This document provides information on several 2015 IEEE Matlab projects related to signal processing and image analysis. It lists the project titles, languages, links, and abstracts for 10 different Matlab projects. The projects cover topics such as target source separation using deep neural networks, hyperspectral image classification using sparse representation, image denoising techniques, and cardiovascular biometrics.

1. IEEE 2015 Matlab Projects
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List Link : http://kasanpro.com/projects-list/ieee-2015-matlab-projects
Title :NMF-based Target Source Separation Using Deep Neural Network
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/nmf-based-target-source-separation-using-deep-neural-network
Abstract : Non-negativematrix factorization (NMF) is one of the most well-known techniques that are applied to
separate a desired source from mixture data. In the NMF framework, a collection of data is factorized into a basis
matrix and an encoding matrix. The basismatrix for mixture data is usually constructed by augmenting the basis
matrices for independent sources. However, target source separation with the concatenated basis matrix turns out to
be problematic if there exists some overlap between the subspaces that the bases for the individual sources span. In
this letter, we propose a novel approach to improve encoding vector estimation for target signal extraction. Estimating
encoding vectors from themixture data is viewed as a regression problem and a deep neural network (DNN) is used
to learn the mapping between the mixture data and the corresponding encoding vectors. To demonstrate the
performance of the proposed algorithm, experiments were conducted in the speech enhancement task. The
experimental results show that the proposed algorithm outperforms the conventional encoding vector estimation
scheme.
Title :Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-classification-based-sparse-representation
Abstract : In many real-world problems, data always lie in a low-dimensional manifold. Exploiting the manifold can
greatly enhance the discrimination between different categories. In this letter, we propose a classification framework
based on sparse representation to directly exploit the underlying manifold. Specifically, using the tangent plane to
approximate the local manifold of each test sample, the proposed method classifies the sample by sparse
representation in tangent space. Unlike several existing sparse-representation-based classification methods, which
sparsely represent the test sample itself, the proposed method sparsely represents the local manifold of the test
sample by tangent plane approximation. Therefore, it goes beyond the sample itself and is more robust to kinds of
variations confronted in hyperspectral image (HSI) such as illustration differences and spectrum mixing. Experimental
results show that the proposed algorithm outperforms several state-of-the-art methods for the classification of HSI
with limited training samples.
Title :Non-Local Means Image Denoising With a Soft Threshold
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/non-local-means-image-denoising-with-soft-threshold
Abstract : Non-local means (NLM) are typically biased by the accumulation of small weights associated with
dissimilar patches, especially at image edges. Hence, we propose to null the small weights with a soft threshold to
reduce this accumulation. We call this method the NLM filter with a soft threshold (NLM-ST). Its Stein's unbiased risk
estimate (SURE) approaches the true mean square error; thus, we can linearly aggregate multiple NLM-STs of
Monte-Carlo-generated parameters by minimizing SURE to surpass the performance limit of single NLM-ST, which is
referred to as the Monte-Carlo-based linear aggregation (MCLA). Finally, we employ a simple moving average filter to
smooth the MCLA image sequence to further improve the denoising performance and stability. Experiments indicate
that the NLM-ST outperforms the classic patchwise NLM and three other well-known recent variants in terms of the
peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual quality. Furthermore, its PSNR is higher
than that of BM3D for certain images.
Title :Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification
Language : Matlab

2. Project Link : http://kasanpro.com/p/matlab/hyperspectral-imagery-classification-gabor-feature
Abstract : Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum
representation error via a sparse linear combination of all the training samples, which has successfully been applied
to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could
yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the
l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery
(HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is
difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative
representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D
Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D
Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and
effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity
of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be
directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to
noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets
have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of
both the classifier complexity and generalization ability from very small training sets.
