Rashba spin splitting in biased semiconductor quantum wells

Rashba spin splitting (RSS) in biased semiconductor quantum wells is investigated theoretically based on the eight-band envelope function model. We find that at large wave vectors, RSS is both nonmonotonic and anisotropic as a function of in-plane wave vector, in contrast to the widely used linear and isotropic model. We derive an analytical expression for RSS, which can correctly reproduce such nonmonotonic behavior at large wave vectors. We also investigate numerically the dependence of RSS on the various band parameters and find that RSS increases with decreasing band gap and subband index, increasing valence band offset, external electric field, and well width. Our analytical expression for RSS provides a satisfactory explanation to all these features.Comment: 5 pages, 4 figures, author names corrected, submitted to Phys. Rev.

The effect of extreme response and non-extreme response styles on testing measurement invariance

Extreme and non-extreme response styles (RSs) are prevalent in survey research using Likert-type scales. Their effects on measurement invariance (MI) in the context of confirmatory factor analysis are systematically investigated here via a Monte Carlo simulation study. Using the parameter estimates obtained from analyzing a 2007 Trends in International Mathematics and Science Study data set, a population model was constructed. Original and contaminated data with one of two RSs were generated and analyzed via multi-group confirmatory factor analysis with different constraints of MI. The results indicated that the detrimental effects of response style on MI have been underestimated. More specifically, these two RSs had a substantially negative impact on both model fit and parameter recovery, suggesting that the lack of MI between groups may have been caused by the RSs, not the measured factors of focal interest. Practical implications are provided to help practitioners to detect RSs and determine whether RSs are a serious threat to MI

A Missing Piece of RSS Technology

In the Information Age, people use RSS (Really Simple Syndication) Technology to help them to easily get the latest contents from websites by accessing only a single website as well as by using mobile devices. Several companies have started to use RSS for distributing their information to customers. However, most contents published via RSS technology are public and not confidential such as credit card information, financial business information etc. Since the RSS technology does not have a mechanism to ensure that the incoming information is really secure, in this paper we have proposed a Secure Information Notifying System with RSS Technology (SInfoNS). We have applied the RSS technology together with the cryptography to make any RSS document become secure before disseminating it to relevant users. The SInfoNS also uses XSL to apply to private information retrieval and XML schema and SchemaPath definitions have been created for validation. The results displayed on a user's mobile device provide users with the latest information. The results of this study confirm that our system will aggregate RSS documents and disseminate information to each user. The SInfoNS enables RSS technology for the use of private information that can be securely distributed.Cryptography, Really Simple Syndication, RSS, XML Schema, XSL

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained