In this research paper, an inverse double V loaded complementary square split ring resonator based double negative (DNG) metamaterial has been developed and examined numerically and experimentally. The electromagnetic (EM) properties of the proposed inverse double V-structure were calculated using computer simulation technology (CST-2019) and the finite integration technique (FIT). The designed metamaterial provides three resonance frequencies are 2.86, 5, and 8.30 GHz, covering S-, C-, and X-bands. The total size of the recommended unit cell is 8 [Formula: see text] 8 [Formula: see text] 1.524 mm3, and a high effective medium ratio (EMR) value of 13.11 was found from it. The - 10 dB bandwidths of this structure are 2.80 to 2.91, 4.76 to 5.17, and 8.05 to 8.42 GHz. The proposed structure's novelty is its small size, simple resonator structure, which provides double negative characteristics, high EMR, maximum coverage band, and required resonance frequencies. Wi-Fi network speeds are generally faster when frequencies in the 5 GHz band are used. Since the proposed structure provides a 5 GHz frequency band, hence the suggested metamaterial can be used in Wi-Fi for high bandwidth and high-speed applications. The marine radars operate in X-band, and weather radar works in S-band. Since the designed cell provides two more resonance frequencies, i.e., 2.86 GHz (S-band) and 8.30 GHz (X-band), the proposed metamaterial could be used in weather radar and marine radar. The design process and various parametric studies have been analyzed in this article. The equivalent circuit is authenticated using the advanced design system (ADS) software compared with CST simulated result. The surface current, E-field, and H-field distributions have also been analyzed. Different types of array structure, i.e., 1 [Formula: see text] 2, 2 [Formula: see text] 2, 3 [Formula: see text] 3, 4 [Formula: see text] 4, and 20 [Formula: see text] 25 is examined and validated by the measured result. The simulated and measured outcome is an excellent agreement for the inverse double V loaded CSSRR unit cell and array. We showed the overall performance of the suggested structure is better than the other structures mentioned in the paper. Since the recommended metamaterial unit cell size is small, provides desired resonance frequency, gives a large frequency band and high EMR value; hence the suggested metamaterial can be highly applicable for Radar and Wi-Fi.
Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich-Pyle (GP) or Tanner-Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.