final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles,

NEXGEN TECHNOLOGY
www.nexgenproject.com
No: 66,4th cross, Venkata nagar, Near SBI ATM, Pondicherry.
Email Id: praveen@nexgenproject.com
Mobile: 9751442511, 9791938249, Telephone: 0413-2211159
MATLAB PROJECTS 2015
Sno. Topic Abstract Year
1. MATLAB2015_01 Machine Learning-Based
Coding Unit Depth
Decisions for Flexible
Complexity Allocation
in High Efficiency Video
Coding
In this paper, we propose a machine learning-based
fast coding unit (CU) depth decision method for High
Efficiency Video Coding (HEVC), which optimizes the
complexity allocation at CU level with given rate-
distortion (RD) cost constraints. First, we analyze quad-
tree CU depth decision process in HEVC
and model it as a three-level of hierarchical binary
decision problem. Second, a flexible CU depth decision
structure is presented, which allows the performances of
each CU depth decision be smoothly transferred between
the coding complexity and RD performance. Then, a
three-output joint classifier consists of multiple binary
classifiers with different parameters is designed to control
the risk of false prediction. Finally, a sophisticated RD-
complexity model is derived to determine the optimal
parameters for the joint classifier, which is capable of
minimizing the complexity in each CU depth at given RD
degradation constraints. Comparative experiments over
various sequences show that the proposed CU depth
decision algorithm can reduce the computational
complexity from 28.82% to 70.93%, and 51.45% on
average when compared with the original HEVC test
model.
2015
2. MATLAB2015_02 Distinguishing Local and
Global Edits for Their
Simultaneous Propagation
in a Uniform Framework
In propagating edits for image editing, some edits are
intended to affect limited local regions, while others act
globally over the entire image. However, the ambiguity
problem in propagating edits is not adequately addressed
in existing methods. Thus, tedious user input
requirements remain since the user must densely or
repeatedly input control samples to suppress ambiguity.
In this paper, we address this challenge to propagate edits
suitably by marking edits for local or global
propagation and determining their reasonable propagation
scopes automatically. Thus, our approach avoids
propagation conflicts, effectively resolving the ambiguity
problem. With the reduction of ambiguity, our method
allows fewer and less-precise control samples than
existing methods. Furthermore, we provide a uniform
framework to propagate local and global edits
simultaneously, helping the user to quickly obtain the
intended results with reduced labor. With our unified
framework, the potentially ambiguous interaction
between local and global edits (evident
in existing methods that propagate these two edit types in
series) is resolved. We experimentally demonstrate the
effectiveness of our method compared with existing
methods.
2015

NEXGEN TECHNOLOGY
3. MATLAB2015_03 Face Recognition Across
Non-Uniform Motion
Blur, Illumination, and
Pose
Existing methods for performing face recognition
in the presence of blur are based on the convolution
model and cannot handle non-uniform blurring situations
that frequently arise from tilts and rotations in hand-held
cameras. In this paper, we propose a methodology for
face recognition in the presence of space-varying motion
blur comprising of arbitrarily-shaped kernels. We model
the blurred face as a convex combination of
geometrically transformed instances of the focused
gallery face, and show that the set of all images obtained
by non-uniformly blurring a given image forms a convex
set. We first propose a non uniform blur-robust algorithm
by making use of the assumption of a sparse camera
trajectory in the camera motion space to build an energy
function with l1-norm constraint on the camera
motion. The framework is then extended to handle
illumination variations by exploiting the fact that the set
of all images obtained from a face image by non-uniform
blurring and changing the illumination forms a bi-convex
set. Finally, we propose an elegant extension to also
account for variations in pose.
2015
4. MATLAB2015_04 Swarm Intelligence for
Detecting Interesting
Events
in Crowded Environments
This work focuses on detecting and localizing
anomalous events in videos of crowded scenes, i.e.
divergences from a dominant pattern. Both motion and
appearance information are considered, so as to robustly
distinguish different kinds of anomalies, for a wide range
of scenarios. A newly introduced concept based on swarm
theory, Histograms of Oriented Swarms (HOS), is applied
to capture the dynamics of crowded environments. HOS,
together with the well known Histograms of Oriented
Gradients (HOG), are combined to build a descriptor that
effectively characterizes each scene. These appearance
and motion features are only extracted within
spatiotemporal volumes of moving pixels to ensure
robustness to local noise, increase accuracy in the
detection of local, non dominant anomalies, and achieve a
lower computational cost. Experiments on benchmark
datasets containing various situations with human crowds,
as well as on traffic data, led to results that
surpassed the current state of the art, confirming the
method’s efficacy and generality. Finally, the experiments
show that our approach achieves significantly higher
accuracy, especially for pixel-level event detection
compared to State of the Art (SoA)
methods, at a low computational cost.
2015
5. MATLAB2015_05 Content-Based Image
Retrieval Using Features
Extracted From Halftoning-
Based Block
Truncation Coding
This paper presents a technique for Content-Based
Image Retrieval (CBIR) by exploiting the advantage of
low complexity Ordered-Dither Block Truncation Coding
(ODBTC) for the generation of image content descriptor.
In encoding step, ODBTC compresses an image block
into corresponding quantizers and bitmap image. Two
image features are proposed to index an image, namely
Color Co-occurrence Feature (CCF) and Bit Pattern
Features (BPF), which are generated directly from
2015

NEXGEN TECHNOLOGY
ODBTC encoded data streams without performing the
decoding process. The CCF and BPF of an image are
simply derived from the two ODBTC quantizers and
bitmap, respectively, by involving the visual codebook.
Experimental results show that the proposed method is
superior to the Block Truncation Coding (BTC) image
retrieval systems and the other former methods, and thus
prove that the ODBTC scheme is not only suited for
image compression since of its simplicity, but also offers
a simple and effective descriptor to index images in CBIR
6. MATLAB2015_06 Approximation and
Compression with Sparse
Orthonormal Transforms
We propose a new transform design method that
targets the generation of compression-optimized
transforms for next-generation multimedia applications.
The fundamental idea behind transform compression is to
exploit regularity within signals such that redundancy is
minimized subject to a fidelity cost. Multimedia signals,
in particular images and video, are well known to contain
a diverse set of localized structures, leading to many
different types of regularity and to non stationary signal
statistics. The proposed method designs sparse
orthonormal transforms (SOT) that automatically exploit
regularity over different signal structures and provides an
adaptation method that determines the best representation
over localized regions. Unlike earlier work that is
motivated by linear approximation constructs and model-
based designs that are limited to specific types of
signal regularity, our work uses general nonlinear
approximation ideas and a data-driven setup to
significantly broaden its reach. We show that our SOT
designs provide a safe and principled
extension of the Karhunen-Loeve transform (KLT) by
reducing to the KLT on Gaussian processes and by
automatically exploiting non-Gaussian statistics to
significantly improve over the KLT on more general
processes. We provide an algebraic optimization
framework that generates optimized designs for any
desired transform structure (multi-resolution, block,
lapped, etc.) with significantly better n-term
approximation performance. For each structure, we
propose a new prototype codec and test over a
database of images. Simulation results show consistent
increase in compression and approximation performance
compared with conventional methods.
2015
7. MATLAB2015_07 High-Resolution Face
Verification Using
Pore-Scale Facial Features
Face recognition methods, which usually represent
face images using holistic or local facial features, rely
heavily on alignment. Their performances also suffer a
severe degradation under variations in expressions or
poses, especially when there is one gallery per subject
only. With the easy access to high resolution (HR) face
images nowadays, some HR face databases
have recently been developed. However, few studies have
tackled the use of HR information for face recognition or
verification. In this paper, we propose a pose-invariant
face-verification method, which is robust to alignment
errors, using the HR information based on pore-scale
2015

NEXGEN TECHNOLOGY
facial features. A new key point descriptor, namely, pore-
Principal Component Analysis (PCA)- Scale Invariant
Feature Transform (PPCASIFT)—adapted from
PCA-SIFT—is devised for the extraction of a compact set
of distinctive pore-scale facial features. Having matched
the porescale features of two-face regions, an effective
robust-fitting scheme is proposed for the face-verification
task. Experiments show that, with one frontal-view
gallery only per subject, our proposed method
outperforms a number of standard verification
methods, and can achieve excellent accuracy even the
faces are under large variations in expression and pose.
8. MATLAB2015_08 DERF: Distinctive
Efficient Robust Features
From
the Biological Modeling of
the P Ganglion Cells
Studies in neuroscience and biological vision have
shown that the human retina has strong computational
power, and its information representation supports vision
tasks on both ventral and dorsal pathways. In this paper, a
new local image descriptor, termed distinctive efficient
robust features (DERF), is derived by modeling the
response and distribution properties of the parvocellular-
projecting ganglion cells in the primate
retina. DERF features exponential scale distribution,
exponential grid structure, and circularly symmetric
function difference of Gaussian (DoG) used as a
convolution kernel, all of which are consistent with the
characteristics of the ganglion cell array found in
neurophysiology, anatomy, and biophysics. In addition,
a new explanation for local descriptor design is presented
from the perspective of wavelet tight frames. DoG is
naturally a wavelet, and the structure of the grid points
array in our descriptor is closely related to the spatial
sampling of wavelets. The DoG wavelet itself forms a
frame, and when we modulate the parameters of our
descriptor to make the frame tighter, the performance of
the DERF descriptor improves accordingly. This
is verified by designing a tight frame DoG, which leads to
much better performance. Extensive experiments
conducted in the image matching task on the multiview
stereo correspondence data set demonstrate that DERF
outperforms stateof the art methods for both hand-crafted
and learned descriptors, while remaining robust and being
much faster to compute.
2015
9. MATLAB2015_09 Blind Inpainting using ℓ0
and Total Variation
Regularization
In this paper, we address the problem of image
reconstruction with missing pixels or corrupted with
impulse noise, when the locations of the corrupted pixels
are not known. A logarithmic transformation is applied to
convert the multiplication between the image and binary
mask into an additive problem. The image and mask
terms are then estimated iteratively with total variation
regularization applied on the image, and ℓ0 regularization
on the mask term which imposes sparseness on the
support set of the missing pixels. The resulting
alternating minimization scheme simultaneously
estimates the image and mask, in the same iterative
2015

