Multi-tone EMC testing strategy for RF-devices

In low-cost miniaturized electronic systems, filters are often omitted in front of active non-linear components, potentially resulting in unwanted intermodulation products in the band of operation. Current immunity tests most often use a single-frequency source and are hence not able to capture all relevant intermodulation products. Relying on an anechoic chamber as test facility and using multiple-tone excitation from a dual-source network analyzer, we present an advanced test methodology to evaluate in-the-band leakage of out-of-band undesired frequencies. To demonstrate our approach we use a frequency-selective active textile antenna with integrated non-linear low-noise amplifier

A Strategy Language for Testing Register Transfer Level Logic

The development of modern ICs requires a huge investment in RTL verification. This is a reflection of brisk release schedules and the complexity of contemporary chip designs. A major bottleneck to reaching verification closure in such designs is the disproportionate effort expended in crafting directed tests; which is necessary to reach those behaviors that other, more automated testing methods fail to cover. This paper defines a novel language that can be used to generate targeted stimuli for RTL logic and which mitigates the complexities of writing directed tests. The main idea is to treat directed testing as a meta-reasoning problem about simulation. Our language is both formalized and prototyped as a proof-search strategy language in rewriting logic. We illustrate its novel features and practical use with several examples.published or submitted for publicatio

Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation

Selecting an estimator for the variance covariance matrix is an important step in hypothesis testing. From less robust to more robust, the available choices include: Eicker/White heteroskedasticity-robust standard errors, Newey and West heteroskedasticity-and-autocorrelation- robust standard errors, and cluster-robust standard errors. The rationale for using a less robust covariance matrix estimator is that tests conducted using a less robust covariance matrix estimator can have better power properties. This motivates tests that examine the appropriate level of robustness in covariance matrix estimation. We propose a new robustness testing strategy, and show that it can dramatically improve inference about the proper level of robustness in covariance matrix estimation. Our main focus is on inference about clustering although the proposed robustness testing strategy can also improve inference about parametric assumptions in covariance matrix estimation, which we demonstrate for the case of testing for heteroskedasticity. We also show why the existing clustering test and other applications of the White (1980) robustness testing approach perform poorly, which to our knowledge has not been well understood. The insight into why this existing testing approach performs poorly is also the basis for the proposed robustness testing strategy.

Asymptotics of Fingerprinting and Group Testing: Capacity-Achieving Log-Likelihood Decoders

We study the large-coalition asymptotics of fingerprinting and group testing, and derive explicit decoders that provably achieve capacity for many of the considered models. We do this both for simple decoders (fast but suboptimal) and for joint decoders (slow but optimal), and both for informed and uninformed settings. For fingerprinting, we show that if the pirate strategy is known, the Neyman-Pearson-based log-likelihood decoders provably achieve capacity, regardless of the strategy. The decoder built against the interleaving attack is further shown to be a universal decoder, able to deal with arbitrary attacks and achieving the uninformed capacity. This universal decoder is shown to be closely related to the Lagrange-optimized decoder of Oosterwijk et al. and the empirical mutual information decoder of Moulin. Joint decoders are also proposed, and we conjecture that these also achieve the corresponding joint capacities. For group testing, the simple decoder for the classical model is shown to be more efficient than the one of Chan et al. and it provably achieves the simple group testing capacity. For generalizations of this model such as noisy group testing, the resulting simple decoders also achieve the corresponding simple capacities.Comment: 14 pages, 2 figure

On the First Order Regression Procedure of Estimation for Incomplete Regression Models

This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing

Will Parent Training Reduce Abuse, Enhance Development, and Save Money? Let's Find Out

Outlines a strategy for testing the feasibility of community-developed parent training initiatives to prevent child abuse and neglect. Calls for a federal grant program to test community-wide implementation of parent training programs in stages