List of Big Data Analytics IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Big Data Analytics for the year 2015

1. Signal Processing IEEE 2015 Projects
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List Link : http://kasanpro.com/projects-list/signal-processing-ieee-2015-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 :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 :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.
Signal Processing IEEE 2015 Projects
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
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

2. discriminative brain tissue segmentation. Experiments on two brain MRI datasets verified the effectiveness and
efficiency of the proposed approach.
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.
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.
Signal Processing IEEE 2015 Projects
Title :Joint Power Splitting and Antenna Selection in Energy Harvesting Relay Channels
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/joint-power-splitting-antenna-selection-energy-harvesting-relay-channels
Abstract : The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple
antennas is considered in this letter, where a "harvest-and-forward" strategy is proposed. In particular, the relay
harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna
selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to
the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable
rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a
two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting,
and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the
two-stage procedure, and demonstrate that the proposed "harvest-and-forward" strategy outperforms the
conventional amplify-and-forward (AF) relaying and the direct transmission.
Title :Joint Power Splitting and Antenna Selection in Energy Harvesting Relay Channels
Language : NS2
Project Link : http://kasanpro.com/p/ns2/joint-power-splitting-antenna-selection
Abstract : The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple
antennas is considered in this letter, where a "harvest-and-forward" strategy is proposed. In particular, the relay
harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna
selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to
the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable
rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a
two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting,
and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the
two-stage procedure, and demonstrate that the proposed "harvest-and-forward" strategy outperforms the
conventional amplify-and-forward (AF) relaying and the direct transmission.
Title :Detectors for Cooperative Mesh Networks With Decode-and-Forward Relays

3. Language : NS2
Project Link : http://kasanpro.com/p/ns2/detectors-cooperative-mesh-networks-with-decode-forward-relays
Abstract : We consider mesh networks composed of groups of relaying nodes which operate in decode-and-forward
mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two
setups, one where the nodes have complete state information about the channels through which they receive the
signals, and anotherwhen they only have the statistics of the channels. We derive recursive expressions for the
probabilities of errors of the nodes and present several implementations of detectors used in these networks. We
compare the mesh networks with multihop networks formed by a set of parallel sections of multiple relaying nodes.
We demonstrate with numerous simulations that there are significant improvements in performance of mesh over
multihop networks in various scenarios.
Signal Processing IEEE 2015 Projects
Title :Detectors for Cooperative Mesh Networks With Decode-and-Forward Relays
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/cooperative-mesh-network-detectors-with-decode-forward-relays
Abstract : We consider mesh networks composed of groups of relaying nodes which operate in decode-and-forward
mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two
setups, one where the nodes have complete state information about the channels through which they receive the
signals, and anotherwhen they only have the statistics of the channels. We derive recursive expressions for the
probabilities of errors of the nodes and present several implementations of detectors used in these networks. We
compare the mesh networks with multihop networks formed by a set of parallel sections of multiple relaying nodes.
We demonstrate with numerous simulations that there are significant improvements in performance of mesh over
multihop networks in various scenarios.