NEXGEN TECHNOLOGY
process. The logarithmic transformation also allows the
method to be extended to the Rayleigh multiplicative and
Poisson observation models. The method can also be
extended to impulse noise removal by relaxing
the regularizer from the ℓ0 norm to the ℓ1 norm.
Experimental results show that the proposed method can
deal with a larger fraction of missing pixels than two
phase methods which first estimate the mask and then
reconstruct the image.
10. MATLAB2015_10 A Source-Channel Coding
Approach to Digital
Image Protection and Self-
Recovery
Watermarking algorithms have been widely applied
to the field of image forensics recently. One of these very
forensic applications is the protection of images against
tampering. For this purpose, we need to design a
watermarking algorithm fulfilling two purposes in case of
image tampering: 1) detecting the tampered area of the
received image and 2) recovering the lost information in
the tampered zones. State-of-the-art techniques
accomplish these tasks using watermarks consisting of
check bits and reference bits. Check bits are used for
tampering detection, whereas reference bits carry
information about the whole image. The problem of
recovering the lost reference bits still stands. This paper is
aimed at showing that having the tampering location
known, image tampering can be modeled
and dealt with as an erasure error. Therefore, an
appropriate design of channel code can protect the
reference bits against tampering. In the present proposed
method, the total watermark bit-budget is dedicated to
three groups: 1) source encoder output bits; 2) channel
code parity bits; and 3) check bits. In watermark
embedding phase, the original image is source coded and
the output bit stream is protected using appropriate
channel encoder. For image recovery, erasure locations
detected by check bits help channel erasure decoder to
retrieve the original source encoded image. Experimental
results show that our proposed scheme significantly
outperforms recent techniques in terms of image quality
for both watermarked and recovered
image. The watermarked image quality gain is achieved
through spending less bit-budget on watermark, while
image recovery quality is considerably improved as a
consequence of consistent performance of designed
source and channel codes.
2015
11. MATLAB2015_11 Structured Sparse Priors for
Image Classification
Model-based compressive sensing (CS) exploits the
structure inherent in sparse signals for the design of better
signal recovery algorithms. This information about
structure is often captured in the form of a prior on the
sparse coefficients, with the Laplacian being the most
common such choice (leading to l1-norm minimization).
Recent work has exploited the discriminative capability
of sparse representations for image classification by
employing class-specific dictionaries in the CS
framework. Our contribution is a logical extension of
these ideas into structured sparsity for classification. We
2015

NEXGEN TECHNOLOGY
introduce the notion of discriminative class-specific priors
in conjunction with class specific dictionaries,
specifically the spike-and-slab prior widely applied in
Bayesian sparse regression. Significantly, the proposed
framework takes the burden off the demand for abundant
training image samples necessary for the success of
sparsity-based classification schemes. We demonstrate
this practical benefit of our approach in important
applications,
such as face recognition and object categorization.
12. MATLAB2015_12 Video Tracking Using
Learned Hierarchical
Features
In this paper, we propose an approach to learn
hierarchical features for visual object tracking. First, we
offline learn features robust to diverse motion patterns
from auxiliary video sequences. The hierarchical features
are learned via a twolayer convolutional neural network.
Embedding the temporal slowness constraint in the
stacked architecture makes the learned
features robust to complicated motion transformations,
which is important for visual object tracking. Then, given
a target video sequence, we propose a domain adaptation
module to online adapt the pre-learned features according
to the specific target object. The adaptation is conducted
in both layers of the deep feature learning module so as to
include appearance information of the specific target
object. As a result, the learned hierarchical features can
be robust to both complicated motion transformations and
appearance changes of target objects. We
integrate our feature learning algorithm into three
tracking
methods. Experimental results demonstrate that
significant improvement can be achieved by using our
learned hierarchical features, especially on video
sequences with complicated motion transformations.
2015
13. MATLAB2015_13 A Global/Local Affinity
Graph for Image
Segmentation
Construction of a reliable graph capturing perceptual
grouping cues of an image is fundamental for
graph-cut based image segmentation methods. In this
paper, we propose a novel sparse global/local affinity
graph over superpixels of an input image to capture both
short and long range grouping cues, thereby
enabling perceptual grouping laws, e.g., proximity,
similarity, continuity, to enter in action through a
suitable graph cut algorithm. Moreover, we also evaluate
three major visual features, namely color, texture
and shape,for their effectiveness in perceptual
segmentation and propose a simple graph fusion scheme
to implement some recent findings from psychophysics
which suggest combining these visual features
with different emphases for perceptual grouping.
Specifically, an input image is first oversegmented into
superpixels at different scales. We postulate a gravitation
law based on empirical observations and divide
superpixels adaptively into small, medium and large sized
sets. Global grouping is achieved using medium
sized superpixels through a sparse representation of
2015

NEXGEN TECHNOLOGY
superpixels’ features by solving a `0-minimization
problem, thereby enabling continuity or propagation of
local smoothness over long range connections.
Small and large sized superpixels are then used to achieve
local smoothness through an adjacent graph
in a given feature space, thus implementing perceptual
laws, e.g., similarity and proximity. Finally, a
bipartite graph is also introduced to enable propagation of
grouping cues between superpixels of different
scales. Extensive experiments are carried out on the
Berkeley Segmentation Database in comparison with
several state of the art graph constructions.
14. MATLAB2015_14 A Database for Evaluating
No-Reference
Image Quality Assessment
Algorithms
This paper presents a new database, CID2013,
to address the issue of using no-reference (NR) image
quality assessment algorithms on images with multiple
distortions. Current NR algorithms struggle to handle
images with many concurrent distortion types, such as
real photographic images captured by different digital
cameras. The database consists of six image sets; on
average, 30 subjects have evaluated 12–14 devices
depicting eight different scenes for a total of 79 different
cameras, 480 images, and 188 subjects (67% female).
The subjective evaluation method was a hybrid absolute
category rating-pair comparison developed for the study
and presented in this paper. This method utilizes a
slideshow of all images within a scene to allow the test
images to work as references to each other. In addition to
mean opinion score value, the images are also rated using
sharpness, graininess, lightness, and color saturation
scales. The CID2013 database contains images used
in the experiments with the full subjective data plus
extensive background information from the subjects. The
database is madefreely available for the research
community.
2015
15. MATLAB2015_15 An Efficient MRF
Embedded Level Set
Method for
Image Segmentation
This paper presents a fast and robust level set
method for image segmentation. To enhance the
robustness against noise, we embed a Markov random
field (MRF) energy function to the conventional level set
energy function. This MRF energy function builds the
correlation of a pixel with its neighbors and encourages
them to fall into the same region. To obtain
a fast implementation of the MRF embedded level set
model, we explore algebraic multigrid (AMG) and sparse
field method (SFM) to increase the time step and decrease
the computation domain, respectively. Both AMG and
SFM can be conducted in a parallel fashion, which
facilitates the processing of our method for big image
databases. By comparing the proposed fast and
robust level set method with the standard level set method
and its popular variants on noisy synthetic images,
synthetic aperture radar (SAR) images, medical images
and natural images, we comprehensively demonstrate the
new method is robust against various kinds of noises.
Especially, the new level set method can segment an
image of size 500 by 500 within three seconds on
2015

NEXGEN TECHNOLOGY
MATLAB R2010b installed in a computer with 3.30GHz
CPU and 4GB memory.
16. MATLAB2015_16 Weighted Guided Image
Filtering
It is known that local filtering-based edgepreserving
smoothing techniques suffer from halo artifacts.
In this paper, a weighted guided image filter (WGIF) is
introduced by incorporating an edge-aware weighting into
an existing guided image filter (GIF) to address the
problem. The WGIF inherits advantages of both global
and local smoothing filters in the sense that: 1) the
complexity of the WGIF is O(N) for an image with N
pixels, which is same as the GIF and 2) the WGIF
can avoid halo artifacts like the existing global smoothing
filters. The WGIF is applied for single image detail
enhancement, single image haze removal, and fusion of
differently exposed images. Experimental results show
that the resultant algorithms produce images with better
visual quality and at the same time halo artifacts can be
reduced/avoided from appearing in the final images with
negligible increment on running times.
2015
17. MATLAB2015_17 Distinctive Efficient
Robust Features From
the Biological Modeling of
the P Ganglion Cells
Studies in neuroscience and biological vision have
shown that the human retina has strong computational
power, and its information representation supports vision
tasks on both ventral and dorsal pathways. In this paper, a
new local image descriptor, termed distinctive efficient
robust features (DERF), is derived by modeling the
response and distribution properties of the parvocellular-
projecting ganglion cells in the primate retina. DERF
features exponential scale distribution, exponential grid
structure, and circularly symmetric function difference of
Gaussian (DoG) used as a convolution kernel, all of
which are consistent with the characteristics of the
ganglion cell array found in neurophysiology, anatomy,
and biophysics. In addition, a new explanation for local
descriptor design is presented from the perspective of
wavelet tight frames. DoG is naturally a
wavelet, and the structure of the grid points array in our
descriptor is closely related to the spatial sampling of
wavelets. The DoG wavelet itself forms a frame, and
when we modulate the parameters of our descriptor to
make the frame tighter, the performance of the DERF
descriptor improves accordingly. This is verified by
designing a tight frame DoG, which leads to much better
performance. Extensive experiments conducted in the
image matching task on the multiview stereo
correspondence data set demonstrate that DERF
outperforms stateof the art methods for both hand-crafted
and learned descriptors, while remaining robust and being
much faster to compute.
2015
18. MATLAB2015_18 Multi-task Pose-Invariant
Face Recognition
Face images captured in unconstrained environments
usually contain significant pose variation, which
dramatically degrades the performance of algorithms
designed to recognize frontal faces. This paper proposes a
novel face identification framework capable of handling
2015

