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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Biasing of FET01:22

Biasing of FET

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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

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Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
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Related Experiment Video

Updated: Jan 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K

Runtime-Robust Edge Inference System with Masking-Based Partial Update on Dynamic Reconfigurable FPGA.

Myeongjin Kang1, Daejin Park2

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces a novel edge inference framework for real-time adaptation in dynamic environments. It uses a server-side FPGA for masked updates, reducing latency and communication costs for robust edge AI.

Keywords:
FPGA acceleratordynamic partial reconfigurationedge-cloud systemlearning accelerator

Related Experiment Videos

Last Updated: Jan 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K

Area of Science:

  • Edge Computing
  • Artificial Intelligence
  • Hardware Acceleration

Background:

  • Edge inference systems require real-time performance amidst environmental changes like sensor noise and new object classes.
  • Static, offline-trained models on edge devices degrade accuracy due to input distribution drift.

Purpose of the Study:

  • To propose a runtime-robust edge inference framework enabling continuous adaptation without execution interruption.
  • To leverage server-side FPGA acceleration for efficient, dynamic model updates on edge devices.

Main Methods:

  • Memory partitioning into active and adaptive regions on the edge device.
  • Server-side FPGA performing layer-wise importance analysis and partial retraining.
  • Dynamic Partial Reconfiguration (DPR) for adaptive mask generation and minimized reconfiguration delay.

Main Results:

  • Adaptation latency reduced by up to 1.3x compared to GPU full retraining.
  • Communication costs reduced to 28% of full model transmission.
  • Demonstrated real-time adaptability, low latency, and communication efficiency.

Conclusions:

  • The proposed framework effectively combines masking-based selective updates with FPGA DPR acceleration.
  • Achieves robust, real-time learning and adaptation in cloud-edge collaborative environments.
  • Offers a viable solution for dynamic edge inference challenges.