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A Planar Quad-Band Bandpass Filter Employing Transmission Lines Loaded with Tri-Stepped Impedance Open-and Dual-Stepped Impedance Short-Ended Resonators

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Progress In Electromagnetics Research C 2024年 147卷 65-72页

作者： Arif, Kamran Phani Kumar, Kanaparthi V. Barik, Rusan Kumar Chakaravarthi, Geetha Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli620015 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India School of Microelectronics Southern University of Science and Technology Guangdong Shenzhen518055 China

A highly miniaturized bandpass filter with quad-band response is demonstrated in this article. The proposed quad-band bandpass filter has a novel topology comprising series quarter wavelength transmission lines loaded with tri-stepped impedance open-ended resonators and a dual-stepped impedance short-ended resonator. The proposed quad-band bandpass filter configuration is validated by theoretically verifying the transmission zeros and pole frequencies using even-odd mode analysis. A prototype operating at 0.475 GHz, 1.695 GHz, 3.48 GHz, and 4.53 GHz is designed, implemented, and experimented. The tested insertion losses at these center frequencies are 0.38 dB, 0.71 dB, 1.03 dB, and 1.22 dB, and the return loss is better than 10 dB in each passband. Each passband is isolated by a transmission zero with a rejection better than 40 dB. The proposed quad-band filter occupies a compact size of 0.146 × 0.087λ2g and is distinguished by its high compactness, wide bandwidth, multiple transmission zeros and poles, and high performance compared to benchmark designs making it more suitable for multi-band wireless applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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EV Integrated PV Water Pumping System with Priority-Based control 3

EV Integrated PV Water Pumping System with Priority-Based Co...

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3rd IEEE International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Tomar, Anuradha Netaji Suhas University of Technology Department of Instrumentation & Control Engineering Delhi India

ISBN: (纸本)9798350319972

Solar photovoltaic-based water pumping system (PVWPS) is emerging as a promising solution for addressing the water needs in rural/remote regions. However, there is a need to increase the utilization of installed PV capacity in such PVWPS as water pumping is required only a few hours a week. To address this issue, in this paper an electric vehicle integrable PVWPS with priority-based control is proposed. It will enable alternative utilization of installed PV capacity and thus would result in improved return on investment. The proposed system is validated through MATLAB simulation. The satisfactory performance of the proposed system has been observed under various operating scenarios. The proposed PVWPS with optimized configuration and design parameters could be further explored in future. © 2023 IEEE.

关键词： Investments

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VAG Signal Classification Using Time Domain Statistical Features and Machine Learning 3

VAG Signal Classification Using Time Domain Statistical Feat...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Patankar, Sanika Durge, Gaurav Joshi, Ashutosh Jaid, Atharva Kalambe, Khomesh Dhale, Hrishikesh Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Maharashtra Pune India

ISBN: (纸本)9798350317022

This research paper presents a novel approach for the classification of vibroarthrographic (VAG) signals to aid in the early diagnosis of knee arthritis, a disease whose prevalence is increasing worldwide. VAG signals are non-invasive and cost-effective, making them a promising tool for screening and detection. In this study, a time-domain statistical feature approach is employed to extract relevant features from VAG signals. Analytical measures like Kurtosis, skewness, Standard Deviation have been used in this work. These features are then fed into machine learning algorithms for the automatic detection of knee arthritis. The classification accuracy of 2 algorithms has been compared in this work, namely SVM and Random Forest. The latter resulted in an overall accuracy of 89% and an AUROC of. 90. The results indicate the potential of this approach to provide an accurate and reliable means for early knee arthritis detection, which would allow the patient to be treated ahead of time. © 2023 IEEE.

关键词： Classification Knee arthritis Machine learning signal processing Time-domain statistical features Vibroarthrography

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Dynamic output feedback control strategy for a satellite orbital model within negative-imaginary systems theory framework

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Aerospace Systems 2024年第4期7卷 747-761页

作者： Choudhary, Santosh Kumar Chokkadi, Shreesha Department of Mathematics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

This article presents the synthesis of a dynamic output feedback controller for a satellite orbital system confronted with uncertainties. The investigated method transforms the closed-loop system, synthesized by the controller, into an α-strictly negative-imaginary system. It utilizes the DC-loop gain condition associated with negative-imaginary systems theory to demonstrate robust stability of the satellite orbital system in the presence of uncertainties. Furthermore, the synthesized negative-imaginary closed-loop system exhibits notable time-domain performance. The numerical simulation outcomes presented in this article validate the investigated synthesis method. © The Author(s) 2024.

