Design of a Low-Power 1.65 Gbps Data Channel for HDMI Transmitter

This paper presents a design of low power data channel for application in High Definition Multimedia Interface (HDMI) Transmitter circuit. The input is 10 bit parallel data and output is serial data at 1.65 Gbps. This circuit uses only a single frequency of serial clock input. All other timing signals are derived within the circuit from the serial clock. This design has dedicated lines to disable and enable all its channels within two pixel-clock periods only. A pair of disable and enable functions performed immediately after power-on of the circuit serves as the reset function. The presented design is immune to data-dependent switching spikes in supply current and pushes them in the range of serial frequency and its multiples. Thus filtering requirements are relaxed. The output stage uses a bias voltage of 2.8 volts for a receiver pull-up voltage of 3.3 volts. The reported data channel is designed using UMC 180 nm CMOS Technology. The design is modifiable for other inter-board serial interfaces like USB and LAN with different number of bits at the parallel input.Comment: TMDS, HDMI, USB, Gbps, data-dependent jitter, supply current, UMC180, low-power consumption, single serial cloc

Combining Multiple Time Series Models Through A Robust Weighted Mechanism

Improvement of time series forecasting accuracy through combining multiple models is an important as well as a dynamic area of research. As a result, various forecasts combination methods have been developed in literature. However, most of them are based on simple linear ensemble strategies and hence ignore the possible relationships between two or more participating models. In this paper, we propose a robust weighted nonlinear ensemble technique which considers the individual forecasts from different models as well as the correlations among them while combining. The proposed ensemble is constructed using three well-known forecasting models and is tested for three real-world time series. A comparison is made among the proposed scheme and three other widely used linear combination methods, in terms of the obtained forecast errors. This comparison shows that our ensemble scheme provides significantly lower forecast errors than each individual model as well as each of the four linear combination methods.Comment: 6 pages, 3 figures, 2 tables, conferenc

A Framework for High-Accuracy Privacy-Preserving Mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques

Back to the Future: The Managed Care Revolution

The evolution to a managed care system did not achieve the complete, fundamental change in the health care delivery system that was envisioned by some of its early proponents. As the managed care movement evolved beyond the prepaid group practice model, it focused primarily on methods used to spread the cost of health care services

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding