A Miniaturized and Highly Sensitive Microwave Sensor Based on CSRR for Characterization of Liquid Materials.

In this work, a miniaturized and highly sensitive microwave sensor based on a complementary split-ring resonator (CSRR) is proposed for the detection of liquid materials. The modeled sensor was designed based on the CSRR structure with triple rings (TRs) and a curve feed for improved measurement sensitivity. The designed sensor oscillates at a single frequency of 2.5 GHz, which is simulated using an Ansys HFSS simulator. The electromagnetic simulation explains the basis of the mode resonance of all two-port resonators. Five variations of the liquid media under tests (MUTs) are simulated and measured. These liquid MUTs are as follows: without a sample (without a tube), air (empty tube), ethanol, methanol, and distilled water (DI). A detailed sensitivity calculation is performed for the resonance band at 2.5 GHz. The MUTs mechanism is performed with a polypropylene tube (PP). The samples of dielectric material are filled into PP tube channels and loaded into the CSRR center hole; the E-fields around the sensor affect the relationship with the liquid MUTs, resulting in a high Q-factor value. The final sensor has a Q-factor value and sensitivity of 520 and 7.032 (MHz)/εr) at 2.5 GHz, respectively. Due to the high sensitivity of the presented sensor for characterizing various liquid penetrations, the sensor is also of interest for accurate estimations of solute concentrations in liquid media. Finally, the relationship between the permittivity and Q-factor value at the resonant frequency is derived and investigated. These given results make the presented resonator ideal for the characterization of liquid materials.

A Novel Low-Cost Compact High-Performance Flower-Shaped Radiator Design for Modern Smartphone Applications.

This manuscript examines the design principle and real-world validation of a new miniaturized high-performance flower-shaped radiator (FSR). The antenna prototype consists of an ultracompact square metallic patch of 0.116λ0 × 0.116λ0 (λ0 is the free space wavelength at 3.67 GHz), a rectangular microstrip feed network, and a partial metal ground plane. A novel, effective, and efficient approach based on open circuit loaded stubs is employed to achieve the antenna's optimal performance features. Rectangular, triangular, and circular disc stubs were added to the simple structure of the square radiator, and hence, the FSR configuration was formed. The proposed antenna was imprinted on a low-cost F4B laminate with low profile thickness of 0.018λ0, relative permittivity εr = 2.55, and dielectric loss tangent δ = 0.0018. The designed radiator has an overall small size of 0.256λ0 × 0.354λ0. The parameter study of multiple variables and their influence on the performance results has been extensively studied. Moreover, the impact of different substrate materials, impedance bandwidths, resonance tuning, and impedance matching has also been analyzed. The proposed antenna model has been designed, simulated, and fabricated. The designed antenna exhibits a wide bandwidth of 5.33 GHz ranging from 3.67 to 9.0 GHz at 10 dB return loss, which resulted in an 83.6% fractional impedance bandwidth; a maximum gain of 7.3 dBi at 8.625 GHz; optimal radiation efficiency of 89% at 4.5 GHz; strong intensity current flow across the radiator; and stable monopole-like far-field radiation patterns. Finally, a comparison between the scientific results and newly published research has been provided. The antenna's high-performance simulated and measured results are in a good agreement; hence, they make the proposed antenna an excellent choice for modern smartphones' connectivity with the sub-6 GHz frequency spectrum of modern fifth-generation (5G) mobile communication application.

A New CPW-Fed Semicircular Inverted Triangular Shaped Antenna Based on Mixed-Alternate Approach for 5G Millimeter-Wave Wireless Applications.

This paper presents the design and development of a new semicircular inverted triangular shaped antenna for 5G millimeter-wave wireless applications. An alternate-mixed approach based on cavity, slots and loaded stubs is employed in the designed antenna lattice. The suggested antenna structure is formed by a radiator, partial defected metal ground plane and a 50 Ω coplanar waveguide. The proposed antenna resonated at multiple frequencies by the setting up of the proper dimensions and locations of the rectangles, elliptical cut slots and cavity stubs. Furthermore, a parametric analysis is carried out to examine the antenna's effectiveness and impedance-matching controls. The proposed structure is realized on the low-cost RT/Duroid Rogers RO3010™ laminate with an overall small size of 1.381λ0 × 1.08λ0 × 0.098λ0, where λ0 represents the wavelength corresponding to the minimum edge frequency of the 23 GHz at 10 dB impedance bandwidth of the antenna. The antenna's key characteristics in terms of bandwidth, gain, radiation patterns and current distribution have been investigated. The antenna exhibits high performance, including an impedance bandwidth of 19 GHz ranging from 23 GHz to 42 GHz, results in 58.46% wider relative bandwidth calculated at 10 dB scaled return loss, a peak realized gain of 6.75 dBi, optimal radiation efficiency of 89%, stable omnidirectional-shaped radiation patterns and robust current distribution across the antenna structure at multiple resonances. The designed antenna has been fabricated and simulation experiments evaluated its performance. The results demonstrate that the antenna is appropriate and can be well integrated into 5G millimeter-wave wireless communication systems.

A novel compact broadband and radiation efficient antenna design for medical IoT healthcare system.

This paper investigates and develops a novel compact broadband and radiation efficient antenna design for the medical internet of things (M-IoT) healthcare system. The proposed antenna comprises of an umbrella-shaped metallic ground plane (UsMGP) and an improved radiator. A hybrid approach is employed to obtain the optimal results of antenna. The proposed solution is primarily based on the utilization of etching slots and a loaded stub on the ground plane and rectangular patch. The antenna consists of a simple rectangular patch, a 50 Ƹ microstrip feed line, and a portion of the ground plane printed on a relatively inexpensive flame retardant material (FR4) thick substrate with an overall compact dimension of 22 × 28 × 1.5 mm3. The proposed antenna offers compact, broadband and radiation efficient features. The antenna is carefully designed by employing the approximate calculation formulae extracted from the transmission line model. Besides, the parameters study of important variables involved in the antenna design and its influence on impedance matching performance are analyzed. The antenna shows high performance, including impedance bandwidth of 7.76 GHz with a range of 3.65Ƀ11.41 GHz results in 103% wider relative bandwidth at 10 dB return loss, 82% optimal radiation efficiency in the operating band, reasonable gain performance, stable monopole-shaped radiation patterns and strong current distribution across the antenna lattice. The suggested antenna is manufactured, and simulation experiments evaluate its performance. The findings indicate that the antenna is well suited for medical IoT healthcare systems applications.

A framework for efficient brain tumor classification using MRI images.

A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.