Today, microcontrollers are of paramount importance in various aspects of life. They are used for design in many industrial fields from simple to highly complex devices. With a COVID-19 crisis going on, blending learning is the ideal solution for a post-pandemic society. This paper proposes a blended learning system as a solution to address today's problem in teaching microcontroller courses through collaboration between distance learning with the proposed training toolkit for real work. Implementation of the proposed solution began by constructing an inexpensive training kit (100$), to empower all students, even those in remote rural areas. The distance learning model allows the simulation of the proposed IoT projects electronically anywhere and at any time using the Proteus design suite, which helps students to conduct them before the actual laboratory appointment. Two learning models are programmed in assembly language which is directly related to the internal architecture of the microcontroller and provides access to all the real capabilities of its central processing unit. To get acquainted with all the features offered by the microcontroller integrated circuit, various IoT projects were constructed, each one dedicated to learning its architecture features, important to engineering students. The proposed IoT systems operate with a minimum consuming power that is very important for portable devices.Questionnaire questions for students were formulated to measure the proposed system benefit over three academic years.
Skin cancer is the deadliest diseases compared with all other kinds of cancer. In this paper various pre- and post-treatments are proposed for improving automated melanoma diagnosis of dermoscopy images. At first pre-processing have done to exclude unwanted parts, a new triple-A segmentation proposes to extract lesion according to their histogram patterns. Lastly, suggest appending process with testing many factors for superior detection decision. This paper offers a novel approach with testing different detection rules: first system used fuzzy rules based on a different features, a second test has been done by modeled local colours with bag-of-features classifier. Then proposed adding lesion shape on two previous systems as their global form in the first one, while distributing it and appending with local colour patches in the second system. For each case, different features; various colour models, and many other parameters are examined to decide which settings are more discriminating. Evaluates performance of each method has carried out on (ISIC2019 Challenge) dermoscopic database. The novel processes with their a specific parameters are rising the classification accuracy to 98.26%.
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