On Training and Training Voids for Receive Antenna Subset Selection in Time-Varying Channels

Antenna selection (AS) provides most of the benefits of multiple-antenna systems at drastically reduced hardware costs. In receive AS, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. The "best" subset to be used for data reception is determined by means of channel estimates acquired using training sequences. Due to the nature of AS, the channel estimates at different antennas are obtained from different transmissions of the pilot sequence, and are, thus, outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We present a "void-filling" method for fully exploiting these voids, which essentially provides more accurate training for some antennas, and derive the optimal subset selection rule for any void-filling method. We also derive new closed-form equations for the performance of receive AS with optimal subset selection.

Training-Embedded, Single-Symbol ML-Decodable, Distributed STBCs for Relay Networks

Recently, a special class of complex designs called Training-Embedded Complex Orthogonal Designs (TE-CODs) has been introduced to construct single-symbol Maximum Likelihood decodable (SSD) distributed space-time block codes (DSTBCs) for two-hop wireless relay networks using the amplify and forward protocol. However, to implement DSTBCs from square TE-CODs, the overhead due to the transmission of training symbols becomes prohibitively large as the number of relays increase. In this paper, we propose TE-Coordinate Interleaved Orthogonal Designs (TE-CIODs) to construct SSD DSTBCs. Exploiting the block diagonal structure of TE-CIODs, we show that the overhead due to the transmission of training symbols to implement DSTBCs from TE-CIODs is smaller than that for TE-CODs. We also show that DSTBCs from TE-CIODs offer higher rate than those from TE-CODs for identical number of relays while maintaining the SSD and full-diversity properties.

Application of an evolutionary fuzzy system for the estimation of workforce deployment and cross-training in an assembly environment

Owing to the increased customer demands for make-to-order products and smaller product life-cycles, today assembly lines are designed to ensure a quick switch-over from one product model to another for companies' survival in market place. The complexity associated with the decisions pertaining to the type of training and number of workers and their exposition to the different tasks especially in the current era of customized production is a serious problem that the managers and the HRD gurus are facing in industry. This paper aims to determine the amount of cross-training and dynamic deployment policy caused by workforce flexibility for a make-to-order assembly. The aforementioned issues have been dealt with by adopting the concept of evolutionary fuzzy system because of the linguistic nature of the attributes associated with product variety and task complexity. A fuzzy system-based methodology is proposed to determine the amount of cross-training and dynamic deployment policy. The proposed methodology is tested on 10 sample products of varying complexities and the results obtained are in line with the conclusions drawn by previous researchers.

Training and Voids in Receive Antenna Subset Selection in Time-Varying Channels

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N , we highlight a new issue of ``training voids'', in which the last pilot transmission is not fully exploited by the receiver. We then present new ``void-filling'' methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void-filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed-form equations for the symbol error probability with and without void-filling are also developed.

Training Young Minds for Nanoscience and Technology: Issues and Challenges

This article explores issues and challenges in the field of education in nanoscience and technology with special emphasis with respect to India, where an expanding programme of research in nano science and technology is in place. The article does not concentrate on actual curricula that are needed in nano science and technology education course. Rather it focuses on the desirability of nanoscience and technology education at different levels of education and future prospect of students venturing into this within the economic and cultural milieu of India. We argue that care is needed in developing the education programme in India. However, the risk is worth taking as the education on nanoscience and technology can bridge the man power gap not only in this area of technology but also related technologies of hardware and micro electronics for which the country is a promising destination at global level. This will also unlock the demographical advantage that India will enjoy in the next five decades.

Estimation of BT Interference in OFDM Sub Carriers with Training Signals

While wireless LAN (WLAN) is very popular now a days, its performance deteriorates in the presence of other signals like Bluetooth (BT) signal that operate in the same band as WLAN. Present interference mitigation techniques in WLAN due to BT cancel interference in WLAN sub carrier where BT has hopped but do not cancel interference in the adjacent sub carriers. In this paper BT interference signal in all the OFDM sub carriers is estimated. That is, leakage of BT in other sub carriers including the sub carriers in which it has hopped is also measured. BT signals are estimated using the training signals of OFDM system. Simulation results in AWGN noise show that proposed algorithm agrees closely with theoretical results.

Antenna Selection Training in MIMO-OFDM/OFDMA Cellular Systems

Antenna selection allows multiple-antenna systems to achieve most of their promised diversity gain, while keeping the number of RF chains and, thus, cost/complexity low. In this paper we investigate antenna selection for fourth-generation OFDMA- based cellular communications systems, in particular, 3GPP LTE (long-term evolution) systems. We propose a training method for antenna selection that is especially suitable for OFDMA. By means of simulation, we evaluate the SNR-gain that can be achieved with our design. We find that the performance depends on the bandwidth assigned to each user, the scheduling method (round-robin or frequency-domain scheduling), and the Doppler spread. Furthermore, the signal-to-noise ratio of the training sequence plays a critical role. Typical SNR gains are around 2 dB, with larger values obtainable in certain circumstances.

