The COVID‐19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID‐19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two‐route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID‐19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c‐means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2%.

In this paper, nicotine (NIC) was detected by cyclic voltammetry (CV) using a modified glassy carbon (GC) electrode. To do this, the surface of the GC electrode was modified by hybrid nanofiber obtained from the electrospinning method. Hybrid nanofibers were produced through the dispersion of carboxylated multi-walled carbon nanotube (MWCNT-COOH) as an inorganic component in the chitosan (CS) polymer matrix as an organic component. The nanofibers showed unique morphology and high surface area value. With the increase of functionalized carbon nanotube content in the nanofibers, the mean pore diameter and average nanofiber diameter increased. The electrochemical properties of nanofibers towards the sensing of NIC were investigated by the CV method. NIC was irreversibly reduced with the use of a CS/MWCNT-COOH electrode, a controlled process with two protons and two electrons. An oxidation signal at lower potential with higher current was obtained for NIC with the use of a polymer-modified electrode compared to a GC electrode. This was as a result of the electrocatalytic effect of the hybrid nanofibers due to the ability of carbon nanotubes to increase the rate of electron transfer. Under optimum conditions, the oxidation of NIC occurred at 0.82 eV with a pH of 7.4. The linear calibration curve was in the concentration range of 0.1–100 μ M NIC ( R ² = 0.9987) with a detection limit of 30 nM. For 100 parallel 10 μ M NIC diagnoses for five replicates, 97.2% with a standard deviation of 4.08 maintained their stability over the first cycle. This indicates that the CS/MWCNT-COOH electrode has excellent reproducibility and stability.

In this paper, a new voltage controlled oscillator (VCO) in a 0.18um CMOS process is proposed, which presents a high voltage swing and low phase noise. This new method offers better specifications with respect to traditional solutions which require higher current dissipation for increasing output voltage swing. The proposed circuit is capable of extra oscillation amplitude without increasing the current level taking advantages of tail current elimination and topology optimization. Simulation results at 2.5GHz with an offset frequency of 1MHz and 3MHz shows a phase noise of about -127.3dB/Hz and -137dBc/Hz, respectively with 1.4mA from 1.8V supply. Also, the Monte Carlo simulation results illustrate that the proposed circuit is very promising to process variations.

In this work, domino logic with a latched keeper technique is proposed. The circuit, which is used to implement the technique, utilizes a latch with standard domino. By using the simple structure, we can obtain better performance, noise immunity, and lower power consumption. The simulation results for a 65 nm CMOS technology show an improvement between 5% and 55.7% in delay and 8% and 15% in power consumption, over its previous suggestions.

The fusion of visible and infrared images has gained great importance and attention with the emergence of machine learning techniques for applications such as surveillance and medical diagnosis. This study proposes a novel Fuzzy Generative Adversarial Network (FGAN) integrated with a Harris Hawks Optimization (HHO) algorithm to fuse visible and infrared images, addressing the limitations of existing approaches by dynamically optimizing a Mamdani-type fuzzy logic system within the generator. The innovation lies in employing HHO to tune fuzzy rules and output membership functions—using entropy, PSNR, and SSIM as training targets—alongside a Support Vector Machine (SVM) enhanced with Frechet Inception Distance (FID) for discriminator training, justifying the use of multiple models to achieve robust feature extraction and classification. Unlike traditional methods, this hybrid approach ensures superior adaptability to diverse image characteristics. This work achieves remarkable adaptability in infrared and visible picture fusion by incorporating a fuzzy logic system, optimized by HHO, within a GAN framework, therefore pioneering a revolutionary fusion paradigm. Superior visual quality and fusion precision are achieved by the suggested FGAN’s unique integration of a Support Vector Machine and FID in the discriminator, which greatly improves differentiation accuracy. Experimental results on the TNO dataset using MATLAB demonstrate that the proposed FGAN outperforms state-of-the-art techniques, achieving a PSNR up to 55 dB, an SSIM up to 0.99, and SF lower than 10, reflecting enhanced visual quality and information retention. By providing high-quality fused images with better clarity and information retention, this method greatly improves remote sensing, surveillance, and medical diagnostics.