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Item Metadata only A highly sensitive modified triple split ring metamaterial-based sensor for blood sample detection based on dielectric property alteration(2024-07-01) Al Mahfazur Rahman A.; Islam M.T.; Kirawanich P.; Bais B.; Alsaif H.; Maash A.A.; Hoque A.; Moniruzzaman M.; Islam M.S.; Soliman M.S.; Al Mahfazur Rahman A.; Mahidol UniversityThis research paper demonstrates a metamaterial (MTM) based sensing technique to detect various blood samples by analyzing their dielectric properties. The performance of this MTM-based sensor is evaluated with the help of mimicked human blood samples that closely resemble the dielectric properties of actual human blood samples. Moreover, the ISM band frequency of 2.4 GHz is chosen as one of the reference resonance frequencies due to its various industrial and medical applications. The resonating patch is developed on the FR-4 substrate with a dimension of 10 × 20 mm2 that provides sharp reference resonances of 2.4 and 4.72 GHz for the spectra of the transmission coefficient with a good quality factor (Q-factor). The MTM sensor can detect the mimicked blood samples with a maximum frequency deviation of up to 650 MHz at 2.4 GHz and up to 850 MHz at 4.72 GHz, with maximum sensitivity of 0.917 and 0.707, respectively. The measured results using the prototype of the sensor support the simulation result with good agreement, indicating high sensing capability. Due to its high sensitivity, figure of merit (FoM), and frequency shifting with dielectric property changes in blood samples, the developed MTM-based sensor can be implemented effectively for quick sensing of infected blood samples and biomedical applications.Item Metadata only Design and experimental validation of a compact dual-band metamaterial perfect absorber for electromagnetic energy harvesting applications(2024-11-01) Ullah N.; Islam M.T.; Hoque A.; Kirawanich P.; Alsaif H.; Soliman M.S.; Islam M.S.; Ullah N.; Mahidol UniversityThis article introduces, characterizes, and experimentally validates an innovative design for a compact-sized metamaterial (MM) perfect absorber (PA) for electromagnetic (EM) energy harvesting (EH) applications. The absorber design, comprised of three octagonal ring resonators made of annealed copper, incorporates split gaps at 45-degree inclinations within the uppermost and middle resonators. A split strip line connects the inner octagonal ring resonators, while the split gaps of the outermost and inner rings are filled with a 50 Ω resistive load. This structure is made on a Rogers RT5880 substrate, and the back side of the proposed design is entirely coated with annealed copper. The proposed absorber achieves precise impedance matching with free space, facilitating efficient absorption and redirecting EM power toward the resistive loads. The absorber demonstrates absorption peaks of 99.98 % at 2.4 GHz and 99.99 % at 4.9 GHz. In addition, the efficiency of absorption is assessed for different incident (θ) and polarization angles (ϕ) in both the Transverse Electric (TE) and Transverse Magnetic (TM) modes. Simulated harvesting efficiencies of 95.33 % at 2.4 GHz and 95.99 % at 4.9 GHz are recorded. An experimental validation is performed using a 3 × 3 array that measures 30 × 30 mm. The tests are carried out in an anechoic chamber. The measured harvesting efficiencies strongly correlate with the simulated results, indicating the reliability of the proposed design. This absorber's efficiency and compact size make it an excellent option for EH systems in wireless sensor networks (WSNs).Item Metadata only FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification(2024-12-01) Hossain A.; Islam R.; Islam M.T.; Kirawanich P.; Soliman M.S.; Hossain A.; Mahidol UniversityThe microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-class MBT image classification purposes. The Anaconda distribution platform with Python 3.7 on the Windows 11 OS is used to implement the models.Item Metadata only A metamaterial-based biosensing approach for detecting dilution level change of blood(2026-01-15) Mahfazur Rahman A.A.; Islam M.T.; Kirawanich P.; Moniruzzaman M.; Shamsan Z.A.; Alenezi A.M.; Soliman M.S.; Mahfazur Rahman A.A.; Mahidol UniversityThis research illustrates a biosensing approach utilizing metamaterial (MTM) to detect variations in dilution levels in blood samples. The MTM resonator utilizes Rogers RT5880 substrate features a distinctive design that attains a reference resonance of 11.47 GHz for the transmission coefficient, S21 along with two additional resonances of 13.46 GHz and 14.33 GHz, respectively. The simulation of the biosensor model is accomplished within 10 GHz–15 GHz in the CST microwave studio platform. The MTM resonator effectiveness is assessed using electric and magnetic fields, as well as surface current movements. The overall biosensing performance is evaluated using mimicked blood samples of different dilution levels that intently align the dielectric properties of the actual sample. The results correspond with the simulation model's outputs, demonstrating its enhanced sensing capability. Furthermore, an unknown sample prediction model is constructed utilizing MATLAB/Simulink, based on the responses of the known samples, to determine the permittivity and dilution level of the samples. This MTM-based biosensor, distinguished by its strong Q-factor, frequency shifts, sensitivity, selectivity, and figure of merit (FoM), is relevant for