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In this paper, non-conventional circuit design techniques has been reviewed. The techniques discussed are widely used for realizing low voltage low power analog circuits. The discussed techniques in this paper are: Bulk Driven, Floating and Quasi-floating Gate followed by operating of Bulk Driven MOSFET in Floating and Quasi-floating Gate mode. In all the approach, the threshold voltage restriction is removed or reduced from the input signal path thereby reducing the power consumption. However, the adverse effect lies is terms of reduced performance parameters of MOSFET compared to conventional gate driven MOSFET parameters as shown in this paper through simulation results. The comparative analysis of MOSFET parameters results in encouragement of two approaches: Quasi-floating Gate and Bulk Driven Quasi-floating Gate MOSFET. Each of these approaches has its advantage in specific domains. Further in this paper, an Operational Transconductance Amplifier is proposed which use the Bulk Driven Quasi-floating Gate MOSFET technique and the same is amplifier under similar conditions is also realized using Bulk Driven MOSFET so as to highlight the advantage of Bulk Driven  Quasi-floating Gate MOSFET over Bulk Driven MOSFET. All the performances metrics are achieved with the help of HSpice simulator using MOSFET models of 180nm technology provided by UMC.

The detection problem in statistical signal processing can be succinctly formulated: Given m (possibly) signal bearing, n-dimensional signal-plus-noise snapshot vectors (samples) and N statistically independent n-dimensional noise-only snapshot vectors, can one reliably infer the presence of a signal? This problem arises in the context of applications as diverse as radar, sonar, wireless communications, bioinformatics, and machine learning and is the critical first step in the subsequent signal parameter estimation phase. The signal detection problem can be naturally posed in terms of the sample generalized eigenvalues. The sample generalized eigenvalues correspond to the eigenvalues of the matrix formed by "whitening" the signal-plus-noise sample covariance matrix with the noise-only sample covariance matrix. In this article we prove a fundamental asymptotic limit of sample generalized eigenvalue based detection of signals in arbitrarily colored noise when there are relatively few signal bearing and noise-only samples. Numerical simulations highlight the accuracy of our analytical prediction and permit us to extend our heuristic definition of the effective number of identifiable signals in colored noise. We discuss implications of our result for the detection of weak and/or closely spaced signals in sensor array processing, abrupt change detection in sensor networks, and clustering methodologies in machine learning., Comment: Submitted to the IEEE Journal of Selected Topics in Signal Processing, Special Issue on Model Order Selection in Signal Processing

The detection and estimation of signals in noisy, limited data is a problem of interest to many scientific and engineering communities. We present a computationally simple, sample eigenvalue based procedure for estimating the number of high-dimensional signals in white noise when there are relatively few samples. We highlight a fundamental asymptotic limit of sample eigenvalue based detection of weak high-dimensional signals from a limited sample size and discuss its implication for the detection of two closely spaced signals. This motivates our heuristic definition of the 'effective number of identifiable signals.' Numerical simulations are used to demonstrate the consistency of the algorithm with respect to the effective number of signals and the superior performance of the algorithm with respect to Wax and Kailath's "asymptotically consistent" MDL based estimator., Comment: To appear in the Proceedings of the 8th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Helsinki, Finland, June 17-20, 2007

We define a class of "algebraic" random matrix channels for which one can generically compute the limiting Shannon transform using numerical techniques and often enumerate the low SNR series expansion coefficients in closed form. We describe this class, the coefficient enumeration techniques and compare theory with simulations., Comment: Submitted to the IEEE Transactions on Communications (Letters)

Random matrix theory is now a big subject with applications in many disciplines of science, engineering and finance. This talk is a survey specifically oriented towards the needs and interests of a computationally inclined audience. We include the important mathematics (free probability) that permit the characterization of a large class of random matrices. We discuss how computational software is transforming this theory into practice by highlighting its use in the context of a stochastic eigen-inference application., Singapore-MIT Alliance (SMA)