NEXGEN TECHNOLOGY
the full range of pose variations within ±90° of yaw. The
proposed framework first transforms the original pose-
invariant face recognition problem into a partial frontal
face recognition problem. A robust patch-based face
representation scheme is then developed to represent the
synthesized partial frontal faces. For each patch,
a transformation dictionary is learnt under the proposed
multitask learning scheme. The transformation dictionary
transforms the features of different poses into a
discriminative subspace. Finally, face matching is
performed at patch level rather than at the holistic level.
Extensive and systematic experimentation on FERET,
CMU-PIE, and Multi-PIE databases shows that the
proposed method consistently outperforms single-task-
based baselines as well as state-of-the-art methods for the
pose problem. We further extend the proposed algorithm
for the unconstrained face verification problem and
achieve top-level performance on the challenging LFW
data set.
19. MATLAB2015_19 A Feature-Enriched
Completely Blind Image
Quality Evaluator
Existing blind image quality assessment (BIQA)
methods are mostly opinion-aware. They learn regression
models from training images with associated human
subjective scores to predict the perceptual quality of test
images. Such opinion-aware methods, however, require a
large amount of training samples with associated human
subjective scores and of a variety of distortion types. The
BIQA models learned by opinion-aware methods often
have weak generalization capability, hereby limiting their
usability in practice. By comparison, opinion-unaware
methods do not need human subjective scores for
training, and thus have greater potential for good
generalization capability. Unfortunately, thus far no
opinion-unaware BIQA method has shown consistently
better quality prediction accuracy than the opinion-aware
methods. Here, we aim to develop an opinion unaware
BIQA method that can compete with, and perhaps
outperform, the existing opinion-aware methods. By
integrating the features of natural image statistics derived
from multiple cues, we learn a multivariate Gaussian
model of image patches from a collection of pristine
natural images. Using the learned multivariate Gaussian
model, a Bhattacharyya-like distance is used to measure
the quality of each image patch, and then an overall
quality score is obtained by average pooling. The
proposed BIQA method does not need any distorted
sample images nor subjective quality scores for training,
yet extensive experiments demonstrate its superior
quality-prediction performance to the state-of-the-art
opinion-aware BIQA methods.
2015
20. MATLAB2015_20 SpatiotemporalSaliency
Detection for Video
Sequences Based on
Random Walk With Restart
A novel saliency detection algorithm for video
sequences based on the random walk with restart (RWR)
is proposed in this paper. We adopt RWR to detect
spatially and temporally salient regions. More
specifically, we first find a temporal saliency distribution
2015

NEXGEN TECHNOLOGY
using the features of motion distinctiveness, temporal
consistency, and abrupt change. Among them, the motion
distinctiveness is derived by comparing the motion
profiles of image patches. Then, we employ the temporal
saliency distribution as a restarting distribution of the
random walker. In addition, we design the transition
probability matrix for the walker using the spatial features
of intensity, color, and compactness. Finally, we estimate
the spatiotemporal saliency distribution by finding the
steady-state distribution of the walker.
The proposed algorithm detects foreground salient objects
faithfully, while suppressing cluttered backgrounds
effectively, by incorporating the spatial transition matrix
and the temporal restarting distribution systematically.
Experimental results on various video sequences
demonstrate that the proposed algorithm outperforms
conventional saliency detection algorithms qualitatively
and quantitatively.
21. MATLAB2015_21 Sorted Consecutive Local
Binary Pattern
for Texture Classification
In this paper, we propose a sorted consecutive local
binary pattern (scLBP) for texture classification.
Conventional methods encode only patterns whosespatial
transitions are not more than two, whereas scLBP
encodes patterns regardless of their spatial transition.
Conventional methods do not encode
patterns on account of rotation-invariant encoding; on the
other hand, patterns with more than two spatialtransitions
have discriminative power. The proposed scLBP encodes
all patterns with any number of spatial transitions while
maintaining their rotation-invariant nature by sorting the
consecutive patterns. In addition, we introduce dictionary
learning of scLBP based on kd-tree which separates data
with a space partitioning strategy. Since the elements of
sorted consecutive patterns lie in different space, it can be
generated to a discriminative code with kd-tree. Finally,
we present a framework in which scLBPs
and the kd-tree can be combined and utilized. The results
of experimental evaluation on five texture data sets—
Outex, CUReT, UIUC, UMD, and KTH-TIPS2-a—
indicate that our proposed framework achieves the best
classification rate on the CUReT, UMD, and KTH-
TIPS2-a data sets compared with conventional methods.
The results additionally indicate that only a marginal
difference exists between the best classification rate
of conventional methods and that of the proposed
framework on the UIUC and Outex data sets.
2015
22. MATLAB2015_22 Robust 2D Principal
Component Analysis:
A Structured Sparsity
Regularized Approach
Principal component analysis (PCA) is widely
used to extract features and reduce dimensionality in
various computer vision and image/video processing
tasks. Conventional approaches either lack robustness to
outliers and corrupted data or are designed for one-
dimensional signals. To address this problem, we propose
a robust PCA model for two-dimensional
images incorporating structured sparse priors, referred to
as structured sparse 2D-PCA. This robust model
2015

NEXGEN TECHNOLOGY
considers the prior of structured and grouped pixel values
in two dimensions. As the proposed formulation is jointly
nonconvex and nonsmooth, which is difficult to tackle by
joint optimization, we develop a two-stage alternating
minimization approach to solve the problem. This
approach iteratively learns the projection matrices by
bidirectional decomposition and utilizes the proximal
method to obtain the structured sparse outliers.
By considering the structured sparsity prior, the proposed
model becomes less sensitive to noisy data and outliers in
two dimensions. Moreover, the computational cost
indicates that the robust two-dimensional model is
capable of processing quarter common intermediate
format video in real time, as well as
handling large-size images and videos, which is often
intractable with other robust PCA approaches that involve
image-to-vector conversion. Experimental results on
robust face reconstruction, video background subtraction
data set, and real-world videos
show the effectiveness of the proposed model compared
with conventional 2D-PCA and other robust PCA
algorithms.
23. MATLAB2015_23 Accurate Vessel
Segmentation With
Constrained B-Snake
We describean active contour framework with
accurate shape and size constraints on the vessel cross-
sectional planes to produce the vessel segmentation. It
starts with a multiscale vessel axis tracing in a 3D
computed tomography (CT) data, followed by vessel
boundary delineation on the cross-sectional planes
derived from the extracted axis. The vessel boundary
surface is deformed under constrained movements on the
cross sections and is voxelized to produce the final
vascular segmentation. The novelty of this paper lies
in the accurate contour point detection of thin vessels
based on the CT scanning model, in the efficient
implementation of missing contour points in the
problematic regions and in the active contour model with
accurate shape and size constraints. The main advantage
of our framework is that it avoids disconnected and
incomplete segmentation of the vessels in the problematic
regions that contain touching vessels (vessels in close
proximity to each other), diseased portions (pathologic
structure attached to a vessel), and thin vessels. It is
particularly suitable for accurate segmentation of thin and
low contrast vessels. Our method is evaluated and
demonstrated on CT data sets from our partner site, and
its results are compared with three related
methods. Our method is also tested on two publicly
available databases and its results are compared with the
recently published method. The applicability of the
proposed method to some challenging clinical problems,
the segmentation of the vessels in the problematic
regions, is demonstrated with good results on both
quantitative and qualitative experimentations; our
segmentation algorithm can delineate vessel boundaries
that have level of variability similar to those obtained
2015

NEXGEN TECHNOLOGY
manually.
24. MATLAB2015_24 PatchMatch With Potts
Modelfor Object
Segmentation and Stereo
Matching
This paper presents a unified variational formulation for
joint object segmentation and stereo matching, which
takes both accuracy and efficiency into account. In our
approach, depth-map consists of compact objects, each
object is represented through three different aspects: 1)
the perimeter in image space; 2) the slanted object depth
plane; and 3) the planar bias, which is to add an additional
level of detail on top of each object plane in order to
model depth variations within an object. Compared with
traditional high quality solving methods in low level, we
use a convex formulation of the multilabel Potts Model
with PatchMatch stereo techniques to generate depth-map
at each image in object level and show that accurate
multiple view reconstruction can be achieved with our
formulation by means of induced homography without
discretization or staircasing artifacts. Our model is
formulated as an energy minimization that is optimized
via a fast primal-dual algorithm, which can handle several
hundred object depth segments efficiently. Performance
evaluations in the Middlebury benchmark data sets
show that our method outperforms the traditional integer-
valued disparity strategy as well as the original
PatchMatch algorithm and its variants in subpixel
accurate disparity estimation. The proposed algorithm is
also evaluated and shown to produce consistently good
results for various real-world data sets (KITTI benchmark
data sets and multiview benchmark
data sets).
2015
25. MATLAB2015_25 Robust Representation and
Recognition of
Facial Emotions Using
Extreme Sparse
Learning
Recognition of natural emotions from human faces is an
interesting topic with a wide range of potential
applications like human-computer interaction, automated
tutoring systems, image and video retrieval, smart
environments, and driver warning systems. Traditionally,
facial emotion recognition systems have been evaluated
on laboratory controlled data, which is not representative
of the environment faced in real-world applications. To
robustly recognize facial emotions in real-world natural
situations, this paper proposes an approach called
Extreme Sparse Learning (ESL), which has the ability to
jointly learn a dictionary (set of basis) and a non-linear
classification model. The proposed approach combines
the discriminative power of Extreme Learning Machine
(ELM) with the reconstruction property of sparse
representation to enable accurate classification when
presented with noisy signals and imperfect data
recorded in natural settings. Additionally, this work
presents a new local spatio-temporal descriptor that is
distinctive and pose-invariant. The proposed framework
is able to achieve state-of-the-art recognition accuracy on
both acted and spontaneous facial emotion databases.
2015