detecting changes in dilution levels in blood samples to identify diseases and anomalies in the samples, as well as for other biomedical applications.Item Metadata only Design and analysis of a dual-band circular split-ring resonator-based metamaterial absorber for sensing applications(2025-01-01) Alawad M.A.; Rabbani M.G.; Islam M.T.; Kirawanich P.; Alkhrijah Y.; Ouda M.; Misran N.; Soliman M.S.; Alawad M.A.; Mahidol UniversityThis study introduces a dual-band circular split-ring resonator (CSRR)-based metamaterial absorber (MTMA) designed for high-sensitivity sensing of both solid and liquid materials. The proposed structure, fabricated on a Rogers RT 5880 substrate with copper layers, achieves near-perfect absorption rates of 99.99% at 10.48 GHz (X-band) and 99.97% at 14.57 GHz (Ku-band), optimized through CST Microwave Studio simulations. The MTMA’s triple-stage design refinement enhances resonance characteristics, enabling precise detection of dielectric variations in substrates and liquids via measurable frequency shifts. Experimental validation confirms robust performance, with sensitivity of up to 2.51 GHz/εᵣ and quality factors reaching 189, thus outperforming existing single-band metamaterial sensors. The absorber’s compact size and consistent response under varying permittivity’s make it suitable for applications in biomedical diagnostics, fuel adulteration detection, and industrial quality control. By bridging gaps between simulation and real-world implementation, this work advances metamaterial-based sensing technology, offering a scalable and efficient solution for electromagnetic wave manipulation in next-generation sensor systems.Item Metadata only 500HN polyimide film sandwich metamaterial absorber with enhanced sensing capabilities and assisted machine learning absorption forecasting(2025-05-01) Hossen M.S.; Islam M.T.; Kirawanich P.; Hoque A.; Alenezi A.M.; Baharuddin M.H.; Alsaif H.; Soliman M.S.; Hossen M.S.; Mahidol UniversityThe proposed research is about unleashing the absorption properties and parametric forecasting of 500HN polyimide film sandwich deposited metamaterial absorber (MMA) in THz regime. The proposed micro-structure unit cell is ultra-thin (7.9μm) and compact (60μm) at its lowest operational frequency, with multiple absorption peaks at 4.66, 5.06, 5.82, 6.59, and 6.75 THz. The proposed MMA exhibits multiple absorption peaks with absorption coefficients of 88.51%, 99.84%, 99.72%, 95.89%, and 84.95%. To analyze the proper characteristics of polyimide absorption values was observed in different MMA configuration (i.e. unit cell dimension, available substrate height, outer patch radiator). The modified meandered line configuration at the top with gold material (Au) gives this sandwich structure a good stability in terms of sensing which have been verified in TE, TM, and TEM mode (E-field, H-field, and surface current distribution). The sensing capabilities were evaluated using six liquid samples, achieving a maximum sensitivity of 1.4 THz/RIU and a figure of merit (FoM) of 185 RIU−1, outperforming existing designs. Machine learning assisted forecasting analysis in TC-40, TC-50, TC-60 for the different MMA configurations indicates the absorption values can be predicted with a good accuracy. The regression algorithm models was assessed using R2, adjusted R2, and MSE which reveal the models goodness of fit, forecasting accuracy, and generalization for MMA.Item Metadata only Miniaturized Metamaterial Microwave Sensor with ML Assisted Optimization for Label-Free Liquid Sensing(2025-01-01) Hossen M.S.; Islam M.T.; Alawad M.A.; Kirawanich P.; Baharuddin M.H.; Alkhrijah Y.; Ouda M.; Soliman M.S.; Hossen M.S.; Mahidol UniversityThis work presents the design and analysis of a compact single-negative (SNG) metamaterial sensor based on a meandered-line configuration for high-resolution microwave sensing of liquids. The sensor, fabricated on a low-cost FR-4 substrate with a unit cell size of 10 × 10 × 1.575 mm³, operates at frequency of 5-11 GHz. The structure demonstrates strong resonance characteristics, including a reflection coefficient (S11) dip of -32 dB and a transmission coefficient (S21) level of -28 dB, indicating excellent impedance matching and field confinement. The sensing capability was validated using five essential bio oils (peppermint, citrus, eucalyptus, lavendar, and rosmary) with known relative permittivity values ranging from 2.5 to 3.25. The presence of each material under test (MUT) induced a consistent 200 MHz shift in resonance frequency, with a calculated normalized sensitivity of 6.94%. The sensor's design was further optimized using random forest and extra trees predictive model and quantitatively assessed using mean squared error and R² score. The meandered structure and single-negative behavior contributed to a high quality factor (70.36, 64.33, 59.23, 54.86, 54.64) and enhanced dielectric interaction with an average relative error of less than 1.1%, confirming strong reproducibility. These results confirm the sensor's utility for compact, non-invasive, and permittivity characterization in skin care and liquid sensing applications.Item Metadata only Characterization and synthesis of MnxCo(0.90−x)Ni0.10Fe2O4 based flexible DNG metamaterial with EMI shielding and sensing application(2024-09-01) Golam Rabbani M.; Hoque A.; Tariqul Islam M.; Alamri S.; Kirawanich P.; Albadran S.; Soliman M.S.; Golam Rabbani M.; Mahidol UniversityThis article presents a sol–gel method based