Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.

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A two-phase permanent magnet synchronous motor is designed for using an AC-AC converter drive system fed from a single-phase source. This motor and drive system are proposed to replace with a traditional single-phase motors fed by electricity grid for improving the efficiency of the system. The proposed AC drive system can be connected directly between AC grid and motor without requiring any storage device such as DClink large capacitors and rectification. The structure of AC-AC converter and modulation technique is presented with vector controlled two-phase PMSM. The converter output voltage and current waveforms and harmonic contents are analyzed with MATLAB simulation program. Additionally, responses of the motor speed and torque are presented for various conditions.

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