Title :Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set
Evolutions
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/extracting-man-made-objects-from-high-spatial-resolution-remote-sensing-images
Abstract : Object extraction from remote sensing images has long been an intensive research topic in the field of
surveying and mapping. Most past methods are devoted to handling just one type of object, and little attention has
been paid to improving the computational efficiency. In recent years, level set evolution (LSE) has been shown to be
very promising for object extraction in the field of image processing because it can handle topological changes
automatically while achieving high accuracy. However, the application of state-of-the-art LSEs is compromised by
laborious parameter tuning and expensive computation. In this paper, we proposed two fast LSEs for manmade
object extraction from high spatial resolution remote sensing images. We replaced the traditional mean
curvature-based regularization term by a Gaussian kernel, and it is mathematically sound to do that. Thus, we can
use a larger time step in the numerical scheme to expedite the proposed LSEs. Compared with existing methods, the
proposed LSEs are significantly faster. Most importantly, they involve much fewer parameters while achieving better
performance. Their advantages over other state-of-the-art approaches have been verified by a range of experiments.
IEEE 2015 Matlab Projects
Title :Enhanced Ridge Structure for Improving Fingerprint Image Quality Based on a Wavelet Domain
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/improving-fingerprint-image-quality-based-wavelet-domain-enhanced-ridge-structure
Abstract : Fingerprint image enhancement is one of the most crucial steps in an automated fingerprint identification
system. In this paper, an effective algorithm for fingerprint image quality improvement is proposed. The algorithm
consists of two stages. The first stage is decomposing the input fingerprint image into four subbands by applying
two-dimensional discrete wavelet transform. At the second stage, the compensated image is produced by adaptively
obtaining the compensation coefficient for each subband based on the referred Gaussian template. The experimental
results indicated that the compensated image quality was higher than that of the original image. The proposed
algorithm can improve the clarity and continuity of ridge structures in a fingerprint image. Therefore, it can achieve
higher fingerprint classification rates than related methods can.
Title :Discriminative Clustering and Feature Selection for Brain MRI Segmentation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/brain-mri-segmentation-discriminative-clustering-feature-selection
Abstract : Automatic segmentation of brain tissues from MRI is of great importance for clinical application and
scientific research. Recent advancements in supervoxel-level analysis enable robust segmentation of brain tissues by
exploring the inherent information among multiple features extracted on the supervoxels.Within this prevalent

3. framework, the difficulties still remain in clustering uncertainties imposed by the heterogeneity of tissues and the
redundancy of theMRI features. To cope with the aforementioned two challenges, we propose a robust discriminative
segmentation method from the view of information theoretic learning. The prominent goal of the method is to
simultaneously select the informative feature and to reduce the uncertainties of supervoxel assignment for
discriminative brain tissue segmentation. Experiments on two brain MRI datasets verified the effectiveness and
efficiency of the proposed approach.
Title :Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral
Image Classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/hyperspectral-dimension-reduction-using-spatial-spectral-regularized-local-discriminant
Abstract : Dimension reduction (DR) is a necessary and helpful preprocessing for hyperspectral image (HSI)
classification. In this paper, we propose a spatial and spectral regularized local discriminant embedding (SSRLDE)
method for DR of hyperspectral data. In SSRLDE, hyperspectral pixels are first smoothed by the multiscale spatial
weighted mean filtering. Then, the local similarity information is described by integrating a spectral-domain regularized
local preserving scatter matrix and a spatial-domain local pixel neighborhood preserving scatter matrix. Finally, the
optimal discriminative projection is learned by minimizing a local spatial-spectral scatter and maximizing a modified
total data scatter. Experimental results on benchmark hyperspectral data sets show that the proposed SSRLDE
significantly outperforms the state-of-the-art DR methods for HSI classification.
