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Sunday, November 30, 2014

IEEEE 2014 VLSI COMPLETED PROJECTS-

IEEEE 2014 VLSI COMPLETED PROJECTS-
1              A Super regenerative QPSK Receiver
2              Defense Against Primary User Emulation Attacks in Cognitive Radio Networks Using Advanced
Encryption Standard
3              Analysis and Design of a Low-Voltage Low-Power Double-Tail Comparator (micro wind)
4           Efficient VLSI Implementation of Neural Networks With HyperbolicTangent Activation
Function
5              Efficient Algorithm and Architecture for Elliptic Curve Cryptography for Extremely Constrained
Secure Applications
6              A Method to Extend Orthogonal Latin Square Codes
7              Increase in Read Noise Margin of Single-Bit-Line SRAM Using A diabatic Change of Word Line
Voltage
8              Efficient Integer Dct Architectures For Hevc
9              Precise VLSI Architecture for AI Based 1-D/ 2-D Daub-6 Wavelet Filter Banks With Low Adder-
Count
10           Simplifying Clock Gating Logic by Matching Factored Forms
11           Sensitization Input Vector Impact on Propagation Delay for Nanometer
CMOS ICs: Analysis and Solutions
12           Non binary LDPC Decoder Based on Simplified Enhanced Generalized      Bit-Flipping
Algorithm
13           An Optimized Modified Booth  Recorder for Efficient Design of the Add- Multiply Operator
14           Eliminating Synchronization Latency Using Sequenced Latching
15           Efficient Implementation of Reconfigurable Warped Digital Filters With Variable Low-Pass, High-
Pass, Band pass, and Band stop Responses
16           Novel Circuit-Level Model for Gate Oxide Short and its Testing Method in SRAMs(micro wind)

17           High-Throughput Multi standard Transform Core Supporting MPEG/H.264/VC-1 Using Common
Sharing Distributed Arithmetic
18           Software/Hardware Parallel Long-Period Random Number Generation Framework Based on the
WELL Method
19           Recursive Approach to the Design of a Parallel Self-Timed Adder.
20           Multi-Zone Digital Crosstalk Reduction by Image Processing in 3D Display
21           A Multichannel Oscillator for a Resonant Chemical Sensor System
22           A 5.8-GHz Wideband TSPC Divide-by-16/17. Dual Modulus Prescaler.
23           Fast Sign Detection Algorithm for the RNS Moduli Set {2n+1 − 1, 2n − 1, 2n}
24           Multifunction Residue Architecture for Cryptography
25           Design of Efficient Binary Comparators in Quantum-Dot Cellular Automata
26           Aging-Aware Reliable Multiplier Design With Adaptive Hold Logic
27           Area-Delay Efficient Binary Adders in QCA
28           Area–Delay–Power Efficient Carry Select Adder
29           Low-Complexity Hardware Design for Fast Solving LSPs with Coordinated Polynomial Solution
30           Fast Design Optimization Through Simple Kriging Metamodeling: A Sense Amplifier Case Study
31           Fully Reused VLSI Architecture of. FM0/Manchester Encoding Using SOLS.Technique for DSRC
Applications.

32.         Input Vector Monitoring Concurrent BIST Architecture Using SRAM Cells

IEEE 2014 POWER ELECTRONICS PROJECTS,IEEE 2014 Power System Completed Projects

IEEE 2014 Power System Completed  Projects

1.     Voltage support control strategies for static synchronous compensators under unbalanced voltage  sags
2.     Adaptive pi control for voltage regulation
3.      Aiding Power System Support by Means of Voltage Control With Intelligent Distribution Substation
4.      Stability Enhancement of DFIG-Based OffshoreWind Farm Fed to a Multi-Machine SystemUsing a STATCOM
5.      Transient stability enhancement of power system equippedwith Power System Stabilizer by Static VAR Compensator
6.      Power Quality Improvement Using DVR in Power System
7.      Comparative Stability Enhancement of PMSG-Based Offshore Wind Farm Fed to an SG-Based Power System Using an SSSC and an SVeC
8.      Performance Improvement of Dynamic VoltageRestorer using Proportional - Resonant Controller
9.      A New Control Method of Cascaded Brushless Doubly Fed Induction Generators Using Direct Power Control
10.  Determination of available transfer capability withimplication of cascading collapse uncertainty
11.  DQ-Frame Modeling of an Active Power Filter Integrated with a Grid-Connected, Multifunctional Electric Vehicle Charging Station
12.  Residential Distribution System HarmonicCompensation Using PV Interfacing Inverter
12.






