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Fluorescence Lifetime Imaging in Living Cells
Published in Guy Cox, Fundamentals of Fluorescence Imaging, 2019
Chittanon Buranachai, John P. Eichorst, Kei Wei Teng, Robert M. Clegg
Fluorescence lifetimes are typically measured in two ways (Fig. 10.2): in the time domain or the frequency domain. The time domain is a direct measurement of the time decay process; data points are acquired at specific times following the excitation of the sample, usually with a very short pulse of light. The frequency domain carries out the measurement by repetitively modulating the light continuously at a very high frequency, and detecting the frequency dispersion of the signal. Both measurement modes can be carried out in scanning mode (such as a scanning confocal microscope, where a focused excitation beam is scanned across the sample, collecting the image points sequentially) or in wide-field mode (such as in a normal wide-field fluorescence microscope, where all locations of the imaged sample are excited and detected simultaneously).
Dynamic System Models and Basic Concepts
Published in Jitendra R. Raol, Girija Gopalratnam, Bhekisipho Twala, Nonlinear Filtering, 2017
Jitendra R. Raol, Girija Gopalratnam, Bhekisipho Twala
In order to analyse the characteristics of signals from dynamic systems, time domain and frequency domain methods are used [15]. For analysing the behaviour of signals over time, and for prediction and regression models used in parameter estimation, time domain methods are very important and useful. The time domain method involves analysing variables that are functions of time and are measured at various discrete points in time. Examples of systems where time domain analysis is predominantly used are electronic signals, economic or market data and biological systems. In frequency domain analysis, mathematical functions of periodic signals with respect to frequency are analysed to determine the dominant characteristics of the signal. For a measured time signal, the amplitude of the signal in each frequency band over a range of frequencies is represented, and the phase shift between the various sinusoidal signals is also a part of the frequency domain representation. Frequency domain analysis is used in control engineering, electronics and statistics. Specifically, in filtering, amplification and mixing of electronic signals, frequency domain methods are used. In control systems design and analysis, time responses of systems with certain types of input signals like impulse, step and doublet are used to characterize the system in terms of transient and steady-state behaviour. Frequency domain analysis is carried out using Bode, Nyquist and Nichols plots.
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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
TIFF TIFF See tagged image file format. time division duplexing (TDD) a technique for achieving duplex (i.e., two-way) communication. One direction of transmission is conducted within specific segments of time, and the reverse direction of transmission is conducted within different segments of time. time division multiple access (TDMA) a technique for sharing a given communication resource amongst a number of users. The available communication resource is divided into a number of distinct time segments, each of which can then be used for transmission by individual users. Sometimes used in cellular and personal communications applications. time division multiplexing (TDM) refers to the multiplexing of signals by taking rounds of samples from the signals. Each round consists of one sample from each signal, taken as a snapshot in time. See also time division multiple access. time domain the specification of a signal as a function of time; time as the independent variable. time domain analysis a type of simulation that allows the user to predict the circuit response over a specified time range. The result of the simulation is a graph of amplitude against time. time domain storage an optical data storage technique in which time-dependent information is stored as a Fourier transform in an inhomogeneously broadened spectral hole burning material. This is usually accomplished with photon echoes or spin echoes. The maximum storage density is given by the ratio of inhomogeneous to homogeneous widths of the absorption spectrum. time frequency analysis any signal analysis method that examines the frequency properties of a signal as they vary over time. time hopping a type of spread spectrum wherein the transmission of the signal occurs as
Robust control for a class of cyber-physical systems with multi-uncertainties
Published in International Journal of Systems Science, 2021
Jing He, Yan Liang, Feisheng Yang
Similarly, to validate the theoretical results, we use MATLAB/Simulink for the time-domain simulation. Consider that the nonlinear signal is chosen as and suppose the function of cyber attacks , Figure 6 depicts the state response curve under deception attacks randomly occurring with the expectation . It can be seen that the state of the system converges to zero over time and the system is stable under given conditions, indicating the effectiveness of the results.
On the generalized Bézier-based integration approach for co-simulation applications
Published in Mechanics Based Design of Structures and Machines, 2023
Reza Nopour, Afshin Taghvaeipour, Mohammad Mohammadi Aghdam, Francisco González
Simulation can be defined as a tool to predict the time-domain evolution of a mathematical model using a computer to reconstruct reality. Nowadays, simulation has become indispensable at the embodiment design step in a large number of engineering applications. Recent advances in computer hardware and software help engineers to run virtual prototypes in the early stages of the product-development cycle, at a lower cost compared with real experimental tests. Apart from engineering analysis on complex systems, which are often performed off-line, real-time simulations can also be used to interface virtual environments to physical components, for instance, in haptic applications.
Monitoring Transient Stability in Electric Power Systems Using a Hybrid Model of Convolutional Neural Network and Random Forest
Published in Electric Power Components and Systems, 2023
Hafsa Ahmad, Muhammad Yousif, Maliha Shah, Najeeb Ullah
Power system stability is the capability of the electrical power system to restore a new balanced point after the system gets disrupted. It enables the power system to operate within its stability limits. The power system stability is classified into steady-state stability and transient stability. Transient stability is the ability of the system to converge to a stable equilibrium point subsequent to a fault [1]. Power system planning and operation rely on transient instability as it has infrequent nature, and it may cause severe events which lead to the interruption of service, cascading failures, and in the worst situation, major blackouts [2]. Precise and real-time Transient Stability Assessment (TSA) is very essential for determining the condition of the power system following a major disturbance, loss of load, or when a network gets disconnected from the system, etc. [3]. In literature, various methods have been proposed for monitoring transient stability in the system. One of the conventional techniques followed by the time-domain simulation is the Ordinary Differential Equation (ODE) method which solves ODEs through a step-by-step method with respect to time [4]. However, this numerical integration method requires high computational resources for processing large datasets, which limits testing to only specific credible contingencies offline and produces limited test results. These results are later utilized as a reference in real-time to estimate other undefined severe conditions, which may lead the operator to make assumptions [5]. It is challenging for the operators to make reliable decisions as the power system’s complexity and the power demand both are increasing. Various techniques are used for reducing the computational requirements in the power system [6].