NEXGEN TECHNOLOGY
26. MATLAB2015_26 AdaptiveImage Denoising
by Targeted Databases
We propose a data-dependent denoising procedure
to restore noisy images. Different from existing denoising
algorithms which search for patches from either the noisy
image or a generic database, the new algorithm finds
patches from a database that contains relevant patches.
We formulate the denoising problem as an optimal filter
design problem and make two contributions. First, we
determine the basis function ofthe denoising filter by
solving a group sparsity minimization problem. The
optimization formulation generalizes existing denoising
algorithms and offers systematic analysis of the
performance. Improvement methods are proposed to
enhance the patch search process. Second, we determine
the spectral coefficients of the denoising filter by
considering a localized Bayesian prior. The
localized prior leverages the similarity of the targeted
database, alleviates the intensive Bayesian computation,
and links the new method to the classical linear minimum
mean squared error estimation. We demonstrate
applications of the proposed method in a variety of
scenarios, including text images, multiview images,
and face images. Experimental results show the
superiority of the new algorithm over existing methods.
2015
27. MATLAB2015_27 Progressive Halftone
Watermarking Using
Multi-layer Table Lookup
Strategy
In this work, a halftoning-based multi-layer watermarking
of low computational complexity is proposed. An
additional data hiding technique is also employed to
embed multiple watermarks into the watermark to be
embedded to improve the security and embedding
capacity. At the encoder, the Efficient Direct Binary
Search (EDBS) method is employed to generate 256
reference tables to ensure the output is in halftone format.
Subsequently, watermarks are embedded by a set of
optimized compressed tables with various textural angles
for table lookup. At the decoder, the Least-MeanSquare
(LMS) metric is considered to increases the differences
among those generated phenotypes.
2015
28. MATLAB2015_28 Learning MultipleLinear
Mappings for Efficient
Single Image Super-
Resolution
Example learning-based superresolution (SR)
algorithms show promise for restoring a high-resolution
(HR) image from a single low-resolution (LR) input. The
most popular approaches, however, are either time- or
space-intensive, which limits their practical applications
in many resource-limited settings. In this paper, we
propose a novel computationally efficient single image
SR method that learns multiple linear
mappings (MLM) to directly transform LR feature
subspaces into HR subspaces. In particular, we first
partition the large nonlinear feature space of LR images
into a cluster of linear subspaces. Multiple LR
subdictionaries are then learned, followed by inferring the
corresponding HR subdictionaries based on the
assumption that the LR–HR features share the same
representation coefficients. We establish MLM from the
input LR features to the desired HR outputs in order to
achieve fast yet stable SR recovery. Furthermore, in order
2015

NEXGEN TECHNOLOGY
to suppress displeasing artifacts generated by the MLM-
based method, we apply a fast nonlocal means algorithm
to construct a simple yet effective similaritybased
regularization term for SR enhancement. Experimental
results indicate that our approach is both quantitatively
and qualitatively superior to other application-oriented SR
methods, while maintaining relatively low time and space
complexity.
29. MATLAB2015_29 Cross-Domain Person Re-
Identification Using
Domain Adaptation
Ranking SVMs
This paper addresses a new person re-identification
problem without label information of persons under non
overlapping target cameras. Given the matched (positive)
and unmatched (negative) image pairs from source
domain cameras, as well as unmatched (negative) and
unlabeled image pairs from target domain cameras, we
propose an Adaptive Ranking Support Vector Machines
(AdaRSVM) method for re-identification under target
domain cameras without person labels. To overcome
the problems introduced due to the absence of matched
(positive) image pairs in the target domain, we relax the
discriminative constraint to a necessary condition only
relying on the positive mean in the target domain. To
estimate the target positive mean, we make use of all the
available data from source and target domains as well as
constraints in person re-identification. Inspired by
adaptive learning methods, a new discriminative
model with high confidence in target positive mean and
low confidence in target negative image pairs is
developed by refining the distance model learnt from the
source domain. Experimental results show that the
proposed AdaRSVM outperforms existing supervised or
unsupervised, learning or non-learning reidentification
methods without using label information in target
cameras. Moreover, our method achieves better re-
identification performance than existing domain
adaptation methods derived under equal conditional
probability assumption.
2015
30. MATLAB2015_30 Structure-Sensitive
Saliency Detection
via Multilevel Rank
Analysis in
Intrinsic Feature Space
This paper advocates a novel multiscale,
structure-sensitive saliency detection method, which can
distinguish multilevel, reliable saliency from various
natural pictures in a robust and versatile way. One key
challenge for saliency detection is to guarantee the entire
salient object being characterized differently from
nonsalient background. To tackle this, our strategy is to
design a structure-aware descriptor based on the intrinsic
biharmonic distance metric. One benefit of introducing
this descriptor is its ability to simultaneously integrate
local and global structure information, which is extremely
valuable for separating the salient object from nonsalient
background in a multiscale sense. Upon devising such
powerful shape descriptor, the remaining challenge is
to capture the saliency to make sure that salient subparts
actually stand out among all possible candidates. Toward
this goal, we conduct multilevel low-rank and sparse
analysis in the intrinsic feature space spanned by the
2015

NEXGEN TECHNOLOGY
shape descriptors defined on over-segmented super-
pixels. Since the low-rank property emphasizes much
more on stronger similarities among super-pixels, we
naturally obtain a scale space along the rank
dimension in this way. Multiscale saliency can be
obtained by simply computing differences among the
low-rank components across the rank scale. We conduct
extensive experiments on some public benchmarks, and
make comprehensive, quantitative evaluation between our
method and existing state-of-the-art techniques. All the
results demonstrate the superiority of our method in
accuracy, reliability, robustness, and versatility.
31. MATLAB2015_31 Depth Reconstruction From
Sparse Samples:
Representation, Algorithm,
and Sampling
The rapid development of 3D technology and
computer vision applications has motivated a thrust of
methodologies for depth acquisition and estimation.
However, existing hardware and software acquisition
methods have limited performance due to poor depth
precision, low resolution, and high computational cost. In
this paper, we present a computationally efficient method
to estimate dense depth maps from sparse measurements.
There are three main contributions. First, we provide
empirical evidence that depth maps can be encoded much
more sparsely than natural images using common
dictionaries, such as wavelets and contourlets. We also
show that a combined wavelet–contourlet dictionary
achieves better performance than using either dictionary
alone. Second, we propose an alternating direction
method of multipliers (ADMM) for depth map
reconstruction. A multiscale warm start procedure
is proposed to speed up the convergence. Third, we
propose a two-stage randomized sampling scheme to
optimally choose the sampling locations, thus maximizing
the reconstruction performance for a given sampling
budget. Experimental results show that the proposed
method produces high-quality dense depth estimates, and
is robust to noisy measurements. Applications to real data
in stereo matching are demonstrated.
2015
32. MATLAB2015_32 Image Denoising by
Exploring External
and Internal Correlations
Single image denoising suffers from limited data
collection within a noisy image. In this paper, we propose
a novel image denoising scheme, which explores both
internal and external correlations with the help of web
images. For each noisy patch, we build internal and
external data cubes by finding similar patches from the
noisy and web images, respectively. We then propose
reducing noise by a two-stage strategy using different
filtering approaches. In the first stage, since the noisy
patch may lead to inaccurate patch selection, we
propose a graph based optimization method to improve
patch matching accuracy in external denoising. The
internal denoising is frequency truncation on internal
cubes. By combining the internal and external denoising
patches, we obtain a preliminary denoising result. In the
second stage, we propose reducing noise by filtering of
external and internal cubes, respectively, on transform
2015

NEXGEN TECHNOLOGY
domain. In this stage, the preliminary denoising result
not only enhances the patch matching accuracy but also
provides reliable estimates of filtering parameters. The
final denoising image is obtained by fusing the external
and internal filtering results. Experimental results show
that our method constantly outperforms state-of-the-art
denoising schemes in both subjective and objective
quality measurements, e.g., it achieves >2 dB gain
compared with BM3D at a wide range of noise levels.
33. MATLAB2015_33 Motion-Compensated
Coding and Frame Rate
Up-Conversion:Models
and Analysis
Block-based motion estimation (ME) and motion
compensation (MC) techniques are widely used in
modern video processing algorithms and compression
systems. The great variety of video applications and
devices results in diverse compression specifications,
such as frame rates and bit rates.
In this paper, we study the effect of frame rate and
compression bit rate on block-based ME and MC as
commonly utilized in inter-frame coding and frame rate
up-conversion (FRUC). This joint examination yields a
theoretical foundation for comparing MC procedures in
coding and FRUC. First, the video signal is locally
modeled as a noisy translational motion of an image.
Then, we theoretically model the motion-compensated
prediction of available and absent frames as in coding and
FRUC applications, respectively. The theoretic MC-
prediction error is studied further and its autocorrelation
function is calculated, yielding useful separable-
simplifications for the coding application. We argue that a
linear relation exists between the variance of the MC-
prediction error and temporal distance. While the relevant
distance in MC coding is between the predicted and
reference frames, MC-FRUC is affected by the distance
between the frames available for interpolation. We
compare our estimates with experimental results and
show that the theory explains qualitatively the empirical
behavior. Then, we use the models proposed to analyze a
system for improving of video coding at low bit rates,
using a spatiotemporal scaling. Although this concept is
practically employed in various forms, so far it lacked a
theoretical justification. We here harness the proposed
MC models and present a comprehensive analysis of the
system, to qualitatively predict the experimental
results.
2015
34. MATLAB2015_34 Fractal Analysis for
Reduced Reference
Image Quality Assessment
In this paper, multifractal analysis is adapted to
reduced-reference image quality assessment (RR-IQA). A
novel RR-QA approach is proposed, which measures the
difference of spatial arrangement between the reference
image and the distorted image in terms of spatial
regularity measured by fractal dimension. An image is
first expressed in Log-Gabor domain. Then, fractal
dimensions are computed on each Log-Gabor subband
and concatenated as a feature vector. Finally, the
extracted features are pooled as the quality score of the
distorted image using 1 distance. Compared with existing
2015

