Mobile devices with passive depth sensing capabilities are ubiquitous, and recently active depth sensors have become available on some tablets and AR/VR devices. Although real-time depth data is accessible, its rich value to mainstream AR applications has been sorely under-explored. Adoption of depth-based UX has been impeded by the complexity of performing even simple operations with raw depth data, such as detecting intersections or constructing meshes. In this paper, we introduce DepthLab, a software library that encapsulates a variety of depth-based UI/UX paradigms, including geometry-aware rendering (occlusion, shadows), surface interaction behaviors (physics-based collisions, avatar path planning), and visual effects (relighting, 3D-anchored focus and aperture effects). We break down the usage of depth into localized depth, surface depth, and dense depth, and describe our real-time algorithms for interaction and rendering tasks. We present the design process, system, and components of DepthLab to streamline and centralize the development of interactive depth features. We have open-sourced our software at https://github.com/googlesamples/arcore-depth-lab to external developers, conducted performance evaluation, and discussed how DepthLab can accelerate the workflow of mobile AR designers and developers. With DepthLab we aim to help mobile developers to effortlessly integrate depth into their AR experiences and amplify the expression of their creative vision.

Augmented reality (AR) for smartphones has matured from a technology for earlier adopters, available only on select high-end phones, to one that is truly available to the general public. One of the key breakthroughs has been in low-compute methods for six degree of freedom (6DoF) tracking on phones using only the existing hardware (camera and inertial sensors). 6DoF tracking is the cornerstone of smartphone AR allowing virtual content to be precisely locked on top of the real world. However, to really give users the impression of believable AR, one requires mobile depth. Without depth, even simple effects such as a virtual object being correctly occluded by the real-world is impossible. However, requiring a mobile depth sensor would severely restrict the access to such features. In this article, we provide a novel pipeline for mobile depth that supports a wide array of mobile phones, and uses only the existing monocular color sensor. Through several technical contributions, we provide the ability to compute low latency dense depth maps using only a single CPU core of a wide range of (medium-high) mobile phones. We demonstrate the capabilities of our approach on high-level AR applications including real-time navigation and shopping.

The motivation for this paper is the analysis of input conditions that cause a short-circuit in a logic circuit, that is, that create a direct path from the power supply to ground. We model the logic circuit as a graph where edges represent transistors which are either open or closed, function of the input conditions. From this graph we derive a Quantified Boolean Formula (QBF) problem whose solution identifies the existence of a valid input combination that creates a path in the graph between the pair of nodes that represent the power source and ground, without ever enumerating all input combinations. We build the QBF problem incrementally, minimizing the number of active nodes and hence of possible states. In the end, we obtain a relatively simple CNF expression, function only of the circuit inputs, that is handled by a generic SAT solver. We present results that demonstrate the practical applicability of our method on circuit instances that are intractable by alternative methods.

The deployment of vehicular network testbeds encompasses a myriad of new challenges regarding resource management, execution of experiments on the nodes, and the collection of the resulting data, when compared to traditional testbeds. Vehicular nodes have very unpredictable mobility patterns, especially in cities, and use a specific communication protocol; therefore, the commonly used solutions for large scale testbeds are not directly applicable. This is the case of our vehicular network testbed developed in the framework of the DRIVE-IN and Future Cities! (DRIVE-IN and Future Cities!)1projects. This paper tackles the aforementioned issues by proposing the design and implementation of a testbed management system based on the cOntrol and Management Framework (OMF). The base functionalities of OMF were extended to integrate node control over cellular networks, as well as to guarantee that experiments are always safely deployed, minimizing the risk of node corruption and malfunctioning. The management platform is capable of uploading new software to the vehicular nodes, conduct experiments using the specific vehicular wireless (IEEE 802.11p) interface, collect the resulting data, as well as storing and managing information about all the nodes. The results obtained from real-world experiments show that the the proposed platform is able to manage a Vehicular Ad-hoc NETwork (VANET) testbed.

In this paper we propose a solution to strengthen the security of domain name system (DNS) servers associated with one or more top level domains (TLD). The proposed solution has been developed and tested at FCCN, the TLD manager for the .PT domain. Through the implementation of network probes that monitor the network in real-time, we are able to dynamically prevent, detect or limit the scope of attempted intrusions or other types of attacks to the DNS service. The platform relies heavily on cross-correlation allowing data from a particular sensor to be shared with the others. Administration tasks such as setting up alarms or performing statistical analysis are made through a Web-based interface.