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Updates found with 'identification'

JAVA/ DOT NET PROJECT ABSTRACT 2016-2017 SBVLC: SECURE BARCODE-BASED VISIBLE LIGHT COMMUNICATION FOR SMART PHONES ABSTRACT:ABSTRACT: As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive applications including payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. In this paper, we propose SBVLC - a secure system for barcode-based visible light communication (VLC) between smart phones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop two secure data exchange schemes. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and mobile payment. SBVLC is evaluated through extensive experiments on both Android and I OS smart phones.EXISTING SYSTEM: Short-range communication technologies including near field communication (NFC) and 2D barcodes have enabled many popular smart phone applications such as contactless payments, mobile advertisements, and device pairing. Evolved from the RFID technology, NFC can enable reliable low-power communication between RF tags and readers. However, NFC requires additional hardware and has been supported by only a few smart phone platforms on the market. Recent studies have shown that NFC is subject to security vulnerabilities such as eavesdropping and jamming. Moreover, most existing barcode applications are based on a single barcode exchange, which is insufficient for establishing a secure communication channel. Whenever a user types in her password in a bank’s sign in box, the key logger intercepts the password. The threat of such key loggers is pervasive and can be present both in personal computers and public kiosks; there are always cases where it is necessary to perform financial transactions using a public computer although the biggest concern is that a user’s password is likely to be stolen in these computers. Even worse, key loggers, often root kitted, are hard to detect since they will not show up in the task manager process list.PROPOSED SYSTEM: Compared with NFC, 2D barcodes have enjoyed a significantly higher penetration rate in mobile applications. This is largely due to the extremely low barrier to adoption – almost every camera-enabled smart phone can read and process 2D barcodes. As an alternative to NFC, 2D barcodes have been increasingly used for security-sensitive applications including mobile payments and personal identification. For instance, PayPal recently rolled out a barcode-based payment service for retail customers. As one of the most anticipated new features of I Phone 5, the Passbook App stores tickets, coupons, and gift/loyalty cards using barcodes. Proposes an iterative Increment Constrained Least Squares filter method for certain 2D matrix bar codes within a Gaussian blurring ersatz. In particular, they use the L-shaped finder pattern of their codes to estimate the standard deviation of the Gaussian PSF, and then restore the image by successively implementing a bi-level constraint, our approach to solving the problem is to introduce an intermediate device that bridges a human user and a terminal. Then, instead of the user directly invoking the regular authentication protocol, she invokes a more sophisticated but user-friendly protocol via the intermediate helping device. Every interaction between the user and an intermediate helping device is visualized using a Quick Response (QR) code. The goal is to keep user-experience the same as in legacy authentication methods as much as possible, while preventing key logging attacks.ADVANTAGE:• Compared with NFC, 2D barcodes have enjoyed a significantly higher penetration rate in mobile applications.• As an alternative to NFC, 2D barcodes have been increasingly used for security-sensitive applications including mobile payments and personal identification.• Every interaction between the user and an intermediate helping device is visualized using a Quick Response (QR) code.• Preventing key logging attacks.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 : Java. Front-End : Eclipse. Data Base : SQLite Manger.CONCLUSION: As an alternative to NFC, 2D barcodes have been increasingly used for security-sensitive applications including mobile payments and personal identification. Compared with NFC, 2D barcodes have enjoyed a significantly higher penetration rate in mobile applications. As an alternative to NFC, 2D barcodes have been increasingly used for security-sensitive applications including mobile payments and personal identification. Every interaction between the user and an intermediate helping device is visualized using a Quick Response (QR) code. Preventing key logging attacks. Thus in our project password hacking, key logging and eavesdropping issues will be overcome.
