Integrated Inductors Modeling for Library Development and Layout Generation

In this work we propose a modification to the conventional lumped equivalent circuit model for integrated inductors. Also the widely used parametric model is modified. The proposed models expand the frequency range where the integrated inductor behavior is accurately predicted. They are useful in developing automatic tools to assist the designers in selecting and automatically laying-out integrated inductors [1]. This work is based on measurements from integrated inductors fabricated in a standard silicon process.     

Biological tissue characterization by magnetic induction spectroscopy (MIS): requirements and limitations

Magnetic induction spectroscopy (MIS) aims at the contactless measurement of the passive electrical properties (PEP) sigma, epsilon, and mu of biological tissues via magnetic fields at multiple frequencies. Whereas previous publications focus on either the conductive or the magnetic aspect of inductive measurements, this article provides a synthesis of both concepts by discussing two different applications with the same measurement system: 1) monitoring of brain edema and 2) the estimation of hepatic iron stores in certain pathologies. We derived the equations to estimate the sensitivity of MIS as a function of the PEP of biological objects. The system requirements and possible systematic errors are analyzed for a MIS-channel using a planar gradiometer (PGRAD) as detector. We studied 4 important error sources: 1) moving conductors near the PGRAD; 2) thermal drifts of the PGRAD-parameters; 3) lateral displacements of the PGRAD; and 4) phase drifts in the receiver. All errors were compared with the desirable resolution. All errors affect the detected imaginary part (mainly related to sigma) of the measured complex field much less than the real part (mainly related to epsilon and mu). Hence, the presented technique renders possible the resolution of (patho-) physiological changes of the electrical conductivity when applying highly resolving hardware and elaborate signal processing. Changes of the magnetic permeability and permittivity in biological tissues are more complicated to deal with and may require chopping techniques, e.g., periodic movement of the object.     

A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.     

Optomagnetic Nanoplatforms for In Situ Controlled Hyperthermia

Magnetic nanoparticles (M:NPs) are unique agents for in vivo thermal therapies due to their multimodal capacity for efficient heat generation under optical and/or magnetic excitation. Nevertheless, their transfer from laboratory to the clinic is hampered by the absence of thermal feedback and by the influence that external conditions (e.g., agglomeration and biological matrix interactions) have on their heating efficiency. Overcoming these limitations requires, first, the implementation of strategies providing thermal sensing to M:NPs in order to obtain in situ thermal feedback during thermal therapies. At the same time, M:NPs should be modified so that their heating efficiency will be maintained independently of the environment and the added capability for thermometry. In this work, optomagnetic hybrid nanostructures (OMHSs) that simultaneously satisfy these two conditions are presented. Polymeric encapsulation of M:NPs with neodymium‐doped nanoparticles results in a hybrid structure capable of subtissue thermal feedback while making the heating efficiency of M:NPs independent of the medium. The potential application of the OMHSs herein developed for fully controlled thermal therapies is demonstrated by an ex vivo endoscope‐assisted controlled intracoronary heating experiment.     

A low complexity mechanism for congestion notification in rural IPSec‐enabled heterogeneous backhaul networks

Management in wireless backhaul networks is a challenging task, especially in rural and isolated environments. In these scenarios, the backhaul network usually consists of a set of heterogeneous wireless links that provide limited and variable bandwidth to the access networks, often 3G/4G small cells. Because of the highly constrained nature of this type of backhaul network, intelligent and joint management in both the backhaul network and the access network is crucial in order to avoid performance degradation caused by traffic congestion. In order to avoid the saturation in the backhaul network, access networks should consider the backhaul state when taking decisions in the admission control and scheduling procedures. However, no standardized mechanisms currently exist for sharing management information between both networks. In this work, we propose to use the Explicit Congestion Notification (ECN) bit in the outer IP headers present in the Iuh 3GPP IPSec‐enabled interfaces in order to notify the backhaul congestion state to the access network. We analyze for the referred scenario, compatibility and security details, validating our approach by running numerically simulations and implementing the notification mechanism. Our low complexity approach offers 2% accuracy and backhaul update latency lower than 10 ms during 80% of the time, which makes the solution appropriate for admission control and scheduling intervals in small cells.     

CORDIC Processor for Variable-Precision Interval Arithmetic

In this paper we present a specific CORDIC processor for variable-precision coordinates. This system allows us to specify the precision to perform the CORDIC operation, and control the accuracy of the result, in such a way that re-computation of inaccurate results can be carried out with higher precision. It permits a reliable and accurate evaluation of a wide range of elementary functions. The specific architecture designed greatly improves the computational time of previous solutions based on classic polynomial approximation. For controlling error in numerical computation (where intervals are normally narrow) the proposed design performs an interval operation in a time close to that of a point operation.     

An adaptive tracking algorithm for direction finding and array shape estimation in a nonstationary environment

This paper presents an adaptive tracking algorithm for the subspace-based direction of arrival estimation of multiple sources in a nonstationary, environment. The nonstationarities are due to moving sources or to timevarying distortions of the sensor array shape. The proposed algorithm relies on the properties of a linear operator, referred to as the Propagator, which only exploits the linear independency of the source steering vectors. The Propagator allows not only the calibration of the array shape, but also the determination of the source and the noise subspaces without any eigendecomposition of the cross-spectral matrix of the received signals. A gradientbased adaptive algorithm is here proposed for the on-line estimation of the Propagator. A theoretical analysis of the behaviour of this algorithm in a nonstationary environment is given. Simulations are carried out in the case of moving sources and in the case of a time-varying array shape. They exhibit the good performances of the proposed algorithm.     

Energy requirements for a swimming pool through a water-atmosphere energy balance

The methodology displayed here is to calculate the energy requirements for heating a swimming pool to a desired temperature. This methodology consists of an energy balance between water-atmosphere as is used in the temperature evaluation of cooling ponds in power plants. Different mathematical expressions are given to calculate such a balance. It is necessary to know the month of the year, the ambient temperature, relative humidity, wind velocity, and solar radiation. With these parameters it is possible to know the natural temperature of the water, natural evaporation, energy needed to reach a determined swimming pool temperature and the evaporation of the heated pool.     

SPETA: Social pervasive e-Tourism advisor

Tourism is one of the major sources of income for many countries. Therefore, providing efficient, real-time service for tourists is a crucial competitive asset which needs to be enhanced using major technological advances. The current research has the objective of integrating technological innovation into an information system, in order to build a better user experience for the tourist. The principal strength of the approach is the fusion of context-aware pervasive systems, GIS systems, social networks and semantics. This paper presents the SPETA system, which uses knowledge of the user’s current location, preferences, as well as a history of past locations, in order to provide the type of recommender services that tourists expect from a real tour guide.     

On the use of genuine-impostor statistical information for score fusion in multimodal biometrics/sur l’usage de l’information statistioue client-imposteur pour la fusion des scores en biométrie multimodale

Matching score level fusion techniques in multimodal person verification conventionally use global score statistics in the normalization and fusion stages. In this paper, novel normalization and fusion methods are presented to take advantage of the separate statistics of the monomodal scores in order to reduce the genuine and impostor pdf lobe overlapping and improve the verification rate. Joint mean normalization is an affine transformation that normalizes the mean of the monomodal biometrics scores separately for the genuine and impostor individuals. Histogram equalization is used to align the statistical distribution of the monomodal scores and make the whole separate statistics comparable. The presented weighting fusion methods have been designed to minimize the variances of the separate multimodal statistics and reduce the multimodal pdf lobe overlapping. The results obtained in speech and face scores fusion upon polycost and xm2vts databases show that the proposed techniques provide better results than the conventional methods.