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Updates found with 'serious developmnt'

JAVA/ DOT NET PROJECTS ABSTRACT 2016-2017 CATCH YOU IF YOU MISBEHAVE: RANKED KEYWORD SEARCH RESULTS VERIFICATION IN CLOUD COMPUTING ABSTRACT: With the advent of cloud computing, more and more people tend to outsource their data to the cloud. As a fundamental data utilization, secure keyword search over encrypted cloud data has attracted the interest of many researchers recently. However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server’s misbehavior. With our systematically designed verification construction, the cloud server cannot know which data owners’ data are embedded in the verification data buffer, or how many data owners’ verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.EXISTING SYSTEM: However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server’s misbehavior.PROPOSED SYSTEM: Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes. proposed to save communication cost; Returning too much verification data would make the top-k ranked search meaningless. Additionally, in the ‘pay as you consume’ cloud computing environment, returning too much data would cause considerable expenses for data users, which would make the cloud computing lose its attractiveness.Hardware Requirements:• System : Pentium IV 2.4 GHz.• Hard Disk : 40 GB.• Floppy Drive : 1.44 Mb.• Monitor : 14’ Colour Monitor.• Mouse : Optical Mouse.• Ram : 512 Mb.Software Requirements:• Operating system : Windows 7 Ultimate.• Coding Language : ASP.Net with C#• Front-End : Visual Studio 2010 Professional.• Data Base : SQL Server 2008.Conclusion: In this paper, we explore the problem of verification for the secure ranked keyword search, under the model where cloud servers would probably behave dishonestly. Different from previous data verification schemes, we propose a novel deterrent-based scheme. During the whole process of verification, the cloud server is not clear of which data owners, or how many data owners exchange anchor data used for verification, he also does not know which data owners’ data are embedded in the verification data buffer or how many data owners’ verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Additionally, when any suspicious action is detected, data owners can dynamically update the verification data stored on the cloud server. Furthermore, our proposed scheme allows the data users to control the communication cost for the verification according to their preferences, which is especially important for the resource limited data users. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.
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JAVA/DOT NET PROJECTS ABSTRACT 2016-2017 PRACTICAL APPROXIMATE K NEAREST NEIGHBOR QUERIES WITH LOCATION AND QUERY PRIVACY ABSTRACT: In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in approximate kNN queries. The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries. Compared with existing solutions for kNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for kNN queries.EXISTING SYSTEM: Existing solutions for kNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for kNN queries. Performance has shown that our basic protocol performs better than the existing PIRbased LBS query protocols in terms of both parallel computation and communication overhead. Experiment evaluation has shown that our basic protocol is practical. Our future work is to implement our protocol on mobile devices. proposed a solution for continuous location-based services, assuming a query server and a service provider exist, where a query server holds the encrypted location while the service provider has the decryption key.Disadvantages: Performance has shown that our basic protocol performs better than the existing PIRbased LBS query protocols in terms of both parallel computation and communication overhead. Experiment evaluation has shown that our basic protocol is practical. Our future work is to implement our protocol on mobile devices.Proposed systeme: The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. proposed an interesting location-based alert system, where a mobile user keeps sending the encryption of his location to a LBS server and only when he enters a disaster area, the server is able to know his location and send an alert to him.Advantages: kNN query protocol does not have location privacy, then there is a PPT adversary A (the LBS provider) who has non-negligible advantage to break the location privacy of our protocol, i.e., winning Game 1. Now we use A to break the semantic security of the Paillier cryptosystem. Suppose our basic kNN query protocol does not have location privacy, then there is a PPT adversary A (the LBS provider) who has non-negligible advantage to break the location privacy of our protocol, i.e., winning Game 1. Now we use A to break the semantic security of the Paillier cryptosystem.SYSTEM CONFIGURATIONHARDWARE REQUIREMENTS: Hardware - Pentium Speed - 1.1 GHz RAM - 1GB Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGASOFTWARE REQUIREMENTS: Operating System : Windows Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS IDE : My Eclipse Web Server : Tomcat Tool kit : Android Phone Database : My SQL Java Version : J2SDK1.5