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DOT NET PROJECTS ABSTRACT 2016-2017 EPAS: A SAMPLING BASED SIMILARITY IDENTIFICATION ALGORITHM FOR THE CLOUD ABSTRACT: The explosive growth of data brings new challenges to the data storage and management in cloud environment. These data usually have to be processed in a timely fashion in the cloud. Thus, any increased latency may cause a massive loss to the enterprises. Similarity detection plays a very important role in data management. Many typical algorithms such as Shingle, Simhash, Traits and Traditional Sampling Algorithm (TSA) are extensively used. The Shingle, Simhash and Traits algorithms read entire source file to calculate the corresponding similarity characteristic value, thus requiring lots of CPU cycles and memory space and incurring tremendous disk accesses. In addition, the overhead increases with the growth of data set volume and results in a long delay. Instead of reading entire file, TSA samples some data blocks to calculate the fingerprints as similarity characteristics value. The overhead oTSA is fixed and negligible. However, a slight modification of source files will trigger the bit positions of file content shifting. Therefore, a failure of similarity identification is inevitable due to the slight modifications. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the cloud by modulo file length. EPAS concurrently samples data blocks from the head and the tail of the modulated file to avoid the position shift incurred by the modifications. Meanwhile, an improved metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. Furthermore, this paper describes a query algorithm to reduce the time overhead of similarity detection. Our experimental results demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time overhead, CPU and memory occupation. Moreover, EPAS makes a more preferable trade off between precision and recall than that of other similarity detection algorithms. Therefore, it is an effective approach of similarity identification for the cloud.
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IEEE 2016 - 2017 Matlab Image Processing TitlesS.No Project Titles 1. Data-driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain 2. Double-Tip Arte fact Removal from Atomic Force Microscopy Images 3. Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition 4. Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation 5. Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation 6. Robust Visual Tracking via Convolutional Networks without Training 7. Context-based prediction filtering of impulse noise images 8. Predicting the Forest Fire Using Image Processing 9. A Review Paper on detection of Glaucoma using Retinal Fundus Images 10. Performance Analysis of Filters on Complex Images for Text Extraction through Binarization 11. Automated Malaria Detection from Blood Samples Using Image Processing 12. Learning Invariant Color Features for Person Re-Identification 13. A Diffusion and Clustering-based Approach for Finding Coherent Motions and Understanding Crowd Scenes 14. Automatic Design of Color Filter Arrays in The Frequency Domain 15. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution 16. Image Segmentation Using Parametric Contours With Free Endpoints 17. CASAIR: Content and Shape-Aware Image Retargeting and Its Applications 18. Texture classification using Dense Micro-block Difference 19. Statistical performance analysis of a fast super-resolution technique using noisy translations 20. Trees Leaves Extraction In Natural Images Based On Image segmentation and generating Its plant details
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IEEE 2016 -2017 BIG DATA ANDROID DOTNET JAVA TITLESBIG DATA1. FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce.ss2. Self-Healing in Mobile Networks with Big Data.ANDROID1. An Exploration of Geographic Authentication Schemes.2. Intelligent Hands Free Speech based SMS System on Android.3. PassBYOP: Bring Your Own Picture for Securing Graphical Passwords.4. Privacy-Preserving Location Sharing Services for Social Networks.5. SBVLC: Secure Barcode-based Visible Light Communication for Smartphones.6. A Shoulder Surfing Resistant Graphical Authentication System.7. A Cloud-Based Smart-Parking System Based on Internet-of-Things Technologies.8. STAMP: Enabling Privacy-Preserving Location Proofs for Mobile Users.9. Understanding Smartphone Sensor and App Data for Enhancing the Security of Secret Questions..NET1. Attribute-based Access Control with Constant-size Ciphertext in Cloud Computing.2. Attribute-Based Data Sharing Scheme Revisited in Cloud Computing3. Catch You if You Misbehave: Ranked Keyword Search Results Verification in Cloud Computing4. CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal5. Cloud workflow scheduling with deadlines and time slot availability6. Dynamic and Public Auditing with Fair Arbitration for Cloud Data7. Dynamic Proofs of Retrievability for Coded Cloud Storage Systems8. Enabling Cloud Storage Auditing with Verifiable Outsourcing of Key Updates9. Identity-Based Encryption with Cloud Revocation Authority and Its Applications10. Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity Checking in Public Cloud11. MMBcloud-tree: Authenticated Index for Verifiable Cloud Service Selection12. Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud13. Providing User Security Guarantees in Public Infrastructure Clouds14. Publicly Verifiable Inner Product Evaluation over Outsourced Data Streams under Multiple Keys15. Reversible Data Hiding in Encrypted Images by Reversible Image Transformation16. Searchable Attribute-Based Mechanism with Efficient Data Sharing for Secure Cloud Storage17. Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption18. Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps19. Shadow Attacks based on Password Reuses: A Quantitative Empirical Analysis20. A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets DetectionJAVA1. A Locality Sensitive Low-Rank Model for Image Tag Completion2. A Shoulder Surfing Resistant Graphical Authentication System3. DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments4. Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search5. KSF-OABE: Outsourced Attribute-Based Encryption with Keyword Search Function for Cloud Storage6. Mining User-Aware Rare Sequential Topic Patterns in Document Streams7. Mitigating Cross-Site Scripting Attacks with a Content Security Policy8. Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy9. Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases10. SecRBAC: Secure data in the Clouds11. Tag Based Image Search by Social Re-rankingCLOUD COMPUTING1. Cost Minimization for Rule Caching in Software Defined Networking.2. Performance Enhancement of High-Availability Seamless Redundancy (HSR) Networks Using OpenFlow.3. Data Plane and Control Architectures for 5G Transport Networks.4. HBD: Towards Efficient Reactive Rule Dispatching in Software-Defined Networks.5. SDN-based Application Framework for Wireless Sensor and Actor Networks.6. Geo-Social Distance-based Data Dissemination for Socially Aware Networking.7. An Open-Source Wireless Mesh Networking Module for Environmental Monitoring.8. Hybrid IP/SDN networking: open implementation and experiment management tools.9. Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges.10. Cloud Computing-Based Forensic Analysis for Collaborative Network Security Management System.NETWORK SECUIRTY1. Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing.DATA MINING1. Systematic Determination of Discrepancies Across Transient Stability Software Packages.2. Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.3. Teaching Network Security With IP Darkspace Data.4. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection.5. Mining High Utility Patterns in One Phase without Generating Candidates.6. An Improved String-Searching Algorithm and Its Application in Component Security Testing.
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ANDROID PROJECT ABSTRACT 2016-2017 SBVLC: SECURE BARCODE-BASED VISIBLE LIGHT COMMUNICATION FOR SMARTPHONES ABSTRACT:2D barcodes have enjoyed a significant penetration rate in mobile applications. This is largely due to the extremely low Barrier to adoption – almost every camera-enabled smartphone can scan 2D barcodes. As an alternative to NFC technology, 2DBarcodes have been increasingly used for security-sensitive mobile applications including mobile payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual Nature, 2D barcodes are subject to eavesdropping when they are displayed on the smartphone screens. On the other hand, the Fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC - a secure System for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based On geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen View angles and leveraging user-induced motions. We then develop three secure data exchange schemes that encode information in Barcode streams. These schemes are useful in many security-sensitive mobile applications including private information sharing, Secure device pairing, and contactless payment. SBVLC is evaluated through extensive experiments on both Android and ios Smartphones.
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JAVA/DOT NET PROJECTS ABSTRACT 2016-2017 MINING USER-AWARE RARE SEQUENTIAL TOPIC PATTERNS IN DOCUMENT STREAMS ABSTRACT: Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users’ characteristics.EXISTING SYSTEMS: Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. Taking advantage of these extracted topics in document streams, most of existing works analyzed the evolution of individual topics to detect and predict social events as well as user behaviors. However, few researches paid attention to the correlations among different topics appearing in successive documents published by a specific user, so some hidden but significant information to reveal personalized behaviors has been neglected. And correspondingly, unsupervised mining algorithms for this kind of rare patterns need to be designed in a manner different from existing frequent pattern mining algorithms. Most of existing works on sequential pattern mining focused on frequent patterns, but for STPs, many infrequent ones are also interesting and should be discovered.PROPOSED SYSTEMS: In order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. In order to characterize user behaviors in published document streams, we study on the correlations among topics extracted from these documents, especially the sequential relations, and specify them as Sequential Topic Patterns (STPs). Each of them records the complete and repeated behavior of a user when she is publishing a series. Topic mining in document collections has been extensively studied in the literature. Topic Detection and Tracking (TDT) task aimed to detect and track topics (events) in news streams with clustering-based techniques on keywords. The experiments conducted on both real (Twitter) and synthetic datasets demonstrate that the proposed approach is very effective and efficient in discovering special users as well as interesting and interpretable URSTPs from Internet document streams, which can well capture users’ personalized and abnormal behaviors and characteristics.ADVANATAGES: Taking advantage of these extracted topics in document streams, most of exist works analyzed the evolution of individual topics to detect and predict social events as well as user behaviors. In order to find significant STPs, a document stream should be divided into independent sessions in advance with the definition. A sketch map of session identification Each ellipse represents a session, and all the sessions in each line constitute a document subsequence for a specific user. we can conclude that the two algorithms have their respective advantages. Which one is appropriate for the real task reflects a trade-off between mining accuracy and execution speed, and should depend on the specific requirements of application scenarios.HARDWARE 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 CONCLUSION: Mining URSTPs in published document streams on the Internet is a significant and challenging problem. It formulates a new kind of complex event patterns based on document topics, and has wide potential application scenarios, such as real-time monitoring on abnormal behaviors of Internet users. In this paper, several new concepts and the mining problem are formally defined, and a group of algorithms are designed and combined to systematically solve this problem. The experiments conducted on both real (Twitter) and synthetic datasets demonstrate that the proposed approach is very effective and efficient in discovering special users as well as interesting and interpretable URSTPs from Internet document streams, which can well capture users’ personalized and abnormal behaviors and characteristics. As this paper puts forward an innovative research direction on Web data mining, much work can be built on it in the future.
