In this paper, a simplified nonquasi-static table-based approach is developed for high-frequency broad-band large-signal field-effect-transistor modeling. As well as low-frequency dispersion, the quadratic frequency dependency of the y-parameters at high frequencies is taken into account through the use of linear delays. This model is suitable for applications related with nonlinear microwave computer-aided design and can be both easily extracted from dc and s-parameter measurements and implemented in commercially available simulation tools. Model formulation, small-signal, and large-signal validation will he described In this paper. Excellent results are obtained from dc up to the device f(T) frequencies, even when f(T) is as high as 100 GHz

Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to create photorealistic images. Due to this fact, intense research has been performed to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deepfake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research is to detect photorealistic AI-created images versus real photos coming from a physical camera. Id est, making a binary decision over an image, asking whether it is artificially or naturally created. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. ELA is being used nowadays by photographers for the manual detection of AI-created images. Both kinds of features are used to train convolutional neural networks to differentiate between AI images and real photographs. Good results are obtained, achieving accuracy rates of over 95%. Both extraction methods are carefully assessed by computing precision/recall and F₁-score measurements.

This letter presents an optimized single-stage MMIC tripler with W-band output frequency (76.5 GHz). The circuit is based on an 0.15 /spl mu/m gate-length AlGaAs/InGaAs/GaAs PHEMT. By using a class AB transistor bias point and carefully selecting its input and output terminations, a high conversion gain of -4.3 dB for an 8.5 dBm input signal and a saturated output power of 7 dBm have been obtained. To our knowledge, these results represent the best performance reported up to date for an active frequency tripler with W-band output frequency.

This survey addresses the cutting-edge load modulation microwave and radio frequency power amplifiers for next-generation wireless communication standards. The basic operational principle of the Doherty amplifier and its defective behavior that has been originated by transistor characteristics will be presented. Moreover, advance design architectures for enhancing the Doherty power amplifier’s performance in terms of higher efficiency and wider bandwidth characteristics, as well as the compact design techniques of Doherty amplifier that meets the requirements of legacy 5G handset applications, will be discussed.

Recently, X -parameters have been introduced to model active device nonlinear behavior. In addition to providing a measurement-based tool to numerically predict nonlinear device behavior in computer-aided design, they can also provide the designer of nonlinear circuits an analytical design tool. Exploiting this design tool aspect, this work presents an application that combines the nonlinear vector network analyzer PNA-X and a passive tuner to extract a transistor load-independent X -parameter model, focused around targeted circuit impedances for optimal performance. Furthermore, an experimental search algorithm, based on X-parameters analytical computations and developed by Peláez-Pérez , has been used and experimentally validated in this paper, aimed to speed up the characterization and design process, minimizing the number of load-pull measurements necessary to provide an accurate transistor X-parameter model for use in analytical and/or numerical circuit design.