The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is time- and storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images. The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence.

In this paper we have proposed an improved miniaturization design of a rectangular microstrip patch antenna based on Minkowski fractal. The design methodology consists in considering a rectangular patch on which the conventional and modified Minkowski curve are successively applied until the second iteration. In total, three miniaturized antennae are proposed. They are structured by a fractal patch engraved on an FR-4 epoxy substrate. Design and simulation are performed by CADFEKO 7.0.1 software, better suited for small size antennae. The proposed miniaturized fractal models operate at the same frequency F= 15.53 GHz, as the conventional Rectangular Microstrip patch antenna. Moreover, each of them enables a good impedance matching, as can be confirmed by the low return loss obtained (< -15 dB) at the same resonance frequency. The proposed antennae presented a significant size reduction of 89.76 % for Minkowski model, 88.8 % and 86% for both modified Minkowskimodels when compared to the conventional inset-fed rectangular patch antenna. Overall, our study illustrated that, after a modified Minkowski miniaturization technique was introduced a better performance and impedance matching was achieved. According to the IEEE Standard Classification of Satellite Frequency Bands, the results of the miniaturized and optimized fractal patch antenna can effectively contribute to Ku-band communications.

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

Many studies on software quality use a variety of techniques and tools to assess quality in IT organizations. However, it is still difficult to ensure the proper use of measures to guarantee software quality. Cameroon, like many developing countries, faces a number of challenges in its software industry including limited market size, poor infrastructure, and lack of software engineering best practices. This study evaluates the software quality measurement practices in Cameroon and identifies potential areas of improvement. This study conducted a questionnaire survey of 30 companies by identifying five main categories and nine research questions. 57% of the companies surveyed consider that the impact of the measures on the success of the project is significant, and the measurement findings are, by large, accessible to executives as well as to the staff concerned. Furthermore, the adoption of a measurement tool can improve the monitoring and management of software projects.

The design of decision-support systems remains a challenge because no method has yet been approved, although several approaches have been proposed. Multidimensional Canonical Partitioning (MCP) is a supply-driven approach to design decision-support systems, previously proposed. In this approach, decision systems are designed from Entity/Relationship (E/R) schema. The schema provided as input is reduced to a flat relation, if it is not already the case, by using universal relations assumption. The design approach uses two main algorithms. The first is a greedy type heuristic algorithm, and the second a matching algorithm. The approach has six steps. In this paper, we implement algorithms used in the design approach. We then propose a graphical interface as a tool for implementing the approach. The tool, called PaCaM, is intended to be used by decision-support systems designers. We after all apply the design approach on urban transactional systems.