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VLSI PROJECTS ABSTRACT 2016-2017: GRAPH-BASED TRANSISTOR NETWORK GENERATION METHOD FOR SUPERGATE DESIGN ABSTRACT: Transistor network optimization represents an effective way of improving VLSI circuits. In VLSI digital design, the signal delay propagation, power dissipation, and area of circuits are strongly related to the number of transistors. This proposed architecture described an efficient graph-based method to generate optimized transistor (switch) networks. The proposed architecture of this paper will be planned to implemented and also analysis the output current, output voltage, area using Dsch31 and micro wind. EXISTING SYSTEM: I N VLSI digital design, the signal delay propagation, power dissipation, and area of circuits are strongly related to the number of transistors (switches). Hence, transistor arrangement optimization is of special interest when designing standard cell libraries and custom gates. Switch based technologies, such as CMOS, FinFET, and carbon nanotubes, can take advantage of such an improvement. Therefore, efficient algorithms to automatically generate optimized transistor networks are quite useful for designing digital integrated circuits (ICs). PROPOSED SYSTEM: The proposed method comprises two main modules: 1) the kernel identification and 2) the switch network composition. The former receives an ISOP F and identifies individual NSP and SP switch networks, representing sub functions of f. The latter composes those networks into a single network by performing logic sharing. The provided output is an optimized switch network representing the target function f.The execution flow of the method is presented in Fig. 2. ADVANTAGES:• reduction in the number of transistor• improve the area and power consumption:DISADVANTAGES• Usage of number of transistor is high.• Power and area are high SOFTWARE IMPLEMENTATION:• Dsch31• micro wind.
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VLSI PROJECTS ABSTRACT 2016-2017 LOW-POWER ECG-BASED PROCESSOR FOR PREDICTING VENTRICULAR ARRHYTHMIA ABSTRACT: This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.EXISTING SYSTEM: Recently, due to the remarkable advancement in technology, the development of dedicated hardware for accurate ECG analysis and classification in real time has become possible. The main requirements are low-power consumption and low-energy operation in order to have longer battery lifetime along with the small area for wearability. Many attempts succeeded to implement ECG signal processing and classification systems in hardware. Shiu et al. implemented an integrated electrocardiogram signal processor (ESP) for the identification of heart diseases using the 90-nm CMOS technology. The system employed an instrumentation amplifier and a low-pass filter (LPF) to remove the baseline wander and the power line interference form the ECG and employed a time-domain morphological analysis for the feature extraction and classification based on the evaluation of the ST segment. The system was carried out in a field programmable gate array and consumed a total of 40.3-μW power and achieved an accuracy of 96.6%. The main disadvantage of the system is that it uses fixed search window with predefined size to locate S and T fiducial points, which is not suitable for real-time scenarios. PROPOSED SYSTEM: The architecture of the proposed ESP is shown in Fig. 1. The architecture includes the modules of the three stages along with a main FSM that controls the flow of the data between the different stages, as shown in Fig. 2. The processing of the data is done using fixed point representation.ADVANTAGES:• small area• low power• high performance DISADVANTAGES:• large area• high power• high performanceSOFTWARE IMPLEMENTATION• Modelsim• Xilinx ISE
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