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A novel Machine Learning (ML) method based on Neural Networks (NN) is proposed to assess radio-frequency (RF) exposure generated by WiFi sources in indoor scenarios. The aim was to build an NN capable of addressing the complexity and variability of real-life exposure setups, including the effects of not only down-link transmission access points (APs) but also up-link transmission by different sources (e.g. laptop, printers, tablets, and smartphones). The NN was fed with easy to be found data, such as the position and type of WiFi sources (APs, clients, and other users) and the position and material characteristics (e.g. penetration loss) of walls. The NN model was assessed using an additional new layout, distinct from that one used to build and optimize the NN coefficients. The NN model achieved a remarkable field prediction accuracy across exposure conditions in both layouts, with a median prediction error of -0.4 to 0.6 dB and a root mean square error of 2.5-5.1 dB, compared with the target electric field estimated by a deterministic indoor network planner. The proposed approach performs well for the different layouts and is thus generally used to assess RF exposure in indoor scenarios. (c) 2021 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.

This paper presents the preliminary results on the use of Machine Learning (ML) for the estimation of the electric-field exposure in indoor scenarios with multiple WiFi sources. Differently from similar previous approaches, the present approach aims to design a Neural Network (NN) capable to address complex indoor scenarios that include not only down-link transmission by access points (APs) but also up-link transmission by several clients (e.g., laptop, printers, tablets, and smartphones). The NN was trained and tested on the field generated by multiple WiFi sources (2400 MHz) in an office indoor setup; the ‘target’ exposure field in such a scenario was derived using a deterministic indoor network planner method. The median prediction accuracy of the ‘target’ field exposure by the proposed NN was 0.0 dB (1st quartile: −0.7 dB; 3rd quartile 0.9 dB), with a root mean square error of 2.1 dB. The proposed approach is fast (the NN training lasts about 30 minutes) and could be useful to assess radio-frequency (RF) exposure in complex indoor scenarios.

Among the novel technologies that 5th Generation (5G) networks will introduce to fulfill the ambitious goals that promise, there are the antenna arrays with three-dimensional (3D) beamforming capabilities. This technology does the RF-EMF exposure scenario evolve with the particular radiation pattern that the Access Point, in an indoor environment, generates in an adaptive way. The problem of setting up an assessment to evaluate the human exposure levels in such a scenario becomes challenging. In this paper, by the jointly use of deterministic and stochastic methods, it was possible to consider up to 1000 different 3D beamforming patterns of the AP with low computational costs. The work allowed to highlight the conditions of major exposure and to prove the validity of stochastic methods, facing the variability and heterogeneity that will characterized 5G scenarios.

The aim of the present paper is to assess the exposure levels to electromagnetic field taking account the innovative aspects that the incoming 5G networks will introduce. The incoming 5G networks will in fact utilize mm-wave spectrum coupled with MIMO base station and beamforming techniques. These innovations will be deployed both in outdoor and indoor areas and will permit to improve services and benefits for all the population, but will also drastically change the exposure levels of the population to RF-EMF. A promptly adequate exposure assessment is thus necessary. For this reason, in this work, a specific case of a 5G exposure scenario in indoor environment is evaluated. More in details, the exposure levels caused by an 8x8 indoor planar array antenna at 3.7 GHz, which simulates the presence of an indoor access point, are here analyzed in a human computational model. Three different worst-case configurations are evaluated by making use of the Sim4Life simulation platform. The analyzed quantity is the specific absorption rate for all the domain of interest and for some specific tissues. The work represents a first attempt to further expand the knowledge about the exposure levels for the incoming 5G indoor scenarios.

The following work is focused on the assessment of exposure levels in indoor environment, taking account the innovations of the upcoming 5G network technology. More in details, it was analyzed the exposure caused by a 5G access point in a room, modelled with an indoor 8x8 planar array antenna. The working frequency of the planar array antenna was selected according to the frequency range that will be used firstly in Italian 5G networks and will be 3.7 GHz. The exposure levels were assessed using the Ella model from the Virtual family. The quantity analyzed will be the specific absorption rate in the head tissues.