http://kasanpro.com/ieee/final-year-project-center-pudukkottai-reviews
Title :Aerial Image Registration for Tracking
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/aerial-image-registration-tracking
Abstract : To facilitate the tracking of moving targets in a video, the relation between the camera and the scene is
kept fixed by registering the video frames at the ground level. When the camera capturing the video is fixed with
respect to the scene, detected motion will represent the target motion. However, when a camera in motion is used to
capture the video, image registration at ground level is required to separate camera motion from target motion. An
image registration method is introduced that is capable of registering images from different views of a 3-D scene in
the presence of occlusion. The proposed method is capable of withstanding considerable occlusion and
homogeneous areas in images. The only requirement of the method is for the ground to be locally flat and sufficient
ground cover be visible in the frames being registered. Experimental results of 17 videos fromthe Brown University
data set demonstrate robustness of the method in registering consecutive frames in videos covering various urban
and suburban scenes. Additional experimental results are presented demonstrating the suitability of the method in
registering images captured from different views of hilly and coastal scenes.
Title :Cardiovascular Biometrics: Combining Mechanical and Electrical Signals
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/cardiovascular-biometrics-combining-mechanical-electrical-signals
Abstract : The electrical signal originating from the heart, the electrocardiogram (ECG), has been examined for its
potential use as a biometric. Recent ECG studies have shown that an inter-session authentication performance below
6% equal error rate (EER) can be achieved using training data from two days while testing with data from a third day.
More recently, a mechanical measurement of cardiovascular activity, the laser Doppler vibrometry (LDV) signal, was
proposed by our group as a biometric trait. The inter-session authentication performance of the LDV biometric system
is comparable to that of the ECG biometric system. Combining both the electrical and mechanical aspects of the
cardiovascular system, an overall improvement in authentication performance can be attained. In particular, the
multibiometric system achieves about 2% EER. Moreover, in the identification mode, with a testing database
containing 200 individuals, the rank-1 accuracy improves from about 80% for each individual biometric system, to
about 92% for the multibiometric system. Although there are implementation issues that would need to be resolved
before this combined method could be applied in the field, this report establishes the basis and utility of the method in
principle, and it identifies effective signal analysis approaches.
IEEE 2015 Matlab Projects

4. Title :An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image Classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/pixon-extraction-technique-multispectral-hyperspectral-image-classification
Abstract : Hyperspectral imaging has gained significant interest in the past few decades, particularly in remote
sensing applications. The considerably high spatial and spectral resolution of modern remotely sensed data often
provides more accurate information about the scene. However, the complexity and dimensionality of such data, as
well as potentially unwanted details embedded in the images, may act as a degrading factor in some applications
such as classification. One solution to this issue is to utilize the spatial-spectral features to extract segments before
the classification step. This preprocessing often leads to better classification results and a considerable decrease in
computational time. In this letter, we propose a Pixon-based image segmentation method, which benefits from a
preprocessing step based on partial differential equation to extractmore homogenous segments.Moreover, a fast
algorithm has been presented to adaptively tune the required parameters used in our Pixon-based schema. The
acquired segments are then fed into the support vector machine classifier, and the final thematic class maps are
produced. Experimental results on multi/hyperspectral data are encouraging to apply the proposed Pixons for
classification.
Title :Saliency-Guided Unsupervised Feature Learning for Scene Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/scene-classification-saliency-guided-unsupervised-feature-learning
Abstract : Due to the rapid technological development of various different satellite sensors, a huge volume of
high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from
such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature
learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set
of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an
unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not
need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that
the statistics generated from the learned feature extractors can characterize a complex scene very well and can
produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ
a recently developed regularization method called "dropout," which has proved to be very effective in image
classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the
UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data
set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that
were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the
highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based
scene classification method provides more accurate classification results than the other
latent-Dirichlet-allocation-based methods and the sparse coding method.
