In this paper a low-noise amplifier (LNA) is designed at 5GHz with the intention of ultra-low-power consumption. First, a spiral inductor is discussed and its equivalent circuit is described. Second, the input impedance, output impedance, and gain of a common-source LNA is calculated precisely. In addition, forward body biasing technique is used to bias all transistors to bring down the power consumption of the LNA. Plus, the comparison between precise calculation performed in this paper and the approximation proposed in other papers is demonstrated by HSPICE and MATLAB. The main simulation of the proposed LNA is carried out by Advanced Design System (ADS) and TSMC 0.18 um CMOS process is used for all elements in the LNA. The circuit is evaluated in different voltage supplies from 0.1 volt to 0.5 volt. The LNA is simulated with both lumped-elements and real elements. With lumpedelements the results are 0.96dB,-19dB,-16dB, 17.9dB, and 140μW for noise figure (NF), input impedance matching (S11), output impedance matching (S22), power gain (S21), and power consumption respectively. Plus, with real elements the results are 1.4dB,-20dB,-19dB, 15.4dB, and 139μW for noise figure (NF), input impedance matching (S11), output impedance matching (S22), power gain (S21), and power consumption respectively.
The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.
In this paper, an ultra-low-power low-noise amplifier (LNA) at 5GHz is proposed. The main focus is on precise computation of output impedance, input impedance, and gain of the LNA. The LNA is composed of a common-source LNA and a cascode LNA. In fact, the casode LNA can assist to have more stability by declining S12 considerably. Plus, it can be beneficial via increasing the gain of the second stage of the final LNA. In addition, in order to emphasize the significance of the meticulous calculations, the formulas calculated in this paper are compared with their counterparts in other papers. The combination of two different supply voltage is mentioned as an approach to bring down the power dissipation of the circuit. Simulation is performed by MATLAB, HSPICE, and Advanced Design System (ADS). TSMC 0.18 um CMOS process is used to evaluate the circuit. The LNA is analyzed with two different voltage supply 0.7 V and 0.9 V. The input matching (S11) is -14 dB and -16 dB for voltage supply 0.7 V and 0.9 V respectively. Plus, power dissipation, noise figure (NF), and gain (S21) are 532 µW, 944 µW, 1.25 dB, 1.05dB, 15dB, and 17dB for voltage supply 0.7 V and 0.9 V respectively.
Feature extraction and lung detection are critical phases for COVID-19 detection. Hence, the features by which normal lungs and abnormal lungs can be differentiated are significantly important. In this paper, the x-ray images are enhanced and the corresponding angles, coming from ribs, are extracted as the major features. According to the behavior of the angles, the image is bisected in order to evaluate each lung individually. The new definition of normal lungs is proposed so as to discriminate normal lungs from COVID-19 lungs. Considering the definition, the right and the left lungs are cropped from the main image. Subsequently, the Histogram of Oriented Gradient (HOG) features are extracted from the cropped images. Two neural networks with the same topology are trained by the features. First, one of the neural networks is trained by cropped images. Second, another neural network is trained by HOG features obtained from the cropped images. The simulation is performed by MATLAB and the database is comprised of 522 images and 96% accuracy is obtained. Furthermore, a novel method by which fingerprints are classified in eight categories is proposed in this paper. In fact, because of inevitable rotation, brought about during data acquisition procedure in fingerprints, the feature extraction procedure might be afflicted with the rotation. Hence, a new approach is suggested so that the rotation is rectified prior to the feature extraction process. From the enhanced images of fingerprints, the angles of ridges are calculated. According to the extracted angles, new points, called Origin Points, are mentioned as the origins around which decisive blocks are cropped. For each block, a Fourier series model is calculated so as to form a training data for the classifier. The classifier chosen is a Generalized Regression Neural Network (GRNN). FVC2004 is utilized for both training and test phases and 98.2% accuracy is achieved.
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