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PurposeMagnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. MethodTo address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments. ResultsThe proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 pixels], MAXD=6.233 pixels] and HD=2.988 pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches. ConclusionsThe proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution. Display Omitted The proposed method performs a fully automatic uterus and fibroids segmentation.We provide the boundary and volume of ablated fibroid regions and uterus.Our approach is based on FCM and iterative optimal threshold selection algorithms.This solution could optimize the current operative MRgFUS methodology.Our approach can improve the follow-up of patients undergone MRgFUS treatments.

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using a lot of training examples, the proposed approach requires only 18 directional images per class. Experimental results, conducted on a consistent subset of the free downloadable PolyU database, show a classification rate of 87.59%.

In this paper, a first application of the method of fundamental solutions in estimating the electric potential and the spatial current density distribution in the brain due to transcranial stimulation, is presented. The coupled boundary value p roblems for the electric potential are solved in a meshless way, so avoiding the use of grid based numerical methods. A multi-spherical geometry is considered and numerical results are discussed.

MRgFUS (Magnetic Resonance guided Focused UltraSound) is a new and non-invasive technique to treat different diseases in the oncology field, that uses Focused Ultrasound (FUS) to induce necrosis in the lesion. Temperature change measurements during ultrasound thermo-therapies can be performed through magnetic resonance monitoring by using Proton Resonance Frequency (PRF) thermometry. It measures the phase variation resulting from the temperature-dependent changes in resonance frequency by subtracting one phase baseline image from actual phase. Referenceless thermometry aims to re-duce artefacts caused by tissue motion and frequency drift, fitting the back-ground phase outside the heated region. The aim of this contribution is to pro-pose a novel background phase reconstruction method using Radial Basis Func-tion (RBF) interpolation. The effectiveness of the method has been demonstrat-ed by comparing it against the classical PRF shift and polynomial referenceless approach. The comparison evaluates temperature rises in uterine fibroids during MRgFUS treatments on a set of 10 patients.

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: The epicardial fat is considered a prognostic marker of CAD [1]; it could locally modulate the coronary artery functions through secretion of anti-inflammatory and pro-inflammatory cytokines [2]. An increase in epicardial adipose tissue volume is...

Clifford Algebra or Geometric Algebra (GA) is a simple and intuitive way to model geometric objects and their transformations. Operating in high-dimensional vector spaces with significant computational costs, the practical use of GA requires, however, dedicated software and/or hardware architectures to directly support Clifford data types and operators. In this paper, a family of embedded coprocessors for the native execution of GA operations is presented. The paper shows the evolution of the coprocessor family focusing on the latest two architectures that offer direct hardware support to up to five-dimensional Clifford operations. The proposed coprocessors exploit hardware-oriented representations of GA elements and operators properly conceived to obtain fast performing implementations. The coprocessor prototypes, implemented on Field Programmable Gate Arrays (FPGA) development boards, show significant speedups of about one order of magnitude with respect to the baseline software library Gaigen running on a general-purpose processor. The paper also presents an execution analysis of different GA-based applications, namely inverse kinematics of a robot, optical motion capture, raytracing, and medical image processing, showing good speedups with respect to the baseline general-purpose implementation.

The purpose of this study was to evaluate the clinical value of PET (Positron Emission Tomography) for early prediction of tumor response to platinum-based therapy in patients with nonsmall cell lung cancer (NSCLC). The evaluation was carried out comparing the standard treatment response using RECIST (Response Evaluation Criteria in Solid Tumors) with metabolic treatment response according to European Organization for Research and Treatment of Cancer (EORTC) recommendations, PET Response Criteria in Solid Tumors (PERCIST), Total Lesion Glycolysis (TLG) and Metabolic Tumor Volume (MTV). Seventeen inoperable patients with stage IV NSCLC were enrolled between October 2011 and June 2013: PET studies were carried out before the initiation of platinum-based therapy and after the first cycle of chemotherapy for an early therapy monitoring. The lesions with the highest uptake in each patient were evaluated according to EORTC recommendations considering a cut-off of 15% (EORTC15%) and 25% (EORTC25%) to discriminat between patients who respond from those who do not respond to treatment. Moreover, PERCIST and RECIST classifications were evaluated too. Receiver operating characteristic curves were used to obtain cut-off points for therapy evaluation based on variations of TLG and MTV in sequential scans. Overall Survival (OS) time was calculated by the Kaplan-Meier test. The Kaplan-Meier analysis showed that RECIST, EORTC15%, ∆TLG, and ∆MTV proved to be a significant prognostic factor for predicting OS (p-value ≤ 0.0251). For responder patients, median OS was 595 days for RECIST, 423 for EORTC15% and ∆MTV, 492 for ∆TLG. For nonresponder patients, median OS was 238 days for RECIST and ∆TLG, 194.5 for EORTC15%, and 188 for ∆MTV. No statistically significant difference was recognized between responder and non-responder patients according to EORTC25% and PERCIST classifications (p-value ≥ 0.13). In addition, a new threshold of 17% for PERCIST classification was proposed for an early therapy monitoring rather than the conventional cut-off of 30%. Even if this is a preliminary analysis, the results suggest that PET examinations could provide an early identification of patients who benefit from platinum-based treatment.

