Signal Processing IEEE 2015 Projects
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This document lists several signal processing projects completed for IEEE in 2015. It includes projects on topics like target source separation using deep neural networks and NMF, image denoising using non-local means and a soft threshold, improving fingerprint image quality in the wavelet domain, brain MRI segmentation using discriminative clustering and feature selection, dynamic texture classification using hidden Markov models, an EEG-based biometric system using eigenvector centrality in brain networks, and cooperative mesh networks with decode-and-forward relays. The projects are implemented in languages like Matlab, C#, and NS2 and cover techniques such as NMF, deep neural networks, wavelets, hidden Markov models, and more.
Signal Processing IEEE 2015 Projects
- 1. Signal Processing IEEE 2015 Projects Web : www.kasanpro.com Email : sales@kasanpro.com 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. 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
- 2. 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 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. Signal Processing IEEE 2015 Projects 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 :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
- 3. 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. http://kasanpro.com/ieee/final-year-project-center-dindigul-reviews Title :Detectors for Cooperative Mesh Networks With Decode-and-Forward Relays 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. 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. Signal Processing IEEE 2015 Projects