Optimal Power and Noise for Analog and Digital Sections of a Low Power Radio Receiver

We determine the optimal allocation of power between the analog and digital sections of an RF receiver while meeting the BER constraint. Unlike conventional RF receiver designs, we treat the SNR at the output of the analog front end (SNRAD) as a design parameter rather than a specification to arrive at this optimal allocation. We first determine the relationship of the SNRAD to the resolution and operating frequency of the digital section. We then use power models for the analog and digital sections to solve the power minimization problem. As an example, we consider a 802.15.4 compliant low-IF receiver operating at 2.4 GHz in 0.13 μm technology with 1.2 V power supply. We find that the overall receiver power is minimized by having the analog front end provide an SNR of 1.3dB and the ADC and the digital section operate at 1-bit resolution with 18MHz sampling frequency while achieving a power dissipation of 7mW.

Bi-directional Switch Commutation for a Resonant Matrix Converter supplying a Contactless Energy Transmission System

This paper describes a bi-directional switch commutation strategy for a resonant matrix converter loaded with a contactless energy transmission system. Due to the different application compared to classical 3 phase to 3 phase matrix converters supplying induction machines a new investigation of possible commutation principles is necessary. The paper therefore compares the full bridge series-resonant converter with the 3 phase to 2 phase matrix converter. From the commutation of the full bridge series-resonant converter, conditions for the bi-directional switch commutation are derived. One of the main benefits of the derived strategy is the minimization of commutation steps, which is independent from the load current sign.

Pickup and delivery problem using metaheuristics techniques

Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.

Coordination-driven self-assembly of 2D-metallamacrocycles using a shape-selective Pt(2)(II)-organometallic 90 degrees acceptor: design, synthesis and sensing study

Synthesis of a series of two-dimensional metallamacrocycles via coordination-driven self-assembly of a shape-selective Pt(2)(II)-molecular building unit incorporating carbazole-ethynyl functionality is described. An equimolar (1 : 1) combination of a Pt(2)(II)-organometallic 90 degrees acceptor, 1, with rigid linear ditopic donors (L(a) and L(b)) afforded [4 + 4] self-assembled octanuclear molecular squares, 2 and 3, in quantitative yields, respectively [L(a) = 4,4'-bipyridine; L(b) = trans-1,2-bis(4-pyridyl)ethylene]. Conversely, a similar treatment of 1 with an amide-based unsymmetrical flexible ditopic donor, L(c), resulted in the formation of a [2 + 2] self-sorted molecular rhomboid (4a) as a single product [L(c) = N-(4-pyridyl)isonicotinamide]. Despite the possibility of several linkage isomeric macrocycles (rhomboid, triangle and square) due to the different connectivity of L(c), the formation of a single and symmetrical molecular rhomboid (4a) as the only product is an interesting observation. All the self-assembled macrocycles (2, 3 and 4a) were fully characterized by multinuclear NMR ((1)H and (31)P) and ESI-MS analysis. Further structural insights about the size and shape of the macrocycles were obtained through energy minimization using density functional theory (DFT) calculations. Decoration of the starting carbazole building unit with Pt-ethynyl functionality enriches the assemblies to be more p-electron rich and luminescent in nature. Macrocycles 2 and 3 could sense the presence of electron deficient nitroaromatics in solution by quenching of the initial intensity upon gradual addition of picric acid (PA). They exhibited the largest quenching response with high selectivity for nitroaromatics compared to several other electron deficient aromatics tested.

Quaternary association in beta-prism I fold plant lectins: Insights from X-ray crystallography, modelling and molecular dynamics

Dimeric banana lectin and calsepa, tetrameric artocarpin and octameric heltuba are mannose-specific beta-prism I fold lectins of nearly the same tertiary structure. MD simulations on individual subunits and the oligomers provide insights into the changes in the structure brought about in the protomers on oligomerization, including swapping of the N-terminal stretch in one instance. The regions that undergo changes also tend to exhibit dynamic flexibility during MD simulations. The internal symmetries of individual oligomers are substantially retained during the calculations. Energy minimization and simulations were also carried out on models using all possible oligomers by employing the four different protomers. The unique dimerization pattern observed in calsepa could be traced to unique substitutions in a peptide stretch involved in dimerization. The impossibility of a specific mode of oligomerization involving a particular protomer is often expressed in terms of unacceptable steric contacts or dissociation of the oligomer during simulations. The calculations also led to a rationale for the observation of a heltuba tetramer in solution although the lectin exists as an octamer in the crystal, in addition to providing insights into relations among evolution, oligomerization and ligand binding.

Traffic Profiling for Efficient Network Resource Utilization.

