Enhanced Performance of Artificial-Neural-Network-Based Equalization for Short-Haul Fiber-Optic Communications.

This work proposes an efficient and easy-to-implement single-layer artificial neural network (ANN)-based equalizer with improved compensation performance. The proposed equalizer is used for effectively mitigating the distortions induced in the short-haul fiber-optic communication systems based on intensity modulation and direct detection (IMDD). The compensation performance of the ANN equalizer is significantly improved, exploiting an introduced advanced training scheme. The efficiency and robustness of the proposed ANN equalizer are illustrated through 10- and 28-Gbaud short-reach optical-fiber communication systems. Compared to the efficient but computationally expensive maximum likelihood sequence estimator (MLSE), the proposed ANN equalizer not only significantly reduces its computational equalization cost and storage memory requirements, but it also outperforms its bit error rate performance.

Regression models and effect size measures for single case designs.

A regression modelling approach for the analysis of single case designs (SCDs) is described in this paper. The approach presented addresses two key issues in the analysis of SCDs. The first issue is that of serial dependence among the observations in SCDs. The second issue is that of an effect size measure appropriate for SCDs. As with traditional between-subjects experimental designs, effect size measures are critical in assessing the impact of interventions in SCDs. Although effect size measures when there is level change without trend are straightforward to obtain and have been well studied, the situation is different when there are changes in both level and trend. An effect size measure that combines changes in levels and slopes and that is comparable to the d-type effect size measure obtained in between-subjects designs is presented. Finally, an inferential procedure for assessing the effect of the intervention based on the effect size measure is provided and illustrated.

Design of "turn-ON and turn-OFF" fluorescence switching based photonic logic gates through multiple input-output models by MoS2 quantum dots.

We demonstrate Boolean logic gates like behavior of Molybdenum disulfide quantum dots (MoS2 QDs) based on "turn-ON" and "turn-OFF" fluorescence switching response upon the addition of various biomolecules, carbon based materials and other organic molecules as inputs. The logic gates are constructed through single input single output (SISO), double inputs single output (DISO) and single input double outputs (SIDO) models. The fluorescence enhancement/"turn-ON" response of MoS2 QDs is observed in the presence of glucose and bovine serum albumin (BSA). The fluorescence quenching/"turn-OFF" response of MoS2 QDs is observed in the presence of graphene oxide (GO), reduced graphene oxide (RGO) and poly aniline (PANI) and interestingly, this quenched fluorescence is recovered back in the presence of additional input BSA. These fluorescence "turn-ON" and "turn-OFF" responses of MoS2 QDs in the presence of various inputs demonstrate the Boolean logic operations include YES, NOT, PASS0, OR, NOR and IMPLICATIOIN.

Between-case standardized effect size analysis of single case designs: Examination of the two methods.

An increasing movement in single case research is to employ statistical analyses as one form of data analysis. Researchers have proposed different statistical approaches. The purpose of this paper is to examine the utility and discriminant validity of two novel types of between-case standardized effect size analyses with two existing systematic reviews. The between-case analyses found greater effect sizes for the studies in the object play review and smaller effect sizes for studies of sensory intervention, which were consistent with the overall conclusions reached in the original systematic reviews. These findings provide evidence of discriminant validity, although concerns remain around the methods' utility across different single case research designs. Future directions for research and development also are provided.

An effect size measure and Bayesian analysis of single-case designs.

This article describes a linear modeling approach for the analysis of single-case designs (SCDs). Effect size measures in SCDs have been defined and studied for the situation where there is a level change without a time trend. However, when there are level and trend changes, effect size measures are either defined in terms of changes in R(2) or defined separately for changes in slopes and intercept coefficients. We propose an alternate effect size measure that takes into account changes in slopes and intercepts in the presence of serial dependence and provides an integrated procedure for the analysis of SCDs through estimation and inference based directly on the effect size measure. A Bayesian procedure is described to analyze the data and draw inferences in SCDs. A multilevel model that is appropriate when several subjects are available is integrated into the Bayesian procedure to provide a standardized effect size measure comparable to effect size measures in a between-subjects design. The applicability of the Bayesian approach for the analysis of SCDs is demonstrated through an example.

Generalizability theory: a practical guide to study design, implementation, and interpretation.

Generalizability Theory (GT) offers increased utility for assessment research given the ability to concurrently examine multiple sources of variance, inform both relative and absolute decision making, and determine both the consistency and generalizability of results. Despite these strengths, assessment researchers within the fields of education and psychology have been slow to adopt and utilize a GT approach. This underutilization may be due to an incomplete understanding of the conceptual underpinnings of GT, the actual steps involved in designing and implementing generalizability studies, or some combination of both issues. The goal of the current article is therefore two-fold: (a) to provide readers with the conceptual background and terminology related to the use of GT and (b) to facilitate understanding of the range of issues that need to be considered in the design, implementation, and interpretation of generalizability and dependability studies. Given the relevance of this analytic approach to applied assessment contexts, there exists a need to ensure that GT is both accessible to, and understood by, researchers in education and psychology. Important methodological and analytical considerations are presented and implications for applied use are described.

A generalized least squares regression approach for computing effect sizes in single-case research: application examples.

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice.