Searching for solar-like oscillations in the delta Scuti star rho Puppis

Despite the shallow convective envelopes of delta Scuti pulsators, solar-like oscillations are theoretically predicted to be excited in those stars as well. To search for such stochastic oscillations we organised a spectroscopic multi-site campaign for the bright, metal-rich delta Sct star rho Puppis. We obtained a total of 2763 high-resolution spectra using four telescopes. We discuss the reduction and analysis with the iodine cell technique, developed for searching for low-amplitude radial velocity variations, in the presence of high-amplitude variability. Furthermore, we have determined the angular diameter of rho Puppis to be 1.68 \pm 0.03 mas, translating into a radius of 3.52 \pm 0.07Rsun. Using this value, the frequency of maximum power of possible solar-like oscillations, is expected at ~43 \pm 2 c/d (498 \pm 23 muHz). The dominant delta Scuti-type pulsation mode of rho Puppis is known to be the radial fundamental mode which allows us to determine the mean density of the star, and therefore an expected large frequency separation of 2.73 c/d (31.6 muHz). We conclude that 1) the radial velocity amplitudes of the delta Scuti pulsations are different for different spectral lines; 2) we can exclude solar-like oscillations to be present in rho Puppis with an amplitude per radial mode larger than 0.5 m/s.Comment: 14 pages, 12 figure, accepted for MNRA

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR estimation system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. By overcoming computational limitations and performing direct-sequence spread-spectrum (DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive CIR estimation. We validate the channel sounder and present a drive test campaign which measures hundreds of channels between WiFi access points and an in-vehicle receiver in urban and suburban areas

Optimizing ISOCAM data processing using spatial redundancy

We present new data processing techniques that allow to correct the main instrumental effects that degrade the images obtained by ISOCAM, the camera on board the Infrared Space Observatory (ISO). Our techniques take advantage of the fact that a position on the sky has been observed by several pixels at different times. We use this information (1) to correct the long term variation of the detector response, (2) to correct memory effects after glitches and point sources, and (3) to refine the deglitching process. Our new method allows the detection of faint extended emission with contrast smaller than 1% of the zodiacal background. The data reduction corrects instrumental effects to the point where the noise in the final map is dominated by the readout and the photon noises. All raster ISOCAM observations can benefit from the data processing described here. These techniques could also be applied to other raster type observations (e.g. ISOPHOT or IRAC on SIRTF).Comment: 13 pages, 10 figures, to be published in Astronomy and Astrophysics Supplement Serie

Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data

The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps on board. Recently, a new theory called compressed-sensing emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors. A previous article by Bobin et al. (2008) showed how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed-sensing theory can indeed be successfully applied to actual Herschel/PACS data and give significant improvements over the standard pipeline. In order to fully use the redundancy present in the data, we perform full sky map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise, glitches, whose behavior is a priori not well compatible with compressed-sensing) can be handled as well in this new framework. Finally, we make a comparison between the methods from the compressed-sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on ground for the creation of sky maps from the data.Comment: 11 pages, 6 figures, 5 tables, peer-reviewed articl

A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

Spatiotemporal dynamics of low frequency fluctuations in bold fMRI

Traditional fMRI utilizes blood oxygenation level dependent (BOLD) contrast to map brain activity. BOLD signal is sensitive to the hemodynamic changes associated with brain activity, and gives an indirect measure of brain activity. Low frequency fluctuations (LFFs) have been observed in the BOLD signal even in the absence of any anesthetic agent, and the correlations between the fluctuations from different brain regions has been used to map functional connectivity in the brain. Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. The research presented in this document investigates the spatiotemporal dynamics of the BOLD fluctuations to identify common spatiotemporal patterns within a scan. First, the presence of a visually detectable spatiotemporal propagation pattern is demonstrated by utilizing single-slice data with high spatial and temporal resolution. The pattern consists of lateral-medial propagation of BOLD signal, demonstrating the presence of time-varying features in spontaneous BOLD fluctuations. Further, a novel pattern finding algorithm is developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm is applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consists of waves of high signal intensity, propagating in a lateral-medial direction across the cortex, replicating the results obtained using visual observation. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points is also observed. The pattern finding algorithm is shown to be reasonably insensitive to the variation in user-defined parameters, and the results are consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.Ph.D.Committee Chair: Keilholz, Shella; Committee Member: Hu, Xiaoping; Committee Member: Jaeger, Dieter; Committee Member: Sathian, Krish; Committee Member: Schumacher, Eri