IEEE 2014 POWER ELECTRONICS PROJECTS

1. ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies,
Hydrogen and Batteries
2.An LED Driver with Dynamic High-Frequency Sinusoidal Bus Voltage Regulation for Multistring Applications
3.Aiding Power System Support by Means of Voltage Control With Intelligent Distribution Substation
4.ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based On Li-ion Battery and Solar
Energy Supply
5.A Novel Reduced Switching Loss Bidirectional AC/DC Converter PWM Strategy With Feedforward
Control for Grid-Tied Microgrid Systems
6.Sliding Mode control of DC/DC switching converters for photovoltaic  applications






IEEE 2014 NS2 COMPLETED PROJECT LIST-

IEEE 2014 NS2 COMPLETED PROJECT  LIST-
1.Ensuring Predictable Contact Opportunity for Scalable Vehicular Internet Access On the Go
2. A QoS-Oriented Distributed Routing Protocol for Hybrid Wireless Networks
3. Secure and Efficient Data Transmission for Cluster-Based Wireless Sensor Networks
4. An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks
5. Hop-by-Hop Message Authentication and Source Privacy in Wireless Sensor Networks
6. Efficient Data Collection for Large-Scale Mobile Monitoring Applications
7. An Alternative Perspective on Utility Maximization in Energy-Harvesting Wireless Sensor Networks
8. Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks
9. Sensor Node Failure Detection Based on Round Trip Delay and Paths in WSNs
10 An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks
11 Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks
12 Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networks
13 The Impact of Rate Adaptation on Capacity-Delay Tradeoffs in Mobile Ad Hoc
Networks
14 AASR: Authenticated Anonymous Secure Routing for MANETs in Adversarial Environments
15 Mobile-Projected Trajectory Algorithm with Velocity-Change Detection for Predicting Residual Link Lifetime in MANET
16 A Probabilistic Misbehavior Detection Scheme toward Efficient Trust Establishment in Delay-Tolerant Networks

17 STARS: A Statistical Traffic Pattern Discovery System for MANETs

18 Distributed and Centralized Hybrid CSMA/CA-TDMA Schemes for Single-Hop Wireless Networks

19 Energy-efficient cooperative spectrum sensing schemes for cognitive radio networks

20 Green Cognitive Mobile Networks With Small Cells for Multimedia Communications in the Smart Grid Environment

21 Distributed Detection in Mobile Access Wireless Sensor Networks under Byzantine Attacks
22 NETWRAP: An NDN Based Real-Time Wireless Recharging Framework for Wireless Sensor Networks
23 Scalable and Reconfigurable All-Optical VPN for OFDM-Based Metro-Access Integrated Network
24 Routing-Toward-Primary-User Attack and Belief Propagation-Based Defense in Cognitive Radio Networks
25 A Novel Approach to Trust Management in Unattended Wireless Sensor Networks
26 Transmission-Efficient Clustering Method for Wireless Sensor Networks Using
Compressive Sensing
27 Optimal Wavebanding in WDM Ring Networks
28 Maximizing Reliability in WDM Networks Through Lightpath Routing
29 Joint Mobile Data Gathering and Energy Provisioning in Wireless Rechargeable Sensor Networks
30 Reliable Cooperative Communications Based on Random Network Coding in
Multi-Hop Relay WSNs
31 Cost-Effective Resource Allocation of Overlay Routing Relay Nodes
32 An Economic Framework for Routing and Channel Allocation in Cognitive Wireless Mesh Networks
33 Green Cognitive Mobile Networks With Small Cells for Multimedia Communications in the Smart Grid Environment

34 TCP CRAHN: A Transport Control Protocol for Cognitive Radio Ad Hoc Networks

IEEE 2014 – 2015 IMAGE PROCESSING PROJECTS




IEEE 2014 – 2015 IMAGE PROCESSING PROJECTS

1. Self-Learning Based Image Decomposition With Applications to Single Image Denoising
2. Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative              Ultrasound Spectroscopy and Maximum Mean Discrepancy
3. Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions
4. LineCast: Line-Based Distributed Coding and Transmission for Broadcasting Satellite Images
5. Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture         Distinctiveness
6. Noise Reduction in Hyperspectral Images through Spectral Unmixing
7. Mining Weakly Labeled Web Facial Images for Search Based Face Annotation
8. An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection
9. Analysis of data hiding using Digital Image Signal Processing
10. Data Hiding in Encrypted H.264AVC Video Streams by Codeword Substitution
11. image tansmissision using wielesss
12. Model-Based Classification Methods Of Global Patterns In Dermoscopic Images
13. On the Choice of the Pair Conjunction-Implication into the Fuzzy Morphological Edge         Detector
14. Robust Object Tracking via Sparse Collaborative Appearance Model
15. Defense Against Primary User Emulation Attacks in Cognitive Radio Networks Using Advanced         Encryption Standard
16. Localization Of License Plate Number Using Dynamic Image Processing