关键词： Feedback control

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A Robust Algorithm for respiration rate monitoring during cognitive load using PPG Signals 10

A Robust Algorithm for respiration rate monitoring during co...

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10th International Conference on Biosignals, Images and instrumentation, ICBSII 2024

作者： Pavithran, Muthu Ganeshmurthy, M.S. Periyasamy, R. Sri Ramakrishna Engineering College Department of Biomedical Engineering Coimbatore India National Institue of Technology Tiruchirappalli Department of Instrumentation and Control Engineering Tiruchirappalli India

ISBN: (纸本)9798350350951

The project introduces a new method for respiration rate (RR) monitoring, utilizing the OB1203 PPG biosensor module and a custom signal processing algorithm. Traditional methods often involve manual intervention or wearable straps, posing challenges, particularly in emergencies. In contrast, the PPG-based system offers a contactless and user-friendly approach, particularly in pandemics. The RR monitoring method includes data acquisition, noise filtering, and feature extraction for systolic peak detection, with real-time RR display for seamless monitoring. The algorithm's validation used the BIDMC dataset & CAPNOBASE Dataset for calibration and validation, comparing with impedance respiratory signals. Results showcase a superior performance of the proposed algorithm, as evidenced by low RMSE values of 0.8 and 1.2 for BIDMC, and CAPNOBASE datasets, respectively. Additionally, the project explores the RR-cognitive load correlation. Through a study involving tasks with varying mental effort, we contribute to cognitive load assessment. This innovative approach holds promise for vital sign monitoring efficiency, especially in emergencies, and cognitive load assessment in clinical and healthcare settings. © 2024 IEEE.

关键词： Biosensors

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Emotion Classification Using Triple Layer CNN with ECG Signals 6th

Emotion Classification Using Triple Layer CNN with ECG Signa...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Puniya, Gaurav Patel, Tanishq Kumar, Harshit Giri, Chaitanya Nandini, Durgesh Yadav, Jyoti Agrawal, Alok Instrumentation and Control Engineering Department Netaji Subhas University of Technology Sector-3 New Delhi Dwarka India

ISBN: (纸本)9789819774227

The growing interest in recognizing human emotions from physiological signals gathered by smart wearable devices is covered in this research study. Although these gadgets have made it feasible to discreetly and continuously record physiological signals, readings can be influenced by user activity, which makes it difficult to create reliable models for wearable sensor-based emotion identification. Though EEG provides the best signal capture to identify human emotions, it significantly interferes with daily activities and is inconvenient for daily use. Electrocardiogram (ECG) signals have been shown to have potential use in emotion identification in recent studies. In this work, a triple-layered 1-D Convolutional Neural Network (CNN) model is applied to improve emotion recognition performance using electrocardiogram (ECG) signals by utilizing hyperparameter tuning methods. We have systematically explored two well-established hyperparameter tuning methods: grid search and random search. In addition, we have harnessed Bayesian optimization, a more advanced technique acknowledged for its superior performance compared to grid and random search. Bayesian optimization has been employed in conjunction with Optuna to augment its efficacy further. Upon careful evaluation, it became evident that Bayesian optimization significantly outperforms the other two techniques. Consequently, we have implemented the model utilizing Bayesian optimization with Optuna. Our results show that the triple-layered 1-D CNN model using Bayesian optimization in conjunction with Optuna performs significantly better than other neural network architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Integrated CNN-LSTM Model for Emotion Detection Using Physiological Signals from Wearables 6th

Integrated CNN-LSTM Model for Emotion Detection Using Physio...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Ankita Singh, Inderveer Nandini, Durgesh Yadav, Jyoti Instrumentation and Control Engineering Department Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India

