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In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.

The fundamental concepts of electronic devices are generally taught in the initial courses of electrical and electronics engineering. For students, it is very important to grasp the basic concepts that make computers and digital systems possible. Among these concepts, boolean algebra and logic gates stand out. In this paper, a smartphone‐based augmented reality (AR) system is proposed which helps in the adequate learning of logic gate integrated circuits (ICs). The proposed system allows the automatic identification of ICs that enables basic logic operations. It uses a smartphone and a breadboard for the identification of 7 basic logic gate ICs that are: AND gate, OR gate, NOT gate, NAND gate, NOR gate, XOR gate, and XNOR gate. The smartphone takes a picture of the electronic circuit and digitally generates a layer of virtual objects which are then mixed with the original image. The markless paradigm identifies the ICs in a typical circuit and places three objects that are: IC identification, pins information, and logic diagram information. Finally, the smartphone screen displays the image of the logic gate obtained. The entire evaluation of the AR system is presented with a technical efficiency of 97.5 and a qualitative satisfaction study.

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The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which works with local texture analysis, and which is developed in a non supervised fashion, clustering the local achieved descriptors and classifying them with the use of a K-means algorithm supported by the genetic algorithm. This method has been deployed in LabVIEW software, programming each part of the procedure in order to implement it in hardware. The results are very promising, reaching up to 90% of recall for natural scene classification.