### 69 results for qubit oscillator frequency

Contributors: Lowet, Eric, Roberts, Mark, Hadjipapas, Avgis, Peter, Alina, van der Eerden, Jan, De Weerd, Peter

Date: 2015-02-23

phase-**oscillator** model part 1...phase-**oscillator** model part 2...phase-**oscillator** model part 3...phase-**oscillator** model part 4...**oscillation** **frequencies** at nearby spatial locations. Similarly to cortical...**oscillation** phase codes, may resolve conflicting experimental observations...**frequency** with increasing input drive. The relates to the experimental...**oscillators**. The gamma phase-locking, the precise phase relation and the...**oscillators**, where input drive determines the intrinsic (natural) **frequency**...**frequency** of gamma **oscillations** varies with input drive (e.g. visual contrast ... Fine-scale temporal organization of cortical activity in the gamma range (~25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise **frequency** of gamma **oscillations** varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common **frequency**. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent **frequency** modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different **oscillation** **frequencies** at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and **frequency** differences, and quantified the stimulus-related information represented by gamma phase and **frequency**. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled **oscillators**, where input drive determines the intrinsic (natural) **frequency** of **oscillators**. The gamma phase-locking, the precise phase relation and the emergent (measurable) **frequencies** were determined by two principal factors: the detuning (intrinsic **frequency** difference, i.e. local input difference) and the coupling strength. In addition to **frequency** coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower **oscillation** phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma **frequencies** could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

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Contributors: Waldman, Zachary, Chervenova, Inna, Berry, Brent, Kucewicz, Michal, Ganne, Chaitanya, He, Xiao-Song, Elahian, Bahareh, Shimamoto, Shoichi, Davis, Leon, Stein, Joel

Date: 2017-08-03

high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical ... Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.

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Contributors: Lowet, Eric, Roberts, Mark Jonathan, Peter, Alina, Gips, Bart, de Weerd, Peter

Date: 2017-09-01

**frequency** modulations applies to gamma in V1, and is likely generalizable...**frequency** by increasing input current) and coupling on their phase dynamics...**frequency** difference. Crucially, the precise dynamics of **frequencies** and...**frequencies**. When similar enough, these **frequencies** continually attracted...**oscillators** influence each other’s phase relations. Hence, the fundamental...**oscillators**. With this code the effects of detuning and coupling are illustrated...**oscillating** neuronal populations to optimize information transmission ... Gamma-band synchronization coordinates brief periods of excitability in **oscillating** neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared **frequency** over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous **frequency** modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma **frequencies**. When similar enough, these **frequencies** continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized **frequency** difference. Crucially, the precise dynamics of **frequencies** and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled **oscillators** influence each other’s phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous **frequency** modulations applies to gamma in V1, and is likely generalizable to other brain regions and rhythms.

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Contributors: Solodov Igor

Date: 2015-10-01

**oscillations**. The modes observed in experiment include sub- and superharmonic...**frequency**-selective imaging.
...**frequency** resonance) is used to enhance the efficiency of **frequency** mixing...**frequency** to nonlinear
**frequency** components. In this paper, it is...**oscillator** brings about new dynamic and **frequency**
scenarios characteristic ... The bottleneck problem of nonlinear NDT is a low efficiency of conversion from fundamental **frequency** to nonlinear
**frequency** components. In this paper, it is proposed to use a combination of mechanical resonance and nonlinearity of defects to
enhance the input-output conversion. The concept of the defect as a nonlinear **oscillator** brings about new dynamic and **frequency**
scenarios characteristic of parametric **oscillations**. The modes observed in experiment include sub- and superharmonic resonances
with anomalously efficient generation of the higher harmonics and subharmonics. A modified version of the superharmonic
resonance (combination **frequency** resonance) is used to enhance the efficiency of **frequency** mixing mode of nonlinear NDT. All
the resonant nonlinear modes are strongly localized in the defect area that provides a background for high-contrast highlysensitive
defect- and **frequency**-selective imaging.

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Contributors: Solodov Igor

Date: 2015-11-04

**frequency**-selective imaging.
...**oscillator** brings about
new dynamic and **frequency** scenarios characteristic...**oscillations**. The experiments confirm transition
to resonant modes...**frequency** conversion in nonlinear NDT. The concept of a defect as a nonlinear ... In this paper, it is proposed to use a combination of mechanical resonance and nonlinearity of defects to enhance substantially the
efficiency of input-output **frequency** conversion in nonlinear NDT. The concept of a defect as a nonlinear **oscillator** brings about
new dynamic and **frequency** scenarios characteristic of nonlinear and parametric **oscillations**. The experiments confirm transition
to resonant modes of nonlinear vibrations in simulated and realistic defects. All resonant nonlinear modes are strongly localised
in the defect area that provides a background for high-contrast highly-sensitive defect- and **frequency**-selective imaging.