Title :A New Framework for SAR Multitemporal Data RGB Representation: Rationale and Products
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/sar-multitemporal-data-rgb-representation
Abstract : This paper presents the multitemporal adaptive processing (MAP3) framework for the treatment of
multitemporal synthetic aperture radar (SAR) images. The framework is organized in three major activities dealing
with calibration, adaptability, and representation. The processing chain has been designed looking at the simplicity,
i.e., the minimization of the operations needed to obtain the products, and at the algorithms' availability in the
literature. Innovation has been provided in the crosscalibration step, which is solved introducing the variable
amplitude levels equalization (VALE) method, through which it is possible to establish a common metrics for the
measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with
an application-based approach, supported by examples with regard to semiarid and temperate regions in which
amplitude maps and interferometric coherence are combined in an original way.
Title :Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random Fields
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/supervised-spectral-spatial-hyperspectral-image-classification-with-weighted-mark
Abstract : This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial
information. Under the maximum a posteriori framework, we propose a supervised classification model which includes

5. a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data
fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while
the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a
spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed
as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and
contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm,
named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of
multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms
many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.
Title :Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing
Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/segmentation-arbitrarily-large-remote-sensing-images-stable-mean-shift-algorithm
Abstract : Segmentation of real-world remote sensing images is challenging because of the large size of those data,
particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on
segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing
applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that
pieceor tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to
processing the whole image at once. We also derive a technique to empirically estimate the stability of a given
segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is
found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for
tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends
and applications.
IEEE 2015 Matlab Projects
Title :Reversible Image Data Hiding with Contrast Enhancement
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/reversible-image-data-hiding-contrast-enhancement
Abstract : In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying
to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual
quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be
performed by repeating the process. The side information is embedded along with the message bits into the host
image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of
images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast
enhancement byRDH. Furthermore, the evaluation results show that the visual quality can be preserved after a
considerable amount of message bits have been embedded into the contrast-enhanced images, even better than
three specificMATLAB functions used for image contrast enhancement.
http://kasanpro.com/ieee/final-year-project-center-pudukkottai-reviews
Title :Hidden Markov Model Based Dynamic Texture Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/dynamic-texture-classification-hidden-markov-model
Abstract : The stochastic signal model, hidden Markov model (HMM), is a probabilistic function of the Markov chain.
In this letter, we propose a general nth-order HMM based dynamic texture description and classification method.
Specifically, the pixel intensity sequence along time of a dynamic texture ismodeled with a HMM that encodes the
appearance information of the dynamic texture with the observed variables, and the dynamic properties over time with
the hidden states. A new dynamic texture sequence is classified to the category by determining whether it is the most
similar to this category with the probability that the observed sequence is produced by the HMMs of the training
samples. The experimental results demonstrate the arbitrary emission probability distribution and the higher-order
dependence of hidden states of a higher-order HMM result in better classification performance, as compared with the
linear dynamical system (LDS) based method.

6. Title :Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/rotation-invariant-object-detection-remote-sensing-images-based-radial-gradient-angle
Abstract : To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection
method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called
radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by
combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of
objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting
process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced.
Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as
the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for
objects with different directions and shapes in complicated background. Moreover, the antinoise performance and
parameter influence are also discussed.
Title :A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble
Strategy
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/self-training-based-unsupervised-satellite-image-classification-techni
Abstract : This letter addresses the problem of unsupervised land-cover classification of remotely sensed
multispectral satellite images fromthe perspective of cluster ensembles and self-learning. The cluster ensembles
combine multiple data partitions generated by different clustering algorithms into a single robust solution. A
cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative
expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters
considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover
classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is
introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set
obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier,
although trained without any external supervision, reduces the effect of data overlapping from different clusters which
otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a
medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual
clustering of the ensemble.
Title :An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/sift-based-mode-seeking-algorithm-sub-pixel-registration-remotely-sensed-images
Abstract : Several image registration methods, based on the scaled-invariant feature transform (SIFT) technique,
have appeared recently in the remote sensing literature. All of these methods attempt to overcome problems
encountered by SIFT in multimodal remotely sensed imagery, in terms of the quality of its feature correspondences.
