High degree of N-functionalization in macroscopically assembled carbon nanotubes

Journal of Materials Science - Nitrogen doping of carbon nanomaterials has emerged as a method to develop novel material properties, though limitations in the form of extended treatment times,...     

Controlled Micro/Nanodome Formation in Proton‐Irradiated Bulk Transition‐Metal Dichalcogenides

At the few‐atom‐thick limit, transition‐metal dichalcogenides (TMDs) exhibit strongly interconnected structural and optoelectronic properties. The possibility to tailor the latter by controlling the former is expected to have a great impact on applied and fundamental research. As shown here, proton irradiation deeply affects the surface morphology of bulk TMD crystals. Protons penetrate the top layer, resulting in the production and progressive accumulation of molecular hydrogen in the first interlayer region. This leads to the blistering of one‐monolayer thick domes, which stud the crystal surface and locally turn the dark bulk material into an efficient light emitter. The domes are stable (>2‐year lifetime) and robust, and host strong, complex strain fields. Lithographic techniques provide a means to engineer the formation process so that the domes can be produced with well‐ordered positions and sizes tunable from the nanometer to the micrometer scale, with important prospects for so far unattainable applications.     

Guide Me in Analysis: A Framework for Guidance Designers

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.     

Modeling and solving the steelmaking and casting scheduling problem

We propose a general model for the problem of planning and scheduling steelmaking and casting activities obtained by combining common features and constraints of the operations from a real plant and the literature. For tackling the problem, we develop a simulated annealing approach based on a solution space made of job permutations, which uses as submodule a chronological constructive procedure that assigns processing times and resources to jobs. Our technique, properly tuned in a statistically principled way, is able to find good solutions for a large range of different settings and horizons. In addition, it outperforms both a greedy procedure and a constraint‐based solver developed for comparison purposes on almost all instances. Finally, we have collected several real‐world instances that we make available on the web along with the solution validator and our best results.     

Correction to: Anomaly Detection Techniques in the Gaia Space Mission Data

Journal of Signal Processing Systems -     

EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU.     

A comparison of volumetric information gain metrics for active 3D object reconstruction

In this paper, we investigate the following question: when performing next best view selection for volumetric 3D reconstruction of an object by a mobile robot equipped with a dense (camera-based) depth sensor, what formulation of information gain is best? To address this question, we propose several new ways to quantify the volumetric information (VI) contained in the voxels of a probabilistic volumetric map, and compare them to the state of the art with extensive simulated experiments. Our proposed formulations incorporate factors such as visibility likelihood and the likelihood of seeing new parts of the object. The results of our experiments allow us to draw some clear conclusions about the VI formulations that are most effective in different mobile-robot reconstruction scenarios. To the best of our knowledge, this is the first comparative survey of VI formulation performance for active 3D object reconstruction. Additionally, our modular software framework is adaptable to other robotic platforms and general reconstruction problems, and we release it open source for autonomous reconstruction tasks.     

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system (BMS). Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system. We propose a new robust method, called ERMES (extendible range multi-model estimator), for determining an estimated state-of-charge (SoC), an estimated state-of-health (SoH) and a prediction of uncertainty of the estimates (state-of-uncertainty—SoU), thanks to which it is possible to monitor the validity of the estimates and adjust it, extending the robustness against a wider range of uncertainty, if necessary. Specifically, a finite number of models in state-space form are considered starting from a modified Thevenin battery model. Each model is characterized by a hypothesis of SoH value. An iterated extended Kalman filter (EKF) is then applied to each model in parallel, estimating for each one the SoC state variable. Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF, yielding the overall SoC and SoH estimates, respectively. In addition, a figure of uncertainty of such estimates is also provided.