A compact fully flexible antenna with 1.22 as dielectric constant with 0.016 loss tangent is designed for wearable biomedical applications. It has dimensions of 40.5 x 23 x 0.97 mm3. The polygon-shaped patch is modified to achieve ultra-wideband frequencies using partial ground as 16.5mm and a periodic vertical slot of 13x2 mm2 structure in defected ground structure to enhance the wide bandwidth of 2.6-11.1 GHz. Using a transmission line equation and a microstrip line feeding method, the resulting antenna operates at a biomedical frequency in open space and on the body-worn case with multi band i.e. 3.1 and 5GHz. The deformation process works well for the proposed antenna without affecting its actual bandwidth with 20 to 60 mm radii. Additionally, the proposed antenna operates at 5GHz for Wi-Fi with 3.67dBi gain, -35.1dB as S11 which is suitable for commercial applications. CST Microwave Studio was used to simulate the antenna and fabricate it for testing.
Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets.
A Hybrid logic style is most popular when compared to other logic styles in implementation of full adder circuits. Conventional hybrid adder uses truth table with true form of carry in and carry out. This will result in non-identical outputs of sum and carry for about 75% of the input combinations. Alternate truth table has been proposed to increase the similarity of sum and carry outputs. In this paper, circuit is designed for complemented carry in and complemented carry out of full adder. This novel structure allowed to design 20-T hybrid adder with process control, low power and low power delay product. The proposed adder structure is applicable for ripple carry adder. The performance of the designs is measured by simulating it in tanner T-spice environment using 0.25um technology. Proposed design has also been implemented up to 64-bit for its scalability. All the results were taken at several operating frequencies with varying word size of the adder. The proposed adder minimizes the power by 9.5%-51.5% and the power delay product by 3%-60% when compared to its counterparts for N-bit adder.
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