Cars are rapidly evolving into smart connected objects that can communicate not only with the infrastructure but also with other cars through vehicle-to vehicle (V2V) communication. By the end of 2023, more than 72 million vehicles worldwide will be equipped with devices and technologies that enable to exchange data and communicate with other cars. This challenging scenario is raising cross-cutting issues, such as those related to new radio-frequency exposures of the human body also when travelling. We evaluate the Specific Absorption Rate (SAR) induced in a realistic smart mobility communication scenario operated at 5.9 GHz. V2V antennas were modeled and placed on a realistic 3D model of a city-car to numerically estimate SAR in the body regions and tissues of a human phantom (adult male) inside the car. We found that both local and whole-body average exposures were below the ICNIRP and IEEE limits for the general public in the 100 kHz-6 GHz band, being equal in the worst case scenario to 1.58 W/kg (head) and 0.008 W/kg, respectively. The highest SAR was found in the most superficial tissues (the skin) of body regions very close to the sources. The distance of the passenger from the antennas played an important role in the resulting SAR. This research has a potentially great clinical impact as it contributes to new and realistic knowledge on the exposure scenario in smart mobility communication to assess possible health effects and for the design of policies for public health management.

This study focused on the assessment of radio-frequency electromagnetic fields (RF-EMF) exposure in a realistic apartment due to the presence of a WiFi source deployed in uncertain position. In order to describe the 2D spatial distribution of electric field induced in the whole apartment for whatever position of the WiFi source, an innovative approach that combines Principal Component Analysis (PCA) and Gaussian process regression (Kriging method) was applied. The 2D surrogate model was used to investigate the exposure in three different usage scenarios of the WiFi sources, i.e. surfing to a new web site, using a Skype video call and watching a You Tube video at 1080p, evaluating the electric field E induced at each location of the apartment for 10,000 different positions of the source. Across all the examined conditions, we found E values distributions with median values in the range 2.2 - 96.1 mV/m and 90th percentiles in the range 4.9-209.3 mV/m. The 2D surrogate model allowed obtaining a complete statistical description of the exposure for any positions of the WiFi source in the apartment, with a computational effort equal to about 10% of the one needed by using only the WiCa Heuristic Indoor Propagation Prediction (WHIPP) network planner.

Innovative approaches, such as stochastic dosimetry and Machine Learning, can be complementary to traditional methods for electromagnetic field (EMF) exposure assessment, overcoming limitations and allowing extraction of new/deeper information. In this study, two examples of innovative EMF exposure assessment approaches are presented: (i) a stochastic approach based on low rank tensor approximations to assess indoor exposure to WLAN access point with unknown location and (ii) an application of Machine Learning to characterize indoor residential exposures to ELF magnetic field in children by considering the type of electric networks near the child home, the age and type of the child home, the type of heating and the family size.

The Urban Intelligence (UI) paradigm proposes an ecosystem of technologies to improve urban environment, wellbeing, quality of life and smart city systems. It fosters the definition of a digital twin of the city, namely a cyber-physical counterpart of all the city systems and sub-systems. Here we propose a novel approach to UI that extends available frameworks combining advanced multidisciplinary modelling of the city, simulation and learning tools with numerical optimization techniques, each of them specialized for the digital representation of city systems and subsystems, including not only city infrastructures, but also city users and their interactions. UI provides sets of candidate policies in complex scenarios and supports policy makers and stakeholders in designing sustainable and personalized solutions. The main characteristics of the proposed UI architecture are (a) fully multidisciplinary integration of city layers, (b) connection and evolution with the city, (c) integration of participative strategies to include 'human-oriented' information, and (d) modularity of application.

Stochastic dosimetry, combining electromagnetic computational techniques and statistics to build surrogate models, allows assessing exposure to EMF accounting for variability and uncertainty intrinsic of real scenarios. In this study, we present some examples of exposure assessment of children and fetuses to RF devices in uncertain scenarios using stochastic dosimetry. Polynomial chaos expansions and low rank tensor approximations, applied to build surrogate models of Specific Absorption Rate (SAR), permitted a fast estimation of the variability of the exposure due to the variation in the RF source position.

