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Numerical Calculations

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...

Bulk Modulus

Bulk Modulus

The bulk modulus is a scientific term used to describe a material's resistance to uniform compression. It is the proportionality constant that links a change in pressure to the resulting relative volume change.

Scaling

Scaling

In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...

Voltage Doubler Circuit

Voltage Doubler Circuit

A voltage doubler circuit integrates two main components: a clamping section and a rectifier section. The clamping section consists of a capacitor (C1) and a diode (D1), whereas the rectifier section is equipped with another diode (D2) and capacitor (C2). This circuit produces an output voltage with twice the amplitude of the sinusoidal input voltage.

Design Example: Capacitance Multiplier Circuit

Design Example: Capacitance Multiplier Circuit

In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.

Real Number Operations

Real Number Operations

The concept of real numbers includes all the values that can be represented on a continuous number line. The system began with basic counting values used for enumeration. It later expanded to include values that represent the absence of quantity and opposites of the counting values. When situations required expressing parts of a whole or dividing quantities evenly, values capable of representing such proportions were developed. When written using decimal notation, these values can end or repeat...

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Related Experiment Video

Updated: Mar 25, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: March 20, 2017

Bidirectional Modulation of Numerical Magnitude.

Qadeer Arshad¹, Yuliya Nigmatullina¹, Ramil Nigmatullin²

¹Division of Brain Sciences, Imperial College London, London W6 8RF, UK.

Cerebral Cortex (New York, N.Y. : 1991)

|February 17, 2016

Summary

This study reveals that numerical cognition involves dynamic interhemispheric competition, influencing how we process number magnitudes. The brain

Keywords:

VOR dynamic interhemispheric competition mental number line numerical magnitude vestibular cognition

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Last Updated: Mar 25, 2026

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Numerical cognition is essential, but its neural basis is unclear.
Number processing and spatial attention share behavioral and neuro-anatomical links.
Interhemispheric competition may regulate both systems.

Purpose of the Study:

To investigate if numerical magnitude allocation is modulated by interhemispheric competition.
To explore the role of the hemispheres in processing numerical magnitudes.
To develop a computational model of numerical magnitude allocation.

Main Methods:

Utilized a physiological paradigm with visual and vestibular stimulation to induce interhemispheric conflict.
Employed transcranial direct current stimulation (tDCS) to confirm unihemispheric inhibition.
Demonstrated bidirectional modulation of numerical magnitude processing.

Main Results:

Showcased the first systematic bidirectional modulation of numerical magnitude.
Confirmed modulation independent of eye movements or spatial attention biases.
Established a novel computational model for numerical magnitude allocation.

Conclusions:

Numerical magnitude allocation is governed by dynamic interhemispheric competition.
The right hemisphere processes smaller magnitudes, while the left processes larger ones.
This competition provides a unified framework for understanding numerical cognition.