiple cells collide, cells proceed and the remaining cells are dropped. If the number of colliding cells is smaller than or equal to , all cells proceed. If more than cells collide, a scheduled cell (if present) always proceeds. Moreover, or (if a scheduled cell is present) randomly chosen speculative cells proceed. Every cell may be speculatively transmitted at most once. Every speculative cell remains stored in its input adapter until it is either acknowledged as a successful STX or receives a scheduled grant. The scheduler acknowledges every successful speculative cell to the sending input by returning an acknowledgment (ACK). To this end, every cell, SRQ, and ACK carries a sequence number. However, when a grant arrives before theACK, a cell is transmitted a second time. These are called duplicate cells as opposed to the pure cells, which are transmitted through grants but are not duplicate. The corresponding grants are classified as duplicate and pure accordingly.Every grant is either regular, spurious, or wasted. It is regular if it is used by the cell that initiated it. A grant corresponding to a successfully speculatively transmitted and acknowledged cell is spurious when used by another cell residing in the same VOQ, resulting in a spurious transmission, or wasted if the VOQ is empty.If it is wasted, the slot can be used for a speculative transmission.

STX Policy

 

According to , an adapter performs an STX in a given time slot if it receives no grant at and has an eligible cell. If it receives a grant, it performs the corresponding scheduled transmission. allows the STX scheme to operate in conjunction with regular scheduled transmissions, which take precedence? over the speculative ones. Accordingly, we distinguish between scheduled and speculative cells. When an adapter is eligible to perform an STX, it selects a non-empty VOQ according to a specific STX policy, dequeue

its HOL cell and stores it in a retransmission buffer, marks the cell as speculative, and sends it to the crossbar. On the control path, it sends an SRQ indicating that a cell has been sent speculatively to the selected output. Both the cell and the SRQ comprise a unique sequence number to enable reliable, in-order, single-copy delivery. The STX policy defines which VOQ the adapter selects when it is eligible to perform an STX. This policy can employ, e.g., a random (RND), oldest-cell-first (OCF), or youngest-cell-first

(YCF) selection. First, we consider the OCF policy. It chooses the cell that has been waiting longest at the input adapter for an STX opportunity.

 

 Collisions:

 

An important consequence of STX is the occurrence of collisions in the switch fabric: As STX cells are sent without prior arbitration, they may collide with either other STX cells or scheduled cells destined to the same output, and as a result they may  be dropped. In OSMOSIS, it is possible to always allow up to cells to “survive” the collision, because the colliding cells do not share a physical medium until they arrive at the crossbar. The scheduler knows about incoming STX cells from the accompanying SRQs on the control path, and it also knows which scheduled cells have been scheduled to arrive in the current time slot. Therefore, it can arbitrate  between arriving STX cells if necessary and configure the crossbar to allow up to to pass, while dropping the others. Therefore, transmissions are always successful, even in the case of a collision. This is an important difference to ALOHA or Ethernet, where all colliding cells are lost. When multiple STX cells collide, we can forward up to of them, but when a scheduled cell collides with one or more STX cells, the scheduled cell always takes precedence to ensure that STX does not interfere with the basic operation of the underlying matching algorithm (see and ). Note also that the matching algorithm ensures that collisions between scheduled cells can never occur. The collision arbitration operates as follows. Before resolving contention among SRQs destined to port , a scheduled matching for the time slot under consideration must be ready. For every matched port , a number of SRQs are randomly accepted and the others denied. For every unmatched port , a number of SRQs are randomly accepted and the others denied. Granting SRQs does not affect the operation of the matching algorithm, e.g., in the case of -SLIP, the round-robin pointers are not updated. The scheduler notifies the sender of a successful SRQ by means of an acknowledgment (ACK). Of course, it also issues the regular grants according to the matching. These grants may cause duplicate cell transmissions as described in . The scheduler does not generate explicit negative acknowledgments (NAK) for dropped cells.

 

 

 Retransmission:

 

Collisions imply cell losses and out-of-order (OOO) delivery, which in turn imply a need for link-level retransmissions and ACKs, as this loss probability is orders of magnitude higher than that due to transmission errors. Reliability and ordering can be restored by means of a reliable delivery (RD) scheme. Any RD scheme requires that an STX cell remain in the input adapter  buffer until successfully transmitted. The ACKs are generated by the scheduler for every successful STX cell and include the

sequence number of the acknowledged cell. specifies that a speculative cell remains stored in the adapter until either of the following two events occurs:

 

• The cell is positively acknowledged, i.e., an ACK arrives  with the corresponding sequence number. The cell is dequeued and dropped.

• A grant for this output arrives and the cell is the oldest unacknowledged STX cell. When a grant arrives and there are any unacknowledged STX cells for the granted output, the oldest of these is dequeued and retransmitted. Otherwise, the HOL cell of the VOQ is dequeued and transmitted, as usual. This rule implies that unacknowledged STX cells take precedence over other cells in the VOQ, to expedite their reliable, in-order delivery.

According to , unacknowledged STX cells are never eligible for STX, because they have already been transmitted speculatively once. Allowing only one STX attempt per cell reduces the number of STXs, which increases their chance of success. Moreover, if an STX cell fails, the potential gain in latency has been lost in any case, so retrying the same cell serves no purpose. This is also the reason for not using explicit NAKs.

 

According to and , a non-wasted grant can be classified in two orthogonal ways: It is either pure or duplicate, and it is either regular or spurious depending on whether it is used by the cell that initiated it.

