Analog circuit synthesis performing fast Pareto frontier exploration and analysis through 3D graphs

This paper presents a technique for performing analog design synthesis at circuit level providing feedback to the designer through the exploration of the Pareto frontier. A modified simulated annealing which is able to perform crossover with past anchor points when a local minimum is found which is used as the optimization algorithm on the initial synthesis procedure. After all specifications are met, the algorithm searches for the extreme points of the Pareto frontier in order to obtain a non-exhaustive exploration of the Pareto front. Finally, multi-objective particle swarm optimization is used to spread the results and to find a more accurate frontier. Piecewise linear functions are used as single-objective cost functions to produce a smooth and equal convergence of all measurements to the desired specifications during the composition of the aggregate objective function. To verify the presented technique two circuits were designed, which are: a Miller amplifier with 96 dB Voltage gain, 15.48 MHz unity gain frequency, slew rate of 19.2 V/mu s with a current supply of 385.15 mu A, and a complementary folded cascode with 104.25 dB Voltage gain, 18.15 MHz of unity gain frequency and a slew rate of 13.370 MV/mu s. These circuits were synthesized using a 0.35 mu m technology. The results show that the method provides a fast approach for good solutions using the modified SA and further good Pareto front exploration through its connection to the particle swarm optimization algorithm.

Preparation of percentile tables through anthropometric, performance, biochemical, hematological, hormonal and psychological parameters in professional soccer players

Introduction: The lack of reference values of anthropometric, performance, biochemical, hematological, hormonal and psychological parameters is an important limitation in the investigations with soccer players. Objective: To elaborate percentile tables to be used as comparison reference for further studies. Methods: 82 professional soccer players were evaluated approximately 30 days after the beginning of the main competition played by their teams. On the first day of evaluation, fast blood samples were collected for measurement of hematological parameters (i.e. erythrocytes, hemoglobin, hematocrit, mean corpuscular volume - MCV, mean corpuscular hemoglobin - MCH, mean corpuscular hemoglobin concentration - MCHC, leukocytes, eosinophils, lymphocytes, monocytes and platelets) and of concentrations of adrenaline, cortisol, creatine kinase, creatinine, norepinephrine, testosterone and urea. Subsequently, the soccer players had their anthropometric characteristics and psychological parameters assessed. In addition, the evaluation of the lactic anaerobic system efficiency was performed on a 400-m track. On the second day, both the alactic anaerobic and aerobic system efficiency was measured. Results: The percentile distribution (P-0, P-15, P-30, P-50, P-70, P-85 e P-100) was used to present the results. Conclusion: The elaboration of the percentile tables can be used as comparison reference for further studies.

Caffeine determination at a carbon fiber ultramicroelectrodes by fast-scan cyclic voltammetry

Caffeine determination using a fast-scan voltammetric procedure at a carbon fiber ultramicroelectrode (CF-UME) is described. The CF-UME was submitted to electrochemical pretreatment. Parameters such as number of acquisition cycles, scan rate, potential window, and the electrochemical surface pretreatment were optimized. Using the optimized conditions, it was possible to achieve a LDR from 10.0 up to 200 μmol L-1, with a LOD of 3.33 μmol L-1. The method has been applied in the determination of caffeine in commercial samples, with errors of 1.0-3.5% in relation to the label values and recoveries of 97-114% within the linear range.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.