fabrication of DNG (Double negative) metamaterials using flexible microwave composites composed of MnxCo(0.90−x)Ni0.10Fe2O4. The sol–gel method is used to synthesize flexible composites with Mn25, Mn50, and Mn75 molecular compositions. XRD, FESEM, and coaxial probe-based dielectric assessment kits (DAK) are used to analyze the structural, morphological, and dielectric properties of synthesized flexible composites to justify their use as microwave dielectric substrates. DAK depicts the substrate dielectric constant as 6.63 and a loss tangent of 0.3254. The proposed flexible substrate performs better scattering parameters than FR4 and RO4533 materials and covers microwave frequencies S- and C-band. The measurement of the fabricated prototype verifies the simulated results of the flexible material (FM), and both results are in close concurrence. The transmission-blocking characteristics of the proposed FM make it a prospective candidate for Electromagnetic interference (EMI) shielding, which shows values of 40 dB, 40 dB, and 49 dB were observed at frequencies of 3.77 GHz, 4.68 GHz, and 5.50 GHz, respectively. Through the process of material characterization, the material selection, optimization, validation of sensor substances, coatings, and composition assure better sensitivity and dependability compared to reported articles. Therefore, the MnxCo(0.90−x)Ni0.10Fe2O4 composites-based flexible DNG metamaterials exhibit suitability for microwave technologies.Item Metadata only DNG metamaterial-inspired slotted Vivaldi antenna development integrated with supervised machine learning for ex-vivo bone fracture diagnosis(2025-08-01) Hossen M.S.; Hoque A.; Islam M.T.; Kirawanich P.; Baharuddin M.H.; Soliman M.S.; Hossen M.S.; Mahidol UniversityThe following research paper describes an Ex vivo bone fracture diagnosis method based on supervised machine learning (ML) using metamaterial-loaded slotted vivaldi antenna numerical parameters (S11). The proposed 26 mm x 14.05 mm compact antenna (operating at 6.89 GHz) is designed and prototyped using commercially available Rogers RT5880 substrate material with a standard substrate height of 0.508 mm (tanδ = 0.009, ϵr = 2.2). The double negative (DNG) metamaterial structure dramatically enhances antenna realized gain from 2.4 dB to 4.1 dB, and notable changes have been noticed in the S11, surface magnitude current distribution, specific absorption rate (SAR) distribution (both 1 g and 10 g), as well as in antenna radiation pattern properties (E field, H field) both in simulation and measurement. Efficiency curve was recorded over 85% in the operating frequency band. A cylindrical bone phantom model, exported from CST MWS, is used to collect the bone penetration S11 data for the ML analysis. 1440 measured data points have been accumulated from different fracture types to run the well-known SVM, adaptive boosting, random forest, XGBoost, decision tree, and logistic regression classifier for supervised learning. Training is carried out to ensure robust model performance with 75% to 85% of data from the dataset. The measured performance metrics and result comparison findings show that predictive models achieve good accuracy, which verifies the frequency-dependent pattern of the dataset and successfully predicts different classes of bone fracture (i.e., transverse, oblique, and green stick), which achieves the primary goal of this research.Item Metadata only DNG Metamaterial-Inspired Slotted Stub Antenna with Enhanced Gain, Efficiency and Distributed Current for Early Stage Bone Fracture Detection Applications(2024-01-01) Hossen M.S.; Hoque A.; Islam M.T.; Kirawanich P.; Baharuddin M.H.; Alsaif H.; Soliman M.S.; Hossen M.S.; Mahidol UniversityThis research focuses on the computational design, prototype development, and experimental validation of a compact microwave antenna for orthopaedic applications in biomedical regimes. The unique resonant structure comprises an embedded double negative (DNG) metamaterial (MTM)-inspired slotted stub, designed and simulated in the CST Microwave studio environment, as well as verified through simulation in Advanced Design Systems (ADS) software. This adaptation of the design technique improves the antenna's performance, as demonstrated in the design and result analysis section of the research paper, which includes experimental comparison results from previously developed antenna prototypes. To make the design suitable for rapid industry prototyping, Rogers RT5880 substrates have been used with a standard substrate height of 0.79 mm (loss tangent of 0.009 and dielectric constant of 2.2). However, the antenna's effectiveness was also compared with other commercially available variations of Rogers RT5880 substrate material (0.508 mm, 4.575 mm, and 3.175 mm) while transmitting the input power into the electromagnetic wave. Antenna performance for measuring bone tissue penetration was checked, and a gain of 3.5 dB and a distributed surface current value of 295 A/m were found to be satisfactory for the early stage bone fracture diagnosis application. The measured radiating efficiency was 97.5%. The far-field experiment measurements show an omnidirectional radiation pattern and a well-performed return loss well below -10 dB in the operating region of 3.8 GHz to 4.8 GHz. During simulation, the stacked bone phantom environment's operational characteristics demonstrated satisfactory gain, directivity, and efficiency performance, which are also compared in this research analysis.