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Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 3.46M | Year: 2009

Medical imaging (MI) is at the heart of many of todays improved diagnostic and treatment technologies. Computer-based solutions are vastly more capable of both quantitative measurement of the medical condition and the pre-processing tasks of filtering, sharpening, and focusing image detail. Bio-inspired and Soft Computing (BC, SC) techniques have been successfully applied in each of the fundamental steps of medical image processing and analysis (e.g. restoration, segmentation, registration or tracking). The natural partnership of humans and intelligent systems and machines in MI is to provide the clinician with powerful tools to take better decisions regarding diagnostic and treatment. The main goal of the network is to create a training programme where the enrolled early-stage researchers (ESRs) will be exposed to a wide variety of SC and BC techniques, as well as to the challenge of applying them to different situations and problems within the different MI stages. A personalised, exhaustive and complementary programme will consist of: i) a personalised research plan based on individual research projects; ii) local and network-wide specific training courses, both in face-to-face and virtual modalities; iii) the networks complementary skills courses, workshops and final conference; and iv) the international research stays among the different partners. The collaboration of experts from the area of MI with those working on BC and SC applications to computer vision will generate new and viable methods and solutions from the combined ideas of these communities. The presence of both research and technical partners in the network, including hospitals and companies, will provide the appropriate framework for application domain focused research. The trained ESRs will acquire a strong background for the development of intelligent systems based on BC-SC providing flexible application-oriented solutions to current MI problems in the clinical and research field.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.3.4 | Award Amount: 3.98M | Year: 2013

Application requirements, power, and technological constraints are driving the architectural convergence of future processors towards heterogeneous many-cores. This development is confronted with variability challenges, mainly the susceptibility to time-dependent variations in silicon devices. Increasing guard-bands to battle variations is not scalable, due to the too large worst-case cost impact for technology nodes around 10 nm. The goal of HARPA is to enable next-generation embedded and high-performance heterogeneous many-cores to cost-effectively confront variations by providing Dependable-Performance: correct functionality and timing guarantees throughout the expected lifetime of a platform under thermal, power, and energy constraints. The HARPA solution employs a cross-layer approach. A middleware implements a control engine that steers software/hardware knobs based on information from strategically dispersed monitors. This engine relies on technology models to identify/exploit various types of platform slack - performance, power/energy, thermal, lifetime, and structural (hardware) - to restore timing guarantees and ensure the expected lifetime amidst time-dependent variations. Dependable-Performance is critical for embedded applications to provide timing correctness; for high-performance applications, it is paramount to ensure load balancing in parallel phases and fast execution of sequential phases. The lifetime requirement has ramifications on the manufacturing process cost and the number of field-returns. The HARPA novelty is in seeking synergies in techniques that have been considered virtually exclusively in the embedded or high-performance domains (worst-case guaranteed partly proactive techniques in embedded, and dynamic best-effort reactive techniques in high-performance). HARPA will demonstrate the benefits of merging concepts from these two domains by evaluating key applications from both segments running on embedded and high-performance platforms.

Ugolotti R.,University of Parma | Sassi F.,University of Parma | Sassi F.,Henesis s.r.l. | Mordonini M.,University of Parma | Cagnoni S.,University of Parma
Journal of Ambient Intelligence and Humanized Computing | Year: 2013

This paper describes a novel system for detecting and classifying human activities based on a multi-sensor approach. The aim of this research is to create a loosely structured environment, where activity is constantly monitored and automatically classified, transparently to the subjects who are observed. The system uses four calibrated cameras installed in the room which is being monitored and a body-mounted wireless accelerometer on each person, exploiting the features of different sensors to maximize recognition accuracy, improve scalability and reliability. The algorithms on which the system is based, as well as its structure, are aimed at analyzing and classifying complex movements (like walking, sitting, jumping, running, falling, etc.) of potentially multiple people at the same time. Here, we describe a preliminary application, in which action classification is mostly aimed at detecting falls. Several instances of a hybrid classifier based on Support Vector Machines and Hierarchical Temporal Memories, a recent bio-inspired computational paradigm, are used to detect potentially dangerous activities of each person in the environment. If such an activity is detected and if the person "in danger" is wearing the accelerometer, the system localizes and activates it to receive data and then performs a more reliable fall detection using a specifically trained classifier. The opportunity to turn on the accelerometer on-demand makes it possible to extend its battery life. Besides and beyond surveillance, this system could also be used for the assessment of the degree of independence of elderly people or, in rehabilitation, to assist patients during recovery. © 2011 Springer-Verlag.

