Lucas Iacono

Eye movements shape all visual input to the brain, making their understanding essential for studying perception and visual guidance in dynamic environments. Research on expert performance indicates that gaze coordination is a key feature of expertise in, for example, sports. Mobile eye tracking provides the opportunity to investigate gaze strategies supporting the skilled actions of an athlete and can deliver insight into the underlying perceptual-cognitive processes. We systematically observed the visual strategy of an expert racing driver performing a domain-representative task. Synchronized gaze, telemetry, and localization data from a high-grade simulator were analyzed to address four classes of research questions: oculomotor, scene analysis, timing, and point of vantage. The results (a) replicate the seminal tangent point orientation (pre-turn-in saccades), (b) describe both the oculomotor signature and timing signature of the steering with the head strategy, (c) identify a novel saccade strategy (pre-full-throttle saccades), and (d) reveal a previously unstudied spatial regularity in the serial organization of behavior: a tight localization of the points of vantage where the pre-turn-in saccades and pre-full-throttle saccades are made. The gaze strategies are not tied to specifics of the task and may be relevant for understanding expert performance in other fields with similar visuomotor and cognitive demands. The method of cross-examining an integrated dataset by multiple parametrizations itself complements traditional research designs with predefined task constraints and restrictions. We are not aware of any study that has simultaneously addressed all four kinds of research questions simultaneously.

Farmers and agronomists require crop health metrics to monitor plantations and detect problems like diseases or droughts at an early stage. This enables them to implement measures to address crop problems. The use of multispectral images and cloud computing is conducive to obtaining such metrics. Drones and satellites capture extensive multispectral image datasets, while the cloud facilitates the storage of these images and provides execution services for extracting crop health metrics, such as the Normalized Difference Vegetation Index (NDVI). The use of the Cloud to compute NDVI poses new research challenges, such as determining which cloud technology offers the optimal balance of execution time and monetary cost. In this article, we present Serverless NDVI (SNDVI), a new framework based on serverless computing for NDVI computation. The objective of SNDVI is to minimize the monetary costs and computing times associated with using a Public Cloud while processing NDVI from large datasets. One of SNDVI's key contributions is to crop the dataset into subsegments to leverage Lambda's ability to run up to 1,000 NDVI computing functions in parallel on each subsegment. We deployed SNDVI using Amazon Lambda and conducted two experiments to analyze and validate its performance. Both experiments focused on two key metrics: (i) execution time and (ii) monetary costs. The first experiment involved executing SNDVI to extract NDVI from a multispectral dataset. The objective was to evaluate the overall SNDVI functionality, assess its performance, and verify the quality of SNDVI output. In the second experiment, we conducted a benchmarking analysis comparing SNDVI with an EC2-based NDVI computing architecture. Results from the first experiment demonstrated that the processing times for the entire SNDVI execution ranged from 9 to 15 seconds, with a total cost (including storage) of 4.19 USD. Results from the second experiment revealed that the monetary costs of EC2 and Lambda were similar, but the computing time for SNDVI was 411 times faster than the EC2 architecture. In conclusion, the investigation reported in this paper demonstrates that SNDVI successfully achieves its goals and that Serverless Computing presents a promising native serverless alternative to traditional cloud services for NDVI computation.