NEXGEN TECHNOLOGY
approaches, the proposed method measures image quality
from the perspective of the spatial distribution of image
patterns. The proposed method was evaluated on seven
public benchmark data sets. Experimental results have
demonstrated the excellent performance of the proposed
method in comparison with state-of-the-art approaches.
35. MATLAB2015_35 Criteria-Based Modulation
for Multilevel Inverters
Pulse-width modulation schemes are aimed at adjusting
the fundamental component while reducing the harmonic
content of an inverter output voltage or current. This
paper addresses the topic of optimal inverter operation in
reference to a given objective function. The objective
function could embody either a single performance
criterion, such as voltage or current total harmonic
distortion, or a weighted sum of multiple criteria.
The proposed method ensures primacy of the chosen
solution while imposing no restriction over its modulation
index. In particular, operating the inverter by the chosen
solution would result in performances superior to any
other modulation scheme commutating in an equal
number of switching angles per fundamental cycle. The
proposed method allows for the consideration
of practical inverter constraints and prevents the
possibility of impractical switching sequence. A detailed
investigation of the method is given, accompanied by two
practical cases minimizing, respectively, phase-voltage
THD and line-current THD of a three level inverter.
Selected simulation and experimental results are
presented to validate the theoretical part.
2015
36. MATLAB2015_36 A Fully Soft-Switched
Single Switch Isolated
DC-DC Converter
This paper proposes a soft-switched single switch
isolated converter. The proposed converter is able to offer
low cost and high power density in step up application
due to the following features: ZCS turn-on and ZVS turn-
off of switch and ZCS turn-off of diodes regardless of
voltage and load variation; low rated lossless snubber;
reduced transformer volume compared to flyback based
converters due to low magnetizing current. Experimental
results on a 100kHz, 250W prototype are provided to
validate the proposed concept.
2015
37. MATLAB2015_37 Functional Modeling of
Symmetrical Multipulse
Autotransformer Rectifier
Units
for Aerospace Applications
This paper aims to develop a functional model of
symmetrical multipulse autotransformer rectifier units
(ATRUs) for more-electric aircraft (MEA) applications.
The ATRU is seen as the most reliable way readily to be
applied in the MEA. Interestingly, there is no model of
ATRUs suitable for unbalanced or faulty
conditions at the moment. This paper is aimed to fill this
gap and develop functional models suitable for both
balanced and unbalanced conditions. Using the fact that
the dc voltage and current are strongly related to the
voltage and current vectors at the ac terminals of ATRUs,
a functional model has been developed for the
asymmetric ATRUs. The developed functional models
are validated through simulation and experiment. The
efficiency of the developed model is also demonstrated by
2015

NEXGEN TECHNOLOGY
comparing with corresponding detailed switching models.
The developed functional model shows significant
improvement of simulation efficiency, especially under
balanced conditions.
38. MATLAB2015_38 ModelPredictive Control
Methods to Reduce
Common-Mode Voltage
for Three-Phase Voltage
Source Inverters
In this paper, we propose model predictive control
methods to reduce the common-mode voltage of three-
phase voltage source inverters (VSIs). In the reduced
common-mode voltage-model predictive control (RCMV-
MPC) methods proposed in this paper, only nonzero
voltage vectors are utilized to reduce the common-mode
voltage as well as to control the load currents. In addition,
two nonzero voltage vectors are selected from the cost
function at every sampling period, instead of using only
one optimal vector during one sampling period. The two
selected nonzero vectors are distributed in one sampling
period in such a way as to minimize the error between
the measured load current and the reference. Without
utilizing the zero vectors, the common-mode voltage
controlled by the proposed RCMV-MPC algorithms can
be restricted within ±Vdc/6. Furthermore, application of
the two nonzero vectors with optimal time sharing
between them can yield satisfactory load current ripple
performance without using the zero vectors. Thus, the
proposed RCMV-MPC methods can reduce the common-
mode voltage as well as control the load currents with fast
transient response and satisfactory load current ripple
performance compared with the conventional model
predictive control method. Simulation and experimental
results are included to verify the effectiveness of the
proposed RCMV-MPC methods.
2015
39. MATLAB2015_39 Interleaved Phase-Shift
Full-Bridge Converter With
Transformer Winding
Series–Parallel
Autoregulated
(SPAR) Current Doubler
Rectifier
The analysis and design guidelines for a two-phase
interleaved phase-shift full-bridge converter with
transformer winding series–parallel autoregulated current
doubler rectifier are presented in this paper. The
secondary windings of two transformers
work in parallel when the equivalent duty cycle is smaller
than 0.25 but in series when the duty cycle is larger than
0.25 owing to the series–parallel autoregulated rectifier.
With the proposed rectifying structure, the voltage stress
of the rectifier is reduced. Also, the interleaving operation
reduces theoutput current ripple. A 1-kW prototype with
200–400-V input and 50-V/20-A output is built up
to verify the theoretical analysis.
2015
40. MATLAB2015_40 Analysis of Active-
Network Converter with
Coupled
Inductors
High step-up voltage gain DC/DC converters are widely
applied in fuel cell stacks, photovoltaic arrays,
battery sources, and high intensity discharge (HID) lamps
power systems. Active-network converters with coupled
inductors (CL-ANC) are derived from switched inductor
active-network converters (SL-ANC). The proposed
converter contains two coupled inductors which can be
integrated into one magnetic core and two power
switches. The converter can provide a relatively high
voltage conversion ratio with a small duty cycle; the
2015

NEXGEN TECHNOLOGY
voltage and current stress of power switches are low
which is helpful to reduce the losses. This paper shows
the key waveforms of the CL-ANC and detailed
derivation of the steady-state operation principle. The
voltage conversion ratio and the effect of the leakage
inductance on voltage gain are discussed. The voltage
stress and current stress on the power devices are
illustrated and the comparison between the proposed
converter and SL-ANC are given. Finally, the prototype
has been established in the lab with 200V and 400V
output under different turn ratios. Experimental results are
given to verify the correctness of the analysis.
41. MATLAB2015_41 Modeling and Controller
Design of a Semi-Isolated
Multi-Input Converter for
Hybrid PV/Wind Power
Charger System
The objective of this paper is to propose the
development of a multi-input dc-dc converter (MIC)
family which is composed of isolated and/or non-isolated
dc-dc converters. By analyzing five basic isolated dc-dc
converters, four isolated pulsating voltage source cells (I-
PVSCs) and three isolated pulsating current source cells
(I-PCSCs) are generated. Moreover, a semi-isolated
multi-input converter (S-MIC) for hybrid PV/wind power
charger system which can simplify the power
system, reduce the cost, deliver continuous power and
overcome high voltage-transfer-ratio problems is
proposed. In this paper, the operational principle of the
proposed S-MIC is explained, the small-signal ac model
is derived and the controller design is developed.
Computer simulations and experimental results are
presented to verify the accuracy of the proposed small
signal ac model and the performance of the proposed S-
MIC.
2015
42. MATLAB2015_42 A Four-Switch Three-
Phase SEPIC-Based
Inverter
The four-switch three-phase (FSTP) inverter has been
proposed as an innovative inverter design to
reduce the cost, complexity, size, and switching losses of
the DC-AC conversion system. Traditional FSTP inverter
usually operates at half the DC input voltage, hence, the
output line voltage cannot exceed this value. This paper
proposes a novel design for the FSTP inverter based on
the topology of the single-ended primary-inductance
converter (SEPIC). The proposed topology provides pure
sinusoidal output voltages with no need for output filter.
Compared to traditional FSTP inverter, the proposed
FSTP SEPIC inverter improves the voltage utilization
factor of the input DC supply, where the proposed
topology provides higher output line voltage which can be
extended up to the full value of the DC input voltage. The
integral sliding-mode control is used with the proposed
topology to optimize its dynamics and to ensure
robustness of the system during different operating
conditions. Derivation of the equations describing the
parameters design, components ratings, and the operation
of the proposed SEPIC inverter is presented in this paper.
Simulation model and experimental setup are used to
validate the proposed concept. Simulations and
experimental results show the effectiveness of the
2015

NEXGEN TECHNOLOGY
proposed inverter.
43. MATLAB2015_43 High-Efficiency Isolated
Single-Input Multiple-
Output
Bidirectional Converter
This study presents a high-efficiency isolated single-input
multiple-output bidirectional (HISMB) converter for a
power storage system. According to the power
management, the proposed HISMB converter can operate
at a step-up state (energy release) and a step-down state
(energy storage). At the step-up state, it can boost the
voltage of a low-voltage input power source to a high-
voltage-side dc bus and middle-voltage terminals. When
the high-voltage-side dc bus has excess energy, one can
reversely transmit the energy. The high-voltage dc bus
can take as the main power, and middle-voltage output
terminals can supply powers for individual middle-
voltage dc loads or to charge auxiliary power sources
(e.g., battery modules). In this study, a coupled-inductor-
based HISMB converter accomplishes the bidirectional
power control with the properties of voltage clamping and
soft switching, and the corresponding device
specifications are adequately designed. As a result, the
energy of the leakage inductor of the coupled inductor
can be recycled and released to the high-voltage-side dc
bus and auxiliary power sources, and the voltage stresses
on power switches can be greatly reduced. Moreover, the
switching losses can be significantly decreased because of
all power switches with zero-voltage-switching (ZVS)
features. Therefore, the objectives of high-efficiency
power conversion, electric isolation, bidirectional energy
transmission, and various output voltage with different
levels can be obtained. The effectiveness of the proposed
HISMB converter is verified by experimental results of a
kW-level prototype in practical applications.
2015
44. MATLAB2015_44 Modularized Control
Strategy and Performance
Analysis of DFIG System
under Unbalanced and
Harmonic Grid Voltage
The paper presents a modularized control
strategy of doubly fed induction generator (DFIG)
system, including the grid-side converter (GSC) and
rotor-side converter (RSC), under unbalanced and
harmonic grid voltage. The sequence decomposition
process and complicated control reference calculation can
be avoided in the proposed control strategy. From the
perspective of power grid friendly-operation, the control
targets of DFIG system in this paper are chosen as: 1)
smooth active and reactive power injected into the power
grid; 2) balanced and sinusoidal current injected into the
power grid. The RSC and GSC can work as two
independent modules and the communication between
RSC and GSC can be removed. Furthermore, the 3rd
harmonic current component, DC link voltage fluctuation
and electromagnetic torque pulsation under the different
control targets are theoretically analyzed. Finally, the
availability of the proposed modularized control strategy
of DFIG system under unbalanced and distorted grid
voltage is verified by experiment results.
2015
45. MATLAB2015_45 Resonant Switched-
Capacitor Voltage
Regulator with Ideal
A new, small and efficient voltage regulator,
realized using a resonant switched capacitor converter
technology, is introduced. Voltage regulation is
2015

