This paper reports a monolithic device integrating a CMOS analog front-end and an array of 192 MR sensors for biomolecular recognition detection. Innovative CMOS building blocks (i.e. current source, multiplexers and pre-amplifier) were designed targeting a negligible noise when compared with the MR sensors noise and a low power consumption. The CMOS front-end was fabricated using AMS 0.35 μm technology and the magnetoresistive sensors were post-fabricated on top of the CMOS chip. Due to its low noise (16 nV/√Hz), the full system was able to detect magnetic nanoparticles with a SNR degradation of only 1.4 dB (circuit imposed). Furthermore, the unprecedented low power consumption (6.5 mW) and small dimensions (7.59 mm2) of the presented solution guarantees the portability of the detection system allowing its usage at the point-of-care.

Non-destructive testing (NDT) based on eddy currents (EC) is commonly used to detect defects in conductive materials. Usually the system includes an emitter coil, and one receiver coil or one Magnetoresistive (MR) sensor. In this work we added an interface ASIC that pre-amplifies and filters the signal from an array of MR sensors. This demo will present a new version based on the work presented at the ECNDT 2014 conference with a paper entitled “A CMOS ASIC for Precise Reading of a Magnetoresistive Sensor Array for NDT”. Since this is an on-going work, improvements have been made, namely the reduction of the system thermal noise to 30 nV/√Hz, the development of a multigain amplifier and the application of the same concept and circuit to a multichannel parallel signal acquisition system. Detection of surface and buried defects will be demonstrated in different material mock-ups.

In order to adapt the Kalman filter (KF), the RLS algorithm or other, to single channel active noise control (ANC) the MFx structure is used. But, this is more complex in the multichannel case. The RLS and others have been adapted to multi-channel ANC. However, there has been no adaptation of the KF based on a random walk state space model. This is similar to the RLS but has some advantages. The RLS algorithm tries to invert the autocorrelation matrix of the reference signal, and can become unstable if this is singular. The same is not true for random walk based KF. In the paper we present an adaptation of the KF to multi-channel ANC.

This paper describes an integrated digital-to-analog converter (DAC) for a intra-cortical microelectrode stimulator. The circuit has a special feature that consists on charging and discharging the same current to prevent electric charge accumulation that might harm the brain. The DAC has 5 bits of resolution supplying a maximum current of 100 µA to one electrode of the cortical stimulator. The circuit is sized to operate at a maximum sampling frequency of 1 MHz, has an active area of 0.077 mm2, and the biasing part consumes only 19.2 µA, from a 3.3 V supply voltage. The circuit has low area to reduce its impact inside the brain and is low power to allow using wireless energy harvesting circuits preventing the use of batteries or wires, to reduce the risk of infections. A circuit prototype was implemented in AMS 0.35um CMOS technology, and tested.

This paper presents a recently developed handheld biochip-based microsystem. The microsystem is based on a magneto-resistive array biochip composed of a number of sensing sites with magnetic tunneling junctions (MTJ) and diodes. To drive the MTJ, different techniques are addressed with different types of signals. Filtering strategies are also presented, which allow the recovery of bio signals from the noise without increasing too much nor the time required to access all the sensors, nor the power consumption of the board. In conclusion, experiments with the system in a setup to detect actual bio signals are presented with encouraging results.

The loudspeaker is a nonlinear transducer that produces harmonic distortion and nonlinear controllers, requiring parameters well tuned to the loudspeaker, are used to reduce it. Unfortunately loudspeaker parameters are not well known and vary during normal operation. Based on a simplified nonlinear model of the loudspeaker, and a modification to the adaptive filters, nonlinear systems for the estimation of parameters for the mechanical part and for the electrical part of the loudspeaker were developed. The parameters estimated are directly usable by the controllers, not requiring additional conversion calculations. The Kalman and RLS adaptive algorithms where applied to this systems and simulation results show that they converge, although the electrical part estimation system was about 15 times slower than the mechanical.

In active noise control there has been some research based in the modified filtered-X least mean square (LMS) algorithm (MFX-LMS). When the secondary path is perfectly modeled, this algorithm is able to perfectly eliminate it's effect. It is also easily adapted to allow the use of fast algorithms such as the recursive least square, or algorithms with good tracking performance based on the Kalman filter. This letter presents the results of a frequency domain analysis about the behavior of the MFX-LMS in the presence of secondary path modeling errors and a comparison with the FX-LMS algorithm. Namely, it states that for small values of the secondary path delay both algorithms perform the same, but that the step-size of the FX-LMS algorithm decreases with increasing delay, while the MFX-LMS algorithm is stable for an arbitrary large value for the secondary path delay, as long as the real part of the ratio of the estimated to the actual path is greater than one half (Re{S/spl circ//sub z//S/sub z/}>1/2). This means that for the case of no phase errors the estimated amplitude should be greater than half the real one and for the case of no amplitude errors the phase error should be less than 60/spl deg/. Analytical expressions for the limiting values for the step-size in the presence of modeling errors are given for both algorithms.