The deterministic method presented in this letter exploits the fact that each SIFT feature is associated with a scale,
orientation, and position to perform mode seeking (in transformation space) to eliminate outlying corresponding key
points (i.e., features) and improve the overall match obtained. We also present an exhaustive empirical study on a
variety of test cases, which demonstrates that our method is highly accurate and rather fast. The algorithm is capable
of automatically detecting whether it succeeded or failed.
IEEE 2015 Matlab Projects
Title :Remote Sensing Image Segmentation Based on an Improved 2-D Gradient Histogram and MMAD Model
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/remote-sensing-image-segmentation-based-improved-2-d-gradient-histogram-mm
Abstract : A novel remote sensing image segmentation algorithm based on an improved 2-D gradient histogram and
minimum mean absolute deviation (MMAD) model is proposed in this letter. We extract the global features as a 1-D
histogram from an improved 2-D gradient histogram by diagonal projection and subsequently use the MMAD model
on the 1-D histogram to implement the optimal threshold. Experiments on remote sensing images indicate that the

7. new algorithm provides accurate segmentation results, particularly for images characterized by Laplace distribution
histograms. Furthermore, the new algorithm has low time consumption.
Title :An Efficient MRF Embedded Level Set Method for Image Segmentation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/mrf-embedded-level-set-method-image-segmentation
Abstract : 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
MATLAB R2010b installed in a computer with 3.30GHz CPU and 4GB memory.
Title :An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/eeg-based-biometric-system-using-eigenvector-centrality-resting-state-brain-networks
Abstract : Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so
far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an
approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end,
the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We
hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109
64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the
sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a
weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively
used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate (EER = 0.044)
and high beta band (EER = 0.102). Slightly lower recognition rate was observed in the low beta band (EER = 0.144),
while poor recognition rates were observed for the others frequency bands. The reported results show that
resting-state functional brain network topology provides better classification performance than using only a measure
of functional connectivity, and may represent an optimal solution for the design of next generation EEG based
biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG
features should be interpreted with caution.
Title :Image Sensor-Based Heart Rate Evaluation From Face Reflectance Using Hilbert-Huang Transform
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/heart-rate-evaluation-from-face-reflectance-using-hilbert-huang-transform
Abstract : Monitoring heart rates using conventional electrocardiogram equipment requires patients to wear adhesive
gel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetry
sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can
be painful to use. Therefore, a novel robust face-based heart rate monitoring technique is proposed to allow for the
evaluation of heart rate variation without physical contact with the patient. Face reflectance is first decomposed from a
single image and then heart rate evaluation is conducted from consecutive frames according to the periodic variation
of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as
heartbeats cause changes to blood volume in the blood vessels in the face. To achieve a robust evaluation, ensemble
empirical mode decomposition of the Hilbert-Huang transform is used to acquire the primary heart rate signal while
reducing the effect of ambient light changes. Our proposed approach is found to outperform the current state of the
art, providing greater measurement accuracy with smaller variance and is shown to be feasible in real-world
environments.
http://kasanpro.com/ieee/final-year-project-center-pudukkottai-reviews

8. Title :Live Video Forensics: Source Identification in Lossy Wireless Networks
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/source-identification-lossy-wireless-networks
Abstract : Video source identification is very important in validating video evidence, tracking down video piracy
crimes, and regulating individual video sources. With the prevalence of wireless communication, wireless video
cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However,
wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless
transmissions. The existing source identification methods experience significant performance degradation or even fail
to work when identifying videos with blocking and blurring. In this paper, we propose a method that is effective and
efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel
signatures and selective frame processing into source identification, which significantly improve the identification
speed. We conduct extensive realworld experiments to validate our method. The results show that the source
identification accuracy of the proposed scheme largely outperforms the existing methods in the presence of video
blocking and blurring. Moreover, our method is able to identify the video source in a near-real-time fashion, which can
be used to detect the wireless camera spoofing attack.
IEEE 2015 Matlab Projects