EESM-Based Link Adaptation in Point-to-Point and Multi-Cell OFDM Systems: Modeling and Analysis

In contemporary wideband orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE) and WiMAX, different subcarriers over which a codeword is transmitted may experience different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems is driven by a vector of subcarrier SNRs experienced by the codeword, and is more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping this vector into a single equivalent fiat-fading SNR. Analysis of AMC using EESM is challenging owing to its non-linear nature and its dependence on the modulation and coding scheme. We first propose a novel statistical model for the EESM, which is based on the Beta distribution. It is motivated by the central limit approximation for random variables with a finite support. It is simpler and as accurate as the more involved ad hoc models proposed earlier. Using it, we develop novel expressions for the throughput of a point-to-point OFDM link with multi-antenna diversity that uses EESM for AMC. We then analyze a general, multi-cell OFDM deployment with co-channel interference for various frequency-domain schedulers. Extensive results based on LTE and WiMAX are presented to verify the model and analysis, and gain new insights.

Modeling Time-Varying Aggregate Interference in Cognitive Radio Systems, and Application to Primary Exclusive Zone Design

Accurately characterizing the time-varying interference caused to the primary users is essential in ensuring a successful deployment of cognitive radios (CR). We show that the aggregate interference at the primary receiver (PU-Rx) from multiple, randomly located cognitive users (CUs) is well modeled as a shifted lognormal random process, which is more accurate than the lognormal and the Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, which depends on path-loss, shadowing, and small-scale fading of the link from the primary transmitter to the CU; the interweave and underlay modes or CR operation, which determine the transmit powers of the CUs; and time-correlated shadowing and fading of the links from the CUs to the PU-Rx. It leads to expressions for the probability distribution function, level crossing rate, and average exceedance duration. The impact of cooperative spectrum sensing is also characterized. We validate the model by applying it to redesign the primary exclusive zone to account for the time-varying nature of interference.

On Averaging Multiview Relations for 3D Scan Registration

In this paper, we present an extension of the iterative closest point (ICP) algorithm that simultaneously registers multiple 3D scans. While ICP fails to utilize the multiview constraints available, our method exploits the information redundancy in a set of 3D scans by using the averaging of relative motions. This averaging method utilizes the Lie group structure of motions, resulting in a 3D registration method that is both efficient and accurate. In addition, we present two variants of our approach, i.e., a method that solves for multiview 3D registration while obeying causality and a transitive correspondence variant that efficiently solves the correspondence problem across multiple scans. We present experimental results to characterize our method and explain its behavior as well as those of some other multiview registration methods in the literature. We establish the superior accuracy of our method in comparison to these multiview methods with registration results on a set of well-known real datasets of 3D scans.

Common-Mode and Differential-Mode Active Damping for PWM Rectifiers

Modern pulse-width-modulated (PWM) rectifiers use LC L filters that can be applied in both the common mode and differential mode to obtain high-performance filtering. Interaction between the passive L and C components in the filter leads to resonance oscillations. These oscillations need to be damped either by the passive damping or active damping. The passive damping increases power loss and can reduce the effectiveness of the filter. Methods of active damping, using control strategy, are lossless while maintaining the effectiveness of the filters. In this paper, an active damping strategy is proposed to damp the oscillations in both line-to-line and line-to-ground. An approach based on pole placement by the state feedback is used to actively damp both the differential-and common-mode filter oscillations. Analytical expressions for the state-feedback controller gains are derived for both continuous and discrete-time model of the filter. Tradeoff in selection of the active damping gain on the lower order power converter harmonics is analyzed using a weighted admittance function. Experimental results on a 10-kVA laboratory prototype PWM rectifier are presented. The results validate the effectiveness of the active damping method, and the tradeoff in the settings of the damping gain.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Scheduling for Stable and Reliable Communication Over Multiaccess Channels and Degraded Broadcast Channels

We model communication of bursty sources: 1) over multiaccess channels, with either independent decoding or joint decoding and 2) over degraded broadcast channels, by a discrete-time multiclass processor sharing queue. We utilize error exponents to give a characterization of the processor sharing queue. We analyze the processor sharing queue model for the stable region of message arrival rates, and show the existence of scheduling policies for which the stability region converges to the information-theoretic capacity region in an appropriate limiting sense.