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Contributors: Mahto, Manpuran, Jain, P. K.

Date: 2016-09-05

**oscillator** and relativistic klystron principles have been extended to ...**oscillator** has been analyzed to understand the device physics. The split...**frequency**. The obtained analytical and simulation results have also been ... In this paper, electromagnetic analysis of the reltron, which is a compact, simple and efficient high power microwave (HPM) source has been presented. The beam wave interaction process of the reltron **oscillator** has been analyzed to understand the device physics. The split cavity **oscillator** and relativistic klystron principles have been extended to demonstrate the electric field responsible for beam bunching and the electron beam modulation process in the reltron. The analytical formulation to obtain the RF energy growth and efficiency of the device has also been presented. To validate the analytical results and to evaluate the overall performance of the device, beam present simulation of reltron has been performed using commercial 3D PIC simulation code "CST Particle Studio". With the parameters of a previously reported experimental reltron device, the present analytical calculation provided ~240 MW RF output power with ~38% efficiency while the PIC simulation provided RF output power of ~225 MW with ~36% efficiency at 2.75 GHz **frequency**. The obtained analytical and simulation results have also been found in agreement of ~6% with this experimental work.

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Contributors: Mahto, Manpuran, Jain, P K

Date: 2016-07-14

**oscillator** and relativistic klystron principles have been extended to ...**oscillator** has been analyzed to understand the device physics. The split...**frequency**. The obtained analytical and simulation results have also been ... In this paper, electromagnetic analysis of the reltron, which is a compact, simple and efficient high power microwave (HPM) source has been presented. The beam wave interaction process of the reltron **oscillator** has been analyzed to understand the device physics. The split cavity **oscillator** and relativistic klystron principles have been extended to demonstrate the electric field responsible for beam bunching and the electron beam modulation process in the reltron. The analytical formulation to obtain the RF energy growth and efficiency of the device has also been presented. To validate the analytical results and to evaluate the overall performance of the device, beam present simulation of reltron has been performed using commercial 3D PIC simulation code "CST Particle Studio". With the parameters of a previously reported experimental reltron device, the present analytical calculation provided ~240 MW RF output power with ~38% efficiency while the PIC simulation provided RF output power of ~225 MW with ~36% efficiency at 2.75 GHz **frequency**. The obtained analytical and simulation results have also been found in agreement of ~6% with this experimental work.

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Contributors: Chitrasen Gabel*, Dr. Dharmendra Kumar Singh

Date: 2017-05-25

**frequency** disturbances are mainly; Loss of synchronism which might be ...**frequency** disturbances which are typically in the **frequency** range of 0.2...**frequency** **oscillations** [1]. The ANFIS based stability enhancement accuracy...**frequency** disturbance occurred in power system.
...**frequency** disturbance in the power line operation. Various techniques ... In the present era, the power system has become a vital part to provide stability enhancement. The stability of a power system depends on how low **frequency** disturbances which are typically in the **frequency** range of 0.2 to 3.0 Hz, accurately find out and cleared so that quick restoration and maintains a stability enhancement of power is accomplished. Loss of synchronism and stability enhancement are needs to be performed using ANFIS controlled based excitation of power system [3]. The significant factors which affect the operation of power system during the occurrence of low **frequency** disturbances are mainly; Loss of synchronism which might be excited by the disturbances in the system or, in some cases, might even build up spontaneously. These factors can be analyzed to find out the occurrence of the low **frequency** disturbance in the power line operation. Various techniques like Power System Stabilizer with algorithm based or logic controlled based, UPFC has been used in past to find out and cleared the different low **frequency** disturbances occurred in the transmission line. The proper selection of enhanced feedback is a very tedious and time consuming task and also requires brief knowledge of the system configuration. To avoid the drawbacks of conventional power system stabilizer with algorithm or logic controller based techniques, this dissertation proposed, an efficient and robust technique of stability enhancement using ANFIS based power system excitation. The advantage of the proposed technique is that; it improves the overshoot of power and reduced the time for low **frequency** **oscillations** [1]. The ANFIS based stability enhancement accuracy of proposed technique has been verified using MATLAB/Simulink 2013(a) software. The obtained results show that the proposed technique is efficient in stability enhancement of all type of loss of synchronism and hence reliable tool for low **frequency** disturbance occurred in power system.