Biometric recognition systems are rapidly evolving technologies and their use in embedded devices for accessing and managing data and resources is a very challenging issue. Usually, they are composed of three main modules: Acquisition, Features Extraction and Matching. In this paper the hardware design and implementation of an efficient fingerprint features extractor for embedded devices is described. The proposed architecture, designed for different acquisition sensors, is composed of four blocks: Image Pre-processor, Macro-Features Extractor, Micro- Features Extractor and Master Controller. The Image Pre- processor block increases the quality level of the input raw image and performs an adaptive binarization, introducing a novel hardware approach. The Macro-Features Extractor extracts singularity points. The Micro-Features Extractor extracts only micro-features around singularity points using an adaptive thinning and a post-processing phase to remove potential false micro-features. The Master Controller synchronizes and coordinates the two extractors. Xilinx ML507 board has been used to develop the prototype, while tests have been conducted on the PolyU (Hong Kong Polytechnic University) and the FVC2002 DB2-B free databases. These two databases have been chosen for their different characteristics in terms of image resolution and dimension in order to test the effectiveness of the proposed architecture. Experimental results show an interesting trade-off between used resources (about 32%) and fingerprint features extraction time (the lower execution time is 21.6 ms while the higher execution time is 28.4 ms, with a working frequency of 25 MHz), obtaining the best rate of false minutiae discharged of 5%.

The Proton Resonance Frequency (PRF) shift provide a method for temperature change measurements during thermotherapy. Conventional PRF thermometry works subtracting one or multiple baseline images. The method leads to artifacts caused by tissue motion and frequency drift. Various works estimating the background phase from each acquired image phase are present in literature. These algorithms are called “referenceless” because they don’t require any subtraction of baseline images for calculating temperature increment. Conventional referenceless methods estimate baseline image by fitting the background phase outside the heated region through a polynomial approach. In this work a background phase reconstruction method using Radial Basis Function (RBF) interpolation is presented. The effectiveness of the proposed approach has been demonstrated with Magnetic Resonance guided Focused UltraSound (MRgFUS) sonications on ex-vivo porcine muscle. A significant improvement of the proposed RBF referenceless method, against classical PRF and polynomial referenceless methods, has been demonstrated.

1. Purpose To supply an useful learning tool aimed to interactively display on mobile devices normal anatomy and variants of the nose and paranasal sinuses as seen on CBCT images. 2. Methods and Materials Images Images of the nose and paranasal sinuses were derived by a study series acquired by a CBCT device. CBCT studies of the paranasal sinuses were acquired in patients referred for nasal obstruction or sinusitis with the following parameters: 90 kVp, 12.5 mA, 20 s rotation time, FOV 13 x 14.5 cm, 0.25 x 0.25 x 0.25 mm voxel size. Software The application has been developed for iOS based mobile devices through the platform XCode provided by Apple®, and it is developed using the Objective-C programming language. The application has been configured as Master-Detail. This configuration splits the mobile device display in two panels. The left panel displays a list of the interesting items, while the right panel shows the relative details. Touching an item from the menu on the left panel, the textual description is shown on the same side, while the panel on the right will show the relative image. The application allows interactively navigation through normal anatomy and variants of the nose and paranasal sinuses, as represented on CBCT images in axial, sagittal and coronal planes. Cross-reference images to localize the same anatomic structures on different section planes are available. The navigation is intuitive, with multiple shortcuts. Different labels have been proposed in accordance with the specific anatomic lessic of the district and current literature references. High image quality with a zooming tool are available. 4. Conclusion An App for IOs devices was developed, that can represent an useful educational tool for medical students, residents and continuous medical education in radiology and other medical specialties dealing with nose and paranasal sinuses. This interactive atlas based on CBCT images could be also an useful option to be implemented on CBCT softwares

Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancement phase. Experimental trials, conducted on a subset of the free downloadable PolyU database, show a classification rate of 91% over a 100 images test database using only 12 training examples.