Traffic Engineering has been the prime concern for Internet Service Providers (ISPs), with the main focus being minimization of over-utilization of network capacity even though additional capacity is available which is under-utilized, Furthermore, requirements of timely delivery of digitized audiovisual information raises a new challenge of finding a path meeting these requirements. This paper addresses the issue of (a) distributing load to achieve global efficiency in resource utilization. (b) Finding a path satisfying the real time requirements of, delay and bandwidth requested by the applications. In this paper we do a critical study of the link utilization that varies over time and determine the time interval during which the link occupancy remains constant across days. This information helps in pre-determining link utilization that is useful in balancing load in the network Finally, we run simulations that use a dynamic time interval for profiling traffic and show improvement in terms number of calls admitted/blocked.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

Methodology for alleviation of voltage excursions in large power systems

This paper presents a method for minimizing the sum of the square of voltage deviations by a least-square minimization technique, and thus improving the voltage profile in a given system by adjusting control variables, such as tap position of transformers, reactive power injection of VAR sources and generator excitations. The control variables and dependent variables are related by a matrix J whose elements are computed as the sensitivity matrix. Linear programming is used to calculate voltage increments that minimize transmission losses. The active and reactive power optimization sub-problems are solved separately taking advantage of the loose coupling between the two problems. The proposed algorithm is applied to IEEE 14-and 30-bus systems and numerical results are presented. The method is computationally fast and promises to be suitable for implementation in real-time dispatch centres.

Comparison of Optimum Reactive Power Schedule with Different Objectives Using LP Technique

Three algorithms for reactive power optimization are proposed in this paper with three different objective functions. The objectives in the proposed algorithm are to minimize the sum of the squares of the voltage deviations of the load buses, minimization of sum of squares of voltage stability L-indices of load buses (:3L2) algorithm, and also the objective of system real power loss (Ploss) minimization. The approach adopted is an iterative scheme with successive power flow analysis using decoupled technique and solution of the linear programming problem using upper bound optimization technique. Results obtained with all these objectives are compared. The analysis of these objective functions are presented to illustrate their advantages. It is observed comparing different objective functions it is possible to identify critical On Load Tap Changers (OLTCs) that should be made manual to avoid possible voltage instability due to their operation based on voltage improvement criteria under heavy load conditions. These algorithms have been tested under simulated conditions on few test systems. The results obtained on practical systems of 24-node equivalent EHV Indian power network, and for a 205 bus EHV system are presented for illustration purposes.

Reactive power optimization with different objectives in large power systems including HVDC systems and FACTS devices

Present day power systems are growing in size and complexity of operation with inter connections to neighboring systems, introduction of large generating units, EHV 400/765 kV AC transmission systems, HVDC systems and more sophisticated control devices such as FACTS. For planning and operational studies, it requires suitable modeling of all components in the power system, as the number of HVDC systems and FACTS devices of different type are incorporated in the system. This paper presents reactive power optimization with three objectives to minimize the sum of the squares of the voltage deviations (ve) of the load buses, minimization of sum of squares of voltage stability L-indices of load buses (¿L2), and also the system real power loss (Ploss) minimization. The proposed methods have been tested on typical sample system. Results for Indian 96-bus equivalent system including HVDC terminal and UPFC under normal and contingency conditions are presented.

Damage-tolerant design optimization of laminated composite structures using dispersion of ply angles by genetic algorithm

This article aims to obtain damage-tolerant designs with minimum weight for a laminated composite structure using genetic algorithm. Damage tolerance due to impacts in a laminated composite structure is enhanced by dispersing the plies such that too many adjacent plies do not have the same angle. Weight of the structure is minimized and the Tsai-Wu failure criterion is considered for the safe design. Design variables considered are the number of plies and ply orientation. The influence of dispersed ply angles on the weight of the structure for a given loading conditions is studied by varying the angles in the range of 0 degrees-45 degrees, 0 degrees-60 degrees and 0 degrees-90 degrees at intervals of 5 degrees and by using specific ply angles tailored to loading conditions. A comparison study is carried out between the conventional stacking sequence and the stacking sequence with dispersed ply angles for damage-tolerant weight minimization and some useful designs are obtained. Unconventional stacking sequence is more damage tolerant than the conventional stacking sequence is demonstrated by performing a finite element analysis under both tensile as well as compressive loading conditions. Moreover, a new mathematical function called the dispersion function is proposed to measure the dispersion of ply angles in a laminate. The approach for dispersing ply angles to achieve damage tolerance is especially suited for composite material design space which has multiple local minima.

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Constraints on axino warm dark matter from X-ray observation at the Chandra telescope and SPI

A sufficiently long lived warm dark matter could be a source of X-rays observed by satellite based X-ray telescopes. We consider axinos and gravitinos with masses between 1 keV and 100 keV in supersymmetric models with sin all R-parity violation. We show that axino dark matter receives significant constraints from X-ray observations of Chandra and SPI, especially for the lower end of the allowed range of the axino decay constant f(a), while the gravitino dark matter remains unconstrained.

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).