There are several methods of achieving reliable, in-order delivery in the presence of STX, e.g., Go-Back-N (GBN) and Selective Retry (SR). First, we consider SR. SR allows a predetermined maximum number of cells per output to be unacknowledged at each input at any given time. STX cells are stored in retransmission (RTX) queues (one RTX queue per VOQ). The output adapter accepts cells in any order and performs resequencing to restore the correct cell order. To this end, it has a resequencing queue (RSQ) per input to store OOO cells until the missing ones arrive. The input adapter accepts ACKs in any order. This implies that only the failed STX cells need to be retransmitted, hence the name Selective   Retry, as opposed to retransmitting the entire RTX queue as is done with GBN. SR requires resequencing logic and buffers at every output adapter. In addition, the RTX queues require a random-out organization, because cells can be dequeued from any point in the queue. However, SR minimizes the number of retransmissions, thus improving performance.

 

Technique used or algorithm used:

The RETIN active learning strategy for interactive learning in content-based image retrieval context is presented. The classification framework for CBIR is studied and powerful classification techniques for information retrieval context are selected. After analyzing the limitation of active learning strategies to the CBIR context, we introduce the general RETIN active learning scheme, and the different components to deal with this particular context. The main contributions concern the boundary correction to make the retrieval process more robust, and secondly, the introduction of a new criterion for image selection that better represents the CBIR objective of database ranking. Other improvements, as batch processing and speed-up process are proposed and discussed.

Advantages:

• It is used to reduce the computational time.

Application:

• The computation time is also an important criterion for CBIR in generalized applications, since people will not wait several minutes between two feedback steps. Furthermore, a fast selection allows the user to provide more labels in the same time. Thus, it is more interesting to use a less efficient but fast method than a more efficient but highly-computational one.
• It will reduce the control-path latency incurred between issuance of a request and arrival of the corresponding grant.

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7.4 Libraries

Android includes a set of C/C++ libraries used by various components of The project for bca final year free download  Android system. These capabilities are exposed to developers through the Android application framework. Some of the core libraries are listed below:

System C library - a BSD-derived implementation of The project for bca final year free download  standard C system library (libc), tuned for embedded Linux-based devices

Media Libraries - based on PacketVideo's OpenCORE; the libraries support playback and recording of many popular audio and video formats, as well as static image files, including MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG

Surface Manager - manages access to the display subsystem and seamlessly composites 2D and 3D graphic layers from multiple applications

LibWebCore - a modern web browser engine which powers both The project for bca final year free download  Android browser and an embeddable web view

SGL - the underlying 2D graphics engine

3D libraries - an implementation based on OpenGL ES 1.0 APIs; the libraries use either hardware 3D acceleration (where available) or the included, highly optimized 3D software rasterizer

FreeType - bitmap and vector font rendering

SQLite - a powerful and lightweight relational database engine available to all applications

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7.5 Android Runtime  Free download MCA projects in VB.NET with documentation

Android includes a set of core libraries that provides most of The project for bca final year free download  functionality available in the core libraries of the Java programming language.

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The Dalvik VM relies on the Linux kernel for underlying functionality such as threading and low-level memory management.

an Android code editor that helps you write valid XML for your Android manifest and resource files.

It will even export your project into a signed APK, which can be distributed to users.

To begin developing Android applications in the Eclipse IDE with ADT, you first need to download The project for bca final year free download  Eclipse IDE and then download and install the ADT plugin. To do so, follow the steps given in Installing the ADT Plugin  Free download MCA BSc CS projects in C#.NET with documentation

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The project for bca final year free download  Android Development Tools (ADT) plugin for Eclipse adds powerful extensions to the Eclipse integrated development environment. It allows you to create and debug Android applications easier and faster. If you use Eclipse, the ADT plugin gives you an incredible boost in developing Android applications:

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7.7 Creating an Android project

The project for bca final year free download  ADT plugin provides a New Project Wizard that you can use to quickly create a new Android project (or a project from existing code). To create a new project:

Select File > New > Project.
Select Android > Android Project, and click Next.
Select the contents for the project:

Enter a Project Name. This will be the name of The project for bca final year free download  folder where your project is created.

Under Contents, select Create new project in workspace. Select your project  Free download MCA projects in PHP with documentation  .workspace location.

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Note: You can change your the Build Target for your project at any time: Right-click the project in The project for bca final year free download  Package Explorer, select Properties, select Android and then check the desired Project Target.

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Enter a Package name. This is the package namespace (following the same rules as for packages in the Java programming language) where all your source code will reside.

Select Create Activity (optional, of course, but common) and enter a name for your main Activity class.

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Enter a Min SDK Version. This is an integer that indicates The project for bca final year free download  minimum API Level required to properly run your application. Entering this here automatically sets the minSdkVersion attribute in the of your Android Manifest file. If you're unsure of the appropriate API Level to use, copy The project for bca final year free download  API Level listed for the Build Target you selected in the Target tab. Free Download MCA BTech BE MSc BSc IT CS projects in PHP with MY SQL

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Once you complete the New Project Wizard, ADT creates The project for bca final year free download  following folders and files in your new project:

src/

Includes your stub Activity Java file. All other Java files for your application go here.

/ (e.g., Android 1.1/)

Includes the android.jar file that your application will build against. This is determined by the build target that you have chosen in the New Project Wizard.

gen/

This contains The project for bca final year free download  Java files generated by ADT, such as your R.java file and interfaces created from AIDL files.

assets/

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res/

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AndroidManifest.xml

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default.properties

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