The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.

This paper presents a high conversion gain and highly linearised up‐conversion mixer designed in 65 nm CMOS process, which translates intermediate frequency of 2.4 GHz and local oscillator frequency of 21.6–24 GHz in radio frequency. In the proposed mixer, the tranconductance stage consists of a common source route with neutralisation technique and dual capacitive cross coupled‐common gate route to achieve high linearity and stability. In addition, it also includes dual negative resistance switches with improved inter‐stage matching network to enhance the conversion gain and to decrease the noise figure. The mixer achieves the conversion gain of 4.7 dB, the measured output 1‐dB compression point of 0.41 dBm and noise figure of 3.8 dB at 24 GHz. In an automotive radar application, the proposed mixer showed a high conversion gain, an excellent output 1‐dB and a low noise figure with power consumption of approximately 5.2 mW at a 1.2 V supply.