Expert Systems can integrate logic based often on computational intelligence methods and they are used in complex problem solving. In this work an Expert System for classifying liver iron concentration in thalassemic patients is presented. In this work, an ANN is used to validate the output of the L.I.O.MO.T (Liver Iron Overload Monitoring in Thalassemia) method against the output of the state-of-the-art method based on MRI T2 assessment for liver iron concentration. The model has been validated with a dataset of 200 samples. The experimental Mean Squared Error results and Correlation show interesting performances. The proposed algorithm has been developed as a plug in for OsiriX Dicom Viewer.

Presentiamo la nostra esperienza riguardo la progettazione e l'utilizzo di un plug-in per il software open-source di visualizzazione di immagini DICOM (OsiriX 32-bit; GNU General Public License) per la valutazione dei criteri dimensionali e densitometrici di metastasi epatiche in TCMS.

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: We present our experience about the improvement of a free DICOM medical imaging software Osirix (OsiriX 32-bit; GNU General Public License) for the evaluation of dimensional and densitometric changes of hepatic metastases on CT multislice...

he main Wireless Sensor Networks purpose is represented by areas of interest monitoring. Even if the Wireless sensor network is properly initialized, errors can occur during its monitoring tasks. The present work describes an approach for detecting faulty sensors in Wireless Sensor Network and for correcting their corrupted data. The approach is based on the assumption that exist a spatio-temporal cross- correlations among sensors. Two sequential mathematical tools are used. The first stage is a probabilistic tools, namely Markov Random Field, for a two-fold sensor classification (working or damaged). The last stage is represented by the Locally Weighted Regression model, a learning techniques modelling each sensor on the basis of its neighbours. If the sensor is working, the approach actives a learning phase and the sensor model is trained, while if the sensor is damaged, a correction phase starts and the related corrupted data are replaced with the data produced by the learned model. The effectiveness of the proposed approach has been proved using real data obtained from the Intel Berkeley Research Laboratory, over which different classes of faults were artificially superimposed. The proposed architecture achieves satisfactory results, since it successfully corrects faulty data produced by sensors.

Fibroids are benign tumors growing in the uterus. Most of fibroids do not require treatment unless they are causing symptoms. Traditional surgery treatments, like myomectomy and hysterectomy, are very invasive therapeutic approaches which not always preserves reproductive potential of the woman. MRgFUS, performed with Insightec ExAblate 2100 equipment, is a new and noninvasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time for patients. An initial assessment of MRgFUS treatment is made by computing the ablated volume of uterine fibroid. In this paper a semi-automatic approach, based on region-growing segmentation technique, is proposed. The implemented approach gives a quantitative and qualitative evaluation of the treatment providing the volume and the three-dimensional (3D) model of the treated fibroid area. Considering these characteristics, the proposed approach can be used as a tool to integrate the information used by a Medical Decision Support System (MDSS). As step in the MRgFUS treatment evaluation, the achieved results improve the current methodology based on the manual uterine fibroid ROT segmentation. © 2013 IEEE.

Medical community needs to extract precise information from a large amount of data. These data are a collection of different types such as text documents, images and video. Currently medical technology do not provide an intelligent methodology for documents recovery and classification of such documents based on their content. In this work the radiological structured reports are analysed with the corresponding mammographic images. The presented system is composed of an Indexing Engine and a Searching Engine, based on innovative methods for IR (Information Retrieval). The proposed work is useful for physicians as support diagnosis system, for students as learning support system, and finally, for epidemiological studies. The system allows for submitting a query through a web interface, consulting a structured report and showing associated mammographic images. The dataset is composed of both healthy and pathological patients. The proposed architecture achieved satisfactory results in terms of Sensitivity and Specificity. Three scenarios are presented and described. © 2013 IEEE.