In the last two decades, motor cortex stimulation has been recognized as a valuable alternative to pharmacological therapy for the treatment of neuropathic pain. Although this technique started to be used in clinical studies, the debate about the optimal settings that enhance its effectiveness without inducing tissue damage is still open. To this purpose, computational approaches applied to realistic human models aimed to assess the current density distribution within the cortex can be a powerful tool to provide a basic understanding of that technique and could help the design of clinical experimental protocols. This study aims to evaluate, by computational techniques, the current density distributions induced in the brain by a realistic electrode array for cortical stimulation. The simulation outcomes, summarized by specific metrics quantifying the efficacy of the stimulation (i.e., the effective volume and the effective depth of penetration) over two cortical targets, were evaluated by varying the interelectrode distance, the stimulus characteristics (amplitude and frequency), and the anatomical human model. The results suggest that all these parameters somehow affect the current density distributions and have to be therefore taken into account during the planning of effective electrical cortical stimulation strategies. In particular, our calculations show that (1) the most effective interelectrode distance equals 2 cm; (2) increasing voltage amplitudes increases the effective volume; (3) increasing frequencies allow enlarging the effective volume; and (4) the effective depth of penetration is strictly linked to both the anatomy of the subject and the electrode placement.

In this study, we used stochastic dosimetry, a promising approach that combines electromagnetic computational techniques and statistics, to assess the exposure of a fetus at 9 months of gestational age to a 4G LTE tablet in realistic scenarios characterized by variability. In particular, we analyzed how the exposure changes when moving the tablet in a range of positions representative of realistic exposure scenarios. Polynomial chaos theory, applied to build surrogate models of Specific Absorption Rate (SAR), permitted a fast estimation of the variability of the exposure due to the variation in the tablet position.

The spread of radio frequency identification (RFID) devices in ubiquitous applications without their simultaneous exposure assessment could give rise to public concerns about their potential adverse health effects. Among the various RFID system categories, the ultra high frequency (UHF) RFID systems have recently started to be widely used in many applications. This study addresses a computational exposure assessment of the electromagnetic radiation generated by a realistic UHF RFID reader, quantifying the exposure levels in different exposure scenarios and subjects (two adults, four children, and two anatomical models of women 7 and 9 months pregnant). The results of the computations are presented in terms of the whole-body and peak spatial specific absorption rate (SAR) averaged over 10 g of tissue to allow comparison with the basic restrictions of the exposure guidelines. The SAR levels in the adults and children were below 0.02 and 0.8 W/kg in whole-body SAR and maximum peak SAR levels, respectively, for all tested positions of the antenna. On the contrary, exposure of pregnant women and fetuses resulted in maximum peak SAR(10 g) values close to the values suggested by the guidelines (2 W/kg) in some of the exposure scenarios with the antenna positioned in front of the abdomen and with a 100% duty cycle and 1 W radiated power.

The continuous development of radio frequency (RF) devices used in everyday life highlights the need to conduct appropriate health risk assessment due to RF electromagnetic field (RF-EMF) exposure, especially for the fetal exposure in realistic scenarios. In this study, we used stochastic dosimetry, an approach that combines electromagnetic computational techniques and statistics, to assess the fetal exposure to a fourth-generation Long Term Evolution (4G LTE) tablet in realistic scenarios, assessing the influence of the position of the tablet, the gestational age of the fetus, and the frequency of the emitting antenna. Results showed that the exposure in terms of specific absorption rate (SAR) was within the limits of the ICNIRP 1998 General Public Guidelines in all the considered scenarios. The position of the tablet was very influential for the induced SAR in the fetus, resulting in quartile coefficient of dispersion always higher than 40%. The level of exposure for the later pregnancy was found to be higher than those for the early pregnancy. As to the effect of the emitting frequency of the tablet, we found that the higher the frequency, the lower the induced SAR in the fetus.

Objective: Deep transcranial magnetic stimulation (dTMS) has been recently used in several clinical studies as diagnostic and therapeutic tool. However, electric field ( E ) distributions induced in the brain by dTMS are still unknown. This paper provides a characterization of the induced E distributions in the brain of a realistic human model due to 16 different coil configurations. Methods: The scalar potential finite-element method was used to calculate the E distributions differentiating the brain structures, e.g., cortex, white matter, anterior cingulated cortex, cerebellum, thalamus, hypothalamus, nucleus accumbens, amygdale, and hippocampus. Results: Our results support that the double cone coils and the large diameter circular coils are more prone to activate deeper brain structures but are also characterized by a reduced focality on the surface of the cortex, with the consequent possible counter effect of stimulating regions not of interest. The Hesed coils, although their ability to reach deep brain tissues is lower, seem to be more able to reduce the effect on other brain regions where the stimulation is undesired. Conclusion: All the coil configurations resulted subjected to a depth-focality tradeoff. Significance: Since there is not a configuration that is capable of achieving a stimulation both deep and focal, the selection of the most suitable coil settings for a specific clinical application should be based on a balanced evaluation between these two different needs.

