A stepper motor is usually operated in position open-loop control for simplicity. However, in order to improve the transient and steady-state performances of the stepper motor-based drive system, a high performance stepper motor drive system is usually designed to feed the encoder signal back to form a closed-loop system such as a general servo motor drive, and high-performance position and speed loop controller can then be used to overcome the nonlinear characteristics of the motor, such as the cogging force and impacts from external load. On the other hand, the existed cogging force of the hybrid stepper motor must be solved to increase the positioning precision. The variable structure control (VSC) is insensitive to the bounded uncertainty and load disturbance, and has been known as a high-performance controller. A predefined sliding surface is used to shape the system performances, and incorporate with switching control to achieve the robustness property. Thus, we applied the VSC to implement the stepper motor drive system about the position and speed control, and the switching control is used to overcome the cogging force. The field programmable gate array (FPGA) is a good alternative to be used to realize a motor drive system by considering its programmable ability and diverse designing environment. It is easily developed as an intellectual property (IP) for future use or combined as a part of a large control system. This paper showed the procedures to develop the hardware circuits for the variable structure controller, and applied it to stepper motor position and velocity control. Functions such as PI controllers, dq-axis transformation and two-phase space vector space vector modulation (SVPWM) designed for the stepper motor drives are also shown in the paper. The system simulation and hardware circuit realization are based on MATLAB/Simulink, and realized on Altera FPGA. Simulations on MATLAB/Simulink with trapezoidal velocity profile command, and experiments with and without the load added are shown to demonstrate the hardware performances and correctness.

Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) spatial and one-dimensional (1D) convolutional neural network (CNN) to early detect possible breast lesions (tumors) to reduce patients’ mortality rates and to develop a classifier for use in mammographic images on regions of interest where breast lesions (tumors) may likely occur. The 2D spatial fractional-order convolutional processes are used to strengthen and sharpen the lesions’ features, denoise, and improve the feature extraction processes. Then, an automatic extraction task is performed using a specific bounding box to sequentially pick out feature patterns from each mammographic image. The multi-round 1D kernel convolutional processes can also strengthen and denoise 1D feature signals and assist in the identification of the differentiation levels of normality and abnormality signals. In the classification layer, a gray relational analysis-based classifier is used to screen the possible lesions, including normal (Nor), benign (B), and malignant (M) classes. The classifier development for clinical applications can reduce classifier’s training time, computational complexity level, computational time, and achieve a more accurate rate for meeting clinical/medical purpose. Mammographic images were selected from the mammographic image analysis society image database for experimental tests on breast lesions screening and K-fold cross-validations were performed. The experimental results showed promising performance in quantifying the classifier’s outcome for medical purpose evaluation in terms of recall (%), precision (%), accuracy (%), and F1 score.

Owing to the benefits of programmable and parallel processing of the field programmable gate arrays (FPGAs), they have been widely used to the realization of digital controllers and motor drive systems. In this study, we adopt the FPGA chip to realize the Linear Shaft Motor (LSM) drive system which includes the position, speed and current vector controls. Linear shaft motor is a special motor which has the magnet as the shaft and stator coils are on the forcer. Thus, it reveals a small mechanical time constant. In addition, the electrical side can be looked as a general three-phase Alternating Current (AC) system which can be powered by inverter and vector control technique can be applied to the system. The designed system needs high performance calculation ability about position/speed control and vector current control loops. The mathematical model of linear shaft motor drive system is first built and simulated by MATLAB/Simulink and the accuracy about the effect from the speed estimation method is proposed. The resulting digital model of the drive system is stored into Verilog Hardware Description Language (Verilog HDL) codes and realized by FPGA. At last, the hardware circuits as well as the power module are used to test the performance of the developed hardware system in terms of the trapezoidal velocity profile. The experimental results show that the designed system realized by FPGA has attained the desired performance.

Digital images can be easily shared or stored using different imaging devices, storage tools, and computer networks or wireless communication systems. However, these digital images, such as headshots or medical images, may contain private information. Hence, to protect the confidentiality, reliability, and availability of digital images on online processing applications, it is crucial to increase the infosecurity of these images. Therefore, an authorization encryption scheme should ensure a high security level of digital images. The present study aimed to establish a multilayer convolutional processing network (MCPN)-based cryptography mechanism for performing two-round image encryption and decryption processes. In the MCPN layer, two-dimensional (2D) spatial convolutional operations were used to extract the image features and perform scramble operations from grayscale to gray gradient values for the first-image encryption and second-image decryption processes, respectively. In the MCPN weighted network, a sine-power chaotic map (SPCM)-based key generator was used to dynamically produce the non-ordered pseudorandom numbers to set the network-weighted values as secret keys in a sufficiently large key space. It performs the second and first encryption processes using the diffusion method, modifying the image pixel values. Children’s headshots and medical images were used to evaluate the security level by comparing the plain and cipher images using the information entropy, number of pixel change rate, and unified averaged changed intensity indices. Moreover, the plain and decrypted images were compared to verify the decrypted image quality using the structural similarity index measurement and peak signal-to-noise ratio.