Fair Scheduling in Cellular Systems in the Presence of Noncooperative Mobiles

We consider the problem of ``fair'' scheduling the resources to one of the many mobile stations by a centrally controlled base station (BS). The BS is the only entity taking decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric alpha-fair scheduling problems from a game-theoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash equilibrium (NE) that we call a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise nonrobust (in presence of noncooperation) alpha-fair scheduling algorithms.

Time-Optimal Convergence to a Rectilinear Path in the Presence of Wind

This paper considers the problem of determining the time-optimal path of a fixed-wing Miniature Air Vehicle (MAV), in the presence of wind. The MAV, which is subject to a bounded turn rate, is required to eventually converge to a straight line starting from a known initial position and orientation. Earlier work in the literature uses Pontryagin's Minimum Principle (PMP) to solve this problem only for the no-wind case. In contrast, the present work uses a geometric approach to solve the problem completely in the presence of wind. In addition, it also shows how PMP can be used to partially solve the problem. Using a 6-DOF model of a MAV the generated optimal path is tracked by an autopilot consisting of proportional-integral-derivative (PID) controllers. The simulation results show the path generation and tracking for cases with steady and time-varying wind. Some issues on real-time path planning are also addressed.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Combining data sets of satellite-retrieved products for basin-scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model

In this study, we applied the integration methodology developed in the companion paper by Aires (2014) by using real satellite observations over the Mississippi Basin. The methodology provides basin-scale estimates of the four water budget components (precipitation P, evapotranspiration E, water storage change Delta S, and runoff R) in a two-step process: the Simple Weighting (SW) integration and a Postprocessing Filtering (PF) that imposes the water budget closure. A comparison with in situ observations of P and E demonstrated that PF improved the estimation of both components. A Closure Correction Model (CCM) has been derived from the integrated product (SW+PF) that allows to correct each observation data set independently, unlike the SW+PF method which requires simultaneous estimates of the four components. The CCM allows to standardize the various data sets for each component and highly decrease the budget residual (P - E - Delta S - R). As a direct application, the CCM was combined with the water budget equation to reconstruct missing values in any component. Results of a Monte Carlo experiment with synthetic gaps demonstrated the good performances of the method, except for the runoff data that has a variability of the same order of magnitude as the budget residual. Similarly, we proposed a reconstruction of Delta S between 1990 and 2002 where no Gravity Recovery and Climate Experiment data are available. Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently, the integrated data sets are model independent and can be used for model calibration or validation.

Sensitive detection of C-reactive protein using optical fiber Bragg gratings

An accurate and highly sensitive sensor platform has been demonstrated for the detection of C-reactive protein (CRP) using optical fiber Bragg gratings (FBGs). The CRP detection has been carried out by monitoring the shift in Bragg wavelength (Delta lambda(B)) of an etched FBG (eFBG) coated with an anti-CRP antibody (aCRP)-graphene oxide (GO) complex. The complex is characterized by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and atomic force microscopy. A limit of detection of 0.01 mg/L has been achieved with a linear range of detection from 0.01 mg/L to 100 mg/L which includes clinical range of CRP. The eFBG sensor coated with only aCRP (without GO) show much less sensitivity than that of aCRP-GO complex coated eFBG. The eFBG sensors show high specificity to CRP even in the presence of other interfering factors such as urea, creatinine and glucose. The affinity constant of similar to 1.1 x 10(10) M-1 has been extracted from the data of normalized shift (Delta lambda(B)/lambda(B)) as a function of CRP concentration. (C) 2014 Elsevier B.V. All rights reserved.

Operand Isolation Circuits with Reduced Overhead for Low Power Data-Path Design

Dynamic power dissipation due to redundant switching is an important metric in data-path design. This paper focuses on the use of ingenious operand isolation circuits for low power design. Operand isolation attempts to reduce switching by clamping or latching the output of a first level of combinational circuit. This paper presents a novel method using power supply switching wherein both PMOS and NMOS stacks of a circuit are connected to the same power supply. Thus, the output gets clamped or latched to the power supply value with minimal leakage. The proposed circuits make use of only two transistors to clamp the entire Multiple Input Multiple Output (MIMO) block. Also, the latch-based designs have higher drive strength in comparison to the existing methods. Simulation results have shown considerable area reduction in comparison to the existing techniques without increasing timing overhead.

SIMULATION STUDY OF THE ADSORPTION DYNAMICS OF CYLINDRICAL SILICA GEL PARTICLES

The paper presents a simulation study of loose cylindrically shaped particles packed within a copper plate and aluminum fins. The model presented solves coupled heat and mass transfer equations using the finite volume method based on ANSY S FLUENT medium. Three different arrangements of cylindrical particles are considered. The model is validated with experimental data. It is found that the arrangements which represented monolayer configurations are only marginally better in heat transfer and uptake efficiency than the tri-layer configuration in the presence of fins. However, there is an appreciable difference in the uptake curve between monoand tri-layer configurations in the absence of fins. Finally, it is found that the fin pitch also plays an important role in determining the time constant for the adsorber design.

