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Digital System Designs
Published in Wen-Long Chin, Principles of Verilog Digital Design, 2022
The throughput, Θ, of a module is the number of tasks a module can complete per unit time. For example, if we have an adder that is able to perform one add operation every 10 ns, we say that the throughput of the adder is 100 MOPS (million operations per second). The latency, T, of a module is the amount of time it takes the module to complete one task from beginning to end. For example, if the adder takes 10 ns to complete an addition from the time the inputs are applied to the time the output is stable, its latency T is 10 ns. For a simple module, throughput and latency are reciprocals of one another: Θ=1/T.
Brain Jelly to Humanoid Avatar—Fractal Reaction Kinetics, Fractal Condensation, and Programmable Matter for Primes
Published in Anirban Bandyopadhyay, Nanobrain, 2020
One way to confirm that time crystal language, GML is to read the brain jelly’s EEG. Figure 9.11a shows as brain-like prototype device filled with organic jelly and several neural network-like cables are connected to input time crystals as a stream of electrical pulses. Figure 9.11b shows fully operational module for such a device, where using a high-resolution camera, the evolution of jelly is observed as a function of time (Figure 9.11c). By shining laser and reading the magnetic vortices one could read the time crystals instantly. One nice way to advance the simple module of Figure 9.11b is to build an ensemble of several such devices and arrange them in a fractal structure. Only the geometric feature alone could trigger EEG similar to a human brain (Figure 9.11c).
Innovations in Mine Safety Engineering
Published in Debi Prasad Tripathy, Mine Safety Science and Engineering, 2019
ViMINE allows mining engineering students to experience various aspects of a mining operation, working together and integrating several types of simulation into one environment. The students can access information from different simulations and make decisions throughout the life of the simulated mine, from initial exploration to final site rehabilitation, and evaluate their effectiveness for building systems-thinking skills. ViMINE allows students to carry out a number of mine design projects where they can link separate mine planning and design simulation software packages as part of one simulation exercise and to design various aspects of a mining operation and assess the feasibility of different design options. The ViMINE mining method selection module is a simple module used for mining method selection. Fourteen different terrains are available to simulate the various possible surface environments, which might exist in proximity to a mineral deposit. The students then decide on the mining method using their knowledge and taking into consideration environment and possible community constraints.
An enhanced text classification model by the inverted attention orthogonal projection module
Published in Connection Science, 2023
Hong Zhao, Chenpeng Zhang, Aolong Wang
In this study, we designed a simple module, called the Inversed Attention Orthogonal Projection Module (IAOPM), for extracting high-quality common features within a single network, thereby increasing the flexibility of the orthogonal projection method. Specifically, common features without classification bias are typically assigned lower weights in the attention distribution map of text features. IAOPM iteratively reverses the attention distribution map of text features through inversed attention (IA) to remove discriminative features, inducing the system to generate complementary latent common features, and then refines the text features through orthogonal projection. Finally, we propose a self-supervised loss function called Orthogonal Projection Loss (OPL). It ensures the shared nature of extracted common features within one or several batches, thereby ensuring high-quality common features extracted by IA. Additionally, OPL stabilises the training process and aids in generalisation. Compared to the original orthogonal projection method (Qin et al., 2020), IAOPM is relatively more flexible because all operations are performed within a single network, and therefore it does not add any branch networks. IAOPM can simply be added after a feature extractor (such as RNN, LSTM, CNN, etc.) to purify text features. In addition, through experiments and visualisation, we have demonstrated that IAOPM extracts higher quality common features, resulting in better purity performance compared to the original method.
Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C†
Published in Optimization Methods and Software, 2018
K. Kulshreshtha, S.H.K. Narayanan, J. Bessac, K. MacIntyre
SWIG generates interfaces based on an input file (e.g. mymodule.i). This input file consists of SWIG macros. A simple module may be defined by using the input file in Figure 1(a). This will create a module with the name mymodule containing a wrapped interface in the scripting language of choice for the C/C++ API declared in the file that is given in the %include macro. In this case it is <myheader.h>. Actual C/C++ code is given between the macro delimiters %{ and %. This is the code required in order to compile and link the generated interface with the original C/C++ library.
A new framework for addressing high-level decisions related to sustainable transportation development
Published in International Journal of Sustainable Transportation, 2020
Karim Abdel Warith, Amr Kandil, John Haddock, Khaled Shaaban
This is a very simple module that calculates the population increase based on a population growth rate. Because simulation modeling has a continuous nature through the use of differential equations, with the independent variable being time, the annual population growth rate was converted to a continuous growth rate using the following equation: where icont is the continuous interest rate and ieff is the effective interest rate, which in most cases is the annual interest rate.