Nunzio Alberto Borghese

Autocalibration of MEMS Accelerometers

In this paper, we present a novel procedure for the on-the-field autocalibration of triaxial micro accelerometers, which requires neither any equipment nor a controlled environment and allows increasing the accuracy of this kind of microsensor. The procedure exploits the fact that, in static conditions, the modulus of the accelerometer output vector matches that of the gravity acceleration. The calibration model incorporates the bias and scale factor for each axis and the cross-axis symmetrical factors. The parameters are computed through nonlinear optimization, which is solved in a very short time. The calibration procedure was quantitatively tested by comparing the orientation produced by MEMS with that measured by a motion capture system. Results show that the MEMS output, after the calibration procedure, is far more accurate with respect to the output obtained using factory calibration data and almost one order of magnitude more accurate with respect to using traditional calibration models.

Multiscale approximation with hierarchical radial basis functions networks

An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a self-organizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale. The grids are not completely filled, but units are inserted only where the local error is over threshold. This guarantees a uniform residual error and the allocation of more units with smaller scales where the data contain higher frequencies. Only local operations, which do not require any iteration on the data, are required; this allows to construct the network in quasi-real time. Through harmonic analysis, it is demonstrated that, although a HRBF cannot be reduced to a traditional wavelet-based multiresolution analysis (MRA), it does employ Riesz bases and enjoys asymptotic approximation properties for a very large class of functions. HRBF networks have been extensively applied to the reconstruction of three-dimensional (3-13) models from noisy range data. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by MRA.

Real-time accurate circle fitting with occlusions

Accurate location of circles inside images is a common problem in many scientific fields. Traditional algorithms, based on fitting a parameterized model, cannot accurately determine the circle in presence of partial occlusions. A novel problem formulation, based on maximum likelihood, allows estimating circles in real-time with sub-pixel accuracy also when occlusions are present.

Autocalibration of Triaxial MEMS Accelerometers With Automatic Sensor Model Selection

Up to now, little attention has been posed on a principled derivation of the cost function used for autocalibration of MEMS tri-axial accelerometers. By formulating the calibration problem in the context of maximum likelihood estimate, we derive here a general formulation that can be reduced to the classical quadratic cost function under certain hypotheses. Moreover, we adopt the Akaike information criterion to automatically choose the most adequate linear sensor model for the given calibration data set. Experiments on simulated and real data show the effectiveness of the proposed approach.

Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing

Although it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta-analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun-verb distribution of the activation peaks included in each cluster. Thirty-two clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical-semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant—but not exclusive—left lateralization of the network.

Duckneglect: Video-games based neglect rehabilitation

BACKGROUND Video-games are becoming a common tool to guide patients through rehabilitation because of their power of motivating and engaging their users. Video-games may also be integrated into an infrastructure that allows patients, discharged from the hospital, to continue intensive rehabilitation at home under remote monitoring by the hospital itself, as suggested by the recently funded Rewire project. OBJECTIVE Goal of this work is to describe a novel low cost platform, based on video-games, targeted to neglect rehabilitation. METHODS The patient is guided to explore his neglected hemispace by a set of specifically designed games that ask him to reach targets, with an increasing level of difficulties. Visual and auditory cues helped the patient in the task and are progressively removed. A controlled randomization of scenarios, targets and distractors, a balanced reward system and music played in the background, all contribute to make rehabilitation more attractive, thus enabling intensive prolonged treatment. RESULTS Results from our first patient, who underwent rehabilitation for half an hour, for five days a week for one month, showed on one side a very positive attitude of the patient towards the platform for the whole period, on the other side a significant improvement was obtained. Importantly, this amelioration was confirmed at a follow up evaluation five months after the last rehabilitation session and generalized to everyday life activities. CONCLUSIONS Such a system could well be integrated into a home based rehabilitation system.

Statistical Based Impulsive Noise Removal in Digital Radiography

A new filter to restore radiographic images corrupted by impulsive noise is proposed. It is based on a switching scheme where all the pulses are first detected and then corrected through a median filter. The pulse detector is based on the hypothesis that the major contribution to image noise is given by the photon counting process, with some pixels corrupted by impulsive noise. Such statistics is described by an adequate mixture model. The filter is also able to reliably estimate the sensor gain. Its operation has been verified on both synthetic and real images; the experimental results demonstrate the superiority of the proposed approach in comparison with more traditional methods.

An integrated low-cost system for at-home rehabilitation

We show here how integrating novel natural user interfaces, like Microsoft Kinect, with a fully adappatients current statustive game engine, a system that can be used for rehabilitation at home can be built. A wide variety of game scenarios, a balanced scoring system, quantitative and qualitative exercise evaluation, automatic gameplay level adaptation to patients current status, and audiovisual feed-back are all implemented inside the Intelligent Game Engine for Rehabilitation here introduced, and are aimed at maximizing patients motivation and rehabilitation effectiveness. The system is integrated into a multi-level platform that provides continuous monitoring by the hospital and it has been developed inside the framework of the EU funded Rewire project.

Automatic monitoring of pest insects traps by Zigbee-based wireless networking of image sensors

Monitoring pest insect population is currently a key issue in crop protection. At farm level it is routinely operated by repeated surveys by a human operator of adhesive traps, disseminated through the field, where insects remain stuck when attracted. This is a labor- and time-consuming activity, and it would be of great advantage for farmers to have an affordable system doing this task automatically. This paper illustrates a system based on a distributed imaging device operated through a wireless sensor network that is able to automatically acquire and transmit images of the trapping area to a remote host station. The station evaluates the insect density evolution at different farm sites and produces an alarm when insect density goes over threshold. The network architecture consists of a master node hosted in a PC and a set of client nodes, spread in the fields, that act as monitoring stations. The master node coordinates the network and retrieves from the client nodes the captured images. Zigbee transmission protocol guarantees a low power consumption. Results from real data acquired on a small scale system deployed inside a greenhouse hall hosting a set of Vinca Catharantus roseus plants are shown. During a monitoring period of four weeks the network operated regularly, producing a pest insects population curve fairly correlated to daily counts obtained by visual observations of the trap and therefore demonstrating the feasibility of this approach.

A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering

The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing the computational complexity to polynomial from the exponential obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method.