Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

Breast cancer is one of the deadliest cancers for women. Early detection of skin cancer gives a high chance for the women to escape from the malady and obtain a cure at the initial stages. In other words, early detection of breast cancer has a direct relation by the women's quality of life. In this case, mammography images are important. Indeed, the main test used for screening and early diagnosis of breast cancer is mammography. In recent years, computer-aided cancer detection has been turned into an active field of research and showed a promising future. In this study, a new optimization algorithm based on thresholding is introduced. A WCO algorithm is employed as the optimization algorithm. WCO is a new meta-heuristic approach which is inspired by the FIFA world cup challenge. The presented method utilizes random samples as candidate solutions from the search space inside the image histogram with considering to the objective function that is utilized by the Kapur's method.

An Emergency Response (ER) Cyber-Physical System (CPS) to avoid landslides and survey areas located on or near slopes is introduced that handles two problems: electronic waste disposal, and environmental disasters. Uncomplicated detection circuits using salvaged components can pinpoint floods in impoverished regions. CPSs simplify hazard prediction and mitigation in disaster supervision. Nonetheless, few green practices and efforts have been accomplished in this regard. Recent technical advances help landslides studies and the evaluation of suitable risk alleviation measures. This work addresses in situ meters, and cameras to observe ground movements more accurately. The ER-CPS identifies and can help mitigate landslides using techniques based on motion detection that can productively predict and monitor the zone conditions to classify it, and the landslide-related data can be transmitted to inspecting stations to lessen the erosion/sedimentation likelihood while increasing security.

Image segmentation is one of the most important tasks of visual machine and image processing that helps to recognize the pattern. Grouping of the pixels of an image helps to understand it. One of the successful methods for segmentation of the complicated images is artificial neural networks. Artificial neural networks are a subset of data science that can be used to extract significant data from the image. There are different methods that are proposed for the development of neural networks. The purpose of this chapter is to study different evolutionary algorithms for optimizing neural networks in different ways for image segmentation purposes.

Field-Programmable Gate Array (FPGA) provides the possibility to design new “electromagnetic compatibility (EMC) friendly” control techniques for power electronic converters. Such control techniques use pseudo-random modulators (RanM) to control the converter switches. However, some issues connected with the FPGA-based design of RanM, such as matching the range of fixed-point numbers, might be challenging. The modern programming tools, such as LabVIEW, may facilitate the design process, but there are still fixed-point operations and limitations in arithmetic operations. This paper presents the design insights on the FPGA-based EMC friendly control system for DC/DC converter. Probability density functions (PDF) are used to analyse and improve pseudo-random algorithms. The theoretical algorithms, hardware details and experimental results are presented and discussed in terms of conducted electromagnetic interference emission.