With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the ongoing increment on the spatial–temporal resolution together with the free availability of some of these services. Typically time series are used as a pre-processing technique and combined with supervised learning techniques in order to build models for crop type identification in remote images. However, these models suffer from the lack of labelled data sets needed to train them. Unsupervised classification can overcome this limitation but has been less frequently used in this research field. This paper proposes to test and analyse the performance of several unsupervised clustering algorithms towards crop type identification on remote images. In this manner combinations of clustering algorithms and distance measures, a key element in the behaviour of these algorithms, are studied using an experimental design with more than twenty datasets built from the combinations of five crops and more than 45000 parcels. Results highlight better clustering methods and distance measures to create accurate and novel crop mapping models for remote sensing images.

The acquisition of 3D MRIs is adversely affected by many degrading factors including low spatial resolution and noise. Image enhancement techniques are commonplace, but there are few proposals that address the increase of the spatial resolution and noise removal at the same time. An algorithm to address this vital need is proposed in this presented work. The proposal tiles the 3D image space into parallelepipeds, so that a median filter is applied in each parallelepiped. The results obtained from several such tilings are then combined by a subsequent median computation. The convergence properties of the proposed method are formally proved. Experimental results with both synthetic and real images demonstrate our approach outperforms its competitors for images with high noise levels. Moreover, it is demonstrated that our algorithm does not generate any hallucinations.

This paper presents a methodology for estimating pollution on roads by analyzing traffic video sequences. The objective is to take advantage of the huge network of static cameras which is possible to lind in the road system of any state or country to estimate the pollution on each area. This proposal uses deep learning neural networks for the object detection, and a pollution estimation model based on the frequency of vehicles and their speed. The experiments show promising results which suggest that the system can be used alone or combined with existing systems for measuring pollution on roads.

The backup taken of a file system must be consistent, preserving data integrity across files in the file system. With file system sizes getting very large, and with demand for continuous access to data, backup has to be taken when the file system is active (is online). Arbitrarily taken online backup may result in an inconsistent backup copy. We propose a scheme referred to as mutual serializability to take a consistent backup of an active file system assuming that the file system supports transactions. The scheme extends the set of conflicting operations to include read-read conflicts, and it is shown that if the backup transaction is mutually serializable with every other transaction individually, a consistent backup copy is obtained. The user transactions continue to serialize within themselves using some standard concurrency control protocol such as Strict 2PL. We put our scheme into a formal framework to prove its correctness, and the formalization as well as the correctness proof are independent of the concurrency control protocol used to serialize user transactions. The scheme has been implemented and experiments show that consistent online backup is possible with reasonable overhead.