The public concern toward the exposure to Radio-Frequency Electromagnetic Fields (RF-EMF) is continuously growing, due also to the increasing use of wireless communication system. The assessment of real exposure scenarios is still an open issue, due to the variability of the input parameters that influence the exposure itself (e.g., the source design, the frequency band used, the morphology of the subject exposed). Classical electromagnetic computational techniques are typically leading to highly time consuming simulations, if the variation of the, before mentioned, parameters is taken into account. A promising alternative to this issue is given by Polynomial Chaos (PC) theory, which is an efficient method to assess the variability of exposure at a lower computational cost. In this study (ANSES project Acte http://whist.mines-telecom.fr), PC theory has been applied to study the exposure of a 1-year-old child to a 3G tablet emitting at the frequency of 1940 MHz with 1 W input power. The exposure has been characterized in terms of Specific Absorption Rate (SAR). A PC expansion has been built to estimate the whole-body SAR and the SAR in the brain, separately, to assess the variability of the child exposure with the change in the tablet position.

The public concern toward the exposure to Radio-Frequency electromagnetic fields (RF- EMF) exists despite the intensive use of wireless communication system. There is also an increasing usage of novel RF devices among very young population. However, there is still a lack of knowledge about the level of exposure of infants (0-3 years-old) to near and far-field sources, simultaneously (uplink and downlink mode) [1]. Furthermore, the assessment of real exposure scenarios is still an open issue, due to the variability of the input parameters that influence the exposure itself (e.g., the source design, the morphology and posture of the subject exposed). Stochastic dosimetry, e.g. using the Polynomial Chaos (PC) theory [2], is a promising method to assess the variability of exposure at a lower computational cost than the deterministic dosimetry. In this study PC theory has been applied to study the exposure of a 1-year-old child to a 3G tablet emitting at 1940 MHz (uplink mode) and to a femtocell at 2100 MHz (downlink mode), separately. The exposure has been characterized in terms of Specific Absorption Rate (SAR). A PC expansion has been built for the whole-body SAR and the SAR in the brain to assess the variability of the child exposure with the change in the tablet and femtocell position, separately.

Click-evoked otoacoustic emissions (CEOAEs) are time-varying signals with a clear frequency dispersion along with the time axis. Analysis of CEOAEs is of considerable interest due to their close relation with cochlear mechanisms. The particular structure of CEOAEs requires a time-frequency method with both a satisfactory time and frequency resolution. In this paper, several basic time-frequency distribution methods are considered and compared on the basis of both simulated signals and real CEOAEs. Results from simulations and real CEOAEs revealed that the wavelet approach is highly suitable for the analysis of such signals. Some examples of the application of the Wavelet Transform to CEOAEs are provided here. Applications range from the extraction of normative data from adult OAEs to the extraction of quantitative parameters for clinical purposes.

This paper implements a filtering technique to enhance the signal-to-noise ratio (SNR) and, in turn, the detection of transient-evoked otoacoustic emissions (TEOAE's), generated by healthy human cochlea. One can increase the SNR by compiling an image of recorded TEOAE from more than one stimulus intensity, averaged over a few sweeps, which can be further processed by means of two-dimensional spatial mean filters. Averaging some 60 sweeps recorded to stimuli at several intensity levels requires one-forth of the collection time needed for a classical set of responses (average of 260 sweeps), and obtains approximately the same final SNR. The relation between the performances of the proposed technique and the SNR of the rapidly acquired responses before filtering is also investigated.

Background A large number of studies explored the biological effects of extremely low-frequency (0–300 Hz) magnetic fields (ELF-MFs) on nervous system both at cellular and at system level in the intact human brain reporting several functional changes. However, the results of different studies are quite variable and the mechanisms of action of ELF-MFs are still poorly defined. The aim of this paper is to provide a comprehensive review of the effects of ELF-MFs on nervous system. Methods We convened a workgroup of researchers in the field to review and discuss the available data about the nervous system effects produced by the exposure to ELF-MFs. Main Findings/Discussion We reviewed several methodological, experimental and clinical studies and discussed the findings in five sections. The first section analyses the devices used for ELF-MF exposure. The second section reviews the contribution of the computational methods and models for investigating the interaction between ELF-MFs and neuronal systems. The third section analyses the experimental data at cellular and tissue level showing the effects on cell membrane receptors and intracellular signaling and their correlation with neural stem cell proliferation and differentiation. The fourth section reviews the studies performed in the intact human brain evaluating the changes produced by ELF-MFs using neurophysiological and neuropsychological methods. The last section shows the limits and shortcomings of the available data, evidences the key challenges in the field and tracks directions for future research.

