An adaptive modulation scheme for two-user fading MAC with quantized fade state feedback

For transmission over the two-user Gaussian Multiple Access Channel with fading and finite constellation at the inputs, we propose a scheme which uses only quantized knowledge of fade state at users with the feedback overhead being nominal. One of the users rotates its constellation without varying the transmit power to adapt to the existing channel conditions, in order to meet certain pre-determined minimum Euclidean distance requirement in the equivalent constellation at the destination. The optimal modulation scheme has been described for the case when both the users use symmetric M-PSK constellations at the input, where M = 2λ, λ being a positive integer. The strategy has been illustrated by considering examples where both the users use QPSK signal set at the input. It is shown that the proposed scheme has considerable better error performance compared to the conventional non-adaptive scheme, at the cost of a feedback overhead of just [log2 (M2/8 - M/4 + 2)] + 1 bits, for the M-PSK case.

Full-rate full-diversity finite feedback space-time schemes with minimum feedback and transmission duration

A Finite Feedback Scheme (FFS) for a quasi-static MIMO block fading channel with finite N-ary delay-free noise-free feedback consists of N Space-Time Block Codes (STBCs) at the transmitter, one corresponding to each possible value of feedback, and a function at the receiver that generates N-ary feedback. A number of FFSs are available in the literature that provably attain full-diversity. However, there is no known full-diversity criterion that universally applies to all FFSs. In this paper a universal necessary condition for any FFS to achieve full-diversity is given, and based on this criterion the notion of Feedback-Transmission duration optimal (FT-optimal) FFSs is introduced, which are schemes that use minimum amount of feedback N for the given transmission duration T, and minimum T for the given N to achieve full-diversity. When there is no feedback (N = 1) an FT-optimal scheme consists of a single STBC, and the proposed condition reduces to the well known necessary and sufficient condition for an STBC to achieve full-diversity. Also, a sufficient criterion for full-diversity is given for FFSs in which the component STBC yielding the largest minimum Euclidean distance is chosen, using which full-rate (N-t complex symbols per channel use) full-diversity FT-optimal schemes are constructed for all N-t > 1. These are the first full-rate full-diversity FFSs reported in the literature for T < N-t. Simulation results show that the new schemes have the best error performance among all known FFSs.

Transport properties of CuIn1-xAlxSe2/AZnO heterostructure for low cost thin film photovoltaics

CuIn1-xAlxSe2 (CIASe) thin films were grown by a simple sol-gel route followed by annealing under vacuum. Parameters related to the spin-orbit (Delta(SO)) and crystal field (Delta(CF)) were determined using a quasi-cubic model. Highly oriented (002) aluminum doped (2%) ZnO, 100 nm thin films, were co-sputtered for CuIn1-xAlxSe2/AZnO based solar cells. Barrier height and ideality factor varied from 0.63 eV to 0.51 eV and 1.3186 to 2.095 in the dark and under 1.38 A. M 1.5 solar illumination respectively. Current-voltage characteristics carried out at 300 K were confined to a triangle, exhibiting three limiting conduction mechanisms: Ohms law, trap-filled limit curve and SCLC, with 0.2 V being the cross-over voltage, for a quadratic transition from Ohm's to Child's law. Visible photodetection was demonstrated with a CIASe/AZO photodiode configuration. Photocurrent was enhanced by one order from 3 x 10(-3) A in the dark at 1 V to 3 x 10(-2) A upon 1.38 sun illumination. The optimized photodiode exhibits an external quantum efficiency of over 32% to 10% from 350 to 1100 nm at high intensity 17.99 mW cm(-2) solar illumination. High responsivity R-lambda similar to 920 A W-1, sensitivity S similar to 9.0, specific detectivity D* similar to 3 x 10(14) Jones, make CIASe a potential absorber for enhancing the forthcoming technological applications of photodetection.

Speed control of brushed DC motor for low cost electric cars

As petrol prices are going up in developing countries in upcoming decades low cost electric cars will become more and more popular in developing world. One of the main deciding factors for success of electric cars specially in developing world in upcoming decades will be its cost. This paper shows a cost effective method to control the speed of low cost brushed D.C. motor by combining a IC 555 Timer with a High Boost Converter. The main purpose of using High Boost Converter since electric cars needs high voltage and current which a High Boost Converter can provide even with low battery supply.

