An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement

Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes.

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors.

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Self-tuning algorithm for dsp-based motion control in cnc applications

This thesis describes the development of an adaptive control algorithm for Computerized Numerical Control (CNC) machines implemented in a multi-axis motion control board based on the TMS320C31 DSP chip. The adaptive process involves two stages: Plant Modeling and Inverse Control Application. The first stage builds a non-recursive model of the CNC system (plant) using the Least-Mean-Square (LMS) algorithm. The second stage consists of the definition of a recursive structure (the controller) that implements an inverse model of the plant by using the coefficients of the model in an algorithm called Forward-Time Calculation (FTC). In this way, when the inverse controller is implemented in series with the plant, it will pre-compensate for the modification that the original plant introduces in the input signal. The performance of this solution was verified at three different levels: Software simulation, implementation in a set of isolated motor-encoder pairs and implementation in a real CNC machine. The use of the adaptive inverse controller effectively improved the step response of the system in all three levels. In the simulation, an ideal response was obtained. In the motor-encoder test, the rise time was reduced by as much as 80%, without overshoot, in some cases. Even with the larger mass of the actual CNC machine, decrease of the rise time and elimination of the overshoot were obtained in most cases. These results lead to the conclusion that the adaptive inverse controller is a viable approach to position control in CNC machinery.

An augmented Lagrangian contact algorithm employing a vertex-based finite volume method

Abstract not available

A Research of Resource Scheduling Algorithm Based on Hadoop

With the development of electronic devices, more and more mobile clients are connected to the Internet and they generate massive data every day. We live in an age of “Big Data”, and every day we generate hundreds of million magnitude data. By analyzing the data and making prediction, we can carry out better development plan. Unfortunately, traditional computation framework cannot meet the demand, so the Hadoop would be put forward. First the paper introduces the background and development status of Hadoop, compares the MapReduce in Hadoop 1.0 and YARN in Hadoop 2.0, and analyzes the advantages and disadvantages of them. Because the resource management module is the core role of YARN, so next the paper would research about the resource allocation module including the resource management, resource allocation algorithm, resource preemption model and the whole resource scheduling process from applying resource to finishing allocation. Also it would introduce the FIFO Scheduler, Capacity Scheduler, and Fair Scheduler and compare them. The main work has been done in this paper is researching and analyzing the Dominant Resource Fair algorithm of YARN, putting forward a maximum resource utilization algorithm based on Dominant Resource Fair algorithm. The paper also provides a suggestion to improve the unreasonable facts in resource preemption model. Emphasizing “fairness” during resource allocation is the core concept of Dominant Resource Fair algorithm of YARM. Because the cluster is multiple users and multiple resources, so the user’s resource request is multiple too. The DRF algorithm would divide the user’s resources into dominant resource and normal resource. For a user, the dominant resource is the one whose share is highest among all the request resources, others are normal resource. The DRF algorithm requires the dominant resource share of each user being equal. But for these cases where different users’ dominant resource amount differs greatly, emphasizing “fairness” is not suitable and can’t promote the resource utilization of the cluster. By analyzing these cases, this thesis puts forward a new allocation algorithm based on DRF. The new algorithm takes the “fairness” into consideration but not the main principle. Maximizing the resource utilization is the main principle and goal of the new algorithm. According to comparing the result of the DRF and new algorithm based on DRF, we found that the new algorithm has more high resource utilization than DRF. The last part of the thesis is to install the environment of YARN and use the Scheduler Load Simulator (SLS) to simulate the cluster environment.

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

The development of an algorithm to track blood pressure accurately based on the pulse transit time method

Oscillometric blood pressure (BP) monitors are currently used to diagnose hypertension both in home and clinical settings. These monitors take BP measurements once every 15 minutes over a 24 hour period and provide a reliable and accurate system that is minimally invasive. Although intermittent cuff measurements have proven to be a good indicator of BP, a continuous BP monitor is highly desirable for the diagnosis of hypertension and other cardiac diseases. However, no such devices currently exist. A novel algorithm has been developed based on the Pulse Transit Time (PTT) method, which would allow non-invasive and continuous BP measurement. PTT is defined as the time it takes the BP wave to propagate from the heart to a specified point on the body. After an initial BP measurement, PTT algorithms can track BP over short periods of time, known as calibration intervals. After this time has elapsed, a new BP measurement is required to recalibrate the algorithm. Using the PhysioNet database as a basis, the new algorithm was developed and tested using 15 patients, each tested 3 times over a period of 30 minutes. The predicted BP of the algorithm was compared to the arterial BP of each patient. It has been established that this new algorithm is capable of tracking BP over 12 minutes without the need for recalibration, using the BHS standard, a 100% improvement over what has been previously identified. The algorithm was incorporated into a new system based on its requirements and was tested using three volunteers. The results mirrored those previously observed, providing accurate BP measurements when a 12 minute calibration interval was used. This new system provides a significant improvement to the existing method allowing BP to be monitored continuously and non-invasively, on a beat-to-beat basis over 24 hours, adding major clinical and diagnostic value.

