Ieee 2013 matlab abstracts part b

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  • 1. VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874 IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech 20. Phase Noise in MIMO Systems: Bayesian Cram´er-Rao Bounds and Soft-Input Estimation Abstract: This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is r -Rao lower bounds (BCRLBs) and soft-input maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' mean-square error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame, a novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use soft statistics of the transmitted symbols given the current observations are proposed. Numerical results indicate that by employing the proposed phase tracking approach, the biterror rate performance of a MIMO system affected by phase noise can be significantly improved. In addition, simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in MIMO systems that employ imperfect oscillators. Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 10 ) Issue Date : May15, 2013 Index Terms—Multi-input multi-output (MIMO), Wiener phase noise, Bayesian Cram´er Rao lower bound (BCRLB), maximum-a-posteriori (MAP), soft-decision extended Kalman filter (EKF), and extended Kalman smoother (EKS). 21. Multi scale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks Abstract: This paper describes and analyzes a hierarchical algorithm called Multi scale Gossip for solving the distributed average consensus problem in wireless sensor networks. The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest scale gossip to compute local averages. Then, using multi-hop communication and geographic routing to communicate between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with k=Θ(loglogn) levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms in terms of message complexity, achieving ε-accuracy with high probability after O(n loglogn log*1/(ε)+ ) single-hop messages. Key to our analysis is the way in which the network is recursively partitioned. We VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874
  • 2. VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874 find that the above scaling law is achieved when sub networks at scale j contain O(n(2/3)j) nodes; then the message complexity at any individual scale is O(n log*1/ε+). Another important consequence of the hierarchical construction is that the longest distance over which messages are exchanged is O(n1/3) hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms based on averaging along paths. Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 9 ) Date of Publication: May1, 2013 22. Joint Estimation of Channel and Oscillator Phase Noise in MIMO Systems Abstract: Oscillator phase noise limits the performance of high speed communication systems since it results in time varying channels and rotation of the signal constellation from symbol to symbol. In this paper, jointestimation of channel gains and Wiener phase noise in multi-input multioutput (MIMO) systems is analyzed. The signal model for the estimation problem is outlined in detail and -Rao lower bounds (CRLBs) for the multiparameter estimation problem are derived. A data-aided least-squares (LS) estimator for jointly obtaining the channel gains and phase noise parameters is derived. Next, a decision-directed weighted least-squares (WLS) estimator is proposed, where pilots and estimated data symbols are employed to track the time-varying phase noise parameters over a frame. In order to reduce the overhead and delay associated with the estimation process, a new decision-directed extended Kalman filter (EKF) is proposed for tracking the MIMO phase noise throughout a frame. Numerical results show that the proposed LS, WLS, and EKF estimators' performances are close to the CRLB. Finally, simulation results demonstrate that by employing the proposed channel and timevarying phase noise estimators the bit-error rate performance of a MIMO system can be significantly improved. Published in: Signal Processing, IEEE Transactions on (Volume:60 , Issue: 9 ) Date of Publication: Sept. 2012 Index Terms—Channel estimation, Cramér-Rao lower bound (CRLB), extended Kalman filter (EKF), multi-input multi-output (MIMO), weighted least squares (WLS), Wiener phase noise. 23. Accurate Computation of the MGF of the Lognormal Distribution and its Application to Sum of Lognormals Abstract: Sums of lognormal random communications and other variables (RVs) are of wide interest in wireless areas of science and engineering. VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874
  • 3. VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874 Since the distribution of lognormal sums is not log-normal and does not have a closed-form analytical expression, many approximations and bounds have been developed. This paper develops two computational methods for the moment generating function (MGF) or the characteristic function (CHF) of a single lognormal RV. The first method uses classical complex integration techniques based on steepest-descent integration. The saddle point of the integrand is explicitly expressed by the Lambert function. The steepest-descent (optimal) contour and two closely-related closed-form contours are derived. A simple integration rule (e.g., the midpoint rule) along any of these contours computes the MGF/CHF with high accuracy. The second approach uses a variation on the trapezoidal rule due to Ooura and Mori. Importantly, the cumulative distribution function of lognormalsums is derived as an alternating series and convergence acceleration via the Epsilon algorithm is used to reduce, in some cases, the computational load by a factor of 106! Overall, accuracy levels of 13 to 15 significant digits are readily achievable. Published in: Communications, IEEE Transactions on (Volume:58 , Issue: 5 ) Date of Publication: May 2010 Index Terms—Sum of lognormals, moment-generating function, characteristic function. 24. Compressed Sensing of EEG for Wireless Tele monitoring with Low Energy Consumption and Inexpensive Hardware Abstract: Tele monitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non sparse in the time domain and also non sparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the tele monitoring of EEG. Experimental results show that its recovery quality is better than state-ofthe-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for tele monitoring of EEG and other non sparse physiological signals. Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 1 ) Date of Publication: Jan. 2013 VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874
  • 4. VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874 Index Terms—Telemonitoring, Healthcare, Wireless Body- Area Network (WBAN), Compressed Sensing (CS), Block Sparse Bayesian Learning (BSBL), electroencephalogram (EEG) 25. Compressed Sensing for Energy-Efficient Wireless Tele monitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning Abstract: Fetal ECG (FECG) tele monitoring is an important branch in telemedicine. The design of a tele monitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as non sparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct non sparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage. Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 2 ) Date of Publication: Feb. 2013 Index Terms—Fetal ECG (FECG), Tele monitoring, Telemedicine, Healthcare, Block Sparse Bayesian Learning (BSBL), Compressed Sensing (CS), Independent Component Analysis (ICA) VENSOFT Technologies www.ieeedeveloperslabs.in Email: vensoftprojects@yahoo.com Contact: 9448847874