ISBN: (纸本)9789819774258

Emotion recognition from physiological signals is pivotal for enhanced human–computer interaction. However, recognizing emotions using conventional electroencephalogram (EEG) signals offers limited wearability in day-to-day scenarios. In addition, the recent advancements in wearable fitness tracking devices offer non-intrusive, real-time insights into individuals’ health parameters. Thus, modeling diverse multimodal physiological modalities to recognize human emotions can enable an emotionally enriched user experience. The work proposes an automated emotion recognition system utilizing the multimodal physiological signals acquired using the wearable smartwatch. The study utilizes the publicly available EMOGNITION database comprising diverse physiological parameters. The parameters considered in this work include Heart Rate, Blood Volume Pulse, 3-axis Accelerometer, 3-axis Gyroscope, and 4-axis Rotation, acquired via the Samsung Galaxy smartwatch wearable device. The work examines the variations in the considered signals to determine nine discrete emotions. The preprocessed signal modalities are investigated using the hybrid Convolutional Neural Networks (CNNs) and one layer of Long Short-term Memory (LSTM) deep learning (DL) architecture. The adopted DL architecture obviated the requirement of manual feature extraction. The developed system successfully determines nine discrete emotions with an average accuracy and F1 score of 96.82% and 96.74%, respectively. The obtained results outperform the existing methods in the literature, demonstrating potential in emotion recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Evaluating the Performance of ResNet-50 and GoogleNet for Damage Detection and Classification 4

Evaluating the Performance of ResNet-50 and GoogleNet for Da...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Baral, Anuj Singh, Vikash Lath, Achyut Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India

ISBN: (纸本)9798331540364

Accurate and rapid assessment of structural damage is critical for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This study exam-ines the use of ResNet50 and GoogLeNet convolutional neural networks (CNNs) to automate the classification of damaged structures. The models were trained on a diverse dataset that includes images of both damaged and undamaged structures, ensuring their adaptability to different conditions. Evaluation metrics such as precision, recall, accuracy, and F1-score were used to measure performance. The results demonstrate that both ResNet50 and GoogLeNet are effective for assessing damage in post-disaster contexts. However, GoogLeNet shows a slight advantage achieving testing accuracy of 97.5% as compared to ResNet50 achieving 97.2% accuracy. This suggests that GoogLeNet offer a more reliable option for real-world disaster response applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Graph-guided masked autoencoder for process anomaly detection

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process Safety and Environmental Protection 2024年 186卷 1345-1357页

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological and nonlinear connections among variables. In this study, a process anomaly detection method based on graph-guided masked autoencoder (GGMAE) is proposed by introducing the concept of the graph to the process industry. GGMAE first constructs a topology graph according to the process flowchart. Then, the patch and mask mechanism forces GGMAE to learn the process intrinsic information to reconstruct the input according to the variable topological relationship and temporal characteristics. Additionally, Kullback-Leibler divergence is used as the loss to ensure that the distribution of input and output is consistent. Experiments on two publicly available anomaly detection benchmarks demonstrate the superiority of GGMAE over existing methods. Visualization of the results demonstrates the physically compliant reconstruction logic. © 2024 The Institution of Chemical Engineers

关键词： Anomaly detection

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Design and implementation of combined liquid pump and active–passive mixer for a drug delivery system utilizing two 1-DOF piezoelectric actuated cantilever beams

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Materials Today: Proceedings 2022年 58卷 823-829页

作者： Ohol, R.M. Vasuki, B. Department of Instrumentation & Control Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India

In a biomedical drug delivery system, liquid medication is provided to the patient through a saline solution, administered in tiny drops. The spread of the medicine in the solution is an essential aspect for the solution to become homogeneous. The present study introduces a combined pump and active–passive mixer arrangement consisting of dual piezoelectrically actuated 1-degree of freedom (DOF) Aluminium cantilever beams. The free end of each beam is attached to a borosilicate glass tube set-up that works as both pump and a mixer. The setup consists of two vertical tubes, connecting channels, a glass bulb, and a coiled tube. When the piezoelectric actuated cantilever beam is excited by an excitation voltage, it deflects and vibrates along with the glass tube arrangement resulting in the vertical tubes pumping liquid from the two separate glass containers due to the mechanical vibratory movement. The connecting channels pass the pumped fluid into the vibrating glass bulb and the coiled tube where these two liquids mix. We tested the mixing executions of four different geometries, G-1 – G-4, based on changed δ (coil curvature ratio), p (pitch), and n (number of windings). Initial pumping was started at 90 VP-P input voltage for all geometries, and the observed flow rate and velocity were 2.91 ml min−1 and 0.9160 mm min−1 for G-1 geometry. A UV–visible spectrophotometer was used to evaluate the mixing execution by measuring the solution's absorbance values before and after mixing. © 2021

关键词： controlled drug delivery

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