NEXGEN TECHNOLOGY
Transient Response implemented by means of simple digital pulse density
modulation. It displays an ideal transient response with a
zero-order nature to all disturbance types. The newly
developed topology acts as a gyrator with a wide range of
voltage conversion ratios (below as well as above unity)
with constant efficiency characteristics for the entire
operation range. The operation of the voltage regulator is
verified on a 20W experimental prototype, demonstrating
ideal transient recovery without over/under-shoots in
response to load and line transients. Simple design
guidelines for the voltage regulation system are provided
and verified by experiments.
46. MATLAB2015_46 On thePerformance of
Multiobjective
Evolutionary
Algorithms in Automatic
Parameter Extraction of
Power Diodes
In this paper, a general, robust, and automatic
parameter extraction of nonlinear compact models is
presented. The parameter extraction is based on
multiobjective optimization using evolutionary algorithms
which allow fitting of several highly
nonlinear and highly conflicting characteristics
simultaneously. Two multiobjective evolutionary
algorithms which have been proved to be robust for a
wide range of multiobjective problems [1]–[3], the
Nondominated Sorting Genetic Algorithm II
and the Multiobjective Covariance Matrix Adaptation
Evolution Strategy, are used in the parameter extraction
of a novel power diode compact model based on the
lumped charge technique. The performance of the
algorithms is assessed using a systematic statistical
approach. Good agreement between the simulated and
measured characteristics of the power diode shows the
accuracy of the used compact model and the efficiency
and effectiveness of the proposed multiobjective
optimization scheme.
2015
47. MATLAB2015_47 Development of a Wind
Interior Permanent-Magnet
Synchronous Generator
Based Microgrid and Its
Operation Control
This paper presents the development of a wind
interior permanent-magnet synchronous generator
(IPMSG) based DC micro-grid and its operation control.
First, the derated characteristics of PMSG systems with
various AC/DC converters and operation controls are
comparatively analyzed. Then the IPMSG followed by
three-phase Vienna switch mode rectifier (SMR) is
developed to establish the common DC bus of DC micro-
grid. Good developed power and voltage regulation
characteristics are achieved via the proposed
commutation tuning, robust current and voltage controls.
Second, a single-phase three-wire (1P3W) inverter is
constructed to serve as the test load. Good AC
220V/110V output voltage waveforms under unknown
and nonlinear loads are preserved by the developed robust
waveform tracking control scheme. Third, a battery
energy storage system (BESS) is established, and the fast
energy storage support response is obtained via the
proposed droop control approach with adaptive predictive
current control method. In addition, a chopped dump load
2015

NEXGEN TECHNOLOGY
is equipped to enhance the energy balance control
flexibility.
48. MATLAB2015_48 A Novel Drive Method for
High-Speed
Brushless DC Motor
Operating in a Wide
Range
In this paper, a novel drive method, which is different
from the traditional motor drive techniques, for high-
speed brushless DC (BLDC) motor is proposed and
verified by a series of experiments. It is well known that
the BLDC motor can be driven by either Pulse-Width
Modulation (PWM) techniques with a constant DC-link
voltage or Pulse-Amplitude Modulation (PAM)
techniques with an adjustable DC-link voltage. However,
to our best knowledge, there is rare study providing a
proper drive method for high-speed BLDC motor with a
large power over a wide speed range. Therefore, the
detailed theoretical analysis comparison of the PWM
control and the PAM control for high-speed BLDC motor
is first given. Then a conclusion that the PAM control is
superior to the PWM control at high speed is obtained
because of decreasing the commutation delay and high
frequency harmonic wave. Meanwhile, a new high-speed
BLDC motor drive method based on the hybrid approach
combining PWM and PAM is proposed. At last, the
feasibility and effectiveness of the performance analysis
comparison and the new drive method are verified by
several experiments.
2015
49. MATLAB2015_49 The Dynamic Controlof
Reactive Power for the
Brushless Doubly Fed
Induction Machine with
Indirect Stator-quantities
Control Scheme
Compared to the doubly fed induction
machine (DFIM), the brushless doubly fed induction
machine (BDFIM) has higher reliability by virtue of the
absence of a brush gear. Recent research on structure
optimization design and control strategy of BDFIM has
made remarkable progress. BDFIM indirect
stator-quantities control (ISC) is a new control strategy,
which, in comparison to vector control strategy, requires
fewer parameters and does not need rotating coordinate
transformation. This paper further develops the dynamic
control of reactive power for the BDFIM with ISC
scheme. Detailed theoretical analysis is done to show the
controller structure of the reactive power. The
experimental results of the prototype show the feasibility
of the proposed scheme. As a result, the proposed ISC
controllers have been able to control not only speed and
torque, but also the reactive power.
2015
50. MATLAB2015_50 An LCL-LC Filter for
Grid-Connected
Converter: Topology,
Parameter and Analysis
In order to further cut down the cost of filter for
grid-connected pulse width modulation (PWM) converter
under the more and more stringent grid code, a new kind
of high order filter, named LCL-LC filter, is presented in
this paper. The resonant frequency characteristics of the
filter are analyzed and a parameter design method on the
base of the characteristics is also proposed in the paper.
The proposed parameter design method can easily make
full use of the existing research results about the
traditional LCL filter parameter design. And then a
parameter robustness analysis method based on
four-dimensional graphics is proposed to analyze
2015

NEXGEN TECHNOLOGY
parameter robustness of the presented filter. Compared
with the traditional one, theproposed analysis method can
analyze the filter performance under variations of several
parameters at a time without any iteration. The
comparative analysis and discussion considering the LCL
filter, the trap filter, and the LCL-LC filter,
are presented and verified through the experiments on a
5kW grid-connected converter prototype. Experiment
results demonstrate the accuracy of theoretical analysis
and prove the presented filter has a better performance
than two others.
51. MATLAB2015_51 3D microtransformers for
DC-DC on-chip
power conversion
We address the miniaturization of power converters by
introducing novel, 3D micro transformers with magnetic
core for low-MHz frequency applications. The core is
fabricated by lamination and micro structuring of
Metglas® 2714A magnetic alloy. The solenoids of the
micro transformers are wound around the core using a
ball-wedge wire bonder. The wire bonding process is fast,
allowing the fabrication of solenoids with up to 40 turns
in 10 s. The fabricated devices yield the high inductance
per unit volume of 2.95 µH/mm3 and energy per unit
volume of 133 nJ/mm3 at the frequency of 1 MHz. The
power efficiency of 64-76% are measured for different
turns ratio with coupling factors as high as 98%.
2015
52. MATLAB2015_52 Indirect Matrix Converter-
Based Topology
and Modulation Schemes
for Enhancing
Input Reactive Power
Capability
A new topology based on indirect matrix converter
(IMC) is proposed to enhance the input reactive power
capability. This topology consists of a conventional IMC
and an auxiliary switching network (ASN), which is
connected to the dc-link of the IMC in parallel. With the
aid of ASN, an implicit current source converter-based
static synchronous compensator can be embedded
into an IMC, which lays a foundation for the input
reactive power control. Based on the proposed topology,
two modulation schemes are presented, and the
formations of the output voltage and input reactive
current are decoupled in both of them. To minimize
power loss and improve input current quality, a double
closed-loop control algorithm is introduced, in which the
current through the dc inductor in ASN is controlled to be
minimum. Different from the conventional IMC, theinput
reactive power of the topology is independent of its load
condition without considering the practical constraints.
The effectiveness of the proposed topology and
modulation scheme is confirmed by experimental results.
2015
53. MATLAB2015_53 Closed Loop Discontinuous
Modulation Technique
for Capacitor Voltage
Ripples and Switching
Losses
Reduction in Modular
Multilevel Converters
In this paper, a new discontinuous modulation
technique is presented for the operation of the modular
multilevel converter (MMC). The modulation technique
is based on adding a zero-sequence to the original
modulation signals so that the MMC arms are clamped to
the upper or lower terminals of the dc-link bus. The
clamping intervals are controlled according to
the absolute value of the output current to minimize the
switching losses of the MMC. A significant reduction in
the capacitor voltage ripples is achieved, especially when
2015

