Speech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, and a learning approach is introduced, which is based on the statistical model to classify internal feelings of the utterance. This approach analyzes and tracks the emotional state changes trend of speaker during the speech. The proposed method classifies utterance emotions in six standard classes including, boredom, fear, anger, neutral, disgust and sadness. For this purpose, it is applied the renowned speech corpus database, EmoDB, for training phase of the proposed approach. In this process, once the pre-processing tasks are done, the meaningful speech patterns and attributes are extracted by MFCC method, and meticulously selected by SFS method. Then, a statistical classification approach is called and altered to employ as a part of the method. This approach is entitled as the LGMM, which is used to categorize obtained features. Aftermath, with the help of the classification results, it is illustrated the emotional states changes trend to reveal speaker feelings. The proposed model also has been compared with some recent models of emotional speech classification, in which have been used similar methods and materials. Experimental results show an admissible overall recognition rate and stability in classifying the uttered speech in six emotional states, and also the proposed algorithm outperforms the other similar models in classification accuracy rates. تفاصيل المقالة

Moving towards nanometer scales, Quantum-dot Cellular Automata (QCA) technology emerged as a novel solution, which can be a suitable replacement for complementary metal-oxide-semiconductor (CMOS) technology. The 3-input majority function and inverter gate are fundamental gates in the QCA technology, which all logical functions are produced based on them. Like CMOS technology, making the basic computational element such as an adder with QCA technology, is considered as one of the most important issues that extensive research have been done about it. In this paper, a new QCA full-adder based on coupled majority-minority and 5-input majority gates is introduced which its novel structure, appropriate design technique selection and its arrangement make it very suitable. The experimental results showed that the proposed QCA full-adder makes only 48 cells and the first output is obtained in the 0.05clock. Therefore, the presented QCA full-adder improves the number of cells and gains a speedup rate of 33% in comparison with the best previous robust QCA full-adders. In addition, temperature analysis of the QCA full-adders shows that our design is more robust compared with other suggested QCA full-adders. تفاصيل المقالة

Nowadays diabetes disease is one of the main problems of health domain and it’s known as the fourth factor of death in the world. The main problem with this dangerous disease is the late or weak diagnosis. The reason of weak diagnosis is because sometimes doctors aren’t able to select the right patterns or they can’t use the standard patterns very well, so the outcome is that the disease will be diagnosed by the patients when it has become late for controlling or curing it. Therefore, implementing a method which can help each person to have an authentic diagnosis of being or not being affected to this disease; can be an important step for prevention and controlling this special disease at the beginning of it. In this paper, a new method is presented for diagnosing diabetes disease which is able to extract the proper knowledge by helping to cluster and analyze the training patterns, after that in recognition phase it can diagnose diabetes disease precisely and fast via a fuzzy reward-penalty mechanism. For evaluating the proposed method, PIMA dataset has been used. The experimental results show that the proposed method has a better performance compared to other existing methods. تفاصيل المقالة