The main challenge of intelligent transportation systems (ITS) is to deal with ‘real-time’ information to improve vehicular traffic management. Road data can be processed and used for dynamic traffic light management in order to reduce waiting times in queues. This paper proposes an innovative distributed architecture based on a wireless sensor network (WSN) with a network coordinator providing remote and ubiquitous authentication module for managing unexpected events. The architecture is completed by a dynamic module for street priority management depending on traffic rate. Many experimental trials have been carried out considering three different levels of traffic intensity to prove the effectiveness of the proposed approach. In a street with high traffic intensity (120 vehicles in queue), the average vehicles/second rate decrease from 6.135, using a fixed cycle traffic light, to 0.365 using the proposed traffic light measurement system. Moreover, to improve the system security, a fingerprints-based embedded authentication system has been implemented as a self-contained sensor to increase the security of distributed personal data storage. The best achieved FAR and FRR indexes are respectively 0.73% and 9.21%, with a mean elaboration time of 192.46 ms and a working frequency of 22.5 MHz.

User identification is a very interesting and complex task. Invasive biometrics is based on traits uniqueness and immutability over time. In forensic field, fingerprints have always been considered an essential element for personal recognition. The traditional issue is focused on full fingerprint images matching. In this paper an advanced technique for personal recognition based on partial fingerprint is proposed. This system is based on fingerprint local analysis and micro-features, endpoints and bifurcations, extraction. The proposed approach starts from minutiae extraction from a partial fingerprint image and ends with the final matching score between fingerprint pairs. The computation of likelihood ratios in fingerprint identification is computed by trying every possible overlapping of the partial image with complete image. The first experimental results conducted on the PolyU (Hong Kong Polytechnic University) free database show an encouraging performance in terms of identification accuracy.

Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the devel- opment of an accurate and fast method for semi-automatic segmentation of me- tabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Va- lidation was first performed on phantoms containing spheres and irregular in- serts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algo- rithm. Experimental results show that the segmentation algorithm is accurate and fast and meets the physician requirements in a radiotherapy environment.

In modern medical domain, documents are created directly in electronic form and stored on huge databases containing documents, text in integral form and images. Retrieving right informations from these servers is challenging and, sometimes, this is very time consuming. Current medical technology do not provide a smart methodology classification of such documents based on their content. In this work the radiological structured reports are analysed classified and assigning an appropriate label. The text classifier is used to label a mammographic structured report. The experimental data are real clinical report coming from a hospital server. Analysing the structured report content, the classifier labels the patient structured report as healthy or pathological. The present work uses Information Retrieval techniques to improve the classification process. These technique provide a light semantic analysis to remove negative terms, a removing stop-word step and, finally, a thesaurus is used to uniform used words. The structured reports are classified using a Bayes Naive Classifier. The experimental results provide interesting performance in terms of specificity and sensibility. Others two indexes are computed in order to assess system's robustness: these are the A z (Area under Curve ROC) and σ A z (A z standard error).

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems represent a valid alternative to the conventional authentication systems providing a flexible e-infrastructure towards an integrated solution supporting the requirement for improved inter-organizational functionality. In this work the study and the implementation of a fingerprints-based embedded biometric system is proposed. Typical strategies implemented in Identity Management Systems could be useful to protect biometric information. The proposed sensor can be seen as a self-contained sensor: it performs the all elaboration steps on board, a necessary requisite to strengthen security, so that sensible data are securely managed and stored inside the sensor, without any data leaking out. The sensor has been prototyped via an FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times of the sensor are reported. Finally, recognition rates of the proposed embedded biometric sensor have been evaluated considering three different databases: the FVC2002 reference database, the CSAI/Biometrika proprietary database, and the CSAI/Secugen proprietary database. The best achieved FAR and FRR indexes are respectively 1.07% and 8.33%, with an elaboration time of 183.32 ms and a working frequency of 22.5 MHz.

