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It is well known that machine learning algorithms can support the classification of EEG data by using deep learning networks, especially convolutional neural networks [1-3]. In this paper, we propose a new method for automatic classification of movement disorders based on an open-access EEG dataset published by Anjun et al. in 2020 [4]. The dataset includes a total of 41 Parkinson's disease (PD) patients and 41 control subjects. Control participants were demographically matched for age and sex with PD patients and did not differ in any measurements of education or premorbid intelligence. Additionally, EEG recordings from OFF medication sessions for 27 PD patients were recorded in the practically defined OFF levodopa period (12 h after the last dose of dopaminergic medication). Resting state EEG recordings were gathered under both eyes-open and eyes-closed at a sampling rate of 500 Hz on a 64-channel system and eye blinks were removed [5]. In the first step of our machine learning process, a connectivity matrix is created using three different approaches based on Granger causality, Pearson correlation and Spearman correlation. In a second step, these matrices are fed into a convolutional neural network that is tuned using random search, hyperband and Bayesian optimization. We will show that this approach can provide very accurate classification results for the given EEG data sets. Comparison with the traditional method considering raw EEG data shows that our method is more accurate, highlighting the importance of network topology in describing brain data [6]. References: [1] J. C. Vasquez-Correa, T. Arias-Vergara, C. D. Rios-Urrego, M. Schuster, J. Rusz, J. R. Orozco-Arroyave, and E. N ̈oth, Convolutional neural networks and a transfer learning strategy to classify parkinson's disease from speech in three different languages, in Iberoamerican Congress on Pattern Recognition(Springer, 2019) pp. 697–706. [2] T. D. Pham, A comprehensive study on classification of covid-19 on computed tomography with pretrained convolutional neural networks, Scientific reports10, 1 (2020). [3] O. Akbilgic, R. Kamaleswaran, A. Mohammed, G. W.Ross, K. Masaki, H. Petrovitch, C. M. Tanner, R. L. Davis, and S. M. Goldman, Electrocardiographic changes predate parkinson's disease onset, Scientific reports 10, 1 (2020). [4] Narayanan lab, Data set https://narayanan.lab.uiowa.edu/article/datasets [5] M.F. Anjum, S. Dasgupta, R. Mudumbai, A. Singh, J. F. Cavanagh, N. S. Narayanan, Linear predictive coding distinguishes spectral EEG features of Parkinson's disease, Parkinsonism & Related Disorders, 79, 2020, pp. 79-85. [6] C. L. Alves, A. M. Pineda, K. Roster, C. Thielemann, and F. A. Rodrigues, EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia, arXiv preprintarXiv:2110.06140 (2021). [ABSTRACT FROM AUTHOR]

As semiconductor devices decrease in size, soft errors are becoming a major issue that must be addressed at all stages of product definition. Even before prototype silicon chips are available for measuring, modeling must be able to predict soft-error rates with reasonable accuracy. As the technology matures, circuit test sites are produced and experimentally tested to determine representative fail rates of critical SRAM and flip-flop circuits. Circuit models are then fit to these experimental results and further test-site and product circuits are designed and modeled as needed. [ABSTRACT FROM AUTHOR]

We demonstrate large-area robotically assisted optical coherence tomography (LARA-OCT), utilizing a seven-degree-of-freedom robotic arm in conjunction with a 3.3 MHz swept-source OCT to raster scan samples of arbitrary shape. By combining multiple fields of view (FOV), LARA-OCT can probe a much larger area than conventional OCT. Also, nonplanar and curved surfaces like skin on arms and legs can be probed. The lenses in the LARA-OCT scanner with their normal FOV can have fewer aberrations and less complex optics compared to a single wide field design. This may be especially critical for high resolution scans. We directly use our fast MHz-OCT for tracking and stitching, making additional machine vision systems like cameras, positioning, tracking or navigation devices obsolete. This also eliminates the need for complex coordinate system registration between OCT and the machine vision system. We implemented a real time probe-to-surface control that maintains the probe alignment orthogonal to the sample by only using surface information from the OCT images. We present OCT data sets with volume sizes of 140 × 170 × 20 mm 3 , captured in 2.5 minutes., Competing Interests: R. Huber: University of Lübeck (P), Optores GmbH (I,P,R), Optovue Inc. (R), Abott (R)., (© 2024 Optica Publishing Group.)