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

This paper addresses the exposure assessment of RFID devices for newborn identity reconfirmation. To that purpose, a realistic newborn model ("Baby") is used to evaluate by a computational approach the levels of exposure due to these devices. Considering the average technical specifications currently in use, the exposure matrix in Baby shows that the systems comply with the ICNIRP exposure guidelines. As second aim, the effects of the change of the tissue dielectric properties with age on the so called "exposure matrix" (set of induced magnetic and electric field together with the derived values of SAR) is addressed. Specifically, three different approaches proposed in literature for the age variation of the dielectric properties at 13.56 MHz (the working frequency of the RFID systems for these applications) have been implemented using the Baby geometrical model. The related exposure matrices were then compared with the results obtained using the adult properties. No clear trend can be identified on the exposure matrices obtained varying the dielectric properties at 13.56 MHz, although the results could suggest a trend toward the underestimation of the exposure using adult properties. (C) 2011 Elsevier Ltd. All rights reserved.

We present how the repeatability and reproducibility of a measurement device can be estimated from a suitably defined hierarchical linear model. The methodology is illustrated using a collection of eight data sets which consist of the distortion product otoacoustic emission recordings collected from both ears of ten young Sprague-Dawley rats at different frequencies under eight different recording conditions. We formulate a model which extends the commonly used one-way random effects model (5) to account for an experimental setup that is more elaborated than the ones traditionally used in interlaboratory experiments. The fitted model is easily interpretable and furnishes as a by-product the frequencies at which the highest response level is achieved under the eight recording conditions. These values together with the repeatability and reproducibility limits of the protocols are crucial in contributing to the enhancement of the research capabilities on the possible causes of hearing impairment.

To improve planning of maxillofacial surgery, a novel method for maxillary-, mandibular-, and facial-nerve 3-D image reconstruction was implemented and optimized. A set of images acquired by a computed tomography (CT) scanner was segmented to reconstruct the 3-D model of the maxilla and mandible. Particular attention was given to the segmentation of the facial nerve, which was obtained through the gradient-vector-flow (GVF) snake method. After segmentation, precise anatomical 3-D plastic models were fabricated through stereolithography from CT scans of five clinical cases that will undergo either dental implant surgery or bone distraction. For all cases, 3-D models delivered essential visual and tactile information for the planning and simulation of surgery as well as for customized implant preparation.

This study assessed the influence of 'non-ideal' operating conditions typical of cochlear implants (CIs) on the behavior of the wavelet transform (WT) when used to process speech. Particular attention was given to the effect of limited stimulation rate and limited number of channels, typical of CI speech processing, on the performance of the WT. Computer simulations and psychoacoustic recognition tests of WT-processed speech were implemented. The crucial role played by the 'non-ideal' operating conditions on WT speech processing was put in evidence. Psychoacoustic recognition tests proved to be fundamental to evaluate feasibility of WT speech processing for CIs.

Mobile phones have become very commonly used worldwide within a short period of time. To date there is only limited knowledge about interaction between electromagnetic fields (EMFs) emitted by mobile phones and the auditory function. Moreover, there is widespread concern that there may be potential for harm. The aim of this study was to assess potential subtle changes in cochlear function by measuring the temporal and spectral fine structure of transiently evoked otoacoustic emissions (TEOAE) in normal hearing subjects after exposure to EMFs emitted by Global System for Mobile Communication (GSM) mobile phones. TEOAEs were recorded in 27 healthy young adults before and after 10 min of real or sham exposure in a double-blind design. TEOAE data were analyzed both globally (broadband analysis) and using the Wavelet Transform (analysis of the time-frequency fine structure). The broadband analysis revealed no significant effect on TEOAEs related to exposure, confirming results of previous studies; in addition, no significant change was detected in the analysis of the elementary wavelet components, suggesting that the temporal and spectral fine structure of TEOAEs is not affected by 10 min exposure to low-intensity EMFs emitted by GSM mobile phones. (c) 2007 Acoustical Society of America.