Although still there is no a clear definition of “adipose tissue dysfunction”, the identification of clinical and biological markers of altered fat distribution and function may provide the needed tools to early identify a condition of cardiometabolic risk. Visceral Adiposity Index (VAI) is a mathematical gender-specific index estimated with the use of simple anthropometric [(BMI and Waist circumference (WC)] and biochemical parameters [HDL cholesterol (HDL) and Triglycerides (Tg)], that in recent studies has shown to reflect accurately the degree of visceral adiposity and insulin resistance. However, although VAI has been indirectly shown to be a marker of impaired fat distribution and function, until to date there are only few studies in which it was correlated with the main adipokines (leptin and adiponectin). Our aim was to evaluate the correlations among various measures of fat distribution [VAI, BMI, WC, WHR, visceral adipose tissue volume (VAT ) and subcutaneous adipose tissue volume (SAT) measured by MR between L2 and L4] and a complete panel of adipocytokines [Visfatin, Resistin, Leptin, Soluble leptin receptors (sOB-R), Leptin/sOb-R ratio, Adiponectin, Ghrelin, Adipsin, PAI-1, vascular endothelial growth factor (VEGF), TNF-alpha,??hs-CRP, IL-6, IL-18, Hepatocyte growth factor (HGF)]?in patients with type 2 diabetes (DM2). Ninety-one DM2 patients (age: 65.25±6.38 years; duration of disease: 9.73±5.99 years; 42 men and 49 women) in stable treatment for the last two months with metformin in monotherapy at 2 g/day were screened for the study. In all patients the serum levels of adipocytokines were assayed by Luminexbased kits. In a subset of 13 patients an assessment of VAT and SAT volume was performed through a MR. At the Pearson's correlation, among all the investigated measures of fat distribution, only VAI showed a significant correlation with almost all analyzed cytokines [visfatin (r=0.331, p=0.001), resistin (r=0.354, p=0.001), leptin (r=0.285, p=0.005), sOb-R (r=-0.214, p=0.041), ghrelin (r=-0.225, p=0.032), adiponectin (r=-0.394, p

E-infrastructures must support the development of heterogeneous applications for workstation network, for mobile and portable systems and devices. In this context and relating to all collaborative and pervasive computational technology a very important role is played by security and authentication systems, which represent the first step of the whole process. Biometric authentication systems represent a valid alternative to conventional authentication systems providing robust procedures for user authentication. On the other hand, Internet of Things involves a heterogeneous set of interacting devices to enable innovative global and local applications and services for users. In this chapter fingerprint and iris based unimodal and multimodal authentication systems will be described, analyzed and compared. Finally, a prototyped embedded multimodal biometric sensor will be outlined. Software and hardware prototypes have been checked against common and widely used databases.

Background: Traditional surgery for uterine fibroids treatments (e.g. myomectomy, hysterectomy) offers very invasive therapeutic approaches, which not always preserve reproductive potential of the woman. MRgFUS (MR guided Focused UltraSound) is a new and non-invasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time [1]. On June 2011 and July 2012 the first treatments were started at HSR-Giglio Hospital in Cefalù and at University Hospital (DIBIMEF) in Palermo. An initial assessment of MRgFUS treatment was made by computing the thermally-ablated volume of uterine fibroid. This volume was evaluated considering the NPV (Non Perfused Volume) on a post-treatment MR dataset acquired with contrast medium. Nowadays, the used approach is a time-expensive and operator-dependent manual segmentation procedure. In this paper, the preliminary results of the two Sicilian facilities are showed and a semi- automatic segmentation approach is proposed, based on multi-seed region-growing technique, to calculate the NPV. The realized approach gives a quantitative evaluation in the post-treatment phase. Materials and Methods: The MRgFUS procedures, regarding women affected by single/multiple uterine fibroid, were performed using the ExAblate 2000 and 2100 (InSightec, Haifa, Israel), which is fully integrated with a 1.5 Tesla MR Scanner (GE Medical System, Milwaukee, WI). For NPV manual and semi-automatic evaluation we have used the sagittal or coronal images acquired after the treatment with a gadolinium-based contrast medium (FSPGR+FS+C protocol). The proposed semi-automatic approach in based on multi-seed region-growing, where it is possible to individualize the processing steps: • pre-processing filtering; • region-growing segmentation; • post-processing filtering; • NPV computation. Results: Among all 23 performed treatments, 69.57% have obtained a NPV, manually evaluated, bigger than 60%, successful treatment threshold, and the 65.22% > 70% and 47.83% > 80%. The evaluation of our segmentation approach was performed by calculating Jaccard and Dice similarity indexes and specificity and sensitivity values. In order to obtain the above indexes, the results of the proposed region-growing approach were compared with the manual segmentation. Preliminary segmentation tests obtained the following results: • Jaccard Index: 87.83%; • Dice Index: 94.12%; • Sensitivity: 91.59%; • Specificity: 89.72%. Conclusion: The MRgFUS provides an important new non-invasive and effective treatment for uterine fibroids. The proposed multi-seed region-growing segmentation approach allows to estimate the NPV in a semi-automatic and operator-independent way. Obtained segmentation indexes show the effectiveness of the implemented technique.

Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images.

Geometric or Clifford algebra (CA) is a powerful mathematical tool that offers a natural and intuitive way to model geometric facts in a number of research fields, such as robotics, machine vision, and computer graphics. Operating in higher dimensional spaces, its practical use is hindered, however, by a significant computational cost, only partially addressed by dedicated software libraries and hardware/software codesigns. For low-dimensional algebras, several dedicated hardware accelerators and coprocessing architectures have been already proposed in the literature. This paper introduces the architecture of CliffordALU5, an embedded coprocessing core conceived for native execution of up to 5D CA operations. CliffordALU5 exploits a novel, hardware-oriented representation of the algebra elements that allows for faster execution of Clifford operations. In this paper, a prototype implementation of a complete system-on-chip (SOC) based on CliffordALU5 is presented. This prototype integrates an embedded processing soft-core based on the PowerPC 405 and a CliffordALU5 coprocessor on a Xilinx XUPV2P Field Programmable Gate Array (FPGA) board. Test results show a 5× average speedup for 4D Clifford products and a 4× average speedup for 5D Clifford products against the same operations in Gaigen 2, a CA software library generator running on the general-purpose PowerPC processor. This paper also presents an execution analysis of three different applications in three diverse domains, namely, inverse kinematics of a robot, optical motion capture, and raytracing, showing an average speedup between 3× and 4× with respect to the baseline Gaigen 2 implementation. Finally, a multicore approach to higher dimensional CA based on CliffordALU5 is discussed.

Integrated Coastal Zone Management is an emerg- ing research area. The aim is to provide a global view of dif- ferent and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate use- ful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second layer, the first-order in- dexes, depending on raw data, of physiographic regions are computed. The third layer estimates the second-order indexes of the analyzed physiographic regions. In the fourth layer, the global Sustainable Coastal Index is inferred. Processed data refers to 13 physiographic regions in the Province of Trapani, western Sicily, Italy. Gathered data describes the environ- mental information, the agricultural, fisheries, and economi- cal behaviors of the local population and land. The Bayesian Network was trained and tested using a real dataset acquired between 2000 and 2006. The developed system presents inter- esting results.

The aim of the study was to compare epicardial adipose tissue (EAT) characteristics assessed with coronary calcium score (CS) and CT coronary angiography (CTCA) image data sets.In 76 patients (mean age 59 ± 13 years) who underwent CS and CTCA owing to suspected coronary artery disease (CAD), EAT was quantified in terms of density (Hounsfield units), thickness and volume. The EAT volume was extracted with a semi-automatic software.A moderate correlation was found between EAT density in CS and CTCA image data sets (-100 ± 19 HU vs -70 ± 24 HU; p 0.05, r = 0.55). The distribution of EAT was not symmetrical with a maximal thickness at the right atrioventricular groove (14.2 ± 5.3 mm in CS, 15.7 ± 5 mm in CTCA; p 0.05, r = 0.76). The EAT volume resulted as 122 ± 50 cm(3) in CS and 86 ± 40 cm(3) in CTCA (Δ = 30%, p 0.05, r = 0.92). After adjustment for post-contrast EAT attenuation difference (Δ = 30 HU), the volume was 101 ± 47 cm(3) (Δ = 17%, p 0.05, r = 0.92). Based on EAT volume median values, no differences were found between groups with smaller and larger volumes in terms of Agatston score and CAD severity.CS and CTCA image data sets may be equally employed for EAT assessment; however, an underestimation of volume is found with the latter acquisition even after post-contrast attenuation adjustment.EAT may be measured by processing either the CS or CTCA image data sets.

Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6× average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40× over Gaigen 2, a geometric algebra software library generator for general-purpose processors, is also demonstrated.

