Internet of Things is becoming widely present in our daily life. In fact, more and more devices able to interact together have been recently designed and launched in the market. Learning Internet of Things technologies is becoming unavoidable in education. In this paper, we propose a practical approach allowing to progressively learn, by practice the, essential concepts of Internet of Things applied to Smart Homes. From basic knowledge of C++ language and the use of Arduino or its derived, students can develop skills and also smart applications in the field of Internet of Things.

The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.

Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defects, assembly errors, or texture inconsistencies, preventing defective products from reaching customers. Predictive maintenance leverages sensor data by analyzing vibrations, temperature, and pressure signals to anticipate failures and avoid production downtime. Image-based quality control has become critical in industries such as automotive, electronics, aerospace, and food processing, where visual appearance is a key quality indicator. Although advances in deep learning and computer vision have significantly improved anomaly detection, industrial deployments remain challenged by the scarcity of labeled anomalies and the variability of defects. These issues increasingly lead to the adoption of unsupervised methods and generative approaches, which, despite their effectiveness, introduce substantial computational complexity. We conduct a unified comparison of ten anomaly detection methods, categorizing them according to their reliance on synthetic anomaly generation and their detection strategy, either reconstruction-based or feature-based. All models are trained exclusively on normal data to mirror realistic industrial conditions. Our evaluation framework combines performance metrics such as recall, precision, and their harmonic mean, emphasizing the need to minimize false negatives that could lead to critical production failures. In addition, we assess environmental impact and hardware complexity to better guide method selection. Practical recommendations are provided to balance robustness, operational feasibility, and sustainability in industrial applications.

With the emergence of smartphones, video surveillance cameras, social networks, and multimedia engines, as well as the development of the internet and connected objects (the Internet of Things—IoT), the number of available images is increasing very quickly. This leads to the necessity of managing a huge amount of data using Big Data technologies. In this context, several sectors, such as security and medicine, need to extract image features (index) in order to quickly and efficiently find these data with high precision. To reach this first goal, two main approaches exist in the literature. The first one uses classical methods based on the extraction of visual features, such as color, texture, and shape for indexation. The accuracy of these methods was acceptable until the early 2010s. The second approach is based on convolutional neuronal networks (CNN), which offer better precision due to the largeness of the descriptors, but they can cause an increase in research time and storage space. To decrease the research time, one needs to reduce the size of these vectors (descriptors) by using dimensionality reduction methods. In this paper, we propose an approach that allows the problem of the “curse of dimensionality” to be solved thanks to an efficient combination of convolutional neural networks and dimensionality reduction methods. Our contribution consists of defining the best combination approach between the CNN layers and the regional maximum activation of convolutions (RMAC) method and its variants. With our combined approach, we propose providing reduced descriptors that will accelerate the research time and reduce the storage space while maintaining precision. We conclude by proposing the best position of an RMAC layer with an increase in accuracy ranging from 4.03% to 27.34%, a decrease in research time ranging from 89.66% to 98.14% in the function of CNN architecture, and a reduction in the size of the descriptor vector by 97.96% on the GHIM-10K benchmark database.

In recent years, we have witnessed an incredible and rapid growth of multimedia content in its different forms (2D and 3D images, text, sound, video, etc