Mobile phones have become very commonly used throughout the world within a short period of time. Although there is no clear evidence to show harmful physiological effects of electromagnetic fields (EMF) at the levels used by mobile phones, there is widespread public concern that there may be potential for harm. Because mobile phones are usually held close to the ear, it is appropriate to study effects on hearing. In this study, the outer hair cell function of 15 subjects was assessed by DPOAE recording before and after a controlled EMF exposure. To increase the sensitivity of DPOAE recording to identify even small changes in hearing function, an inverse fast Fourier transform (IFFT) analysis and time-domain windowing was applied to separate the two generation mechanisms of DPOAE, the so-called place-fixed and wave-fixed mechanisms, in order to verify if EMF can affects the two DPOAE emission mechanisms. Statistical analysis of the data showed that 10 min of EMF exposure at the maximum power (2 W at 900 MHz or 1 W at 1800 MHz) does not induce any changes in either DPOAE generation mechanism.

In this paper, a simple method for direct acquisition of a cloud of points from an object surface is presented together with a novel surface reconstruction algorithm used to obtain an explicit 3D model for the measured object. This technique is aimed at relatively accurate but simple and reliable 3D reconstruction of anatomical parts for biomedical applications. The whole system is based on acquisition of unorganized range data by laser scanning and successive image processing, by expanding and fitting a regular geometrical model within the range data, for surface reconstruction. The prototype system proved to be working finely, with an estimated resolution

The hearing aid shell (or earmold) couples the hearing aid with the user ear. Proper fitting of the earmold to the subject ear canal is required to achieve satisfactory wearing comfort, reduction in acoustic feedback, and unwanted changes in the electroacoustic characteristics of the aid. To date, the hearing aid shell manufacturing process is fully manual: the shell is fabricated as a replica of the impression of the subject ear canal. The typical post-impression processes made on the ear impression modify the physical dimensions and the shape of the final shell thus affecting the overall performance of the hearing aid. In the proposed approach, the surface of the original ear impression is 3D laser scanned by a prototype equipment consisting of a pair of CCD cameras and a commercial He-Ne laser. The digitized surface is reconstructed by means of iterative deformations of a geometrical model of simple and regular shape. The triangular mesh thus obtained is smoothed by a non-shrinking low-pass spatial filter. With this approach, post-impression processes are no more needed because the digitally reconstructed impression can be directly fed to rapid prototyping equipments, thus achieving a better accuracy in obtaining an exact replica of the ear impression. Furthermore, digital reconstruction of the impression allows for simple and reliable storage and transmission of the model without handling a physical object.

A prototype 3D scanning system is presented together with a novel surface reconstruction algorithm to obtain an explicit 3D reconstruction of both open and closed surfaces, with particular attention to anatomical parts for biomedical applications. The whole system is based on acquisition of unorganized range data by laser scanning and successive image processing, by expanding and fitting a regular geometrical model within the range data, for surface reconstruction. The prototype system proved to be working finely, with an estimated resolution < 10 μm, a repeatability < 50 mum, and an acquisition noise of similar to 170 mum (rms value). Simulations with a synthetic test surface are described to provide quantitative figures on the robustness to noise of the proposed reconstruction algorithm. Reconstruction of 3D models of human organs are presented as well.

The analysis of variability in the EEG signal is a relatively new field of investigation. This is mainly due to the objective difficulty to develop quantitative methods of analysis. Autoregressive modeling of the EEG signal is proposed to quantify its variability. Model coefficients were computed from adjacent epochs and their temporal behavior was analyzed: background activity produced only very slow temporal changes, while a variability in the EEG provoked sharp changes in the AR sequences. To quantify the variability with a numerical value (Difference Measure, DM), the AR sequences were processed by means of a segmentation algorithm. DMs were derived for all EEG leads and analyzed under visual inspection. Preliminary results show that this approach could be of some help in the study of temporal and spatial characteristics of interictal epileptiform discharges.

