Cramer-Rao bounds and maximum likelihood estimation for randomamplitude phase-modulated signals

The problem of estimating the phase parameters of a phase-modulated signal in the presence of colored multiplicative noise (random amplitude modulation) and additive white noise (both Gaussian) is addressed. Closed-form expressions for the exact and large-sample Cramer-Rao Bounds (CRBs) are derived. It is shown that the CRB is significantly affected by the color of the modulating process when the signal-to-noise ratio (SNR) or the intrinsic SNR is small. Maximum likelihood type estimators that ignore the noise color and optimize a criterion with respect to only the phase parameters are proposed. These estimators are shown to be equivalent to the nonlinear least squares estimators, which consist of matching the squared observations with a constant amplitude phase-modulated signal when the mean of the multiplicative noise is forced to zero. Closed-form expressions are derived for the efficiency of these estimators and are verified via simulations     

Dynamic machine learning-based heuristic energy optimization approach on multicore architecture

In the present era, energy is progressively turning into the major limitation in designing multicore chips. However, power and performance are the primary segments of energy, which are contrarily correlated in multicore architectures. This research primarily focused on optimizing energy level of multicore chips using parallel workloads by utilizing either power or execution advancement based on machine learning computation on dynamic programming. To do as such, the novel dynamic machine learning-based heuristic energy optimization (DML-HEO) algorithm has been designed and developed in this research on application-specific controllers to optimize energy-level on multicore architecture. Here DML-HEO is implemented on the controller to maximize the execution inside a fixed power spending plan or to limit the expended capacity to accomplish a similar pattern execution. The controller is additionally scalable as it does not bring about critical overhead due to the increase in quantity of cores. The strategy has been assessed utilizing controllers on a full-framework test system at lab-scale analysis. The experimental results demonstrate that our proposed DML-HEO system shows improving performance than the traditional system.     

Macromodeling ideal switches for SPICE

Representing switches in terms of SPICE-acceptable elements is important for simulating DC-DC converters, switching mode power supplies, and power electronic circuits. Some time back, Xu and Yu [1988] proposed an equivalent circuit model for a switch employing independent and controlled voltage sources and resistors. Two basic questions come to mind in examining this model: (i) Is it possible to devise other equivalent models? (ii) If so, can we create superior models? The answer to both questions is yes     

Time and lag recursive computation of cumulants from a state-spacemodel

Time and lag recursive algorithms for the computation of the cumulants of the state vector and the output process of a multiple-input multiple-output time-varying state-space model are derived, using a Kronecker product representation for the cumulants of vector processes. The noise processes are not assumed to be stationary. Symmetry relations for the cumulants of vector processes are discussed. Computational aspects are examined in detail. It is conjectured that the recursive equations derived will be useful in developing state estimators and even optimal controllers based on higher order statistics     

Synthesis of hydrogel for drug delivery studies utilizing photoinitiator‐free photopolymerization based on the donor/acceptor pair,N‐vinylpyrrolidinone and hydroxypentyl maleimide

Photo‐differential scanning calorimetry (photo‐DSC) was used to study the rate of photoinitiator‐free copolymerization of a donor/acceptor pair involving N‐vinylpyrrolidinone (NVP), and a water‐soluble N‐substituted maleimide, hydroxypentyl maleimide (HPMI). Glucose, 1,1‐diethoxy ethane (DEE) and isopropyl alcohol (IPA) were included for the evaluation of their efficiencies as hydrogen donors, and glucose was shown to be the most efficient in enhancing the rate of polymerization. This photoinitiator (PI)‐free system was extended to hydrogel preparation using the radiation method with UV as the radiation source based on the same donor/acceptor pair, ie NVP/HPMI, in the presence of glucose as the hydrogen donor. Swelling and drug release tests showed that this hydrogel exhibited high swelling ability and a rather fast drug release rate when using theophylline as the model drug. These tests also revealed that the drug release kinetics and the water diffusion into this hydrogel did not adhere to the Fickian model. Cytotoxicity testing showed no evidence of this hydrogel being cytotoxic. © 2002 Society of Chemical Industry     

Digital multi-carrier spread spectrum versus direct sequence spreadspectrum for resistance to jamming and multipath

We compare single user digital multi-carrier spread spectrum (MC-SS) modulation with direct sequence (DS) SS (with a modified implementation) in the presence of narrowband interference (NBI) and multipath fading. We derive closed-form expressions for the symbol error probability for both the linear MMSE receiver as well as the conventional matched-filter receiver under different scenarios: additive white Gaussian noise (AWGN) channel with NBI, multipath channel with or without NBI. We show that DS-SS can achieve the same performance as MC-SS if the spreading code is carefully designed to have perfect periodic autocorrelation function (PACF). On the other hand, MC-SS is more robust to narrowband interference and multipath fading than is DS-SS with the widely used spreading codes that do not possess perfect PACE. Our analysis reveals that the performance improvement of MC-SS is precisely due to the implicit construction of an equivalent spreading code having nonconstant amplitude but possessing perfect periodic autocorrelation     

Set-oriented data mining in relational databases

Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing.

In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases.     

Characterization of Capacitance Standards at High Frequency at National Physical Laboratory,India

Four-terminal-pair air dielectric capacitance standards with nominal values of 1000 and 100 pF have been characterized up-to 10 MHz at NPLI. The procedure employed involves the determination of all capacitive and inductive parameters of the simple electrical-equivalent-circuit-model of these capacitance standards. The effective capacitance of each standard has also been computed as a function of frequency from 1 kHz to 10 MHz. The capacitive parameters have been measured at 1 kHz while inductive parameters have been estimated up to 10 MHz using linear regression analysis by employing least-squares-approximation method. The paper highlights the computation procedure of impedance terms which further requires the determination of various capacitive and inductive terms involved in the calculation of effective capacitance. The method employed for the estimation of inductive parameters as a function of frequency is also discussed in detail. The present work will help in the establishment of metrological traceability of capacitance standards at high-frequency at NPLI which will be further used to establish calibration facility for LCR meters and RF impedance analyzers for capacitance parameter up-to 10 MHz.     

A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard

We present a new software tool called CDN (Collaborative Data Network) for sharing and querying of clinical documents modeled using HL7 v3 standard (e.g., Clinical Document Architecture (CDA), Continuity of Care Document (CCD)). Similar to the caBIG initiative, CDN aims to foster innovations in cancer treatment and diagnosis through large-scale, sharing of clinical data. We focus on cancer because it is the second leading cause of deaths in the US. CDN is based on the synergistic combination of peer-to-peer technology and the extensible markup language XML and XQuery. Using CDN, a user can pose both structured queries and keyword queries on the HL7 v3 documents hosted by data providers. CDN is unique in its design – it supports location oblivious queries in a large-scale, network wherein a user does not explicitly provide the location of the data for a query. A location service in CDN discovers data of interest in the network at query time. CDN uses standard cryptographic techniques to provide security to data providers and protect the privacy of patients. Using CDN, a user can pose clinical queries pertaining to cancer containing aggregations and joins across data hosted by multiple data providers. CDN is implemented with open-source software for web application development and XML query processing. We ran CDN in a distributed environment using Amazon EC2 as a testbed. We report its performance on real and synthetic datasets of discharge summaries. We show that CDN can achieve good performance in a setup with large number of data providers and documents.