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Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5.

Modern application-specific integrated circuits (ASICs) are increasingly employed in domains where they must fulfill security requirements. Traditional ASIC design flows include numerous steps to ensure the correctness of a circuit and its freedom from manufacturing defects, but they do not cover security vulnerabilities. In this paper, we show how to leverage state-of-the-art electronic design automation (EDA) tools to validate the resistance of a circuit against fault injection attacks in early design steps (before fabrication). While the approach is generic, we demonstrate it on a specific physical attack vector: Fault Sensitivity Analysis (FSA). We show how existing tools (especially for logic and timing simulation) can be extended by custom scripts to assess the vulnerability of an implementation to such attacks.

Modern mobile communication networks and Internet of Things are paving the way to ubiquitous and mobile computing. On the other hand, several new computing paradigms, such as edge computing, demand for high computational capabilities on specific network nodes. Ubiquitous environments require a large number of distributed user identification nodes enabling a secure platform for resources, services and information management. Biometric systems represent a useful option to the typical identification systems. An accurate automatic fingerprint classification module provides a valuable indexing scheme that allows for effective matching in large fingerprint databases. In this work, an efficient embedded fingerprint classification node based on the fusion of a Weightless Neural Network architecture and a technique, namely Virtual Neuron, which efficiently maps a neural network architecture into hardware resources, is presented. The key novelty of the proposed paper is a new neural-based classification methodology that can leverage devices and sensors with limited number of resources, allowing for resource-efficient hardware implementations. Furthermore, the classifier efficiency and the accuracy have been optimized to obtain high classification rate with the best trade-off between minimum area on chip and execution time. The proposed neural-based classifier analyzes a directional image, which is extracted from the original fingerprint image without any enhancement, and classifies the processed item into the five NIST NBIS classes. This approach has been designed for FPGA devices, by exploiting pipeline techniques for execution time reduction. Experimental results, based on a 10-fold cross-validation strategy, show an overall average classification rate of 90.08% on the whole official FVC2002DB2 database.

Aim: PET imaging is increasingly utilized for radiation treatment planning. Nevertheless, accurate segmentation of PET images is a complex and unresolved problem. Aim of this work is the development of an automatic segmentation method of Biological Target Volume (BTV) in patients with cervical cancer. Materials and methods: Random walks (RW) is a graph-based method that represents a DICOM (Digital Imaging and COmmunications in Medicine) image as a graph. The voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. Then, RW partitions the nodes into target and background subsets. To create an automatic method starting from previous work (A. Stefano, et al. A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study, in A. Petrosino, ed., Image Analysis and Processing - ICIAP 2013: LNCS, v. 8157, Springer Berlin Heidelberg, p. 711-720), we propose an automated RW seed localization approach. The algorithm identifies the PET slice with the highest SUVmax and a maximum of 10 target and 8 background seeds for each volume slice. The voxels with a SUV>95% of SUVmax are marked as target seeds. Then, the method explores the hottest voxel neighborhood through searching in 8 directions to identify the background voxels with a SUV