Standardizing Patent Data Cleaning in a University Technology Transfer Office

There are multiple goals of a technology transfer office (TTO) based in a university system. Whilst commercialization is a critical goal, maintenance and cleaning of the TTO's database needs detailing. Literature in the area is scarce and only some researchers make reference to TTO data cleaning. During an attempt to understand the commercial strategy of a university TTO in Bangalore the challenge of data cleaning was encountered. This paper describes a case study of data cleaning at an Indian university based TTO. 382 patent records were analyzed in the study. The case study first describes the back ground of the university system. Second, the method to clean the data and the experiences encountered are highlighted. Insights drawn indicate that patent data cleaning in a TTO is a specialized area which needs attention. Overlooking this activity can have legal implications and may result in an inability to commercialize the patent. Two levels of patent data cleaning are discussed in this case study. Best practices of data cleaning in academic TTOs are discussed.

Optimal Power Allocation Policies for Low Powered Wireless Communication Systems

We consider optimal average power allocation policies in a wireless channel in the presence of individual delay constraints on the transmitted packets. Power is consumed in transmission of data only. We consider the case when the power used in transmission is a linear function of the data transmitted. The transmission channel may experience multipath fading. We have developed a computationally efficient online algorithm, when there is same hard delay constraint for all packets. Later on, we generalize it to the case when there are multiple real time streams with different hard deadline constraints. Our algorithm uses linear programming and has very low complexity.

Tailored mechanical behavior of magnetic particles loaded carbon nanotube foam in presence of magnetic field

The compressive behavior of carbon nanotube (CNT) foam with an entangled microstructure has become an important research area due to its excellent energy absorption capability. This report presents a tailored mechanical behavior of CNT foam under an applied magnetic field when all CNTs in the foam are coated with magnetic nanoparticles. The presence of nanoparticles not only enhanced the stiffness of the foam to four times but also revealed a nonlinear variation in both the stress and energy absorption capability with the gradual increase of the magnetic field. Magnetization of both CNT and attached nanoparticles along the magnetic field direction are shown to play a crucial role in determining the dominant deformation mechanism.

Suspensions of polymer-grafted nanoparticles with added polymers-Structure and effective pair-interactions

We present the results of combined experimental and theoretical (molecular dynamics simulations and integral equation theory) studies of the structure and effective interactions of suspensions of polymer grafted nanoparticles (PGNPs) in the presence of linear polymers. Due to the absence of systematic experimental and theoretical studies of PGNPs, it is widely believed that the structure and effective interactions in such binary mixtures would be very similar to those of an analogous soft colloidal material-star polymers. In our study, polystyrene-grafted gold nanoparticles with functionality f = 70 were mixed with linear polystyrene (PS) of two different molecular weights for obtaining two PGNP: PS size ratios, xi = 0.14 and 2.76 (where, xi = M-g/M-m, M-g and M-m being the molecular weights of grafting and matrix polymers, respectively). The experimental structure factor of PGNPs could be modeled with an effective potential (Model-X), which has been found to be widely applicable for star polymers. Similarly, the structure factor of the blends with xi = 0.14 could be modeled reasonably well, while the structure of blends with xi = 2.76 could not be captured, especially for high density of added polymers. A model (Model-Y) for effective interactions between PGNPs in a melt of matrix polymers also failed to provide good agreement with the experimental data for samples with xi = 2.76 and high density of added polymers. We tentatively attribute this anomaly in modeling the structure factor of blends with xi = 2.76 to the questionable assumption of Model-X in describing the added polymers as star polymers with functionality 2, which gets manifested in both polymer-polymer and polymer-PGNP interactions especially at higher fractions of added polymers. The failure of Model-Y may be due to the neglect of possible many-body interactions among PGNPs mediated by matrix polymers when the fraction of added polymers is high. These observations point to the need for a new framework to understand not only the structural behavior of PGNPs but also possibly their dynamics and thermo-mechanical properties as well. (C) 2015 AIP Publishing LLC.

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Review: Overcoming the paradox of strength and ductility in ultrafine-grained materials at low temperatures

Ultrafine-grained (UFG) materials with grain sizes in the submicrometer or nanometer range may be prepared through the application of severe plastic deformation (SPD) to bulk coarse-grained solids. These materials generally exhibit high strength but only very limited ductility in low-temperature testing, thereby giving rise to the so-called paradox of strength and ductility. This paradox is examined and a new quantitative diagram is presented which permits the easy insertion of experimental data. It is shown that relatively simple procedures are available for achieving both high strength and high ductility in UFG materials including processing the material to a very high strain and/or applying a very short-term anneal immediately after the SPD processing. Significant evidence is now available demonstrating the occurrence of grain boundary sliding in these materials at low temperatures, where this is attributed to the presence of non-equilibrium grain boundaries and the occurrence of enhanced diffusion along these boundaries.

Estimation of nonlinearities from pseudodynamic and dynamic responses of bridge structures using the Delay Vector Variance method

Analysis of the variability in the responses of large structural systems and quantification of their linearity or nonlinearity as a potential non-invasive means of structural system assessment from output-only condition remains a challenging problem. In this study, the Delay Vector Variance (DVV) method is used for full scale testing of both pseudo-dynamic and dynamic responses of two bridges, in order to study the degree of nonlinearity of their measured response signals. The DVV detects the presence of determinism and nonlinearity in a time series and is based upon the examination of local predictability of a signal. The pseudo-dynamic data is obtained from a concrete bridge during repair while the dynamic data is obtained from a steel railway bridge traversed by a train. We show that DVV is promising as a marker in establishing the degree to which a change in the signal nonlinearity reflects the change in the real behaviour of a structure. It is also useful in establishing the sensitivity of instruments or sensors deployed to monitor such changes. (C) 2015 Elsevier B.V. All rights reserved.