In this letter, we derive a new Miller-Chang lower bound (MCB) for the variance of unbiased non-data-aided (NDA) carrier phase estimates obtained from a block of N samples of a linearly modulated pulse-amplitude modulation or quadrature-amplitude modulation information signal, transmitted through an additive white Gaussian noise channel. This bound is tighter than the corresponding Crame/spl acute/r-Rao lower bound (CRB) for data-aided or continuous-wave carrier phase estimation (CW-CRB), particularly when N is small. For some given N and sufficiently high signal-to-noise ratios, the MCB is tighter than the corresponding true NDA CRB. The main limitation of this new MCB is that its application is restricted to carrier phase estimators which are unbiased for all possible values of the received symbol sequence.

We try to answer the following set of questions: "is it possible to design step edge detection filters given the desired response to an input step edge signal?"; "the method extends easily to two dimensions?" and "do we improve edge detection using this method?". Answers to these questions are given by formulating the step edge detection problem in terms of a linear first kind Fredholm integral equation. The unidimensional solution to the problem and the extension to the bidimensional case is presented. Several filter designs are presented for different types of filter responses. We show the filters obtained for abrupt and exponential type responses. The first derivative of Gaussian (DG) and the exponential filter (ISEF) are immediately obtained after solving the integral equation. A novel mixed exponential filter (MEXP) is proposed that combines the behaviors of the ISEF and the DG filters. Experimental results are presented for the proposed MFXP filter.

The Modified FX-LMS (MFX-LMS) algorithm has been the subject of some research. It effectively removes the delay from the adaptation mechanism allowing faster convergence rates than the FX-LMS. It also allows the use of more sophisticated algorithms such as the RLS or the Kalman. Although this is true for perfect secondary path modeling, there is no extensive study of the influence of secondary path modeling errors in this algorithm. This paper tries to do just that. It shows that its maximum allowed step-size is in fact almost independent of the delay in the secondary path, even in the presence of modeling errors. It also shows that for very large delays the algorithm is stable for phase errors of /spl plusmn/60/spl deg/ and for an estimated amplitude of more than half of the correct one.

This paper presents an algorithm for the segmentation of multiple moving objects. Our proposal is based on dominant motion estimation, static segmentation and Markov random field (MRF) classification of the regions obtained by static segmentation. Dominant motion estimation is based on efficient variants of the Hough transform applied over a hierarchy of motion models with increasing complexity. The final segmentation is only performed after all the motion models have been determined and is based on motion information, which includes the explicit detection of uncovered regions and on contextual properties between neighboring static regions.

We derive new Cramer-Rao lower bounds (CRBs) for the estimation of carrier phase and frequency offset from an unmodulated carrier or from a M-PSK data signal. The new CRBs are obtained formally from the exact likelihood function for the carrier phase and frequency offset, build from a block of received phase samples, and are applicable for a general alphabet size M. The bounds are compared with other previously obtained bounds (which also take into account the effect of random phase modulation) and with the performance of some popular feedforward algorithms for carrier phase and frequency offset estimation.

A data-aided (DA) feedforward symbol synchronizer based upon the maximum-likelihood (ML) criterion is proposed. The algorithm is carrier-phase independent and is designed for operation in burst-mode, narrow-band communication systems employing linear modulation. The accuracy of the proposed algorithm is assessed by simulation using QPSK signaling. The results show that the performance of the proposed synchronizer is very close to the theoretical minimum limit (variance of the estimates near the Cramer-Rao lower bound) and is also significantly better than that of other popular circuits such as the square-law synchronizer.

This paper presents an algorithm for image sequence segmentation based on clustering and on motion estimation with the Hough transform. The clustering criterion uses the estimated motion information, simultaneously with intensity and position information. This results in a very robust segmentation and motion estimation.

The authors propose a new orthogonal partially shared memory architecture for the design of multiprocessor systems. The architecture allows processors to partially share a 2-D array of memory modules in an orthogonal way with fewer limitations than those imposed by the traditional orthogonal (OMP) architecture. Processors have direct access to large neighborhoods of memory modules which can be used to improve processing efficiency, namely with respect to local processing. The main characteristics of the new architecture are described and compared with the traditional orthogonal architecture. Image processing algorithms have been mapped onto the new architecture, in order to evaluate its efficiency for local processing. The fault tolerant characteristics of the system are also discussed.

A comparative study of the performance of several digital image gradient direction estimators is presented. FIR and IIR implementations of gradient direction filters have different experimental gradient direction errors and these are confronted in this paper. Experiments were done under several noise conditions are the results are presented.