Moisture Aided Degradation of Oil Impregnated Paper Insulation in Power Transformers

The presence of moisture in oil impregnated paper insulation (OIP) is detrimental to its long time performance. Until recently, it was thought insulation ageing was only a function of temperature and electrical stress. It has now been realized that moisture in all its forms causes rapid degradation of the electrical and mechanical properties with time. In this study, insulation paper samples were conditioned for desired level of moisture and were impregnated with premium quality transformer oil. The oil impregnated samples with 1 to 3 % moisture content were aged at 90 to 130 C. The indices for determining the extent of ageing considered in this work are degree of polymerization (DP), furan, carbon monoxide and carbon dioxide content. These quantities were monitored throughout the ageing experimental run. End-of-life (EOL) criterion used here is the reduction in the value of DP. Phenomenological models for estimating the service life of insulation are proposed and are validated against actual experimental data.

Alumina and Silica based Epoxy Nano-composites for Electrical Insulation

Plain epoxy resins or resin impregnated cellulose have found application as electrical insulation in power equipment. In the past, their performance was improved by the use of inorganic oxide fillers of microscopic dimensions. In the recent past nano-particle doped epoxy insulation came into use with a view to further enhance the dielectric properties. This paper reports dielectric investigations into epoxy nano-composites based on a class of metal oxides, Al2O3 and SiO2. In particular, consideration has been given to the partial discharge performance and electrical breakdown under different voltage profiles as a function of the volumetric composition of the nano-particles in epoxy resin.

Bit Error Probability Analysis of SSK in DF Relaying with Threshold-Based Best Relay Selection and Selection Combining

In this letter, we analyze the end-to-end average bit error probability (ABEP) of space shift keying (SSK) in cooperative relaying with decode-and-forward (DF) protocol, considering multiple relays with a threshold based best relay selection, and selection combining of direct and relayed paths at the destination. We derive an exact analytical expression for the end-to-end ABEP in closed-form for binary SSK, where analytical results agree with simulation results. For non-binary SSK, approximate analytical and simulation results are presented.

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

Size Control and Magnetic Property Trends in Cobalt Ferrite Nanoparticles Synthesized Using an Aqueous Chemical Route

Cobalt ferrite (CoFe2O4) is an engineering material which is used for applications such as magnetic cores, magnetic switches, hyperthermia based tumor treatment, and as contrast agents for magnetic resonance imaging. Utility of ferrites nanoparticles hinges on its size, dispersibility in solutions, and synthetic control over its coercivity. In this work, we establish correlations between room temperature co-precipitation conditions, and these crucial materials parameters. Furthermore, post-synthesis annealing conditions are correlated with morphology, changes in crystal structure and magnetic properties. We disclose the synthesis and process conditions helpful in obtaining easily sinterable CoFe2O4 nanoparticles with coercive magnetic flux density (H-c) in the range 5.5-31.9 kA/m and M-s in the range 47.9-84.9 A.m(2)Kg(-1). At a grain size of similar to 54 +/- 2 nm (corresponding to 1073 K sintering temperature), multi-domain behavior sets in, which is indicated by a decrease in H-c. In addition, we observe an increase in lattice constant with respect to grain size, which is the inverse of what is expected of in ferrites. Our results suggest that oxygen deficiency plays a crucial role in explaining this inverse trend. We expect the method disclosed here to be a viable and scalable alternative to thermal decomposition based CoFe2O4 synthesis. The magnetic trends reported will aid in the optimization of functional CoFe2O4 nanoparticles

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.

A Committee Machine Approach for Compressed Sensing Signal Reconstruction

Although many sparse recovery algorithms have been proposed recently in compressed sensing (CS), it is well known that the performance of any sparse recovery algorithm depends on many parameters like dimension of the sparse signal, level of sparsity, and measurement noise power. It has been observed that a satisfactory performance of the sparse recovery algorithms requires a minimum number of measurements. This minimum number is different for different algorithms. In many applications, the number of measurements is unlikely to meet this requirement and any scheme to improve performance with fewer measurements is of significant interest in CS. Empirically, it has also been observed that the performance of the sparse recovery algorithms also depends on the underlying statistical distribution of the nonzero elements of the signal, which may not be known a priori in practice. Interestingly, it can be observed that the performance degradation of the sparse recovery algorithms in these cases does not always imply a complete failure. In this paper, we study this scenario and show that by fusing the estimates of multiple sparse recovery algorithms, which work with different principles, we can improve the sparse signal recovery. We present the theoretical analysis to derive sufficient conditions for performance improvement of the proposed schemes. We demonstrate the advantage of the proposed methods through numerical simulations for both synthetic and real signals.