Swarm Intelligence Algorithm-Based Optimal Design of Microwave Microfluidic Sensors

Zhao, Wen‐Sheng; Wang, Bin-Xiao; Wang, Dawei; You, Bo; Liu, Qi

doi:10.1109/tie.2021.3063873

Cited by 36 publications

(18 citation statements)

References 30 publications

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“…erefore, in this study, we develop a corresponding optimization strategy based on the swarm intelligence optimization algorithm [20] and improve the classical swarm intelligence algorithm to improve the speed, accuracy, and stability of the convergence of the algorithm while improving the classical swarm intelligence algorithm, which is prone to "premature" problems. In the meantime, we seek to balance the "development" and "exploration" capabilities of the algorithm to improve the effectiveness of the target system.…”

Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

“…e constraints in formulas ( 16)- (20) correspond to the communication resource allocation problem, indicating that the communication channels are allocated for the end devices during offloading and the sub-bands do not exceed the total bandwidth; ( 17)- (20) correspond to the computational resource allocation problem, indicating that the computational resources are allocated for the tasks offloaded to the edge nodes or fog nodes and do not exceed the maximum server limit; (16) corresponds to the offloading decision, indicating that there are 2 n kinds of decisions for each layer when offloading is performed at the terminal and edge layers.…”

Section: Edge Layer Modelmentioning

confidence: 99%

“…Existing studies mostly aim at minimizing the energy consumption under the maximum delay constraint or minimizing the delay under the energy consumption constraint, and existing studies have confirmed that the multi-objective optimization problem is an NP-hard problem [ 19 ]. Therefore, in this study, we develop a corresponding optimization strategy based on the swarm intelligence optimization algorithm [ 20 ] and improve the classical swarm intelligence algorithm to improve the speed, accuracy, and stability of the convergence of the algorithm while improving the classical swarm intelligence algorithm, which is prone to “premature” problems. In the meantime, we seek to balance the “development” and “exploration” capabilities of the algorithm to improve the effectiveness of the target system.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Han

Huang

et al. 2022

Computational Intelligence and Neuroscience

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To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the “cloud, fog, edge, and end” collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.

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Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

Section: Edge Layer Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Han

Huang

et al. 2022

Computational Intelligence and Neuroscience

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“…Any variation in the electrical properties of the MUTs leads to alterations in the magnitude and resonance frequency of the response between the microwave sensor and the MUTs. TL-loaded and split-ring resonators (SRRs) [36] and complement split-ring resonators (CSRRs) integrated with microfluidic channels have recently been designed and investigated [37][38][39][40][41][42][43][44][45][46]. From our literature survey, it is apparent that biosensor devices operating in the microwave frequency band have been proposed for the measurement of various biomolecules, including DNA, streptavidin, and biotin.…”

Section: Introductionmentioning

confidence: 99%

Sensing High 17β-Estradiol Concentrations Using a Planar Microwave Sensor Integrated with a Microfluidic Channel

2023

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The global issue of pollution caused by endocrine-disrupting chemicals (EDCs) has been gaining increasing attention. Among the EDCs of environmental concern, 17β-estradiol (E2) can produce the strongest estrogenic effects when it enters the organism exogenously through various routes and has the potential to cause harm, including malfunctions of the endocrine system and development of growth and reproductive disorders in humans and animals. Additionally, in humans, supraphysiological levels of E2 have been associated with a range of E2-dependent disorders and cancers. To ensure environmental safety and prevent potential risks of E2 to human and animal health, it is crucial to develop rapid, sensitive, low cost and simple approaches for detecting E2 contamination in the environment. A planar microwave sensor for E2 sensing is presented based on the integration of a microstrip transmission line (TL) loaded with a Peano fractal geometry with a narrow slot complementary split-ring resonator (PF-NSCSRR) and a microfluidic channel. The proposed technique offers a wide linear range for detecting E2, ranging from 0.001 to 10 mM, and can achieve high sensitivity with small sample volumes and simple operation methods. The proposed microwave sensor was validated through simulations and empirical measurements within a frequency range of 0.5–3.5 GHz. The E2 solution was delivered to the sensitive area of the sensor device via a microfluidic polydimethylsiloxane (PDMS) channel with an area of 2.7 mm2 and sample value of 1.37 µL and measured by a proposed sensor. The injection of E2 into the channel resulted in changes in the transmission coefficient (S21) and resonance frequency (Fr), which can be used as an indicator of E2 levels in solution. The maximum quality factor of 114.89 and the maximum sensitivity based on S21 and Fr at a concentration of 0.01 mM were 1746.98 dB/mM and 40 GHz/mM, respectively. Upon comparing the proposed sensor with the original Peano fractal geometry with complementary split-ring (PF-CSRR) sensors without a narrow slot, several parameters were evaluated, including sensitivity, quality factor, operating frequency, active area, and sample volume. The results showed that the proposed sensor exhibited an increased sensitivity of 6.08% and had a 40.72% higher quality factor, while the operating frequency, active area, and sample volume showed decreases of 1.71%, 25%, and 28.27%, respectively. The materials under tests (MUTs) were analyzed and categorized into groups using principal component analysis (PCA) with a K-mean clustering algorithm. The proposed E2 sensor has a compact size and simple structure that can be easily fabricated with low-cost materials. With the small sample volume requirement, fast measurement with a wide dynamic range, and a simple protocol, this proposed sensor can also be applied to measure high E2 levels in environmental, human, and animal samples.

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“…Recently, various types of microwave sensors have been reported, such as liquid and displacement sensors. 4,5 Among them, microwave angular displacement sensor is used to measure the rotation angle and has attracted much attention due to their low manufacturing and measurement cost. 6 It is usually composed of a stator and a rotor, and the stator is fixed onto the test platform with the rotor rotating.…”

Section: Introductionmentioning

confidence: 99%

An ultrahigh‐sensitivity microwave angular displacement sensor with a wide dynamic range

Lin

Wang

Zhao

et al. 2022

Micro & Optical Tech Letters

Self Cite

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A microwave angular displacement sensor with dynamic range up to 180°and ultrahigh sensitivity is designed and fabricated. In the proposed sensor, a stator is designed for reference and a rotatable rotor is attached to it to implement real-time measurement of the rotation angle. As the rotor is attached to the stator, a CSRR-like resonant structure would be formed, with the resonant frequency linearly changing with the angular displacement. The measurement results show that the resonant frequency of the designed angular displacement sensor increases from 2.398 to 6.348 GHz linearly as the angle varies from 0°to 180°, with an excellent sensitivity of 21.94 MHz/°a chieved.

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Swarm Intelligence Algorithm-Based Optimal Design of Microwave Microfluidic Sensors

Cited by 36 publications

References 30 publications

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Sensing High 17β-Estradiol Concentrations Using a Planar Microwave Sensor Integrated with a Microfluidic Channel

An ultrahigh‐sensitivity microwave angular displacement sensor with a wide dynamic range

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