This paper presents a semi-automatic classification approach to evaluate steatotic liver tissues using B-scan ultrasound images. Several features have been extracted and used in three different classifiers, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). The classifiers were trained using the 10-cross validation method. A feature selection method based on stepwise regression was also exploited resulting in better accuracy predictions. The results showed that the SVM have a slightly higher performance than the kNN and the ANN, appearing as the most relevant one to be applied to the discrimination of pathologic tissues in clinical practice

The conditions in which images are obtained to perform multispectral detection of landmines have a direct influence on the methods that are used to perform automatic detection of landmines. In this article, two methodologies are proposed, one using traditional classifiers and the other using Deep Learning, namely a Convolutional Neuronal Network (CNN). In the first methodology, classifier fusion techniques are also used. The performance of the first methodology was evaluated as a function of the number of landmine features, the environment and the depth of the mine. In Deep Learning, a study was carried out based on the feature map, the type of landmine and the environment. A quantitative analysis shows that traditional classifiers achieved an overall accuracy above 97% in indoor and outdoor environments for the detection of landmines. The adopted Deep Learning methodology presented an increase in the performance for larger mines and a decrease for smaller ones. These experimental results shed light on the factors that influence the detection of mines and into the advantages and disadvantages of CNN compared with classical classifier methods.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments.

Multispectral images are images with more than one channel acquired in different bands or spectral ranges of the electromagnetic spectrum. Each one has specific details that can be exploited in facial recognition applications. In particular, to detect facial expression variations, pose variations and presentation attacks, a facial analysis system can benefit not only of images from the visible spectral band but also of infrared images. In this paper we perform a review of the state of the art methods used in multispectral facial recognition using images from the visible spectral band and also from the Near Infrared, Short Wavelength Infrared and Long Wavelength Infrared sub-bands. The public multispectral databases for facial analysis are identified, and a comparison is made, taking into consideration their specifications. The multispectral facial recognition methods are classified according to their basic working principle, from the traditional Fusion and Subspace methods to the more recent Deep Neural Networks.