Location Averaging is a nearest electrical neighbor method of Statistical Outlier Screening (SOS) for screening outliers from probe test data used at Texas Instruments (TI). Location Averaging is sensitive to variances, not only from die parametrics, but also the test solution. Variance in outlier counts specific to Location Averaging that present either a strong commonality in the test solution, or test pattern on the wafer map, or both, can be indicative of a problem in the test solution. Analysis of outlier counts provides a method of monitoring and maintaining the test solution through data-driven techniques to optimize execution through the reduction of downtime and excursions.

Digital Enhanced Cordless Telecommunications (DECT) is a world-wide wireless standard sustained by ETSI and widely used in cordless telephony. Whilst domestic DECT cordless phones were primarily designed to be used in connection with the Public Switched Telephone Network, their presence in Unified Communications systems has become increasingly common given their reliability, flexibility and interoperability. The DECT protocol foresees the usage of authentication and encryption in order to protect the privacy of the voice communications. Unfortunately, the cryptographic mechanisms envisaged by the standard do not provide support for forward secrecy. As a consequence, the compromise of the long-term secret cryptographic key leads to the decryption of any previous, present and future encrypted communication. In this paper, we describe and demonstrate experimentally a new physical attack, able to recover the long-term cryptographic key from the memory of DECT devices and use it to decrypt voice communications previously intercepted in encrypted form. In order to mitigate this threat to the privacy of the DECT communications, we propose a set of countermeasures and proposals for modification of the standards to provide forward secrecy in the communications.

Abstract The flexibility of software‐defined radio (SDR) technology has been proposed to support various applications from the dynamic usage of the radio frequency spectrum to the implementation of radio access technologies in the commercial and military domain. A new generation of low‐cost SDR devices has emerged in the last few years, opening new possibilities to implement inexpensive security attacks against radio protocols and systems. Digital Enhanced Cordless Telecommunication (DECT) is a European Telecommunications Standards Institute standard for short‐range cordless communications with a worldwide installed base. In this paper, we survey the state of art for the application of low‐cost SDRs to the security domain, and we describe a complete implementation of a passive eavesdropping attack against DECT digital voice communication, where the DECT communication is not encrypted. The framework, which implements the attack, has been deployed on two different types of low‐cost SDR platforms to show the flexibility and extendibility of the implementation. The performance of the framework has been evaluated on the basis of different parameters and different approaches, and the experimental results are presented in the paper. The framework has demonstrated its effectiveness to implement an eavesdropping attack against DECT even at great distances with low‐cost equipment.Copyright © 2014 John Wiley & Sons, Ltd.

Makey Makey is a new platform for improvising tangible user interfaces. It enables people to make nature-based interfaces, it is compatible with all software, and it does not require the user to program or to assemble electronics. It is designed for a wide range of audiences, supporting ideation for experts and access for beginners. In the studio, participants will rapidly create several different user interfaces incorporating a wide variety of found objects, both physical and digital. There will also be opportunities to test out the newly created interfaces with each other, and reflect on the design of UI prototyping toolkits.

Summary form only given. Molecular dynamics (MD) simulation has long been recognized as a potentially transformative tool for understanding the behavior of proteins and other biological macromolecules, and for developing a new generation of precisely targeted drugs. Many biologically important phenomena, however, occur over timescales that have previously fallen far outside the reach of MD technology. We have constructed a specialized, massively parallel machine, called Anton, that is capable of performing atomic-level simulations of proteins at a speed roughly two orders of magnitude beyond that of the previous state of the art. The machine has now simulated the behavior of a number of proteins for periods as long as a millisecond -- approximately 100 times the length of the longest such simulation previously published -- revealing aspects of protein dynamics that were previously inaccessible to both computational and experimental study. The speed at which Anton performs these simulations is the result of a tightly coupled codesign process in which novel algorithms and architectural features were developed in concert, guided in large part by an iterative process of performance analysis and optimization.

This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments.

Streaming media on the Internet has experienced rapid growth over the last few years and will continue to increase in importance as broadband technologies and authoring tools continue to improve. As the Internet becomes an increasingly popular alternative to traditional communications media, Internet streaming will become a significant component of many content providers' communications strategies. Internet streaming, however, poses significant challenges for content providers, since it has significant distribution problems. Scalability, quality, reliability, and cost are all issues that have to be addressed in a successful streaming media offering. Streaming content delivery networks (streaming CDNs) attempt to provide solutions to the bottlenecks encountered by streaming applications on the Internet. However, only a small number of them has been deployed, and little is known about the internal organization of these systems. In this paper, we discuss the design choices made during the evolution of Akamai's CDN for streaming media. In particular, we look at the design choices made to ensure the network's scalability, quality of delivered content, and reliability while keeping costs low. Performance studies conducted on the evolving system indicate that our design scores highly on all of the above categories.

It would be a great advantage if remote classification of species is possible under a variety of vegetation conditions, such as various levels of moisture stress. The primary goal of this study is to investigate the use of hyperspectral data for the detection of soybean (Glycine max) from weeds, specifically sicklepod (Senna obtusifolia) and cocklebur (Xanthium strumarium) at various levels of moisture stress and to determine the effects of moisture stress on automated weed detection systems. A secondary goal of this study is to investigate the use of hyperspectral data for the detection of moisture stress within a given vegetative species. Two feature extraction techniques were investigated, including the use of spectral bands' amplitudes and the use of discrete wavelet transform coefficients. These features were used in a traditional maximum-likelihood classification system. Experimental results showed that higher moisture stress (less moisture) corresponded to higher weed detection accuracies.

Drift can be an undesirable effect of herbicide spraying. Remote detection would be a beneficial step towards effectively monitoring herbicide drift. The goal of this study is to investigate the use of hyperspectral reflectance imagery for remote detection of herbicide drift. Spectral reflectance data for corn and soybean collected with a handheld ASD Spectroradiometer is used in this study. Four different rates of herbicide drift were simulated during the experiment stage, viz. 1/2 (maximum drift), 1/8, 1/32, and 1/64 (minimum drift) as well as no drift. Reflectance values of the 5 best spectral bands were used as features where optimization was conducted using ROC analysis. The features were subjected to linear discriminant analysis to increase drift detection accuracy. Classification was then performed using a maximum-likelihood decision. The methods were tested on the experimental data using cross-validation methods.

In this work we illustrate a means of monitoring probing processes in-situ. In addition, we show meaningful ways of interpreting data gathered during the probing process that allow real time process modification that leads to increased yields and throughput. Through this new technique, a test operation is able to minimize yield loss due to the probing process.