For more than 100 years, recording people's fingerprints has involved them pressing their fingertips against a surface. Originally this involved ink but has since moved to sensors embedded in scanners at airports and phone screens. The next stage of fingerprinting doesn't involve touching anything at all. So-called contactless fingerprinting technology uses your phone's camera and image processing algorithms to capture people's fingerprints. Hold your hand in front of the camera lens and the software can identify and record all the lines and swirls on your fingertips.

NOTE: Controls wasn't the only area of interest at PACK EXPO. CONTROLS INNOVATIONS Two PACK EXPO Las Vegas exhibitors a few aisles apart in the Lower South Hall featured analytics platforms that provide better real-time visibility into the manufacturing process. From Oden Technologies comes The Oden Platform (1). It's a comprehensive industrial Internet of Things analytics platform that provides employees at each level of a manufacturing plant with clear visibility into multiple data sets pertaining to the manufacturing process. Oden helps manufacturers monitor their production process and improve operational efficiency in real time by diagnosing problems that otherwise would have been missed. Oden helps users track performance metrics of multiple assets and accurately predict downtime based on historical data. In addition, by utilizing the platform, manufacturers can reduce bottlenecks at each stage of production and can save costs by eliminating quality issues, waste, and downtime.Photo 1 This platform is designed to help manufacturing units attain the best performance out of their manufacturing assets and leverage artificial intelligence (AI) and machine learning (ML) algorithms to empower prescriptive analytics. This allows employees on the floor to diagnose and mitigate issues as soon as they arise or offer alerts to avoid issues. In a connected manufacturing environment, companies need real-time accurate insights to improve the productivity and efficiency of their production lines. While manufacturers are increasingly willing to adopt manufacturing analytics practices, legacy equipment and limited technical know-how among machine operators are holding them back. In addition, employees on the floor are unable to make the most of the analytics tools at their disposal and are simply not achieving the expected impact on their profitability.

The recorded traffic was aggregated into 100x100 meters bins and made available as three-channel images. Within these images, the first channel depicts the traffic volume in each cell, the second one the average speed of vehicles, and the third one the majority of vehicles' directions (as one of four cardinal directions). The data spanned a whole year in 5-minute intervals, where certain days were left out from the training data, to be used for prediction and upload to the traffic4cast servers, which then would assess the quality of the prediction. The prediction itself consisted of "three images into the future" (spanning a 15-minute interval), based on the previous hour (12 images). Given the problem formalization, our efforts mostly focused on the application of well-known image processing algorithms, though we also explored various simple baselines, neural networks taking into account spatiotemporal context, as well as more complex network architectures that should be able to take advantage of the fact that the origin of the data stems from probes that move on a known graph. Ultimately, we did not manage to outperform the "simple" application of a widely-used image processing algorithm, which might be a hint that either a lot more research on networks targeted specifically at this problem or a different formulation of the problem altogether is required.

Here is a list of best practices we've identified for designing advanced algorithms. We hope this can help you and your team. Let's start off by defining the problem. It's important to keep the problem's scope as narrow as possible. Write the documentation and define the API before you start programming and implementing. What issue are you trying to solve?

The robotics designers offer to the farmers the opportunity to significantly reduce the costs of manual labor for harvesting. The robots can replace the seasonal manual work or even permanent employees on farms. In this article, I made a presentation of the robots designed to replace the manual work in harvesting the fruits. All of the below robots have the ability to detect, recognize, and determine if these are ripe enough to be picked. In addition, they are able to harvest the fruits without damaging them.

Abstract: This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robo tics system is successfully designed and developed to perform target tracking and intelligent navigation. Th is study focuses on developing image processing algorithms and fuzzy inference system for the analys is of the terrain. The visi on system developed is capable of interpreting underwater scene by extracting subjective uncertainties of the object of interest.