NEXGEN TECHNOLOGY
operating with low modulation indices. Furthermore, a
circulating current control strategy suitable for this
modulation technique is also proposed. Simulation and
experimental results under various operating points are
reported along with evaluation and comparison results
against a conventional carrier-based pulse-width
modulation method.
54. MATLAB2015_54 Decentralized Inverse-
Droop Controlfor
Input-Series-Output-
Parallel DC-DC Converters
Input-series-output-parallel (ISOP) DC-DC converters are
suited for high input-voltage and low output-voltage
applications. This letter presents a decentralized inverse-
droop control for this configuration. Each module is self-
contained and no central controller is needed, thus
improving the system modularity, reliability and
flexibility. With the proposed inverse-droop control,
the output voltage reference rises as the load becomes
heavy. Even though the input voltages is not used in the
inverse-droop loop, the power sharing including input
voltage sharing (IVS) and output current sharing (OCS)
can still be well achieved. Besides, the output voltage
regulation characteristic is not affected by the variation
of input voltage. The operation principle is introduced,
and stability of the strategy is also revealed based on
small signal modeling. Finally, the experiment is
conducted to verify the effectiveness of the control
strategy.
2015
55. MATLAB2015_55 Detailed Analysis of DC-
Link Virtual Impedance
based Suppression Method
for Harmonics Interaction
in High-Power PWM
Current-Source Motor
Drives
For high-power PWM current-source motor drive
systems, due to the low converter switching frequency
and the relative small dc choke for reduced cost/weight,
the converters’ switching harmonics may interact through
dc link and produce inter harmonics in the entire system.
Such harmonics interaction phenomenon may give rise to
the system resonance at certain motor speeds, which
degrades the grid-side power quality and generates
excessive torque ripples on the motor side. The resonance
caused by the harmonics interaction in high-power PWM
current-source motor drives is investigated in previous
work. In addition, to actively suppress such resonance,
the basic idea of a dc-link virtual impedance based
suppression method has also been proposed. This paper
extends the previous work to thoroughly analyze the
mechanism and realization of resonance suppression by
the dc-link virtual impedance based method. The indepth
analysis shows that the dc-link virtual impedance based
method successfully enables the active inter harmonics
compensation capability of high-power PWM current-
source drives, which is not addressed in previous
researches. Moreover, simulations and experiments
demonstrate that, by following the selection of coefficient
in the suppression method discussed in this paper, the
dc-link virtual impedance based method can effectively
enhance the attenuation effect of dc link in high-power
PWM current source drive systems so as to suppress the
resonance due to the harmonics interaction under all
resonance conditions.
2015

NEXGEN TECHNOLOGY
56. MATLAB2015_56 An Online Frequency-
Domain Junction
TemperatureEstimation
Method for IGBT Modules
This letter proposes a new frequency-domain
thermal model for online junction temperature estimation
of insulated-gate bipolar transistor (IGBT) modules. The
proposed model characterizes the thermal behavior of an
IGBT module by a linear time-invariant (LTI) system,
whose frequency responseis obtained by applyingthe fast
Fourier transform (FFT) to the time derivative of the
transient thermal impedance from junction to a
reference position of the IGBT module. The junction
temperature of the IGBT is then estimated using the
frequency responses of the LTI system and the heat
sources of the IGBT module. Simulation results show that
the proposed method is computationally efficient
for an accurate online junction temperature estimation of
IGBT modules in both steady-state and transient loading
conditions.
2015
57. MATLAB2015_57 Characterization of a
Silicon IGBT and Silicon
Carbide MOSFET Cross
Switch Hybrid
A parallel arrangement of a Silicon (Si) IGBT and a
Silicon Carbide (SiC) MOSFET is experimentally
demonstrated. The concept referred to as the Cross
Switch “XS” hybrid aims to reach optimum power device
performance by providing low static and dynamic losses
while improving the overall electrical and thermal
properties due to the combination of both the bipolar Si
IGBT and unipolar SiC MOSFET characteristics. For the
purpose of demonstrating the XS hybrid, the parallel
configuration was implemented experimentally in a single
package for devices rated at 1200V. Test results were
obtained to validate this approach with respect to the
static and dynamic performance when compared to
a full Si IGBT and a full SiC MOSFET reference devices
having the same power ratings as for the XS hybrid
samples.
2015
58. MATLAB2015_58 LCL Filter Design and
Inductor Current Ripple
Analysis for 3-
level NPC Grid Interface
Converter
The harmonic filter for a 3-level neutral point
clamped (NPC) grid interface converter is designed in this
paper with good filtering performance and small
component size. LCL topology is selected because of the
attenuation and size tradeoff. The design of the inverter
side inductor L1 is emphasized due to its cost. A detailed
inductor current ripple analysis is given based on the
space vector modulation (SVM). The analysis derives the
inductor volt-second and the maximum current
ripple equation in line cycle. It also reveals the switching
cycle current ripple distribution over a line cycle, with the
consideration of power factor. The total system loss is
calculated with different ripple current. Inductor L1 is
determined by the loss and size tradeoff. Also the
capacitor and grid side inductor L2 is designed based on
attenuation requirement. Different damping circuits for
LCL filter are compared and investigated in detail. The
filter design is verified by both simulation and a 200kVA
3-level NPC converter hardware.
2015
59. MATLAB2015_59 Virtual RC Damping of Active damping and harmonic compensation are 2015

NEXGEN TECHNOLOGY
LCL-Filtered Voltage
Source Converters with
Extended Selective
Harmonic Compensation
two common challenges faced by LCL-filtered voltage
source converters. To manage them holistically, this
paper begins by proposing a virtual RC damper in parallel
with the passive filter capacitor. The virtual damper is
actively inserted by feeding back the passive capacitor
current through a high-pass filter, which indirectly,
furnishes two superior features. They are the
mitigation of phase lag experienced by a conventional
damper and the avoidance of instability caused by the
negative resistance inserted unintentionally. Moreover,
with the virtual RC damper, the frequency region, within
which the harmonic compensation is effective, can be
extended beyond the gain crossover frequency. This is of
interest to some high-performance applications, but has
presently not been achieved by existing schemes.
Performance of the proposed scheme has been tested in
the laboratory with results obtained for demonstrating
stability and harmonic compensation.
60. MATLAB2015_60 Versatile Control of
Unidirectional AC-DC
Boost Converters for
Power Quality Mitigation
This paper introduces a versatile control scheme for
unidirectional ac-dc boost converters for the purpose of
mitigating grid power quality. Since most power factor
correction circuits available in the commercial market
utilize unidirectional ac-dc boost converter topologies,
this is an almost no-cost solution for compensating
harmonic current and reactive power in residential
applications. Harmonic current and reactive power
compensation methods in the unidirectional ac-dc boost
converter are investigated. The additional focus of this
paper is to quantify the input current distortions by the
unidirectional ac-dc boost converter used for supplying
not only active power to the load but also reactive power.
Due to input current distortions, the amount of reactive
power injected from an individual converter to the grid
should be restricted. Experimental results are presented to
validate the effectiveness of the proposed control method.
2015
61. MATLAB2015_61 Aalborg Inverter — A new
typeof “Buck in
Buck, Boost in Boost”
Grid-tied Inverter
This paper presents a new family of high
efficiency DC/AC grid-tied inverter with a wide
variation of input DC voltage. It is a “Boost in Boost,
Buck in Buck” inverter, meaning that only one power
stage works at high frequency in order to achieve
minimum switching loss. The minimum voltage drop of
the filtering inductor in the power loop is achieved to
reduce the conduction power loss in both “Boost” and
“Buck” mode. The principle of operation is
demonstrated through the analysis on the equivalent
circuits of a “half-bridge” single-phase inverter. The
theoretical analysis shows that when input DC voltage
is larger than the magnitude of the AC voltage, it is a
Voltage Source Inverter (VSI), and on the contrary it is
Current Source Inverter (CSI) in the other mode. A
220 V/50 Hz/ 2000 W prototype has been constructed.
Simulations and experiments show it has a good control
and system performance.
2015

NEXGEN TECHNOLOGY
62. MATLAB2015_62 Grid-connected Forward
Micro-inverter with
Primary-Parallel
Secondary-Series
Transformer
This paper presents a primary-parallel secondaryseries
multicore forward micro-inverter for photovoltaic
ACmodule application. The presented micro-inverter
operates with a constant off-time boundary mode control,
providing MPPT capability and unity power factor. The
proposed multi transformer solution allows using low-
profile unitary turns ratio transformers. Therefore, the
transformers are better coupled and the overall
performance of the micro-inverter is improved. Due to the
multiphase solution the number of devices increases but,
the current stress and losses per device
are reduced contributing to an easier thermal
management. Furthermore, the decoupling capacitor is
split among the phases, contributing to a low-profile
solution without electrolytic capacitors suitable to be
mounted in the frame of a PV module. The proposed
solution is compared to the classical parallel interleaved
approach, showing better efficiency in a wide power
range and improving the weighted efficiency.
2015
63. MATLAB2015_63 A Single-Stage
PhotoVoltaic System for a
DualInverter fed Open-End
Winding Induction Motor
Drive for Pumping
Applications
This paper presents an integrated solution for
PhotoVoltaic (PV) fed water-pump drive system, which
uses an Open-End Winding Induction Motor (OEWIM).
The dualinverter fed OEWIM drive achieves the
functionality of a threelevel inverter and requires low
value DC bus voltage. This helps in an optimal
arrangement of PV modules, which could avoid large
strings and helps in improving the PV performance with
wide band-width of operating voltage. It also reduces the
voltage rating of the DC-link capacitors and switching
devices used in the system. Theproposed control strategy
achieves an integration of both Maximum Power Point
Tracking (MPPT) and V/f control for the efficient
utilization of the PV panels and the motor. The
proposed control scheme requires the sensing of PV
voltage and current only. Thus, the system requires less
number of sensors. All the analytical, simulation and
experimental results of this work under different
environmental conditions are presented in this paper.
2015

