Estimating Stellar Radial Velocity Variability from Kepler and GALEX: Implications for the radial velocity confirmation of exoplanets.

We cross match the GALEX and Kepler surveys to create a unique dataset with both ultraviolet (UV) measurements and highly precise photometric variability measurements in the visible light spectrum. As stellar activity is driven by magnetic field modulations, we have used UV emission from the magnetically heated gas in the stellar atmosphere to serve as our proxy for the more well-known stellar activity indicator, R' HK . The R' HK approximations were in turn used to estimate the level of astrophysical noise expected in radial velocity (RV) measurements and these were then searched for correlations with photometric variability. We find significant scatter in our attempts to estimate RV noise for magnetically active stars, which we attribute to variations in the phase and strength of the stellar magnetic cycle that drives the activity of these targets. However, for stars we deem to be magnetically quiet, we do find a clear correlation between photometric variability and estimated levels of RV noise (with variability up to ~10 m s–1). We conclude that for these quiet stars, we can use photometric measurements as a proxy to estimate the RV noise expected. As a result, the procedure outlined in this paper may help select targets best-suited for RV follow-up necessary for planet confirmation.

Octavius 4D characterization for flattened and flattening filter free rotational deliveries

Purpose: In this study the Octavius detector 729 ionization chamber (IC) array with the Octavius 4D phantom was characterized for flattening filter (FF) and flattening filter free (FFF) static and rotational beams. The device was assessed for verification with FF and FFF RapidArc treatment plans.

Methods: The response of the detectors to field size, dose linearity, and dose rate were assessed for 6 MV FF beams and also 6 and 10 MV FFF beams. Dosimetric and mechanical accuracy of the detector array within the Octavius 4D rotational phantom was evaluated against measurements made using semiflex and pinpoint ionization chambers, and radiochromic film. Verification FF and FFF RapidArc plans were assessed using a gamma function with 3%/3 mm tolerances and 2%/2 mm tolerances and further analysis of these plans was undertaken using film and a second detector array with higher spatial resolution.

Results: A warm-up dose of >6 Gy was required for detector stability. Dose-rate measurements were stable across a range from 0.26 to 15 Gy/min and dose response was linear, although the device overestimated small doses compared with pinpoint ionization chamber measurements. Output factors agreed with ionization chamber measurements to within 0.6% for square fields of side between 3 and 25 cm and within 1.2% for 2 x 2 cm(2) fields. The Octavius 4D phantom was found to be consistent with measurements made with radiochromic film, where the gantry angle was found to be within 0.4. of that expected during rotational deliveries. RapidArc FF and FFF beams were found to have an accuracy of >97.9% and >90% of pixels passing 3%/3 mm and 2%/2 mm, respectively. Detector spatial resolution was observed to be a factor in determining the accurate delivery of each plan, particularly at steep dose gradients. This was confirmed using data from a second detector array with higher spatial resolution and with radiochromic film.

Conclusions: The Octavius 4D phantom with associated Octavius detector 729 ionization chamber array is a dosimetrically and mechanically stable device for pretreatment verification of FF and FFF RapidArc treatments. Further improvements may be possible through use of a detector array with higher spatial resolution (detector size and/or detector spacing). (C) 2013 American Association of Physicists in Medicine.

Using entropy based mean shift filter and modified watershed transform for grain segmentation

Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper,we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice.However,due to the over-segmentation issue,this technique has experienced poor performance in various applications,such as inhomogeneous background and connected targets. To solve this problem,we present a combination of two classical techniques to handle this issue.In the first step,a mean shift filter is used to eliminate the inhomogeneous background, where entropy is used to be a converging criterion. Secondly,a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.

The management of radial scars of the breast - does core biopsy help?

Purpose: To compare the effectiveness of fine needle aspiration cytology (FNAC) with core biopsy (CB) in the pre-operative diagnosis of radial scar (RS) of the breast.

Patients and methods: A retrospective analysis was made of all radial scars diagnosed on surgical histology over an 8-year period. Comparison was made between the results of different preoperative needle biopsy techniques and surgical histology findings.

Results: Forty of 47 patients with a preoperative radiological diagnosis of radial scar were included in this analysis. Thirty-eight patients had impalpable lesions diagnosed on mammography and two presented with a palpable lump. FNAC (n=17) was inadequate in 47% of patients, missed two co-existing carcinomas found in this group, and gave a false positive or suspicious result for malignancy in 4 patients. CB (n=23) suggested a RS in 15 patients, but only diagnosed 4 out of 7 co-existing carcinomas found in this group.

Conclusion: CB is more accurate than FNAC in the diagnosis of RS. However, these data demonstrate that CB may offer little to assist in the management of patients with RS. In summary, this paper advocates the use of CB in any lesion with a radiological suspicion of carcinoma and diagnostic excision of all lesions thought to be typical of RS on mammography.

An optimal algorithm for low power multiplierless FIR filter design using chebychev criterion

In this paper, we propose a novel finite impulse response (FIR) filter design methodology that reduces the number of operations with a motivation to reduce power consumption and enhance performance. The novelty of our approach lies in the generation of filter coefficients such that they conform to a given low-power architecture, while meeting the given filter specifications. The proposed algorithm is formulated as a mixed integer linear programming problem that minimizes chebychev error and synthesizes coefficients which consist of pre-specified alphabets. The new modified coefficients can be used for low-power VLSI implementation of vector scaling operations such as FIR filtering using computation sharing multiplier (CSHM). Simulations in 0.25um technology show that CSHM FIR filter architecture can result in 55% power and 34% speed improvement compared to carry save multiplier (CSAM) based filters.

An Improvement of IP Address Lookup based on Rule Filter Analysis

Multi-bit trie is a popular approach performing the longest prefix matching for packet classification. However, it requires a long lookup time and inefficiently consumes memory space. This paper presents an in-depth study of different variations of multi-bit trie for IP address lookup. Our main aim is to study a method of data structure which reduces memory space. The proposed approach has been implemented using the label method in two approaches. Both methods present better results regarding lookup speed, update time and memory bit consumptions.

The influence of high air entry filter on the testing of unsaturated soils using the axis translation technique

Suction is an important stress variable that is required for reliable predictions of the likely performance of unsaturated soils. The axis translation technique is the best established method of measuring or controlling suction; however, the success of this application is heavily dependent on the rating of the high air entry filter (HAF) and how it is incorporated into the testing system. This paper reports some basic experiments in which samples of unsaturated kaolin were brought to saturation in stages using 5 bar and 15 bar HAFs. The results have shown that the water equilibrium in unsaturated soils is greatly affected by the rating of filters. The findings also suggest that the flow through unsaturated soils is not necessarily governed by the one-dimensional consolidation theory that was developed for saturated soils, and this may be attributed to the bimodal pore size distribution of unsaturated soils.

A high performance IIR digital filter chip

The design of a high-performance IIR (infinite impulse response) digital filter is described. The chip architecture operates on 11-b parallel, two's complement input data with a 12-b parallel two's complement coefficient to produce a 14-b two's complement output. The chip is implemented in 1.5-µm, double-layer-metal CMOS technology, consumes 0.5 W, and can operate up to 15 Msample/s. The main component of the system is a fine-grained systolic array that internally is based on a signed binary number representation (SBNR). Issues addressed include testing, clock distribution, and circuitry for conversion between two's complement and SBNR.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.