Biologically Inspired Computing places its emphasis on robustness, adaptability, and emergent organization considering the interaction of many processes. Bio-inspired algorithms exhibit strength and flexibility in poorly defined or time-variable tasks, as well as when the global behaviour is achieved by simple interacting of species or agents. The above philosophy links various disciplines such as artificial and computationally intelligence, evolutionary computation, bio-robotics, agent-based systems, and Digital Ecosystems. In this paper, the main branches of bio-inspired computing are briefly discussed. Successively, the impact of the bioinspired paradigm on innovative hardware structures is outlined.

Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times.

The present work deals with modern computing systems security issues, focusing on biometric based asymmetric keys generation process. Conventional PKI systems are based on private/public keys generated through RSA or similar algorithms. The present solution embeds biometric information on the private/public keys generation process. In addition the corresponding private key depends on physical or behavioural biometric features and it can be generated when it is needed. Starting from fingerprint acquisition, the biometric identifier is extracted, cyphered, and stored in tamper-resistant smart card to overcome the security problems of centralized databases. Biometric information is then used for user authentication and for public/private keys generation. The initial trials have shown that the asymmetric keys generation distinctive power depends on biometric authentication accuracy, assuring unique asymmetric keys for each authenticated user.

In recent years biological processes modelling and simulation have become two key issues in analyzing complex cellular systems. The computational requirements suggest to investigate alternative solutions to the common supercomputers and clusters in order to optimize and overcome computational bottleneck. The goal of this work is the design and the realization of an embedded processor for metabolic networks optimization in order to examine their behaviour and robustness under malfunctions of one or more nodes. The embedded processor has been prototyped on the Celoxica RC203E board, equipped with programmable FPGA technologies. A case studied outlining the E. Coli bacteria metabolic network is also presented.

The interaction ways made available by the evolution of the human-computer interfaces, led to novel Human-to-Human Interaction (HHI) modes, enabling people to cooperate for almost any task any time and any where. HHI nowadays is largely indirect and mediated by a wide variety of technologies and devices. This new and exciting field of design originates from the convergence of a few well-established research fields within the HCI area, such as traditional Graphical User Interfaces (GUI), Tangible User Interfaces (TUI), Touchless Gesture User Interface (TGUI), Voice User Interfaces (VUI), and Brain Computer Interfaces (BCI). We analyze and describe the evolution of the HCI in those fields, and how it paved the way for novel current and upcoming human-to human interaction ways.

Clifford algebra (geometric algebra) offers a natural and intuitive way to model geometry in fields as robotics, machine vision and computer graphics. This paper proposes a new representation based on fixed-size elements (quadruples) of 4D Clifford algebra and demonstrates that this choice leads to an algorithmic simplification which in turn leads to a simpler and more compact hardware implementation of the algebraic operations. In order to prove the advantages of the new, quadruple-based representation over the classical representation based on homogeneous elements, a coprocessing core supporting the new fixed-size Clifford operands, namely Quad-CliffoSor (Quadruple-based Clifford coprocesSor) was designed and prototyped on an FPGA board. Test results show the potential to achieve a 23× speedup for Clifford products and a 33× speedup for Clifford sums and differences compared to the same operations executed by a software library running on a general-purpose processor.

In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.

Computer graphics applications require efficient tools to model geometric objects and their transformations. Clifford algebra (also known as geometric algebra) is receiving a growing attention in many research fields, such as computer graphics, machine vision and robotics, as a new, interesting computational paradigm that offers a natural and intuitive way to perform geometric calculations. At the same time, compute-intensive graphics algorithms require the execution of million Clifford operations. Clifford algebra based software intensive systems need therefore the support of specialized hardware architectures capable of accelerating Clifford operations execution. In this paper the architecture of CliffoSorII (Clifford coprocessor II), an embedded coprocessor that offers direct hardware support to Clifford algebra operations, is introduced. The coprocessor has been designed, implemented and tested on a Field Programmable Gate Array (FPGA) board. The experimental results show the potential to achieve a 20x speedup for Clifford sums and differences and a 5x speedup for Clifford products against the analogous operations in Gaigen, a standard geometric algebra software library generator for general purpose processors. An execution analysis of a ray tracing application is also presented.

