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Polaronic effect in the x-ray absorption spectra of La1-x Ca x MnO3 manganites.
X-ray absorption spectroscopy (XAS) is performed to study changes in the electronic structures of colossal magnetoresistance (CMR) and charged ordered (CO) La1-x Ca x MnO3 manganites with respect to temperature. The pre-edge features in O and Mn K-edge XAS spectra, which are highly sensitive to the local distortion of MnO6 octahedral, exhibit contrasting temperature dependence between CMR and CO samples. The seemingly counter-intuitive XAS temperature dependence can be reconciled in the context of polarons. These results help identify the most relevant orbital states associated with polarons and highlight the crucial role played by polarons in understanding the electronic structures of manganites
Design, fabrication, and experimental validation of novel flexible silicon-based dry sensors for electroencephalography signal measurements
© 2014 IEEE. Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S A c(SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications
Triple-Mode Cavity Bandpass Filter on Doublet with Controllable Transmission Zeros
© 2013 IEEE. On the basis of doublet and its properties, a class of multiple-mode narrow band bandpasss filter is designed and fabricated by simultaneously exploiting the three resonant modes in a single rectangular cavity: TE101, TE011, and TM110 modes. The input/output ports of the proposed filter are fed by coupling a microstrip line to a slot on the side wall of a rectangular cavity. Different modes are excited by changing the position and shape of the two slots at input and output of the rectangular cavity without any intra-cavity coupling. Besides three poles within the passband, a pair of transmission zeros (TZs) is achieved, which can be controlled independently by setting the positions of the two TZs at the lower and/or upper stopband. High stopband attenuation and high filtering selectivity are achieved by considerably allocating three transmission poles and two zeros. In order to verify the proposed theory, two filter prototypes are fabricated and measured
Individually Frequency Tunable Dual- and Triple-band Filters in a Single Cavity
© 2013 IEEE. This paper presents a new class of second-order individually and continuously tunable dual- and triple-band bandpass filters in a single metal cavity. Each passband is realized by two identical metal posts. These dual- and triple-band tunable filters are achieved by putting two or three identical sets of metal-post pair in a single metal cavity. Metal screws are co-designed as a part of the metal posts to control their insertion depth inside the cavity. In this way, the resonant frequencies can be continuously controlled and designed at the desired frequency bands. Moreover, the distance between the two metal posts in a post pair can be freely tuned. Thus, the external quality factor (Qe) and coupling coefficient (k) between the adjacent modes can be easily adjusted to meet the specified requirement in synthesis design. At the bottom of the cavity, some grooves are used to extend the tunable frequency range and make the resonant frequency linearly varied with the height of the metal post. The center frequency of each passband can be independently tuned with a frequency range of 0.8-3.2 GHz and tunable ratio of 4. Finally, the continuously tunable dual- and triple-band bandpass filters prototypes with second order response are designed and fabricated, of which each passband can be individually tuned with a large tuning range
An inflatable and wearable wireless system for making 32-channel electroencephalogram measurements
© 2001-2011 IEEE. Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications
A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors
Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer's disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a sizeadjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions. © 2014 IEEE
Generalized Painleve-Gullstrand descriptions of Kerr-Newman black holes
Generalized Painleve-Gullstrand metrics are explicitly constructed for the
Kerr-Newman family of charged rotating black holes. These descriptions are free
of all coordinate singularities; moreover, unlike the Doran and other proposed
metrics, an extra tunable function is introduced to ensure all variables in the
metrics remain real for all values of the mass M, charge Q, angular momentum
aM, and cosmological constant \Lambda > - 3/(a^2). To describe fermions in
Kerr-Newman spacetimes, the stronger requirement of non-singular vierbein
one-forms at the horizon(s) is imposed and coordinate singularities are
eliminated by local Lorentz boosts. Other known vierbein fields of Kerr-Newman
black holes are analysed and discussed; and it is revealed that some of these
descriptions are actually not related by physical Lorentz transformations to
the original Kerr-Newman expression in Boyer-Lindquist coordinates - which is
the reason complex components appear (for certain ranges of the radial
coordinate) in these metrics. As an application of our constructions the
correct effective Hawking temperature for Kerr black holes is derived with the
method of Parikh and Wilczek.Comment: 5 pages; extended to include application to derivation of Hawking
radiation for Kerr black holes with Parikh-Wilczek metho
Inactivation of myosin binding protein C homolog in zebrafish as a model for human cardiac hypertrophy and diastolic dysfunction
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Single channel wireless EEG device for real-time fatigue level detection
© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments
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