A Novel Design Method for SOGI-PLL for Minimum Settling Time and Low Unit Vector Distortion

Phase-locked loops (PLLs) are necessary in grid connected systems to obtain information about the frequency, amplitude and phase of the grid voltage. In stationary reference frame control, the unit vectors of PLLs are used for reference generation. It is important that the PLL performance is not affected significantly when grid voltage undergoes amplitude and frequency variations. In this paper, a novel design for the popular single-phase PLL topology, namely the second-order generalized integrator (SOGI) based PLL is proposed which achieves minimum settling time during grid voltage amplitude and frequency variations. The proposed design achieves a settling time of less than 27.7 ms. This design also ensures that the unit vectors generated by this PLL have a steady state THD of less than 1% during frequency variations of the grid voltage. The design of the SOGI-PLL based on the theoretical analysis is validated by experimental results.

Vertically aligned ZnO nanorods on flexible substrates for multifunctional device applications: Easy and cost-effective route

Zinc oxide nanorods (ZnO NRs) have been synthesized on flexible substrates by adopting a new and novel three-step process. The as-grown ZnO NRs are vertically aligned and have excellent chemical stoichiometry between its constituents. The transmission electron microscopic studies show that these NR structures are single crystalline and grown along the < 001 > direction. The optical studies show that these nanostructures have a direct optical band gap of about 3.34 eV. Therefore, the proposed methodology for the synthesis of vertically aligned NRs on flexible sheets launches a new route in the development of low-cost flexible devices. (C) 2014 Elsevier B.V. All rights reserved.

Capacity of Gaussian Channels With Energy Harvesting and Processing Cost

Energy harvesting sensor nodes are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting green communication. In this paper, we focus on communicating reliably over an additive white Gaussian noise channel using such an energy harvesting sensor node. An important part of this paper involves appropriate modeling of energy harvesting, as done via various practical architectures. Our main result is the characterization of the Shannon capacity of the communication system. The key technical challenge involves dealing with the dynamic (and stochastic) nature of the (quadratic) cost of the input to the channel. As a corollary, we find close connections between the capacity achieving energy management policies and the queueing theoretic throughput optimal policies.

Minimum switching loss pulse width modulation for reduced power conversion loss in reactive power compensators

Advanced bus-clamping switching sequences, which employ an active vector twice in a subcycle, are used to reduce line current distortion and switching loss in a space vector modulated voltage source converter. This study evaluates minimum switching loss pulse width modulation (MSLPWM), which is a combination of such sequences, for static reactive power compensator (STATCOM) application. It is shown that MSLPWM results in a significant reduction in device loss over conventional space vector pulse width modulation. Experimental verification is presented at different power levels of up to 150 kVA.

Low-Cost Cloud-Based Design of Smart Rural Energy Device in Microgrid: Perspective India An Energy-On-Demand Service for Rural India

In this paper we present a combination of technologies to provide an Energy-on-Demand (EoD) service to enable low cost innovation suitable for microgrid networks. The system is designed around the low cost and simple Rural Energy Device (RED) Box which in combination with Short Message Service (SMS) communication methodology serves as an elementary proxy for Smart meters which are typically used in urban settings. Further, customer behavior and familiarity in using such devices based on mobile experience has been incorporated into the design philosophy. Customers are incentivized to interact with the system thus providing valuable behavioral and usage data to the Utility Service Provider (USP). Data that is collected over time can be used by the USP for analytics envisioned by using remote computing services known as cloud computing service. Cloud computing allows for a sharing of computational resources at the virtual level across several networks. The customer-system interaction is facilitated by a third party Telecom Service provider (TSP). The approximate cost of the RED Box is envisaged to be under USD 10 on production scale.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

A Modified Fluorimetric Method for Determination of Hydrogen Peroxide Using Homovanillic Acid Oxidation Principle

Hydrogen peroxide (H2O2) level in biological samples is used as an important index in various studies. Quantification of H2O2 level in tissue fractions in presence of H2O2 metabolizing enzymes may always provide an incorrect result. A modification is proposed for the spectrofluorimetric determination of H2O2 in homovanillic acid (HVA) oxidation method. The modification was included to precipitate biological samples with cold trichloroacetic acid (TCA, 5% w/v) followed by its neutralization with K2HPO4 before the fluorimetric estimation of H2O2 is performed. TCA was used to precipitate the protein portions contained in the tissue fractions. After employing the above modification, it was observed that H2O2 content in tissue samples was >= 2 fold higher than the content observed in unmodified method. Minimum 2 h incubation of samples in reaction mixture was required for completion of the reaction. The stability of the HVA dimer as reaction product was found to be > 12 h. The method was validated by using known concentrations of H2O2 and catalase enzyme that quenches H2O2 as substrate. This method can be used efficiently to determine more accurate tissue H2O2 level without using internal standard and multiple samples can be processed at a time with additional low cost reagents such as TCA and K2HPO4.

A Low-Cost System for Measurement and Spectral Analysis of Motor Acoustic Noise

Workplace noise has become one of the major issues in industry not only because of workers’ health but also due to safety. Electric motors, particularly, inverter fed induction motors emit objectionably high levels of noise. This has led to the emergence of a research area, concerned with measurement and mitigation of the acoustic noise. This paper presents a lowcost option for measurement and spectral analysis of acoustic noise emitted by electric motors. The system consists of an electret microphone, amplifier and filter. It makes use of the windows sound card and associated software for data acquisition and analysis. The measurement system is calibrated using a professional sound level meter. Acoustic noise measurements are made on an induction motor drive using the proposed system as per relevant international standards. These measurements are seen to match closely with those of a professional meter.

Cost-effective wear and oxidation resistant electrodeposited Ni-pumice coating

In the search for newer distributed phases that can be used in Ni-composite coatings, inexpensive and naturally available pumice has been identified as a potential candidate material. The composition of the pumice mineral as determined by Rietveld analysis shows the presence of corundum, quartz, mulllite, moganite and coesite phases. Pumice stone is crushed, ball-milled, dried and dispersed in a nickel sulfamate bath and Ni-pumice coatings are electrodeposited at different current densities and magnetic agitation speeds. Pumice particles are uniformly incorporated in the nickel matrix and Ni-pumice composite coatings with microhardness as high as 540 HK are obtained at the lowest applied current density. In the electrodeposited Ni-pumice coatings, the grain size of Ni increases with the applied current density. The overall intensity of texture development is slightly stronger for the Ni-pumice composite coating compared to plain Ni coating and the texture evolution is possibly not the strongest deciding factor for the enhanced properties of Ni-pumice coatings. The wear and oxidation resistances of Ni-pumice coating are commensurate with that of Ni-SiC coating electrodeposited under similar conditions. (C) 2014 Elsevier B.V. All rights reserved.

Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks

In this paper, we consider an intrusion detection application for Wireless Sensor Networks. We study the problem of scheduling the sleep times of the individual sensors, where the objective is to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous stateaction spaces, in a manner similar to Fuemmeler and Veeravalli (IEEE Trans Signal Process 56(5), 2091-2101, 2008). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation. Feature-based representations and function approximation is necessary to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation architecture for the Q-values) is updated in an on-policy temporal difference algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model and this is useful in settings where the latter is not known. Our simulation results on a synthetic 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work.

AN OPTIMUM SHRINKAGE ESTIMATOR BASED ON MINIMUM-PROBABILITY-OF-ERROR CRITERION AND APPLICATION TO SIGNAL DENOISING

We address the problem of designing an optimal pointwise shrinkage estimator in the transform domain, based on the minimum probability of error (MPE) criterion. We assume an additive model for the noise corrupting the clean signal. The proposed formulation is general in the sense that it can handle various noise distributions. We consider various noise distributions (Gaussian, Student's-t, and Laplacian) and compare the denoising performance of the estimator obtained with the mean-squared error (MSE)-based estimators. The MSE optimization is carried out using an unbiased estimator of the MSE, namely Stein's Unbiased Risk Estimate (SURE). Experimental results show that the MPE estimator outperforms the SURE estimator in terms of SNR of the denoised output, for low (0 -10 dB) and medium values (10 - 20 dB) of the input SNR.