Gonzalez-Villanueva L.,Henesis S.r.l. | Gonzalez-Villanueva L.,University of Parma | Cagnoni S.,University of Parma | Ascari L.,Henesis S.r.l.
Sensors (Switzerland) | Year: 2013

Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Cagnoni S.,University of Parma | Bacchini A.,University of Parma | Mussi L.,Henesis s.r.l.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

GPU-based parallel implementations of algorithms are usually compared against the corresponding sequential versions compiled for a single-core CPU machine, without taking advantage of the multi-core and SIMD capabilities of modern processors. This leads to unfair comparisons, where speed-up figures are much larger than what could actually be obtained if the CPU-based version were properly parallelized and optimized. The availability of OpenCL, which compiles parallel code for both GPUs and multi-core CPUs, has made it much easier to compare execution speed of different architectures fully exploiting each architecture's best features. We tested our latest parallel implementations of Particle Swarm Optimization (PSO), compiled under OpenCL for both GPUs and multi-core CPUs, and separately optimized for the two hardware architectures. Our results show that, for PSO, a GPU-based parallelization is still generally more efficient than a multi-core CPU-based one. However, the speed-up obtained by the GPU-based with respect to the CPU-based version is by far lower than the orders-of-magnitude figures reported by the papers which compare GPU-based parallel implementations to basic single-thread CPU code. © 2012 Springer-Verlag.

Gonzalez-Villanueva L.,Henesis S.r.l. | Gonzalez-Villanueva L.,University of Parma | Alvarez-Alvarez A.,European Center for Soft Computing | Ascari L.,Henesis S.r.l. | Trivino G.,European Center for Soft Computing
Applied Soft Computing Journal | Year: 2014

In this paper, human motion analysis is performed by modeling a physical complex exercise in order to provide feedback about the patient's performance in rehabilitation therapies. The Sun Salutation exercise, which is a flowing sequence of 12 yoga poses, is analyzed. This exercise provides physical benefits as improving the strength and flexibility of the muscles and the alignment of the spinal column. A temporal series of measures that contains a numerical description of this sequence is obtained by using a wearable sensing system for monitoring, which is formed by five high precision tri-axial accelerometer sensors worn by the patient while performing the exercise. Due to the complexity of the exercise and the huge amount of available data, its interpretation is a challenging task. Therefore, this paper describes the design of a computational system able of interpreting and generating linguistic descriptions about this exercise. Previous works on both Granular Linguistic Models of Phenomena and Fuzzy Finite State Machines are used to create a basic linguistic model of the Sun Salutation. This model allows generating human friendly reports focused on the assessment of the exercise quality based on its symmetry, stability and rhythm. © 2013 Elsevier B.V.

Ugolotti R.,University of Parma | Nashed Y.S.G.,University of Parma | Mesejo P.,University of Parma | Ivekovic S.,University of Strathclyde | And 3 more authors.
Applied Soft Computing Journal | Year: 2013

Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition. The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation. In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subject's posture in space. Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIATM CUDA computing architecture. © 2012 Elsevier B.V. All rights reserved.

Ahmadian P.,Henesis s.r.l | Ahmadian P.,University of Parma | Cagnoni S.,University of Parma | Ascari L.,Henesis s.r.l
Frontiers in Human Neuroscience | Year: 2013

In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) EEG data in humans from both Neuroscientists and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential. Using the premovement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users. © 2013 Ahmadian, Cagnoni and Ascari.

This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operators action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.

A measurement system (1) of the relative position between two structural parts (2a, 2b) of a building, that have separated for example following the formation of a crack, comprising a cylindrical permanent magnet (3) adapted to generate a magnetic field (B) and a sensor (5), the sensor (5) being fixed and the magnet (3) being moveable according to a direction perpendicular to its axis of symmetry (h) so as to vary the direction of the lines of force of the magnetic field B which cross the sensor 5.

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