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Parma, Italy

Ascari L.,SantAnna School of Advanced Studies | Ascari L.,HENESIS Srl | Stefanini C.,SantAnna School of Advanced Studies | Bertocchi U.,SantAnna School of Advanced Studies | Dario P.,SantAnna School of Advanced Studies
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science | Year: 2010

This work presents the design and development of an integrated image-guided robot-assisted endoscopic system for the safe navigation within the spinal subarachnoid space, providing the surgeon with the direct vision of the structures (i.e. spinal cord, roots, vessels) and the possibility of performing some particularly useful operations, like local electrostimulation of nerve roots. The modelling, micro-fabrication, fluidic sustentation, and cable-based actuation system of a steerable tip for a multilumen flexible catheter is described; the hierarchical control system shared between the surgeon and the computer, and based on machine vision techniques and a simple but effective three-dimensional reconstruction is detailed. The Blind Expected Perception sensory-motor scheme is proposed in robot-assited endoscopy. Results from in vitro, ex vivo, and in vivo experiments show that the described model can accurately predict the shape of the catheter given the tension distribution on the cables, that the proposed actuation system can assure smooth and precise control of the catheter tip, that the fluidic sustentation of the catheter is essential in in vivo navigation, and that the proposed rear view mirror interface to show non-visible obstacles is appropriate; in conclusion, the results proved the validity of the proposed solution to develop an intrinsically safe robotic system for navigation and intervention in a narrow and challenging environment such as the spinal subarachnoid space. © 2010 Authors. Source

Cagnoni S.,University of Parma | Bacchini A.,University of Parma | Mussi L.,HENESIS Srl
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. Source

Ugolotti R.,University of Parma | Sassi F.,University of Parma | Sassi F.,HENESIS Srl | 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. Source

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. Source

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