Training-Symbol Embedded, High-Rate, Single-Symbol ML-Decodable, Distributed STBCs for Relay Networks

Distributed space-time block codes (DSTBCs) from complex orthogonal designs (CODs) (both square and nonsquare), coordinate interleaved orthogonal designs (CIODs), and Clifford unitary weight designs (CUWDs) are known to lose their single-symbol ML decodable (SSD) property when used in two-hop wireless relay networks using amplify and forward protocol. For such networks, in this paper, three new classes of high rate, training-symbol embedded (TSE) SSD DSTBCs are constructed: TSE-CODs, TSE-CIODs, and TSE-CUWDs. The proposed codes include the training symbols inside the structure of the code which is shown to be the key point to obtain the SSD property along with the channel estimation capability. TSE-CODs are shown to offer full-diversity for arbitrary complex constellations and the constellations for which TSE-CIODs and TSE-CUWDs offer full-diversity are characterized. It is shown that DSTBCs from nonsquare TSE-CODs provide better rates (in symbols per channel use) when compared to the known SSD DSTBCs for relay networks. Important from the practical point of view, the proposed DSTBCs do not contain any zeros in their codewords and as a result, antennas of the relay nodes do not undergo a sequence of switch on/off transitions within every codeword, and, thus, avoid the antenna switching problem.

A new 6-component accelerometer force balance for short duration ground testing facilities

A new six-component accelerometer force balance is developed and used in the HST2 shock tunnel of Indian Institute of Science. Aerodynamic forces and moments for a hypersonic slender body measured using this balance system at a free stream Mach number of 5.75 and Reynolds number of 1.5 million and stagnation enthalpy of 1.5 and 2 MJ/kg are presented. These measured values compare well with the theoretical values estimated using modified Newtonian theory.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Oscillatory exchange bias and training effects in nanocrystalline Pr0.5Ca0.5MnO3

We report on exchange bias effects in 10 nm particles of Pr0.5Ca0.5MnO3 which appear as a result of competing interactions between the ferromagnetic (FM)/anti-ferromagnetic (AFM) phases. The fascinating new observation is the demonstration of the temperature dependence of oscillatory exchange bias (OEB) and is tunable as a function of cooling field strength below the SG phase, may be attributable to the presence of charge/spin density wave (CDW/SDW) in the AFM core of PCMO10. The pronounced training effect is noticed at 5 K from the variation of the EB field as a function of number of field cycles (n) upon the field cooling (FC) process. For n > 1, power-law behavior describes the experimental data well; however, the breakdown of spin configuration model is noticed at n >= 1. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.3696033]

Robust solver based on modified particle swarm optimization for improved solution of diffusion transport through containment facilities

Accurate estimation of mass transport parameters is necessary for overall design and evaluation processes of the waste disposal facilities. The mass transport parameters, such as effective diffusion coefficient, retardation factor and diffusion accessible porosity, are estimated from observed diffusion data by inverse analysis. Recently, particle swarm optimization (PSO) algorithm has been used to develop inverse model for estimating these parameters that alleviated existing limitations in the inverse analysis. However, PSO solver yields different solutions in successive runs because of the stochastic nature of the algorithm and also because of the presence of multiple optimum solutions. Thus the estimated mean solution from independent runs is significantly different from the best solution. In this paper, two variants of the PSO algorithms are proposed to improve the performance of the inverse analysis. The proposed algorithms use perturbation equation for the gbest particle to gain information around gbest region on the search space and catfish particles in alternative iterations to improve exploration capabilities. Performance comparison of developed solvers on synthetic test data for two different diffusion problems reveals that one of the proposed solvers, CPPSO, significantly improves overall performance with improved best, worst and mean fitness values. The developed solver is further used to estimate transport parameters from 12 sets of experimentally observed diffusion data obtained from three diffusion problems and compared with published values from the literature. The proposed solver is quick, simple and robust on different diffusion problems. (C) 2012 Elsevier Ltd. All rights reserved.

Power Controlled Reverse Channel Training Achieves an Infinite Diversity Order in a TDD-SIMO System with Perfect CSIR

In this letter, we analyze the Diversity Multiplexinggain Tradeoff (DMT) performance of a training-based reciprocal Single Input Multiple Output (SIMO) system. Assuming Channel State Information (CSI) is available at the Receiver (CSIR), we propose a channel-dependent power-controlled Reverse Channel Training (RCT) scheme that enables the transmitter to directly estimate the power control parameter to be used for the forwardlink data transmission. We show that, with an RCT power of (P) over bar (gamma), gamma > 0 and a forward data transmission power of (P) over bar, our proposed scheme achieves an infinite diversity order for 0 <= g(m) < L-c-L-B,L-tau/L-c min(gamma, 1) and r > 2, where g(m) is the multiplexing gain, L-c is the channel coherence time, L-B,L-tau is the RCT duration and r is the number of receive antennas. We also derive an upper bound on the outage probability and show that it goes to zero asymptotically as exp(-(P) over bar (E)), where E (sic) (gamma - g(m)L(c)/L-c-L-B,L-tau), at high (P) over bar. Thus, the proposed scheme achieves a significantly better DMT performance compared to the finite diversity order achieved by channel-agnostic, fixed-power RCT schemes.

A survey of cloud storage facilities

There are many applications such as software for processing customer records in telecom, patient records in hospitals, email processing software accessing a single email in a mailbox etc. which require to access a single record in a database consisting of millions of records. A basic feature of these applications is that they need to access data sets which are very large but simple. Cloud computing provides computing requirements for these kinds of new generation of applications involving very large data sets which cannot possibly be handled efficiently using traditional computing infrastructure. In this paper, we describe storage services provided by three well-known cloud service providers and give a comparison of their features with a view to characterize storage requirements of very large data sets as examples and we hope that it would act as a catalyst for the design of storage services for very large data set requirements in future. We also give a brief overview of other kinds of storage that have come up in the recent past for cloud computing.