Improvement of power amplifier’s performance is the desired topic in communication systems. There are many efforts are made to provide good input and output matching, high efficiency, sufficient power gain and appropriate output power. This paper presents a power amplifier with optimized input and output matching networks. In the proposed approach, a new structure of the Hidden Markov Model with 20 hidden states is used for modeling the power amplifier. The widths and lengths of the microstrip lines in the input and output matching networks are defined as the parameters that the Hidden Markov Model should optimize. For validating our algorithm, a power amplifier has been realized based on a 10W GaN HEMT with part number CG2H40010F from the Cree corporation. Measurement results have shown a PAE higher than 50%, a Gain of about 14 dB, and input and output return losses lower than -10 dB over the frequency range of 1.8–2.5 GHz. The proposed PA can be used in wireless applications such as radar systems.

<p><span>Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted.  HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.</span></p>

Timely diagnosis of Alzheimer's diseases(AD) is crucial to obtain more practical treatments. In this paper, a novel approach Based on Multi-Level Fuzzy Neural Networks (MLFNN) for early detection of AD is proposed. The focus of study was on the problem of diagnosing AD and MCI patients from healthy people using MLFNN and selecting the best feature(s) and most compatible classification algorithm. In this way, we achieve an excellent performance using only a single feature i.e. MMSE score, by fitting the optimum algorithm to the best area using optimum possible feature(s) namely one feature for a real life problem. It can be said, the proposed method is a discovery that help patients and healthy people get rid of painful and time consuming experiments. Experiments shows the effectiveness of proposed method in current research for diagnosis of AD with one of the highest performance (accuracy rates of 96.6%), ever reported in the literature.