Click-evoked otoacoustic emissions (CEOAEs) are currently used in neonatal hearing screening programs for the early identification of hearing impairment present at birth. CEOAEs have been shown to be a rapid, cost-effective test with a high sensitivity but a low specificity: it fails a relatively large number of babies having a normal hearing sensitivity, CEOAE scoring is greatly influenced by the decision rules separating passes from fails, the acquisition procedure and the analysis software. On the other hand, no consensus exists regarding the numerical criteria to be used when scoring CEOAEs. Both simulated signals and CEOAEs from more than 3,400 newborns were here used to examine the effects that different data processing options, such as time-windowing, digital band-pass filtering, type of synchronous averaging, and frequency decomposition, have on the specificity of the test. The specificity was found to vary from less than 40% to more than 95%, depending on the decision rules and on the type of signal processing used. In most cases, the procedures used by defaults by the most widespread equipment for CEOAE recording were relatively far away from the optimal solution and that there is still a wide possibility to increase the accuracy of the test.

Wavelet analysis was performed to obtain time-frequency representations of otoacoustic emissions (OAEs) from normal ears. Otoacoustic emissions evoked by click stimuli at 8 different intensity levels were recorded from healthy subjects and used as test material. Time-frequency representations were used to analyse the relationships between frequency components and latencies. To test whether click-evoked OAEs can be used to derive reliable estimates of cochlear latencies, the authors' latency results were compared with data from ABR.

Mobile phones have become very commonly used throughout the world within a short period of time. To date there is only limited knowledge about interaction between electromagnetic fields (EMFs) emitted by mobile phones and the auditory function. Moreover, there is widespread concern that there may be potential for harm. The aim of this study, performed in the framework of the European Commission Project GUARD “Potential adverse effects of GSM cellular phones on hearing” (5FP, QLK4‐CT‐2001‐00150, 2002‐2004), was to assess potential subtle changes in cochlear function of normal hearing subjects by measuring the time‐frequency fine structure of TEOAEs after exposure to low‐intensity EMFs emitted by GSM mobile phones. TEOAEs were recorded in 27 healthy young adults before and after 10 min of real or sham exposure in a double‐blind design. TEOAE data were analyzed both globally (broadband analysis) and using the wavelet transform (analysis of time‐frequency fine structure). The broadband analysis revealed no significant effect on TEOAEs related to exposure, confirming results of previous studies; in addition, no significant change was detected in the analysis of the elementary wavelet components, suggesting that a 10‐min exposure to EMFs emitted by GSM mobile phones has no immediate after‐effect on TEOAE time‐frequency fine structure.

Hearing aid shells (or earmolds) must couple the hearing aid with the user's ear. Earmolds have to fit the subject's outer ear canal properly to ensure a good performance of the aid. Because of the great variability in the anatomical pattern of the ear, earmolds are custom made. At present, an impression of the subject's ear canal is taken and used to fabricate the silicon-made mold. The postimpression activities that typically are performed during the fabrication process modify the physical dimensions of the resulting earmold and thus affect the fit of the product. A novel system for 3-D laser scanning and mesh reconstruction of the surface of ear canal impressions is presented. The reconstructed impression can be digitally stored and passed directly to dedicated CAD 3-D printing machines to model the silicon earmold and thus achieve the best possible fit. The proposed system is based on a couple of cameras and a commercial laser for the surface digitization and on a straightforward algorithm, based on the deformation of a geometric model, for the reconstruction of the acquired surface. Measurements on objects of well-known geometric features and dimensions are performed to assess the accuracy and repeatability levels of this 3-D acquisition system. Robustness to noise of the proposed reconstruction algorithm is determined by simulations with a synthetic test surface. Finally, the first measurements (acquisition+reconstruction) of closed surfaces from ear canal impressions are reported.

The present work aims to prove the validity of the well know stochastic dosimetry method for a low frequency near-field source exposure scenario. The interest in this thematic has grown since it was discovered that house electric devices could be significant for children ELF-MF levels of exposure. More in details, it was conducted the assessment of children exposure variability caused by 10000 different positions of a hairdryer as near-field source, in order to test the goodness of the method.

Radio frequency identification (RFID) is an innovative technology currently applied in a large number of industrial and consumer applications. The spread of RFID technology does not correspond to a parallel increase in studies on its possible impact on health in terms of electromagnetic field (EMF) exposure. The aim of this paper is to estimate, by computational techniques, the EMF generated by passive RFID systems for mother–newborn identity reconfirmation. The computation was performed on realistic models of newborn and mother for three different reader positions. The compliance with EMF exposure guidelines was investigated as a function of the change in reader–tag specifications (magnetic field threshold and maximum distance of the reader to awake the tag) and time of use of the reader close to the body. The results show that attention should be paid to the identification of the optimal reader–tag technical specifications to be used in this type of application. That should be done by an accurate exposure assessment investigation, in particular for newborn exposure. The need to reduce the exposure time as much as possible indicates the importance of specific training on the practical applications of the RFID (DATALOGIC J-series, Bologna, Italy) device. Bioelectromagnetics 32:408–416, 2011. © 2011 Wiley-Liss, Inc.

