5284748
A novel frequency compensation technique for low-voltage low-dropout regulator
A novel frequency compensation technique for low-voltage low-dropout regulator (LDR) is presented. The proposed technique, called pole-control frequency compensation (PCFC), provides faster loop response and does not require a large filtering capacitor. Both theoretical analysis and simulation show that the stability of the LDR using PCFC is independent of the load current, temperature and equivalent series resistance of the filtering capacitor. LDRs are well suited to portable equipment such as cellular phones, pagers and personal digital assistants.
18463934
Design of a 1.5-V high-order curvature-compensated CMOS bandgap reference
A low-voltage low-noise high-order curvature-compensated CMOS bandgap reference is proposed in this paper. Two resistor strings are added to the bandgap circuit to reduce the supply voltage. In addition, a temperature-dependent resistor ratio generated by a high-resistive poly resistor is used to provide the high-order compensation. The proposed bandgap voltage reference can operate down to a 1.5V supply (where V/sub th/ = 0.9V at 0/spl deg/C) with a temperature coefficient of less than 15.2ppm//spl deg/C and an average line regulation of 5.5mV/V.
15996223
A low-voltage CMOS low-dropout regulator with enhanced loop response
A 1.5-V 100-mA CMOS low-dropout regulator with a double pole-zero cancellation scheme and a linearly operated power PMOS transistor at dropout to enhance the loop-gain response, is presented. The circuit realization is well-studied and developed with respect to the loop-gain response, the transient response, the output noise and the output accuracy, as well as the standby power consumption. Implemented in a 0.6 /spl mu/m CMOS process, experimental results show that the regulator provides a full load transient response of less than 1-/spl mu/s settling time and less than 50-mV overshoots and undershoots. Moreover, it provides a ripple rejection of better than -26 dB and an output noise of 0.07 /spl mu/V/sqrt Hz at 100 kHz.
9755080
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme
A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.
28941391
Optimum nested Miller compensation for low-voltage low-power CMOS amplifier design
An optimum structure of nested Miller compensation (NMC) is developed. The developed structure using a feedforward transconductance stage and null resistor on NMC, called NMCFNR, solves the bandwidth reduction and poor transient response problems of low-voltage low-power NMC amplifiers. For comparison, amplifiers compensated by NMC and NMCFNR have been fabricated. Experimental results show that NMCFNR improves the gain-bandwidth product, slew rate and settling time by more than three times with 8/spl deg/ increase on the phase margin and no increase of the power consumption. Moreover, it improves the negative power supply rejection ratio by at least 54 dB. The die area of the NMCFNR amplifier is about half of that of the NMC amplifier.
13138848
Data Classification Using Genetic Parallel Programming
A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.
16120959
Scalable model-based clustering for large databases based on data summarization
The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources such as memory and computation time. In this paper, two scalable clustering algorithms, bEMADS and gEMADS, are presented based on the Gaussian mixture model. Both summarize data into subclusters and then generate Gaussian mixtures from their data summaries. Their core algorithm, EMADS, is defined on data summaries and approximates the aggregate behavior of each subcluster of data under the Gaussian mixture model. EMADS is provably convergent. Experimental results substantiate that both algorithms can run several orders of magnitude faster than expectation-maximization with little loss of accuracy.
15025885
Scalable model-based cluster analysis using clustering features
We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm-EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably converges. The experiments show that our clustering systems run one or two orders of magnitude faster than the traditional EM algorithm with few losses of accuracy.
17959915
An Enhanced Annealing Genetic Algorithm For Multi-objective Optimization Problems
In this paper, we present a new algorithm — an Enhanced Annealing Genetic Algorithm for Multi-Objective Optimization problems (MOPs). The algorithm tackles the MOPs by a new quantitative measurement of the Pareto front coverage quality — Coverage Quotient. We then correspondingly design an energy function, a fitness function and a hybridization framework, and manage to achieve both satisfactory results and guaranteed convergence.
2011139
Discovering knowledge from noisy databases using genetic programming
In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this paper, we present a framework that combines Genetic Programming (Koza 1992; 1994) and Inductive Logic Programming (Muggleton, 1992) to induce knowledge represented in various knowledge representation formalisms from noisy databases. The framework is based on a formalism of logic grammars and it can specify the search space declaratively. An implementation of the framework, LOGENPRO (The Logic grammar based GENetic PROgramming system), has been developed. The performance of LOGENPRO is evaluated on the chess endgame domain. We compare LOGENPRO with FOIL and other learning systems in detail and find its performance is significantly better than that of the others. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid overfitting and identify important patterns at the same time. Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains. Area: Data mining, Genetic Programming, Evolutionary Computation, Rule Learning