Coordination driven self-assembly of metallamacrocycles using ambidentate linkers and self-selection of single linkage isomer

Nicotinate-N-oxide and isonicotinate-N-oxide have been employed to synthesize four heterometallic metallamacrocycles (dppf)(2)Pd-2(nicotinate-N-oxide)(2)](OTf)(2) (1), (dppf)(2)Pt-2(nicotinate-N-oxide)(2)](OTf)(2) (2), (dppf) 2Pd2(isonicotinate-N-oxide)(2)](OTf)(2) (3) and (dppf)(2)Pt-2(isonicotinate-N-oxide)(2)](OTf)(2) (4). The complexes represent the first examples of metallamacrocycles driven by solely Pd(II)/Pt(II)-O coordination using carboxylate-N-oxide donor. All the complexes 1-4 are characterized by IR, UV-Vis, multinuclear NMR spectroscopic and ESI-MS studies. The molecular structures of the complexes 1 and 3 are unambiguously determined by single crystal X-ray diffraction analysis. Despite the possibility of formation of several linkage isomers due to ambidentate nature of the donors, exclusive formation of 2 + 2] self-assembled single isomeric metallamacrocycle in each case is interesting observation. (C) 2011 Elsevier B.V. All rights reserved.

Reverse flotation of silica from Kudremukh iron ore .1. Selection of cationic reagents

Four types of cationic collectors were tested and evaluated for their performance in the reverse flotation of silica from the spiral preconcentrate of Kudremukh iron ore. A stagewise flotation was conducted by adding the reagent in three stages. Starch was used to depress hematite. Silica flotation was found to be very sensitive to the amount of cationic reagent added. The performance of the reagents was evaluated based on the percentage of silica and iron in the concentrate and percent recovery of iron obtained in the concentrate. Tests of significance, namely, t-test and F-test were performed to select the best two reagents for further investigations.

Synthesis of nonlinear controller to recover an unstable aircraft from poststall regime

Dynamics of the aircraft configuration considered in this paper show a unique characteristic in that there are no stable attractors in the entire high angle-of-attack flight envelope. As a result, once the aircraft has departed from the normal flight regime, no standard technique can be applied to recover the aircraft. In this paper, using feedback linearization technique, a nonlinear controller is designed at high angles of attack, which is engaged after the aircraft departs from normal flight regime. This controller stabilizes the aircraft into a stable spin. Then a set of synthetic pilot inputs is applied to cause an automatic transition from the spin equilibrium to low angles of attack where the second controller is connected. This controller is a normal gain-scheduled controller designed to have a large domain of attraction at low angles of attack. It traps the aircraft into a low angle-of-attack level flight. This entire concept of recovery has been verified using six-degrees-of-freedom nonlinear simulation. Feedback linearization technique used to design a controller ensures internal stability only if the nonlinear plant has stable zero dynamics. Because zero dynamics depend on the selection of outputs, a new method of choosing outputs is described to obtain a plant that has stable zero dynamics. Certain important aspects pertaining to the implementation of a feedback linearization-based controller are also discussed.

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.

A Complete Characterization of an Optimal Timer Based Selection Scheme

Timer-based mechanisms are often used in several wireless systems to help a given (sink) node select the best helper node among many available nodes. Specifically, a node transmits a packet when its timer expires, and the timer value is a function of its local suitability metric. In practice, the best node gets selected successfully only if no other node's timer expires within a `vulnerability' window after its timer expiry. In this paper, we provide a complete closed-form characterization of the optimal metric-to-timer mapping that maximizes the probability of success for any probability distribution function of the metric. The optimal scheme is scalable, distributed, and much better than the popular inverse metric timer mapping. We also develop an asymptotic characterization of the optimal scheme that is elegant and insightful, and accurate even for a small number of nodes.

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model. (C) 2002 Elsevier Science B.V. All rights reserved.

Automatic decomposition of unstructured meshes employing genetic algorithms for parallel FEM computations

Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques. These mesh-partitioning techniques divide the mesh into specified number of submeshes of approximately the same size and at the same time, minimise the interface nodes of the submeshes. This paper describes a new mesh partitioning technique, employing Genetic Algorithms. The proposed algorithm operates on the deduced graph (dual or nodal graph) of the given finite element mesh rather than directly on the mesh itself. The algorithm works by first constructing a coarse graph approximation using an automatic graph coarsening method. The coarse graph is partitioned and the results are interpolated onto the original graph to initialise an optimisation of the graph partition problem. In practice, hierarchy of (usually more than two) graphs are used to obtain the final graph partition. The proposed partitioning algorithm is applied to graphs derived from unstructured finite element meshes describing practical engineering problems and also several example graphs related to finite element meshes given in the literature. The test results indicate that the proposed GA based graph partitioning algorithm generates high quality partitions and are superior to spectral and multilevel graph partitioning algorithms.

On optimal timer-based distributed selection for rate-adaptive multi-user diversity systems

We develop an optimal, distributed, and low feedback timer-based selection scheme to enable next generation rate-adaptive wireless systems to exploit multi-user diversity. In our scheme, each user sets a timer depending on its signal to noise ratio (SNR) and transmits a small packet to identify itself when its timer expires. When the SNR-to-timer mapping is monotone non-decreasing, timers of users with better SNRs expire earlier. Thus, the base station (BS) simply selects the first user whose timer expiry it can detect, and transmits data to it at as high a rate as reliably possible. However, timers that expire too close to one another cannot be detected by the BS due to collisions. We characterize in detail the structure of the SNR-to-timer mapping that optimally handles these collisions to maximize the average data rate. We prove that the optimal timer values take only a discrete set of values, and that the rate adaptation policy strongly influences the optimal scheme's structure. The optimal average rate is very close to that of ideal selection in which the BS always selects highest rate user, and is much higher than that of the popular, but ad hoc, timer schemes considered in the literature.

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.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

Performance analysis of TCM with generalized selection combining on Rayleigh fading channels

We derive the computational cutoff rate, R-o, for coherent trellis-coded modulation (TCM) schemes on independent indentically distributed (i.i.d.) Rayleigh fading channels with (K, L) generalized selection combining (GSC) diversity, which combines the K paths with the largest instantaneous signal-to-noise ratios (SNRs) among the L available diversity paths. The cutoff rate is shown to be a simple function of the moment generating function (MGF) of the SNR at the output of the (K, L) GSC receiver. We also derive the union bound on the bit error probability of TCM schemes with (K, L) GSC in the form of a simple, finite integral. The effectiveness of this bound is verified through simulations.