Noise reduction is a problem of practical concern in the measurement of click-evoked otoacoustic emissions. Instrumentation and environmental noise are suppressed by synchronous averaging and rejection-level techniques that are technically used in most recording systems. Patient noise (such as swallowing, breathing, heart beat) is often of low frequency and can be reduced by digital filtering. A high-pass filtering approach is proposed to reduce low-frequency noise. Cross-correlation was used to determine the cut-off frequency that maximizes the S/N ratio. Click-evolved emissions from normal and hearing impaired adults have been used as test materials. Preliminary results show a remarkable improvement in the S/N ratio of the recorded trials.

In this paper, a simple method for direct acquisition of a cloud of points from an object surfaces is presented together with a surface reconstruction algorithms used to obtain an explicit 3D model for the measured object. This technique is aimed at relatively accurate but simple and reliable 3D reconstruction of anatomical parts for biomedical applications. The whole system is based on acquisition of unorganized range data by laser scanning and successive image processing, by expanding and fitting a regular geometrical model within the range data, for surface reconstruction. The prototype system proved to be working finely, with an estimated resolution

Clinical aspects of many medical devices are often limited since they need a great deal of averaging before yielding an acceptable signal-to-noise ratio. By compiling the data into a two-dimensional image arrays, powerful image processing tools become available. Image filtering improves the S/N ratio much more than classical one-dimensional filtering: acquisition time of otoacoustic emissions may even be halved.

In the clinical use of evoked otoacoustic emissions the identification of the cochlear response and the reduction of the duration of the recording session are of great concern, especially if the recorded responses are to be used in hearing screening tasks. The aim of this paper is two-fold: to examine the potential and limits of optimal band-pass filtering to reduce the noise and increase identification of the cochlear response, and to introduce a technique of two-dimensional processing for reducing the acquisition time of TEOAEs. Band-pass filtering must guard against the loss of significant frequency components of the response; that is, the signals have to be filtered only when the filter bandwidth meets given conditions. As to test duration, preliminary results clearly indicate that two-dimensional filtering can substantially reduce the acquisition time, with only negligible losses in the basic response features, when a set of responses recorded at different stimulus levels is filtered.

Transient-Evoked Otoacoustic Emissions (TEOAE) are nonstationary acoustic signals coming from the inner ear after acoustic stimulation by clicks and they are applied as tools in newborn hearing screening programs to allow the early identification of hearing loss and the consequent diagnosis and intervention. In any screening program, the duration of each test is a crucial parameter. For TEOAE, it is strongly influenced by the acquisition procedure, typically based on classical synchronous averaging technique over 260 sweeps, with an average acquisition time of about 2-3 minutes. This paper present an application of Principal Component Analysis (PCA) to rapidly-acquired TEOAE (averaged over only 10, 60 or 100 sweeps) for the detection of this type of cochlear response. The PCA approach is shown to be able to enhance the signal-to-noise ratio (SNR) and, in turn, to allow a correct detection of the responses. Results of the application of this approach in comparison with responses recorded, from the same ears, with classical technique will be shown. The reduction of the acquisition time to about one forth with respect to Its typical value and with approximately the same final signal-to-noise ratio will be discussed.

Interpretation of multichannel electric potentials in terms of equivalent dipolar sources has often been based upon the analysis of averaged data, i.e. of the grand averages of the individual recordings obtained from normal subjects. Some considerations are presented here to show that the above procedure may lead to erroneous results. Examples of individual data fits are discussed in relation to the possibility of (i) defining a 'norm' and (ii) extracting some information about the 'resolving power' of the electric method. Interpretation of multichannel electric potentials in terms of equivalent dipolar sources has often been based upon the analysis of averaged data, i.e. of the grand averages of the individual recordings obtained from normal subjects. Some considerations are presented here to show that the above procedure may lead to erroneous results. Examples of individual data fits are discussed in relation to the possibility of (i) defining a 'norm' and (ii) extracting some information about the 'resolving power' of the electric method.