A power‐area‐efficient, 3‐band, 2‐RX MIMO,TD‐LTE receiver with direct‐coupled ADC

In this work, a power‐area‐efficient, 3‐band, 2‐RX MIMO, and TD‐LTE (backward compatible with the HSPA+, HSUPA, HSDPA, and TD‐SCDMA) CMOS receiver is presented and implemented in 0.13‐μm CMOS technology. The continuous‐time delta‐sigma A/D converters (CT ?Σ ADCs) are directly coupled to the outputs of the transimpedance amplifiers, eliminating the need of analog anti‐aliasing filters between RX front‐end and ADCs in conventional structures. The strong adjacent channel interference without low‐pass filter attenuation is handled by proper gain control. A low‐power small‐area solution for excess loop delay compensation is implemented in the CT ?Σ ADC. At 20 MHz bandwidth, the CT ?Σ ADC achieves 66 dB dynamic range and 3.5 dB RX chip noise figure is measured. A maximum of 2.4 dB signal‐to‐noise ratio degradation is measured in all the adjacent channel selectivity (ACS) and blocking tests, demonstrating the effectiveness of the strategy against the low‐pass filter removal from the conventional architecture. The receiver dissipates a maximum of 171 mW at 2‐RX MIMO mode. To our best knowledge, it is the first research paper on the design of fully integrated commercial TD‐LTE receiver. Copyright © 2014 John Wiley & Sons, Ltd.     

Troubleshooting in geoelectrical prospecting using real‐coded genetic algorithm with chromosomal extrapolation

This work presents a genetic operator developed from mathematical methods of curve extrapolation applied in solving problems in geoelectrical prospection. This operator will assist the production of fitter individuals in the population of a genetic algorithm in which inherent patterns of the best individuals of each generation are recognized. The proposed operator in conjunction with a real‐coded genetic algorithm is compared to five alternative optimization techniques known and used in the application to problems in geoelectrical prospection. Copyright © 2014 John Wiley & Sons, Ltd.     

Visually weighted neighbor voting for image tag relevance learning

Multimedia Tools and Applications - The presence of non-relevant tags in image folksonomies hampers the effective organization and retrieval of user-contributed images. In this paper, we propose to...     

Recent Lessons Learned in Structural Fire Engineering for Composite Steel Structures

The knowledge in the field of structural fire engineering has been greatly advanced through assessment of a number of real fires (WTC, Torre Windsor, Broadgate, etc.) and, especially, by the Cardington series of full scale structural fire tests. This knowledge has been used to validate and verify the use of computational finite element models that have expanded the range of structures that can be investigated under severe fire exposure. This paper presents a selection of key lessons learned by the authors through the assessment of structures in fire for real commercial building projects. The key areas of sensitivity that have been encountered are described and a discussion of each point presented. The paper is aimed at describing potential weaknesses that have been observed in the commercial work of the authors, often driven by the requirements for efficient ambient structural design. The paper concludes with some suggested advice for structural engineers aimed at increasing the general robustness of building structures. This is based on designing out as far as possible in the ambient design of a structure the potential weaknesses identified in past project work.     

Must psychologists change the way they analyze their data?

Wagenmakers, Wetzels, Borsboom, and van der Maas (2011) argued that psychologists should replace the familiar “frequentist” statistical analyses of their data with Bayesian analyses. To illustrate their argument, they reanalyzed a set of psi experiments published recently in this journal by Bem (2011), maintaining that, contrary to his conclusion, his data do not yield evidence in favor of the psi hypothesis. We argue that they have incorrectly selected an unrealistic prior distribution for their analysis and that a Bayesian analysis using a more reasonable distribution yields strong evidence in favor of the psi hypothesis. More generally, we argue that there are advantages to Bayesian analyses that merit their increased use in the future. However, as Wagenmakers et al.'s analysis inadvertently revealed, they contain hidden traps that must be better understood before being more widely substituted for the familiar frequentist analyses currently employed by most research psychologists. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm. Received 14 May 2001 / Revised 5 September 2001 / Accepted in revised form 26 October 2001 Correspondence and offprint requests to: Qinghua Zou, Department of Computer Science, California University–Los Angeles, CA 90095, USA. Email: zou@cs.ucla.eduau     

Parametric cure models of relative and cause-specific survival for grouped survival times

With parametric cure models, we can express survival parameters (e.g. cured fraction, location and scale parameters) as functions of covariates. These models can measure survival from a specific disease process, either by examining deaths due to the cause under study (cause-specific survival), or by comparing all deaths to those in a matched control population (relative survival). We present a binomial maximum likelihood algorithm to be used for actuarial data, where follow-up times are grouped into specific intervals. Our algorithm provides simultaneous maximum likelihood estimates for all the parameters of a cure model and can be used for cause-specific or relative survival analysis with a variety of survival distributions. Current software does not provide the flexibility of this unified approach.