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JAVA/DOT NET PROJECTS ABSTRACT 2016-2107 TRUST AGENT-BASED BEHAVIOR INDUCTION IN SOCIAL NETWORKS ABSTRACT: The essence of social networks is that they can influence people's public opinions and group behaviors form quickly. Negative group behavior influences societal stability significantly, but existing behavior-induction approaches are too simple and inefficient. To automatically and efficiently induct behavior in social networks, this article introduces trust agents and designs their features according to group behavior features. In addition, a dynamics control mechanism can be generated to coordinate participant behaviors in social networks to avoid a specific restricted negative group behavior. This article investigates the importance of the endogenous selection of partners for trust and cooperation in market exchange situations, where there is information asymmetry between investors and trustees. We created an experimental-data driven agent-based model where the endogenous link between interaction outcome and social structure formation was examined starting from heterogeneous agent behaviour. By testing various social structure configurations, we showed that dynamic networks lead to more cooperation when agents can create more links and reduce exploitation opportunities by free riders. Furthermore, we found that the endogenous network formation was more important for cooperation than the type of network. Our results cast serious doubt about the static view of network structures on cooperation and can provide new insights into market efficiency.EXISTING SYSTEMS Online behavioral analysis and modeling has aroused considerable interest from closely related research fields such as data mining, machine learning, and information retrieval. This special issue provides a forum for researchers in behavior analysis to review pressing needs, discuss challenging research issues, and showcase state-of-the-art research and development in modern Web platforms. Research on network group behavior tendency generally can be divided into two areas: negative tendencies and hot-issue tendencies. For a negative tendency in group behavior, Yiting Zhang explained why violent behavior exists on the Internet and proposed countermeasure research to avoid it.PROPOSED SYSTEMS In Proposed systems by focusing on short texts published on social networks, one group of researchers proposed a biterm topic model that learns behavior topics by directly modeling the generation of word co-occurrence patterns (that is, biterms) in the corpus. The core problem of behavior induction in this article is as follows: with some restricted behaviors predetermined, how to induct participants in social networks to avoid these behaviors? There are all kinds of interaction relations between participants in social networks, but the most important one is trust. Abstractly, trust is the measure taken by one party about the willingness and ability of another party to act in the interest of the former party in a certain situation. However, there’s still no research on trust related to behavior induction in social networks—in particular, how to design features that make trust agents trusted by participants, maximize the effect of participant behaviors, and enhance the effectiveness of behavior induction.System Configuration:H/W System Configuration: Processor - Pentium –IIISpeed - 1.1 GhzRAM - 256 MB(min)Hard Disk - 20 GBFloppy Drive - 1.44 MBKey Board - Standard Windows KeyboardMouse - Two or Three Button MouseMonitor - SVGA S/W System Configuration: Operating System :Windows95/98/2000/XP  Application Server : Tomcat5.0/6.X  Front End : HTML, Java, Jsp Scripts : JavaScript. Server side Script : Java Server Pages. Database : Mysql Database Connectivity : JDBC.CONCLUSION: Now that we’ve proposed and experimentally validated our trust agent-based social behavior induction approach. In future work we’ll introduce Latent Dirichlet Allocation to abstract the behavior features of users in social networks, such as Twitter. We can construct links in behavior feature-driven social networks using the Pearson similarity of users’ behavior features.The explicit formulation of trust, reputation, and related quantities suggests a straightforward implementation of the model in a multi-agent environment.
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VLSI PROJECTS ABSTRACT: 2016-2017 EXPLOITING INTRACELL BIT-ERROR CHARACTERISTICS TO IMPROVE MIN-SUM LDPC DECODING FOR MLC NAND FLASH-BASED STORAGE IN MOBILE DEVICE ABSTRACT: A multilevel per cell (MLC) technique significantly improves the storage density, but also poses serious data integrity challenge for NAND flash memory. This consequently makes the low-density parity-check (LDPC) code and the soft-decision memory sensing become indispensable in the next-generation flash-based solid-state storage devices. However, the use of LDPC codes inevitably increases memory read latency and, hence, degrades speed performance. Motivated by the observation of intracell unbalanced bit error probability and data dependence in the MLC NAND flash memory, this paper proposes two techniques, i.e., intracell data placement interleaving and intracell data dependence aware LDPC decoding, to efficiently improve the LDPC decoding throughput and energy efficiency for the MLC NAND flash-based storage in a mobile device. Experimental results show that, by exploiting the intracell bit-error characteristics, the proposed techniques together can improve the LDPC decoding throughput by up to 84.6% and reduce the energy consumption by up to 33.2% while only incurring less than 0.2% silicon area overhead. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.EXISTING SYSTEM: Compared with single-level cell, MLC NAND flash memory allows more bits to be stored in a single cell. As the penalty for its high storage density, each MLC NAND flash memory cell has much less margin between two adjacent storage states and is thereby much more prone to errors. In order to reduce its raw bit error probability, Gray code is commonly used when mapping information bits to voltage levels in the MLC cells, so that neighboring levels only differ in 1 bit. Fig. 1 shows an example for 2-bit/cell NAND flash memory. The voltage levels S0, S1, S2, and S3 denote information bits 11, 10, 00, and 01, respectively. The most significant bit (MSB) and the least significant bit (LSB) are associated with upper page and lower page, respectively.PROPOSED SYSTEM: This section describes the proposed techniques and VLSI architecture that exploit intracell bit-error characteristics to improve the throughput and energy efficiency of min-sum LDPC decoding for the MLC NAND flash-based solid-state storage in the mobile devices.The proposed intracell data placement interleaving technique is shown in Fig. 2. To make one codeword contain both the upper page bit and the lower page bit belonging to the same flash memory cell, we divide each codeword into several subblocks, and place the subblocks into upper page and lower page alternately in an interleaved form. In particular, the size of each subblock is chosen to be half of the parallel processing bits of an LDPC decoder, and thus, the decoder could process data that are stored in the same flash cells simultaneously. One potential benefit of the proposed intracell data placement interleaving technique is to reduce the hard-decision decoding failure rate. Due to intracell unbalanced bit error probability, to interleave data placement into both the upper and lower pages can significantly reduce the percentage of data pages that have relatively high BER, even though their average BER almost remains the same. ADVANTAGES:• reduce the number of decoding iteration• increase the error correction strengthDISADVANTAGES:• decoding iteration is high• error correction strength is lowSOFTWARE IMPLEMENTATION:• Modelsim• Xilinx ISE
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VLSI PROJECTS ABSTRACT 2016-2017 LOW-POWER SYSTEM FOR DETECTION OF SYMPTOMATIC PATTERNS IN AUDIO BIOLOGICAL SIGNALS ABSTRACT:In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average, and coastline parameter for various wavelet coefficients of interest depending on the type of pattern to be detected. Furthermore, a multi-frequency spectrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have a similar frequency response and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This helps in implementing a low-power system as well as maintaining the efficacy of detection. The system is scalable in terms of user specificity as well as the type of signal to be analyzed for an audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the algorithmic modification of multi spectrum computation to implement low power system in the 65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90% correct classification of five types of audio health symptoms. We also scale the supply voltage due to lower frequency of operation and report a total power consumption of ∼184 µW at 700 mV supply. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2. EXISTING SYSTEM: In the past decade, rapid advancements in the development of low-power design methodologies have resulted in feasible designs for various wearable and implantable medical systems. Numerous wearable health monitoring systems have been proposed in order to deliver early warning of an impending health condition. These systems monitor various internal as well as external parameters related to the human health, such as temperature, heart rate, and so on. Apart from these parameters, it is well known that acoustic symptoms, such as cough, sneeze, belching, and so on, are early markers of serious health issues, such as influenza, diarrhea, and whooping cough, especially among children. If repetitive occurrence of these symptoms is detected in advance, it is possible for the patient or the healthcare personnel to commence remedial action prior to aggravation of the problem. In the literature, most of the developed systems detect a single acoustic symptom (cough or sneeze). The Kids Health Monitoring System (KiMS) proposed in uses wearable sensors and acoustic signal processing in order to provide health monitoring in children. Using the neural network-based processing, the KiMS classifies various symptoms and activities and, subsequently, transmits the record to a parent or doctor for further analysis.PROPOSED SYSTEM: We describe the proposed algorithm and the methodology used to modify the various computational tools in order to make it implementable into low-power hardware. In Section II, we had described the basics and justified the basis for selecting specific computational techniques used in developing this algorithm. The application of these computations is dependent on the characteristic property of the symptom to be detected. The algorithm methodology is shown in Fig. 1. We also describe the details along with the mapping of algorithm to specific signals as follows. ADVANTAGES:• efficient low-power health monitoring system DISADVANTAGES• High power for monitoring system.SOFTWARE IMPLEMENTATION:• Modelsim• Xilinx ISE
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DOT NET PROJECTS ABSTRACT 2016-2017 CATCH YOU IF YOU MISBEHAVE: RANKED KEYWORD SEARCH RESULTS VERIFICATION IN CLOUD COMPUTINGABSTRACT: With the advent of cloud computing, more and more people tend to outsource their data to the cloud. As a fundamental data utilization, secure keyword search over encrypted cloud data has attracted the interest of many researchers recently. However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server’s misbehavior. With our systematically designed verification construction, the cloud server cannot know which data owners’ data are embedded in the verification data buffer, or how many data owners’ verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.
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