The Crystal-t Algorithm: A New Approach To Calculate The Sle Of Lipidic Mixtures Presenting Solid Solutions.

Lipidic mixtures present a particular phase change profile highly affected by their unique crystalline structure. However, classical solid-liquid equilibrium (SLE) thermodynamic modeling approaches, which assume the solid phase to be a pure component, sometimes fail in the correct description of the phase behavior. In addition, their inability increases with the complexity of the system. To overcome some of these problems, this study describes a new procedure to depict the SLE of fatty binary mixtures presenting solid solutions, namely the Crystal-T algorithm. Considering the non-ideality of both liquid and solid phases, this algorithm is aimed at the determination of the temperature in which the first and last crystal of the mixture melts. The evaluation is focused on experimental data measured and reported in this work for systems composed of triacylglycerols and fatty alcohols. The liquidus and solidus lines of the SLE phase diagrams were described by using excess Gibbs energy based equations, and the group contribution UNIFAC model for the calculation of the activity coefficients of both liquid and solid phases. Very low deviations of theoretical and experimental data evidenced the strength of the algorithm, contributing to the enlargement of the scope of the SLE modeling.

Geographical Distribution Of The Association Between El Niño South Oscillation And Dengue Fever In The Americas: A Continental Analysis Using Geographical Information System-based Techniques.

El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20(th) century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AIy) by country was computed using Pan-American Health Association data. SOI and AIy values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AIy (rSOI,AI) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6° 31' S in 1995 to 21° 12' S in 1999 and the rSOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The rSOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

A revision of the Neotropical Solenopsidini ant genus Oxyepoecus Santschi, 1926 (Hymenoptera: Formicidae: Myrmicinae): 2. Final. Key for species and revision of the Rastratus species-group

In the first paper of this series (Albuquerque & Brandão, 2004) we revised the Vezenyii species group of the exclusively Neotropical solenopsidine (Myrmicinae) ant genus Oxyepoecus. In this closing paper we update distribution information on the Vezenyii group species and revise the other Oxyepoecus species-group (Rastratus). We describe two species (Oxyepoecus myops n. sp. and O. rosai n. sp.) and redescribe previously known species of the group [O. daguerrei (Santschi, 1933), O. mandibularis (Emery, 1913), O. plaumanni Kempf, 1974, O. rastratus Mayr, 1887, and O. reticulatus Kempf, 1974], adding locality records and comments on the meagre biological data of these species. We also present an identification key to Oxyepoecus species based on workers.

Rediscovery of the putatively extinct ant species Simopelta minima (Brandão) (Hymenoptera, Formicidae), with a discussion on rarity and conservation status of ant species

Simopelta minima (Brandão, 1989) was originally described based on four workers collected in soil samples from a small cocoa plantation in Ilhéus, state of Bahia, northeastern Brazil. In the subsequent years after the description, this cocoa plantation was eliminated and the species was then considered extinct by the Brazilian environmental institutions. The recent rediscovery of S. minima workers in subterranean pitfall trap samples from Viçosa, state of Minas Gerais, southeastern Brazil, over 1.000 km distant from type locality, suggests that the rarity and vulnerability status of some ant species may be explained by insufficient sampling of adequate microhabitats, in time and space.

A new species of the fungus-farming ant genus Mycetagroicus Brandão & Mayhé-Nunes (Hymenoptera, Formicidae, Attini)

The fungus-farming ant genus Mycetagroicus Brandão & Mayhé-Nunes was proposed based on three species from the Brazilian "Cerrado": M. cerradensis, M. triangularis and M. urbanus. Here we describe a new species of Attini ant of the genus Mycetagroicus, M. inflatus n. sp., based on two workers collected in eastern Pará State, Brazil. A new key for species identification, comments on differences among species and new geographical distribution data are furnished.

Description of the immatures of the ant, Myrmelachista catharinae

The Neotropical ant genus Myrmelachista Roger comprises 69 described species and subspecies, and still is a poorly studied group. Larvae play a paramount role in colony nutrition in social hymenopterans and bear considerable value in the reconstruction of group phylogenies, however, they are generally neglected. Larvae of different instars of Myrmelachista catharinae Mayr (Hymenoptera: Formicidae) are herein described in detail by light and scanning electron microscopy. The number of larval instars was estimated as three based on the frequency distribution of maximum head capsule widths. The described larvae confirmed some traits typical of the genus: general shape of body and mandibles, general aspect and distribution of body hairs, and the number of sensilla on the palps and galea. Differently from other Myrmelachista larvae previously described, M. catharinae presented two distinct kinds of second instars, some additional types of body hairs, different number of antennal sensilla, and a distinct labrum shape. M. catharinae presented ten pairs of spiracles, which is the first record for this genus.