Source separation based processing for integrated Hall sensor arrays

Paraschiv-Ionescu, Anisoara; Jutten, Christian; Bouvier, Gérard

doi:10.1109/jsen.2002.807302

Cited by 21 publications

(11 citation statements)

References 39 publications

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“…However, despite the wide applicability of such technique, it is not employed in several scenarios. Exceptions are the works [10][11][12], but in different applications than wireless communication systems.…”

Section: Proposed Methods For Estimation Of the Number Of Usersmentioning

confidence: 99%

Multiuser processing using blind source separation methods

Cavalcante¹,

Filho²,

Romano³

2008

Trans Emerging Tel Tech

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Multiuser Processing refers to a set of fundamental techniques for improving the performance of the modern wireless communications systems. Most of these techniques have been proposed and studied for specific scenarios, according to the multiple access technology to be employed. The theory and methods of blind source separation (BSS) can provide a more general framework over which multiuser techniques may be investigated and analysed as a particular case. This is the main idea to be exploited along the present paper. We show how this point of view opens perspectives to deal with important problems of multiuser systems such as signal detection and collision resolution. For this task, an information-theoretic based criterion and algorithm, issued from BSS theory, is presented together with an algorithm for estimation of the number of active users in the system. Simulation results are carried out to illustrate the performance of the designed approach, compared to other blind and trained solutions.

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Section: Proposed Methods For Estimation Of the Number Of Usersmentioning

confidence: 99%

Multiuser processing using blind source separation methods

Cavalcante¹,

Filho²,

Romano³

2008

Trans Emerging Tel Tech

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“…For example, many mixing systems in communication, speech, and biomedical areas (Larson, 1998;Korenberg & Hunter, 1986;Prakriya & Hatzinakos, 1995;Maeda, Song, & Ishii, 2005;Lee & Batzoglou, 2003;Parashiv-Ionescu, Jutten, & Bouvier, 2002) can be modeled using equation 2.7, which is also illustrated in Figure 1, such as the nonlinearities introduced by the sensor's nonlinear saturation distortions and the nonlinear characteristics in individual receiver channels (Parashiv-Ionescu et al, 2002). Therefore, this research may have many potential applications (Maeda, Song, & Ishii, 2005;Lee & Batzoglou, 2003).…”

Section: Pnl Modelmentioning

confidence: 99%

MISEP Method for Postnonlinear Blind Source Separation

Zheng

Huang

Kang³

et al. 2007

Neural Computation

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In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

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“…Suppose we have an electronic tongue based on m ISFETs in which its outputs are proportional to the steady-state IS-FET response of (14) or (15). Accordingly, we can write that the output x in an heterogeneous array is…”

Section: Electronic Tongues and Their Mixing Problemmentioning

confidence: 99%

“…where constants d 1i and d 2i are proportional to those defined in (14) and (15). Observe that (16) is a particular form of the mixing problem in (1), i.e.…”

Section: Electronic Tongues and Their Mixing Problemmentioning

confidence: 99%

“…The aim of this second experimentation is two-fold: (1) to determine how the recovery process is affected when ISFETs behave as the Nikoskyan model suggests but with some error, i.e. following (14) or (15); (2) to quantify to which extent separation is possible when main and interfering ion activities are not fully independent, i.e. when the activity coefficients γ i, j in (11) are not constant and thus are correlated.…”

Section: Learning In a Real Isfet Arraymentioning

confidence: 99%

See 1 more Smart Citation

Independent Component Analysis for Solid-State Chemical Sensor Arrays

Bermejo

2006

Appl Intell

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Electronic noses and tongues are two recent examples in chemical sensing that employ statistical array techniques in order to overcome the intrinsic limitations of current solid-state chemical sensors like ion-selective field transistors (ISFET). In particular, ISFETs are sensitive to the concentration of a particular ion in a solution to be measured, but they can be also strongly affected by several interfering ions found in the solution. Hence, they must be employed in regions in which the effect of interferences is negligible thus limiting their range of operation. However, as we show, ISFETs behave as non-linear mixers of main ion activities and interfering ones and thus an attempt to separate the original main ion activity and interferences from the mixed response is suitable with independent component analysis (ICA) methods. In this direction, a novel learning algorithm is proposed which synergistically combines ICA and linear regression for dealing with the separation in ISFET responses and further reconstruction of ion activities in those operating regions in which interferences notably affect their response. Several experiments with real ISFET measurements demonstrate the interest of proposed methods.

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Source separation based processing for integrated Hall sensor arrays

Cited by 21 publications

References 39 publications

Multiuser processing using blind source separation methods

Multiuser processing using blind source separation methods

MISEP Method for Postnonlinear Blind Source Separation

Independent Component Analysis for Solid-State Chemical Sensor Arrays

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