Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps on two-dimensional (2D) digital images, a multilayer CNN has multiple convolutional-pooling layers and fully connected networks, which can increase the screening accuracy and reduce the error rate. However, this multilayer architecture presents some limitations, such as high computational complexity, large-scale training dataset requirements, and poor suitability for real-time clinical applications. Hence, this study designs an optimal multilayer architecture for a CNN-based classifier for automatic breast tumor screening, consisting of three convolutional layers, two pooling layers, a flattening layer, and a classification layer. In the first convolutional layer, the proposed classifier performs the fractional-order convolutional process to enhance the image and remove unwanted noise for obtaining the desired object’s edges; in the second and third convolutional-pooling layers, two kernel convolutional and pooling operations are used to ensure the continuous enhancement and sharpening of the feature patterns for further extracting of the desired features at different scales and different levels. Moreover, there is a reduction of the dimensions of the feature patterns. In the classification layer, a multilayer network with an adaptive moment estimation algorithm is used to refine a classifier’s network parameters for mammography classification by separating tumor-free feature patterns from tumor feature patterns. Images can be selected from a curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM), and K-fold cross-validations are performed. The experimental results indicate promising performance for automatic breast tumor screening in terms of recall (%), precision (%), accuracy (%), F1 score, and Youden’s index.

To address the defects in lithium-ion battery lifespan, this paper proposes a composite waveform generation strategy that offers capacity-recovering effect. Based on digital architecture, this study exploits direct digital synthesis (DDS) to generate data, which are then processed in an analog-to-digital converter to produce a predefined voltage waveform signal. In the process, an arbitrary waveform is converted to digital voltage waveform signal through pulse width modulation (PWM) technology, thus realizing waveform generation through DDS. Subsequently, analog-to-digital conversion is accomplished by going through a buck circuit, resulting in a composite sinusoidal waveform that is used to charge the battery with a recovering effect. This paper comprises an introduction of effective waveforms for capacity recovering, methods of generating composite sinusoidal waveforms, and an example of the application of composite sinusoidal waveform generation. The waveform produced by the circuit may recover the capacity of an aged 18650 lithium-ion battery by about 8%.