17. Low-Order Dominant Harmonic Estimation Using Adaptive Wavelet Neural Network.
18. Robust Text Detection in Natural Scene Images
19. Near-duplicate video retrieval by using pattern-based Prefix tree and temporal relation         forest
20. Speech Enhancement for Listeners With Hearing Loss Based on a Model for Vowel Coding in         the Auditory Midbrain
21. Neuromorphic Pitch Based Noise Reduction for  Monosyllable Hearing Aid System Application
22. A Rain Pixel Recovery Algorithm for Videos With Highly Dynamic Scenes



Monday, November 24, 2014

Data mining With Big Data(Hadoop+Mango Db)

Data Mining with Big Data
ABSTRACT:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
EXISTING SYSTEM:
Ø The rise of Big Data applications where data collection has grown tremen dously and is beyond the ability of commonly used software tools to capture, manage, and process within a “tolerable elapsed time.” The most fundamental challenge for Big Data applications is to explore the large volumes of data and extract useful information or knowledge for future actions. In many situations, the knowledge extraction process has to be very efficient and close to real time because storing all observed data is nearly infeasible.
Ø The unprecedented data volumes require an effective data analysis and prediction platform to achieve fast response and real-time classification for such Big Data.

DISADVANTAGES OF EXISTING SYSTEM:
] The challenges at Tier I focus on data accessing and arithmetic computing procedures. Because Big Data are often stored at different locations and data volumes may continuously grow, an effective computing platform will have to take distributed large-scale data storage into consideration for computing.
] The challenges at Tier II center around semantics and domain knowledge for different Big Data applications. Such information can provide additional benefits to the mining process, as well as add technical barriers to the Big Data access (Tier I) and mining algorithms (Tier III).
] At Tier III, the data mining challenges concentrate on algorithm designs in tackling the difficulties raised by the Big Data volumes, distributed data distributions, and by complex and dynamic data characteristics.

PROPOSED SYSTEM:
Ø We propose a HACE theorem to model Big Data characteristics. The characteristics of HACH make it an extreme challenge for discovering useful knowledge from the Big Data.
Ø The HACE theorem suggests that the key characteristics of the Big Data are 1) huge with heterogeneous and diverse data sources, 2) autonomous with distributed and decentralized control, and 3) complex and evolving in data and knowledge associations.
Ø To support Big Data mining, high-performance computing platforms are required, which impose systematic designs to unleash the full power of the Big Data.
ADVANTAGES OF PROPOSED SYSTEM:
      Provide most relevant and most accurate social sensing feedback to better understand our society at realtime.

SYSTEM CONFIGURATION:
HARDWARE CONFIGURATION:
] Processor    -        Pentium IV
] Speed                   -        1.1 Ghz
] RAM          -        512 MB (min)
] Hard Disk   -        20GB
] Keyboard    -        Standard Keyboard
] Mouse         -        Two or Three Button Mouse
] Monitor      -        LCD/LED Monitor

SOFTWARE CONFIGURATION:
ü Operating System          -        Windows XP/7
ü Programming Language -        Java/J2EE
ü Software Version           -        JDK 1.7 or above
ü Database                        -        MYSQL

REFERENCE:

Xindong Wu, Fellow, IEEE, Xingquan Zhu, Senior Member, IEEE, Gong-Qing Wu, and Wei Ding, Senior Member, IEEE, “Data Mining with Big Data”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014.

Discovering Emerging Topics in Social Streams via Link-Anomaly Detection

Discovering Emerging Topics in Social Streams via Link-Anomaly Detection

ABSTRACT:
Detection of emerging topics is now receiving renewed interest motivated by the rapid growth of social networks. Conventional-term-frequency-based approaches may not be appropriate in this context, because the information exchanged in social-network posts include not only text but also images, URLs, and videos. We focus on emergence of topics signaled by social aspects of theses networks. Specifically, we focus on mentions of user links between users that are generated dynamically (intentionally or unintentionally) through replies, mentions, and retweets. We propose a probability model of the mentioning behavior of a social network user, and propose to detect the emergence of a new topic from the anomalies measured through the model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social-network posts. We demonstrate our technique in several real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-based approaches, and in some cases much earlier when the topic is poorly identified by the textual contents in posts.

EXISTING SYSTEM:
Ø A new (emerging) topic is something people feel like discussing, commenting, or forwarding the information further to their friends. Conventional approaches for topic detection have mainly been concerned with the frequencies of (textual) words.