NEXGEN TECHNOLOGY
MATLAB PROJECTS 2014
SN PROJECT
CODE
PROJECT TOPIC YEAR
1 MAT1425
Topic: Diagnosis of diabetic retinopathy by employing image processingtechniqueto detect exudates
in retinal images
Abstract: Diabeticretinopathy (DR)is a micro vascular complication of long-term diabetes and it is the
major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss
because of DR is highly preventable with regular screeningandtimelyinterventionat theearlierstages. The
presence of exudates is one ofthe primitive signs of DR andthedetectionof these exudates is the first step
in automatedscreeningforDR. Hence, exudates detection becomes a significant diagnostic task, in which
digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the
presence of exudates automatically andthis helps the ophthalmologists in the diagnosis and follow-up of
DR. Exudates are normally detected by their high grey-level variations and they have used an artificial
neural network to perform this task by applying colour, size, shape and texture as the features. The
performance ofthe authors algorithm has been prospectively tested by using DIARETDB1 database and
evaluatedby comparingthe results withthe ground-truth images annotatedby expert ophthalmologists. They
have obtainedillustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based
evaluation criterion and achieved a classification accuracy of 99.7%.
2014
2 MAT1424
Topic: Data Hidingin Encrypted H.264/AVC Video Streams by Codeword Substitution
Abstract:Digital videosometimes needs tobe stored and processed in an encrypted format to maintain
security andprivacy.For thepurpose of content notation and/or tampering detection, it is necessary to
perform data hiding in these encrypted videos. In this way, data hiding in encrypted domain without
decryption preserves theconfidentialityof thecontent Inaddition, it is more efficient without decryption
followedby data hiding and re-encryption. In this paper, a novel scheme of data hiding directly in the
encryptedversionof H.264/AVC video stream is proposed, which includes the following three parts, i.e.,
H.264/AVC video encryption, data embedding, and data extraction. By analyzing the property of
H.264/AVC codec, the code words of intra predictionmodes, the code words of motion vector differences,
andthe code words of residual coefficients are encryptedwith streamciphers.Then, a data hidermayembed
additional data in the encrypted domain by using codeword substitution technique, without knowing the
original video content.In order toadapt to different applicationscenarios, data extractioncan be done either
in the encrypteddomainor in the decrypteddomain. Furthermore,video file size is strictly preserved even
after encryptionanddataembedding. Experimental results have demonstratedthe feasibility and efficiency
of the proposed scheme.
2014
3 MAT1423
Topic: Edge Detection Method for Image Processing based on Generalized Type-2FuzzyLogic
Abstract: This paper presents an edge detection method based on the morphological gradient
technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement
generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and
approximationmethods are used. Simulation results with a type-1 fuzzyinference system (T1FIS), an
interval type-2 fuzzy inferencesystem (IT2FIS) and with a generalized type-2 fuzzy inference system
(GT2FIS) for edge detection arepresented. The proposedgeneralized type-2 fuzzy edge detection method
2014

NEXGEN TECHNOLOGY
was tested with benchmark images and synthetic images. We used the merit of Pratt measure to
illustrate the advantages of using generalized type-2 fuzzy logic.
4 MAT1422
Topic: Deblurred images post-processingby Poisson warping
Abstract:In this work we developa post-processingalgorithmwhich enhances the results of the existing
image deblurring methods. It performs additional edge sharpening using grid warping. The idea of the
proposedalgorithmis to transformthe neighborhoodofthe edge so that theneighboringpixels move closer
to the edge, andthen resample theimage from thewarpedgridto the original uniform grid. The proposed
technique preserves image textures while making the edges sharper. The effectiveness of the method is
shown for basic deblurring methods on LIVE database images with added blur and noise.
2014
5 MAT1421
Topic: Image Contrast EnhancementUsing Color and Depth Histograms
Abstract:In this letter,we propose a newglobal contrast enhancement algorithm using the histograms of
color anddepthimages. On thebasis of the histogram-modificationframework, the color and depth image
histograms arefirst partitionedintosubintervals usingthe Gaussian mixturemodel.The positions partitioning
the color histogramare then adjustedsuch that spatially neighboring pixels with the similar intensity and
depth values can be groupedinto thesame sub-interval.By estimating the mapping curve of the contrast
enhancement foreach sub-interval, the global image contrast can be improvedwithout over-enhancing the
local image contrast. Experimental results demonstrate the effectiveness
of the proposedalgorithm.
2014
6 MAT1420
Topic: ObjectTrackingBased on Active Contour Modeling
Abstract:Object Trackingbasedon Active Contour Modelingis an image processingbasedtechnologythat
uses snapshots of the object under consideration to track it via robot in the realworld. The objective has been
to implement a unique methodology that employs the pursuingandadaptingof contour tothe current state of
image, andhence trackthe object.The systemcan be implementedin drone planes wherein this algorithm
can be used to guide the movement ofthe gun basedon themovements of the object, or,in robot games with
a slightly more advancedrobot. InitiallyImage Processingis performedtoreduce operationcomplexity and
achieve swift real-time performance. A set ofcontour-based modeling algorithms is then implemented to
‘actively’ track the subject. Also, relative
transformation calculations are made tolockthe target viarobot, continuously. MATLAB is usedto simulate
andimplement thesystem andit is testedon fieldwith a ball placedon it anda robot tracking the ball. The
experiments prove that thesystemsuccessfully detects andtracks the object efficientlyin the real world for
all horizontal and vertical transitions.
2014
7 MAT1419
Topic: Vision Based DataExtraction of Vehicles in Traffic
Abstract:Withthe rise in traffic relatedcrimes theneed foran efficient automatedsurveillance system has
become of utmost importance. This paper proposes a system to monitor video from traffic cameras and
process it in real time for storingessential information of the vehicles in traffic. Histogram of Oriented
Gradients (HOG) of extractedframes is usedas features for classification (vehicle frame and non vehicle
frame). Theclassifier is designedbasedon Support VectorMachine(SVM) . The subtracted image acquired
from a dynamically updatedbackgroundimage is used to extract the vehicle image for recognition using
trainedArtificial Neural Network(ANN). Thesystem is designedto storedetails like vehicle make, model,
color andtime of passing the camera in a database (Microsoft Access (MS Access)). Finally the stored
details are made available through a Graphical User Interface(GUI) designed using Visual Basic(VB) that
will provide an user with theoptions of selecting a time windowto look for the vehicles that have passed
within that interval or to enter a car model tocheck if it has passed that point at any time. The system is
modeledin MATLAB andtestedin a real time environment in oneof the busiest roadin Kamrup district of
Assam and provides satisfactory performance.
2014
Topic: Digital RightManagement Control forJointOwnership of Digital Images using Biometric

NEXGEN TECHNOLOGY
8 MAT1418 Features
Abstract: This paper proposes a method to establish joint ownership of digital images by embedding
imperceptible digital patternin the image. This digital patternis generatedfrom biometric features of more
than onesubject in a strategic matter so that the identification of individual subject can be done and the
multiple ownership ofthe digital images canbe established. This digital pattern was embeddedandextracted
from theimage andthe experiments were also carriedout whenthe image was subjected to signal processing
attacks. Coefficients of mid frequency band discrete cosine transform was used for
embeddingas these coefficients do not adverselyaffect the perceptual transparencyandis also significantly
robust to normal signal processingattacks. Experimental results indicate that the insertion of this digital
pattern does not change the perceptual properties of the image and the pattern survives
signal processing attacks which can be extracted for unique identification.
2014
9 MAT1417
Topic: IntelligentWater Metering System:An Image ProcessingApproach (MATLAB simulations)
Abstract: The scarcity and misuse of fresh water pose a serious and growing threat to sustainable
development. The populationgrowth, severe droughts and uneven distribution of water resources are the
reasons for water scarcity, and this scarcity will only continue to grow more severe. The technical
sophisticationof meters formeasuringwater flows has increased noticeably in recent decades in order to
improve management ofwater.This paperproposes simple image processing approach for an intelligent
meteringsystem. The proposedsystemuses simple image processingalgorithms andDSP processor,capable
of executingMIPS; which makes whole systemrespondfaster. As meter image is being captured from set
distance, meter mask generationreduces theneedof algorithms for detection and segmentation of meter
reading. The proposedsystemimproves the efficiency of drinking water management and reduces power
consumption as image sensor is activated as per predefined billing cycle.
2014
10 MAT1416
Topic: Fingerprint Recognition Using Gabor Filter
Abstract:Fingerprint recognitionis the most popularmethods usedfor identificationwith higher degree of
success. The fingerprint has unique characteristics called minutiae, which are points where a curve track
finishes, intersect orbranches off. Inthis work a method for Fingerprint recognition is considered using a
combinationof Fast FourierTransform (FFT) and Gabor Filters for enhancing the image. The proposed
methodinvolves combinationofGabor filterand Frequencydomain filteringfor enhancing the fingerprint.
With eight different orientations of Gabor filter, features of the fingerprint
extractingare combined. InFrequencydomain filtering, the fingerprint image is subdividedinto 32*32small
frames. Features are extractedfromthese frames in frequencydomain.Final enhancedfingerprint is obtained
with the results of Gabor filter and frequency domain filtering. Binarization and Thinning follows next
where the enhancedfingerprint is convertedinto binary andthe ridges are thinned to one pixel width. This
helps in extractingthe Minutiae parts (ridge bifurcation andridge endings). The overall recognition rate for
the proposedmethodis 95% which is much betterthanhistogram methodwhere the recognitionrate is 64%.
This project is implemented in MATLAB.
2014
11 MAT1415
Topic: ARIMA Model based BreastCancerDetection and Classification through Image Processing
Abstract:Computer Aided Diagnosis (CAD) has changed the way of medical diagnostics. As similar to
other walk of diagnostics field, CAD is having high potential in breast cancer prognosis because of its
highest accuracy. CAD mayplaya veryimportant role in developing countries i.e. EIT -MEM (Electrical
Impedance Tomography –Multi-frequency Electrical Impedance Mammography) device being used for
breast cancer defection. MEM-EIT produces tomography based mammograms which are
consideredmost reliable methodof early detectionof breast cancer. Cancer diagnostic expert all over the
world findthis noninvasive technique very accurate as it is one dimensional representation of images in
terms of temperature howeverthe accuracy is limitedandinvestigatorfail totake intoaccount the spatial co-
ordination between the pixels which is crucial in cancerous tumour detection and their classification
2014

final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles,

final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles,

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (18)

Destacado

Destacado (9)

Similar a final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles,

Similar a final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles, (20)

Último

Último (20)

final year ieee pojects in pondicherry,bulk ieee projects ,bulk 2015-16 ieee projects titles,