A Note on the Error Function

Nandagopal, M.; Sen, Soubhadra; Rawat, Ajay Singh

doi:10.1109/mcse.2010.79

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…A function with such a pattern can be described using the error function. 12 The model defined medical system coefficient ( ) to help calculating the theoretical mortality rate, which will grow as more medical resource is occupied. The function about medical system coefficient ( ), hospitalized cases ( + ), and medical system capacity ( + ) is shown (Equation 7).…”

Section: = ∑ ( )mentioning

confidence: 99%

A fatality data based on an optimized SEIR Model for Epidemic: A study about the testing and quarantining

Yang¹,

Chen²,

Du³

et al. 2020

Preprint

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by a newly discovered coronavirus, has been announced as a global pandemic by WHO on March 12, 2020. We aimed to provide a more accurate, object-oriented SEIR model for Epidemic as well as a thorough analysis for different intervention strategies and critical parameters. Based on the object-oriented mindset in computer science, we assigned properties to every independent patient, which includes age, likelihood of catching the virus, independent mortality rate, social activeness, awareness of the danger of the virus. We also considered macro level parameters including testing rate, positive rate. Based on the fatality data obtained, we estimated the occurrence time of the first case in China (excluding Hubei), Russia, Germany, and the United States using the model. Eight groups of virus-transmission simulation with different testing rate and quarantine ratio were conducted. The mortality data was found to be an effective and crucial parameter while the number cases can be decisive reflecting the severity of the pandemic.

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Section: = ∑ ( )mentioning

confidence: 99%

A fatality data based on an optimized SEIR Model for Epidemic: A study about the testing and quarantining

Yang¹,

Chen²,

Du³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…After observation of actual output spike train and prediction of firing probability in (8), R acts as the learning rate for the parameter estimations in (9). The estimated Laguerre coefficients are used to reconstruct the feedforward and feedback kernels [2].…”

Section: System Identificationmentioning

confidence: 99%

“…The value of w is acquired at the root. U3 is designed to calculate estimated firing probability P, its gradient and ∂ log P(t)/∂C in (9). The calculation intensive stage in hardware includes the implementation of the error function erf(x) and the exponential function.…”

Section: Generalized Volterra Model Hardware Architecturementioning

confidence: 99%

“…The calculation intensive stage in hardware includes the implementation of the error function erf(x) and the exponential function. Here, we transforms the calculation of the error function into the calculation of the exponential function by using the approximation for the error function reported in recent literature [9] as…”

Section: Generalized Volterra Model Hardware Architecturementioning

confidence: 99%

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A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities

Chan

Zhang

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A parallelized and pipelined architecture based on FPGA and a higher-level Self Reconfiguration Platform are proposed in this paper to model Generalized Laguerre-Volterra MIMO system essential in identifying the time-varying neural dynamics underlying spike activities. Our proposed design is based on the Xilinx Virtex-6 FPGA platform and the processing core can produce data samples at a speed of 1.33 × 10(6)/s, which is 3.1 × 10(3) times faster than the corresponding C model running on an Intel i7-860 Quad Core Processor. The ongoing work of the construction of the advanced Self Reconfiguration Platform is presented and initial test results are provided.

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Modelocked Thermal Frequency Combs for Ultrashort Chaotic Quantum Optics

Wakui,

Tsujimoto,

Kishimoto

et al. 2024

Adv Quantum Tech

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Modelocked thermal frequency combs (MTCs) are generated by employing spectrally narrowed amplified spontaneous emission (ASE) seeded into an electro‐optic frequency comb generator. The MTC emits 2‐ps duration ultrashort pulses at a repetition rate of 10 GHz. Autocorrelation of the MTC pulses confirms a reduced coherence time, ps, aligning with the narrowed bandwidth of the ASE seed. Intensity correlations of optically gated MTC pulses at a repetition rate of 250 MHz reveal nearly ideal thermal photon statistics with an experimental , yielding an intrinsic after background noise removal. As a practical application, second harmonic generation (SHG) is performed utilizing the optically gated MTC pulses as a pump and experimental intensity correlations, , are examined for the SH photons. An entire transition in , continuously changing from six to two by increasing the pump strength, agrees with the single‐mode analytical model. Furthermore, time‐resolved pulse height correlations allow to simultaneously acquire power variations in SHG and third harmonic generation against the pump. With the maximum peak intensity, , realized in a periodically poled waveguide for SHG, the demonstration highlights the potential for various applications in chaotic quantum optics experiments that necessitate ultrashort, high‐intensity, single‐spatiotemporal‐mode thermal pulses.

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A Note on the Error Function

Cited by 10 publications

References 6 publications

A fatality data based on an optimized SEIR Model for Epidemic: A study about the testing and quarantining

A fatality data based on an optimized SEIR Model for Epidemic: A study about the testing and quarantining

A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities

Modelocked Thermal Frequency Combs for Ultrashort Chaotic Quantum Optics

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