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Contributors: Alagapan, Sankaraleengam, Schmidt, Stephen, Lefebvre, Je̒re̒mie , Hadar, Eldad, Shin, Hae Won, Frohlich, Flavio

Date: 2016-02-09

**Oscillations** by Low-**Frequency** Direct Cortical Stimulation is State-Dependent...**Frequencies** at which spectral power was estimated.
NetworkModel...**frequencies** at which spectral power is calculated, nChannels corresponds...**frequencies**. Refer **MI_Summary_Names**
<strong...**oscillation** strength. The **oscillation** strength parameter was varied from ... Dataset accompanying publication:
"Modulation of Cortical **Oscillations** by Low-**Frequency** Direct Cortical Stimulation is State-Dependent", Alagapan, Schmidt, Lefebvre, Hadar, Shin and Frohlich
For questions, contact flavio_frohlich@med.unc.edu
The mat file consists of the following Matlab variables
Electrode Distance: 3 x 1 cell array containing the arrays (trial x electrode) of distance from stimulating electrode to recording electrode for the three ECoG participants. (First array corresponds to P001, Second array corresponds to P005 and Third array corresponds to P008)
Spectra_Electrode_EC: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-closed experiment. nTrial corresponds to number of trials, nFreq corresponds to **frequencies** at which spectral power is calculated, nChannels corresponds to number of electrodes in the analysis and nEpochs corresponds to “Before Stimulation”, “During Stimulation” and “After Stimulation” epochs.
Spectra_Electrode_EO: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-open experiment. The dimensions are the same as above. The first array consists of task-engaged dataset from Participant P001.
MI_Summary: 8 x 1 cell array consisting of 3 x 1 cell arrays of modulation indexes for the three participants. The 8 arrays stand for the modulation indexes in different epochs and different **frequencies**. Refer **MI_Summary_Names**
**MI_Summary_Names**: 8 x 1 cell array consisting of strings denoting the arrays in MI_Summary. During in text corresponds to “During Stimulation” epoch and After corresponds to “After Stimulation” epoch.
f: **Frequencies** at which spectral power was estimated.
NetworkModel: Matlab struct containing the time series generated by the network model and corresponding spectra. The timeseries consists of 4 columns – 1st column corresponds to time, 2nd column corresponds to task-engaged state data, 3rd column corresponds to eyes-open state data and 4th column corresponds to eyes-closed state data. The spectra struct consists of spectral powers estimated in the different epochs. 1st column of each epoch array corresponds to task-engaged state, 2nd column corresponds to eyes-open state and the 3rd column corresponds to eyes-closed state.
SummationModel: Matlab struct containing the time series generated by the summation model and the peak values in spectra before and during stimulation by varying the two strength parameters. The columns correspond to stimulation strength while rows correspond to **oscillation** strength. The **oscillation** strength parameter was varied from 0.5 to 50 in steps of 0.5 and the stimulation strength parameter was varied from 0.1 to 10 in steps of 0.1.

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Contributors: Sergio Pinna, Suzanne Melo, Francesco Laghezza, Filippo Scotti, Emma Lazzeri, Mirco Scaffardi, Paolo Ghelfi, Antonella Bogoni

Date: 2016-12-05

**oscillator** assure very low system phase noise and phase coherence among...**oscillators**. The ultra-wide band and high stability of photonics and the...**frequency** span up to 7.4GHz. The high coherence among the two **frequency**...**oscillator** that replaces the conventional cascades of electrical local...**frequency** continuous waves simultaneously in the S- and X-band, measuring ... Photonics-based multiband radars have been demonstrated where photonics is exploited for multiple RF signal generation and detection by means of a single optical local **oscillator** that replaces the conventional cascades of electrical local **oscillators**. The ultra-wide band and high stability of photonics and the use of a single local **oscillator** assure very low system phase noise and phase coherence among the RF signals. This phase coherence among multi-band signals is exploited to perform differential phase estimation in enhanced sub-millimeter displacement measures. The system employs stepped **frequency** continuous waves simultaneously in the S- and X-band, measuring the differential phase over a **frequency** span up to 7.4GHz. The high coherence among the two **frequency** bands, provided by the photonic architecture, enables very precise displacements measures, allowing to obtain sub-millimeter precision without using correction algorithms.
The presented experimental results demonstrate a precision < 200m in a range up to 3km. Moreover, the sharing of the same hardware to handle a multi-band operation allows a great reduction of size, weight, power and footprint of the overall system.

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