Improved error correction technique for on-wafer lightwavemeasurements of photodetectors

An accurate correction technique for on-wafer small-signal lightwave measurements of photodetectors is presented. This technique is an improvement of the conventional calibration methods for on-wafer lightwave measurements. Mathematical expressions for the dominant error sources that exist in the measurement system are derived. Experimental results for an InGaAs-InP pin photodiode show a smoother modulation response characteristic when the presented technique is used     

Human-Swarm-Teaming Transparency and Trust Architecture

Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature. This is due, in part, to the lack of an agreed definition and the overlap between the connected — sometimes used synonymously — concepts of interpretability and explainability. We assert that transparency is the overarching concept, with the tenets of interpretability, explainability, and predictability subordinate. We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture (HST3-Architecture). The architecture reinforces transparency as a key contributor towards situation awareness, and consequently as an enabler for effective trustworthy human-swarm teaming (HST).      

Correction technique for on-chip modulation response measurementsof optoelectronic devices

A new and accurate error correction technique for on-chip intensity modulation response measurements of high-frequency optoelectronic devices is presented. Mathematical expressions for the different sources of errors that exist in the measurement system are derived. The new correction technique applied to the modulation response measurement of a strained quantum well laser diode shows excellent agreement with the theoretically expected result. Simulation results for a small-signal circuit model of the laser diode show excellent agreement with the measured input reflection coefficient S₁₁ and the modulation response S₂₁. With the corrected modulation response measurement, more accurate parameters for this model are extracted     

On Taxonomy and Evaluation of Feature Selection‐Based Learning Classifier System Ensemble Approaches for Data Mining Problems

Ensemble methods aim at combining multiple learning machines to improve the efficacy in a learning task in terms of prediction accuracy, scalability, and other measures. These methods have been applied to evolutionary machine learning techniques including learning classifier systems (LCSs). In this article, we first propose a conceptual framework that allows us to appropriately categorize ensemble‐based methods for fair comparison and highlights the gaps in the corresponding literature. The framework is generic and consists of three sequential stages: a pre‐gate stage concerned with data preparation; the member stage to account for the types of learning machines used to build the ensemble; and a post‐gate stage concerned with the methods to combine ensemble output. A taxonomy of LCSs‐based ensembles is then presented using this framework. The article then focuses on comparing LCS ensembles that use feature selection in the pre‐gate stage. An evaluation methodology is proposed to systematically analyze the performance of these methods. Specifically, random feature sampling and rough set feature selection‐based LCS ensemble methods are compared. Experimental results show that the rough set‐based approach performs significantly better than the random subspace method in terms of classification accuracy in problems with high numbers of irrelevant features. The performance of the two approaches are comparable in problems with high numbers of redundant features.     

Implications of the curse of dimensionality for supervised learning classifier systems: theoretical and empirical analyses

Pattern Analysis and Applications - Learning classifier systems are leading evolutionary machine learning systems that employ genetic algorithms to search for a set of optimally general and correct...