ADC Static Characterization Using Nonlinear Ramp Signal

Static characteristics of an analog-to-digital converter (ADC) can be directly determined from the histogram-based quasi-static approach by measuring the ADC output when excited by an ideal ramp/triangular signal of sufficiently low frequency. This approach requires only a fraction of time compared to the conventional dc voltage test, is straightforward, is easy to implement, and, in principle, is an accepted method as per the revised IEEE 1057. However, the only drawback is that ramp signal sources are not ideal. Thus, the nonlinearity present in the ramp signal gets superimposed on the measured ADC characteristics, which renders them, as such, unusable. In recent years, some solutions have been proposed to alleviate this problem by devising means to eliminate the contribution of signal source nonlinearity. Alternatively, a straightforward step would be to get rid of the ramp signal nonlinearity before it is applied to the ADC. Driven by this logic, this paper describes a simple method about using a nonlinear ramp signal, but yet causing little influence on the measured ADC static characteristics. Such a thing is possible because even in a nonideal ramp, there exist regions or segments that are nearly linear. Therefore, the task, essentially, is to identify these near-linear regions in a given source and employ them to test the ADC, with a suitable amplitude to match the ADC full-scale voltage range. Implementation of this method reveals that a significant reduction in the influence of source nonlinearity can be achieved. Simulation and experimental results on 8- and 10-bit ADCs are presented to demonstrate its applicability.

A Non Quasi-Static Small Signal Model for Long Channel Symmetric DG MOSFET

We propose a compact model for small signal non quasi static analysis of long channel symmetric double gate MOSFET The model is based on the EKV formalism and is valid in all regions of operation and thus suitable for RF circuit design Proposed model is verified with professional numerical device simulator and excellent agreement is found well beyond the cut-off frequency

Signal subspace approach in localization of sound source in shallow water

A new algorithm based on signal subspace approach is proposed for localizing a sound source in shallow water. In the first instance we assumed an ideal channel with plane parallel boundaries and known reflection properties. The sound source is assumed to emit a broadband stationary stochastic signal. The algorithm takes into account the spatial distribution of all images and reflection characteristics of the sea bottom. It is shown that both range and depth of a source can be measured accurately with the help of a vertical array of sensors. For good results the number of sensors should be greater than the number of significant images; however, localization is possible even with a smaller array but at the cost of higher side lobes. Next, we allowed the channel to be stochastically perturbed; this resulted in random phase errors in the reflection coefficients. The most singular effect of the phase errors is to introduce into the spectral matrix an extra term which may be looked upon as a signal generated coloured noise. It is shown through computer simulations that the signal peak height is reduced considerably as a consequence of random phase errors.

Composite MR image reconstruction and unaliasing for general trajectories using neural networks

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called ``Composite Reconstruction And Unaliasing using Neural Networks'' (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively. (C) 2010 Elsevier Inc. All rights reserved.

Signal characterization using fractal dimension

Fractal Dimensions (FD) are one of the popular measures used for characterizing signals. They have been used as complexity measures of signals in various fields including speech and biomedical applications. However, proper interpretation of such analyses has not been thoroughly addressed. In this paper, we study the effect of various signal properties on FD and interpret results in terms of classical signal processing concepts such as amplitude, frequency, number of harmonics, noise power and signal bandwidth. We have used Higuchi's method for estimating FDs. This study may help in gaining a better understanding of the FD complexity measure itself, and for interpreting changing structural complexity of signals in terms of FD. Our results indicate that FD is a useful measure in quantifying structural changes in signal properties.

Dual-DSP system for signal and image processing

The design of a dual-DSP microprocessor system and its application for parallel FFT and two-dimensional convolution are explained. The system is based on a master-salve configuration. Two ADSP-2101s are configured as slave processors and a PC/AT serves as the master. The master serves as a control processor to transfer the program code and data to the DSPs. The system architecture and the algorithms for the two applications, viz. FFT and two-dimensional convolutions, are discussed.

On the Nash-Williams′ Lemma in Graph Reconstruction Theory

A generalization of Nash-Williams′ lemma is proved for the Structure of m-uniform null (m − k)-designs. It is then applied to various graph reconstruction problems. A short combinatorial proof of the edge reconstructibility of digraphs having regular underlying undirected graphs (e.g., tournaments) is given. A type of Nash-Williams′ lemma is conjectured for the vertex reconstruction problem.

Processing of Ultrasonic Signal for Transducer Characterization and for Improving Signal-To-Noise Ratio

This paper describes the work related to characterisation of an ultrasonic transducer fabricated in the laboratory. The response of the medium to the ultrasonic wave was obtained by converting the time domain signal to frequency domain, using the FFT algorithm. Cross-correlation technique was adopted to increase the S/N ratio in the raw time domain signal and subsequently, to determine the ultrasonic velocity in the medium.

Can Dufour's gland compounds honestly signal fertility in the primitively eusocial wasp Ropalidia marginata?

Unlike queens of typical primitively eusocial species, Ropalidia marginata queens are docile and non-interactive, and hence cannot be using dominance to maintain their status. It appears that the queen maintains reproductive monopoly through a pheromone, of which the Dufour's gland is at least one source. Here, we reconfirm earlier results showing that queens and workers can be correctly classified on a discriminant function using the compositions of their respective Dufour's glands, and also demonstrate consistent queen-worker differences based on categories of compounds and on single compounds also in some cases. Since the queen pheromone is expected to be an honest signal of the fecundity of a queen, we investigate the correlation of Dufour's gland compounds with ovarian activation of queens. Our study shows that Dufour's gland compounds in R. marginata correlate with the state of ovarian activation of queens, suggesting that such compounds may portray the fecundity of a queen, and may indeed function as honest signals of fertility.