We present a new research domain, human-to-human interaction (HHI) that describes how today's human interaction is largely indirect and mediated by a wide variety of technologies and devices. We show how this new and exciting field of design originates from the convergence of a few well-established research areas, such as traditional graphical user interfaces (GUIs), tangible user interfaces (TUIs), touchless gesture user interfaces (TGUIs), voice user interfaces (VUIs), and brain computer interfaces (BCIs). We analyse and describe current research in those areas and offer a first-hand view and presentation of its salient aspects for the human-to human interaction domain.

Metabolic networks are composed of several functional modules, reproducing metabolic pathways and describing the entire cellular metabolism of an organism. In the last decade, an enormous interest has grown for the study of tolerance to errors and attacks in metabolic networks. Studies on their robustness have suggested that metabolic networks are tolerant to errors, but very vulnerable to targeted attacks against highly connected nodes. However, many findings on metabolic networks suggest that the above classification is too simple and imprecise, since hub node attacks can be by-passed if alternative metabolic paths can be exploited. On the contrary, non-hub nodes attacks can affect cell survival when the node is the only path within a functional module. In this paper an integrated approach for metabolic networks robustness analysis is presented. With more details, statistical, topological, and functional analysis are used together to evaluate metabolic network behavior under normal operation conditions and under random or targeted attacks. Two real biological metabolic networks have been used to test the effectiveness of the proposed approach.

Real-time 3D imaging represents a developing trend in medical imaging. However, most of the 3D medical imaging algorithms are computationally intensive. In this paper, a programmable networked node for 3D brain vessels reconstruction is proposed. Starting from 2D PC-MRA (Phase-Contrast Magnetic Resonance Angiography) sequences, the node is able to generate the 3D brain vasculature using the MIP (Maximum Intensity Projection) algorithm. The node has been prototyped on the Celoxica RC203E board, equipped with a Virtex II FPGA, to get the advantages of an hardware implementation, reaching a better throughput with respect to analogous software implementations. Its generality and programmable capabilities make the proposed node easy to be reprogrammed and customized with different medical imaging algorithms.

Open, distributed multi-platform agent systems require new management approaches for resources and data secure access. In this paper a Jade-S based multi-platform agent system implementing dynamic reputation policy management is proposed. The implemented extension deals with biometrics, X-Security, DES cryptography and agent reputation. With more details, the proposed reputation management system helps to assess the agent's behavior and reliability, in order to select trusted agents. This is made possible by the knowledge that agents are able to acquire, over time, and that allows them to choose the best solution using own intelligence in total autonomy.

Driver’s drowsiness is stated as an important cause of road and highway accidents. Therefore, the development of a system for monitoring the driver’s level of fatigue is desirable in order to prevent accidents. The paper presents the design and the implementation of a system able to find and evidence the drowsiness level of a driver in an ordinary motor vehicle, in order to prevent car accidents. The system, made up of a car installed infrared video camera connected to the Celoxica RC203E FPGA based board, is able to perform a real time video stream processing. The system exploits the “bright pupil” phenomenon produced by the retina, that reflects the 90% of the incident light when a radiation of 850 nm wavelength hit the retina itself. While acquiring the video, a processing chain is executed to detect driver’s eyes and to compute a PERCLOS (Percentage of Eye Closure) function linked to the drowsiness level of a driver. The achieved experimental results show that an entire 720*576 frame processing requires only 16.7ms, so that the system is able to perform real-time PAL video stream processing and has the potentiality to process 60 frames/sec. The effectiveness of the proposed drowsiness detection system has been successfully tested with a human subject in real operating condition, tracking driver’s eyes and detecting drowsiness failures.

The main objective of this work is to analyze metabolic networks evolution in terms of their robustness and fault tolerance capabilities. In metabolic networks, errors can be seen as random removal of network nodes, while attacks are high-connectivity-degree node deletion aimed at compromising network activity. This paper proposes a software framework, namely BioAnalysis, used to test the robustness and the fault tolerance capabilities of real metabolic networks, when mutations and node deletions affect the network structure. The performed simulations are related to the central metabolic network of the well-known E. coli single-celled bacterium and involve either hub nodes or non-hub nodes, whose influence on the network robustness and activity is different. The performed trials have shown that the node connectivity degree as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network robustness and activity. With more details, functional analysis has demonstrated that low-connectivity-degree nodes may drastically influence the normal behaviour of the network, while high-connectivity-degree nodes may produce soft failure in network operations. The results coming from described simulations have been confirmed by similar in vivo laboratory tests on real cluster of E. Coli bacteria.