A DATA-INTENSIVE APPROACH TO EXPLOIT NEW GNSS SCIENCE OPPORTUNITIES

With the current GNSS infrastructure development plans, over 120 GNSS satellites (including European Galileo satellites)will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis.This global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications. In themeantime, recent advances in technology have contributed "de-facto" to the deployment of a large GNSS receiver arraybased on Internet of Things (IoT), affordable smart devices easy to find in everybody’s pockets. These devices – evolvingfast at each new generation – feature an increasing number of capabilities and sensors able to collect a variety ofmeasurements, improving GNSS performance. Among these capabilities, Galileo dual band smartphones receivers andAndroid’s support for raw GNSS data recording represent major steps forward for Positioning, Navigation and Timing (PNT)data processing improvements. Information gathering from these devices, commonly referred as crowdsourcing, opensthe door to new data-intensive analysis techniques in many science domains. At this point, collaboration between variousresearch groups is essential to harness the potential hidden behind the large volumes of data generated by thiscyberinfrastructure. Cloud Computing technologies extend traditional computational boundaries, enabling execution ofprocessing components close to the data. This paradigm shift offers seamless execution of interactive algorithms andanalytics, skipping lengthy downloads and setups. The resulting scenario, defined by a GNSS Big Data repository with colocatedprocessing capabilities, sets an excellent basis for the application of Artificial Intelligence / Machine Learning (ML)technologies in the context of GNSS. This unique opportunity for science has been recognized by the European SpaceAgency (ESA) with the creation of the Navigation Scientific Office, which leverages on GNSS infrastructure to deliverinnovative solutions across multiple scientific domains.

Drone interactions within the field of Augmented Reality

Using drones and augmented reality paradigm, new forms of interactive algorithms has been created and proposed.We start with a first person view interaction where the drone mimics the movement of one person’s head wearing aHMD so that movements of the head can be mapped to actions by the drones. We then provide two novel AR/VRapplications of drones to create something similar to third person view in 2D and 3D. To get started, our firstidea is to control a drone using head movements. The second application which we implemented is to provide animplementation where tangible platforms are used by the drone to react to the movements of the character. Finallyour third implementaton if to create and AR world using real outdoor scenery and asking a drone to mimic a thirdperson view combining the real scenery with a synthetic actor so that based on the synthetic actor movement thedrone changes its behavior correctly in the real-word trying to provide a synchronized view of the real and syntheticword. There are three novel ideas providing a new form of interactions which will improve with drones functionalityin future. Our implementation shows the feasibility of our idea as discussed in the paper.

Advances in Deep Learning in Mobile Interactive Algorithms and Learning Technologies

<p> </p><p>The Special Issue of the <strong>International Journal of Interactive Mobile Technologies (iJIM)</strong> is publishing very selective papers presented at the <strong>6^th edition of International Conference on Communication, Management, and Information Technology- ICCMIT 2020.</strong> The <strong>ICCMIT 2020</strong> was scheduled from 1-3 April 2020 in Athens, Greece which was postponed due to the COVID’19 outbreak, and the papers were presented online. </p><p> For this special issue, the <strong>ICCMIT 2020</strong> received several papers from researchers, engineers, and industry professionals, from all around the world to publish their research in the field of science and technologies. But we selected best contributions from Russia, Romania, Cyprus, Saudi Arabia, Egypt, and Pakistan. These contributions are in the field of cellular automata, wireless sensor networks, mechatronic systems, deep learning, heterogeneous LTE-a networks, GPS services, web services, feature selection, data stream, mobile e-learning, traffic routing solution, and encoding systems.</p>

Interactive Algorithms in Complex Image Processing Systems Based on Big Data

In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive algorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an image processing experiment system based on Matlab GUI platform is established for different levels of image processing knowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper, experimental simulations were performed on complex natural images and complex human eye images. The speed of the USB camera is generally between 15 frames/second and 25 frames/second, and in a 240 × 320 picture, the interactive algorithm in this article only needs about 59 ms, which is enough to complete the automatic interaction in the video in real time, which is convenient for subsequent image extraction. The experimental results show that the interactive algorithm in the complex image processing system in this paper optimizes the image extraction rate and improves the antinoise performance of the segmentation and the segmentation effect of the deep depression region.

Gram matrix associated to controlled frames

Controlled frames have been recently introduced in Hilbert spaces to improve the numerical efficiency of interactive algorithms for inverting the frame operator. In this paper, unlike the cross-Gram matrix of two different sequences which is not always a diagnostic tool, we define the controlled-Gram matrix of a sequence as a practical implement to diagnose that a given sequence is a controlled Bessel, frame or Riesz basis. Also, we discuss the cases that the operator associated to controlled Gram matrix will be bounded, invertible, Hilbert–Schmidt or a trace-class operator. Similar to standard frames, we present an explicit structure for controlled Riesz bases and show that every [Formula: see text]-controlled Riesz basis [Formula: see text] is in the form [Formula: see text], where [Formula: see text] is a bijective operator on [Formula: see text]. Furthermore, we propose an equivalent accessible condition to the sequence [Formula: see text] being a [Formula: see text]-controlled Riesz basis.