This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.
Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren’t yet ready for real time applications. In this work, we propose a compact representation of audio using conventional autoencoders for dimensionality reduction, and test the approach on two benchmark publicly available datasets. Such compact and simple classification systems where the computing cost is low and memory is managed efficiently may be more useful for real time application. System is evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). Three classifiers, namely, support vector machines (SVM), decision tree classifier, and convolutional neural networks (CNN) have been implemented to judge the impact of the approach. The results obtained by attempting classification with Alexnet and Resnet50 are also reported. Observations proved that this introduction of autoencoders indeed can improve the classification accuracy of the emotion in the input audio files. It can be concluded that in emotion recognition from speech, the choice and application of dimensionality reduction of audio features impacts the results that are achieved and therefore, by working on this aspect of the general speech emotion recognition model, it may be possible to make great improvements in the future.
In this paper, we describe constructing an algorithm and providing an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors at the continental scale. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine and USDA agricultural production data at the county level across the United States through Application Programming Interface (API). We (1) integrated satellite images of MODIS-based net primary productivity (NPP) and gross primary productivity (GPP), and ECMWF-based climatic variables, (2) calculated CUE and commonly used climate metrics, and then (3) investigated the spatial heterogeneity of the variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using time series data in each county across the United States. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. We also found that yield increases with greater GDD and precipitation in most areas of the United States, but with more mixed and fragmented interactions in the southern regions—and that CUE decreases with GDD in the northern regions but increases with GDD in the southern ones. As global warming increases, both carbon sequestration and yield will increase in the south, though CUE will decrease in the north.
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