Novel signal demodulation technique to estimate the amount of chirp in fiber Bragg gratings

A novel detection technique to estimate the amount of chirp in fiber Bragg gratings (FBGs) is proposed. This method is based on the fact that reflectivity at central wavelength of FBG reflection changes with strain/temperature gradient (linear chirp) applied to the same. Transfer matrix approach was used to vary different grating parameters (length, strength and apodization) to optimize variation of reflectivity with linear chirp. Analysis is done for different sets of `FBG length-refractive index strength' combinations for which reflectivity vary linearly with linear chirp over a decent measurement range. This article acts as a guideline to choose appropriate grating parameters in designing sensing apparatus based on change in reflectivity at central wavelength of FBG reflection.

Chemical communication in Ropalidia marginata: Dufour's gland contains queen signal that is perceived across colonies and does not contain colony signal

Queens of the primitively eusocial wasp Ropalidia marginata appear to maintain reproductive monopoly through pheromone rather than through physical aggression. Upon queen removal, one of the workers (potential queen, PQ) becomes extremely aggressive but drops her aggression immediately upon returning the queen. If the queen is not returned, the PQ gradually drops her aggression and becomes the next queen of the colony. In a previous study, the Dufour's gland was found to be at least one source of the queen pheromone. Queen-worker classification could be done with 100% accuracy in a discriminant analysis, using the compositions of their respective Dufour's glands. In a bioassay, the PQ dropped her aggression in response to the queen's Dufour's gland macerate, suggesting that the queen's Dufour's gland contents mimicked the queen herself. In the present study, we found that the PQ also dropped her aggression in response to the macerate of a foreign queen's Dufour's gland. This suggests that the queen signal is perceived across colonies. This also suggests that the Dufour's gland in R. marginata does not contain information about nestmateship, because queens are attacked when introduced into foreign colonies, and hence PQ is not expected to reduce her aggression in response to a foreign queen's signal. The latter conclusion is especially significant because the Dufour's gland chemicals are adequate to classify individuals correctly not only on the basis of fertility status (queen versus worker) but also according to their colony membership, using discriminant analysis. This leads to the additional conclusion (and precaution) that the ability to statistically discriminate organisms using their chemical profiles does not necessarily imply that the organisms themselves can make such discrimination. (C) 2010 Elsevier Ltd. All rights reserved.

Accelerating frequency-domain diffuse optical tomographic image reconstruction using graphics processing units

Diffuse optical tomographic image reconstruction uses advanced numerical models that are computationally costly to be implemented in the real time. The graphics processing units (GPUs) offer desktop massive parallelization that can accelerate these computations. An open-source GPU-accelerated linear algebra library package is used to compute the most intensive matrix-matrix calculations and matrix decompositions that are used in solving the system of linear equations. These open-source functions were integrated into the existing frequency-domain diffuse optical image reconstruction algorithms to evaluate the acceleration capability of the GPUs (NVIDIA Tesla C 1060) with increasing reconstruction problem sizes. These studies indicate that single precision computations are sufficient for diffuse optical tomographic image reconstruction. The acceleration per iteration can be up to 40, using GPUs compared to traditional CPUs in case of three-dimensional reconstruction, where the reconstruction problem is more underdetermined, making the GPUs more attractive in the clinical settings. The current limitation of these GPUs in the available onboard memory (4 GB) that restricts the reconstruction of a large set of optical parameters, more than 13, 377. (C) 2010 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3506216]

Accelerated gradient based diffuse optical tomographic image reconstruction

Purpose: Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). Methods: DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Results: Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. Conclusions: We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data. (C) 2011 American Association of Physicists in Medicine. DOI: 10.1118/1.3531572]

Large-Signal Model for Independent DG MOSFET

In this paper, we show the limitations of the traditional charge linearization techniques for modeling terminal charges of the independent double-gate metal-oxide-semiconductor field-effect transistors. Based on our recent computationally efficient Poisson solution for independent double gate transistors, we propose a new charge linearization technique to model the terminal charges and transcapacitances. We report two different types of quasistatic large-signal models for the long-channel device. In the first type, the terminal charges are expressed as closed-form functions of the source- and drain-end inversion charge densities and found to be accurate when the potential distribution at source end of the channel is hyperbolic in nature. The second type, which is found to be accurate in all regimes of operations, is based on the quadratic spline collocation technique and requires the input voltage equation to be solved two more times, apart from the source and drain ends.

A Sub-Nyquist Sampling Method For Computing The Level-crossing-times Of An Analog Signal: Theory And Applications

We address the problem of computing the level-crossings of an analog signal from samples measured on a uniform grid. Such a problem is important, for example, in multilevel analog-to-digital (A/D) converters. The first operation in such sampling modalities is a comparator, which gives rise to a bilevel waveform. Since bilevel signals are not bandlimited, measuring the level-crossing times exactly becomes impractical within the conventional framework of Shannon sampling. In this paper, we propose a novel sub-Nyquist sampling technique for making measurements on a uniform grid and thereby for exactly computing the level-crossing times from those samples. The computational complexity of the technique is low and comprises simple arithmetic operations. We also present a finite-rate-of-innovation sampling perspective of the proposed approach and also show how exponential splines fit in naturally into the proposed sampling framework. We also discuss some concrete practical applications of the sampling technique.