Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Carbon nanotube. The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

From point defects in graphene to two-dimensional amorphous carbon.

Abstract: While crystalline two-dimensional materials have become an experimental reality during the past few years, an amorphous 2D material has not been reported before. Here, using electron irradiation we create an $s{p}^{2}$-hybridized one-atom-thick flat carbon membrane with a random arrangement of polygons, including four-membered carbon rings. We show how the transformation occurs step by step by nucleation and growth of low-energy multivacancy structures constructed of rotated hexagons and other polygons. Our observations, along with first-principles calculations, provide new insights to the bonding behavior of carbon and dynamics of defects in graphene. The created domains possess a band gap, which may open new possibilities for engineering graphene-based electronic devices.

Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives

Abstract: Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

How to Construct Deep Recurrent Neural Networks

Abstract: In this paper, we explore different ways to extend a recurrent neural network (RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN (Schmidhuber, 1992; El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs.

Hybrid Full-Duplex/Half-Duplex Relaying with Transmit Power Adaptation

Abstract: Focusing on two-antenna infrastructure relays employed for coverage extension, we develop hybrid techniques that switch opportunistically between full-duplex and half-duplex relaying modes. To rationalize the system design, the classic three-node full-duplex relay link is first amended by explicitly modeling residual relay self-interference, i.e., a loopback signal from the transmit antenna to the receive antenna remaining after cancellation. The motivation for opportunistic mode selection stems then from the fundamental trade-off determining the spectral efficiency: The half-duplex mode avoids inherently the self-interference at the cost of halving the end-to-end symbol rate while the full-duplex mode achieves full symbol rate but, in practice, suffers from residual interference even after cancellation. We propose the combination of opportunistic mode selection and transmit power adaptation for maximizing instantaneous and average spectral efficiency after noting that the trade-off favors alternately the modes during operation. The analysis covers both common relaying protocols (amplify-and-forward and decode-and-forward) as well as reflects the difference of downlink and uplink systems. The results show that opportunistic mode selection offers significant performance gain over system design that is confined to either mode without rationalization.

Nanotube and graphene saturable absorbers for fibre lasers

Abstract: Nanotubes and graphene have emerged as promising materials for use in ultrafast fibre lasers. Their unique electrical and optical properties enable them to be used as saturable absorbers that have fast responses and broadband operation and that can be easily integrated in fibre lasers.