DISADVANTAGES OF EXISTING SYSTEM:
A term-frequency-based approach could suffer from the ambiguity caused by synonyms or homonyms. It may also require complicated preprocessing (e.g., segmentation) depending on the target language. Moreover, it cannot be applied when the contents of the messages are mostly nontextual information. On the other hand, the “words” formed by mentions are unique, require little preprocessing to obtain (the information is often separated from the contents), and are available regardless of the nature of the contents.

PROPOSED SYSTEM:
Ø In this paper, we have proposed a new approach to detect the emergence of topics in a social network stream.
Ø The basic idea of our approach is to focus on the social aspect of the posts reflected in the mentioning behavior of users instead of the textual contents.
Ø We have proposed a probability model that captures both the number of mentions per post and the frequency of mentionee.

ADVANTAGES OF PROPOSED SYSTEM:
Ø The proposed method does not rely on the textual contents of social network posts, it is robust to rephrasing and it can be applied to the case where topics are concerned with information other than texts, such as images, video, audio, and so on.

Ø The proposed link-anomaly-based methods performed even better than the keyword-based methods on “NASA” and “BBC” data sets.


SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                         :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.

SOFTWARE REQUIREMENTS:

Ø Operating system           :         Windows XP/7.
Ø Coding Language :         JAVA/J2EE
Ø IDE                      :         Netbeans 7.4
Ø Database              :         MYSQL



REFERENCE:

Toshimitsu Takahashi, Ryota Tomioka, and Kenji Yamanishi, Member, IEEE,“Discovering Emerging Topics in Social Streams via Link-Anomaly Detection”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014.

Sunday, November 23, 2014

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data


The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

ABSTRACT:
The location tracking functionality of modern mobile devices provides unprecedented opportunity to the understanding of individual mobility in daily life. Instead of studying raw geographic coordinates, we are interested in understanding human mobility patterns based on sequences of place visits which encode, at a coarse resolution, most daily activities. This paper presents a study on place characterization in people’s everyday life based on data recorded continuously by smartphones. First, we study human mobility from sequences of place visits, including visiting patterns on different place categories. Second, we address the problem of automatic place labeling from smartphone data without using any geo-location information. Our study on a large-scale data collected from 114 smartphone users over 18 months confirm many intuitions, and also reveals findings regarding both regularly and novelty trends in visiting patterns. Considering the problem of place labeling with 10 place categories, we show that frequently visited places can be recognized reliably (over 80 percent) while it is much more challenging to recognize infrequent places.

EXISTING SYSTEM:
Previous works on human mobility understanding differ from our work on the variables under study. Besides seminal works on individual mobility, there are recent works which focus on urban environments. In existing system, it was shown that social relationships can explain a significant fraction of all human movement on data from LBSNs. In another system, location data were transformed into activity data to study daily activity patterns. Using a continuous sensing framework, Eagle was an early proponent of the identification of daily mobility patterns from simplified cell-tower data, in which each cell-tower ID was mapped to three semantic categories: home, work, and other. Similar tasks were also addressed by other authors

DISADVANTAGES OF EXISTING SYSTEM:
] The lack of continuous mobility traces due to the fact that location is only available either when connections to a cellular network are made (through voice, text, or data) or when users explicitly check-in within a LBSN.
] We face multiple challenges such as noisy data recorded in real-life conditions; obtaining human annotation of places and self-reports of place visits; and performing automatic place recognition without knowing the geographic location.

PROPOSED SYSTEM:
This paper presents a study on 1) characterization of real-life place visiting patterns from smartphone data; and 2) automatic place labeling in a location privacy-sensitive setting.
Our paper has three contributions. We first conduct an analysis of place visits in daily life, where places are inferred continuously from phone sensor data. We demonstrate that in practice, beyond the few places that represent an individual’s routine structure, people tend to visit new places on a regular basis, resulting in large number of places that are visited infrequently. In the second place, we demonstrate that this aspect of human behavior has key implications, showing (through an experiment involving manual labeling of visited places) that infrequently visited places are significantly harder to remember and label accurately. In the third place, we addressed the problem of automatic place labeling without using raw geolocation coordinates.

ADVANTAGES OF PROPOSED SYSTEM:
Our system achieves an accuracy of 75 percent in a privacy-preserving setting, and further analysis shows that the accuracy is bounded by the frequency with which a place is visited: while the few frequently visited places in phone users’ daily life can be recognized reliably, the largest fraction of places are more challenging to label.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                         :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.
Ø MOBILE                        :         ANDROID

SOFTWARE REQUIREMENTS:

Ø Operating system           :         Windows XP/7.
Ø Coding Language :         Java 1.7
Ø Tool Kit               :         Android 2.3 ABOVE
Ø IDE                      :         Eclipse

REFERENCE:
Trinh Minh Tri Do and Daniel Gatica-Perez, Member, IEEE, “The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data,” IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 3, MARCH 2014.