We report on the first subpicometer interferometer flown in space. It was part of ESA's Laser Interferometer Space Antenna (LISA) Pathfinder mission and performed the fundamental measurement of the positional and angular motion of two free-falling test masses. The interferometer worked immediately, stably, and reliably from switch on until the end of the mission with exceptionally low residual noise of 32.0_{-1.7}^{+2.4}  fm/sqrt[Hz], significantly better than required. We present an upper limit for the sensor performance at millihertz frequencies and a model for the measured sensitivity above 200 mHz.

The Laser Interferometer Space Antenna Pathfinder (LPF) main observable, labeled Δg, is the differential force per unit mass acting on the two test masses under free fall conditions after the contribution of all non-gravitational forces has been compensated. At low frequencies, the differential force is compensated by an applied electrostatic actuation force, which then must be subtracted from the measured acceleration to obtain Δg. Any inaccuracy in the actuation force contaminates the residual acceleration. This study investigates the accuracy of the electrostatic actuation system and its impact on the LPF main observable. It is shown that the inaccuracy is mainly caused by the rounding errors in the waveform processing and also by the random error caused by the analog to digital converter random noise in the control loop. Both errors are one order of magnitude smaller than the resolution of the commanded voltages. We developed a simulator based on the LPF design to compute the close-to-reality actuation voltages and, consequently, the resulting actuation forces. The simulator is applied during post-processing the LPF data.

We report on the results of the LISA Pathfinder (LPF) free-fall mode experiment, in which the control force needed to compensate the quasistatic differential force acting on two test masses is applied intermittently as a series of "impulse" forces lasting a few seconds and separated by roughly 350 s periods of true free fall. This represents an alternative to the normal LPF mode of operation in which this balancing force is applied continuously, with the advantage that the acceleration noise during free fall is measured in the absence of the actuation force, thus eliminating associated noise and force calibration errors. The differential acceleration noise measurement presented here with the free-fall mode agrees with noise measured with the continuous actuation scheme, representing an important and independent confirmation of the LPF result. An additional measurement with larger actuation forces also shows that the technique can be used to eliminate actuation noise when this is a dominant factor.

In the months since the publication of the first results, the noise performance of LISA Pathfinder has improved because of reduced Brownian noise due to the continued decrease in pressure around the test masses, from a better correction of noninertial effects, and from a better calibration of the electrostatic force actuation. In addition, the availability of numerous long noise measurement runs, during which no perturbation is purposely applied to the test masses, has allowed the measurement of noise with good statistics down to 20  μHz. The Letter presents the measured differential acceleration noise figure, which is at (1.74±0.05)  fm s^{-2}/sqrt[Hz] above 2 mHz and (6±1)×10  fm s^{-2}/sqrt[Hz] at 20  μHz, and discusses the physical sources for the measured noise. This performance provides an experimental benchmark demonstrating the ability to realize the low-frequency science potential of the LISA mission, recently selected by the European Space Agency.

We report the first results of the LISA Pathfinder in-flight experiment. The results demonstrate that two free-falling reference test masses, such as those needed for a space-based gravitational wave observatory like LISA, can be put in free fall with a relative acceleration noise with a square root of the power spectral density of 5.2±0.1  fm s^{-2}/sqrt[Hz], or (0.54±0.01)×10^{-15}  g/sqrt[Hz], with g the standard gravity, for frequencies between 0.7 and 20 mHz. This value is lower than the LISA Pathfinder requirement by more than a factor 5 and within a factor 1.25 of the requirement for the LISA mission, and is compatible with Brownian noise from viscous damping due to the residual gas surrounding the test masses. Above 60 mHz the acceleration noise is dominated by interferometer displacement readout noise at a level of (34.8±0.3)  fm/sqrt[Hz], about 2 orders of magnitude better than requirements. At f≤0.5  mHz we observe a low-frequency tail that stays below 12  fm s^{-2}/sqrt[Hz] down to 0.1 mHz. This performance would allow for a space-based gravitational wave observatory with a sensitivity close to what was originally foreseen for LISA.