Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

State-of-the-Art of High-Power Gyro-Devices. Update of Experimental Results 2021

This report presents an update of the experimental achievements published in the review “State- of-the-Art of High-Power Gyro-Devices and Free Electron Masers”, Journal of Infrared, Millime-ter, and Terahertz Waves, 41, No. 1, pp 1-140 (2020) related to the development of gyro-devices (Tables 2-34). Emphasis is on high-power gyrotron oscillators for long-pulse or continuous wave (CW) operation and pulsed gyrotrons for any applications