Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

A proposal on frequency management methodologies for WCDMA systems using cell coupling matrices

International audienceAlthough a single carrier frequency is usually considered in Wideband Code Division Multiple Access (WCDMA) systems, each operator has more than one carrier frequency in practical 3G systems. Moreover, QoS levels and the throughput within a given frequency highly depend on interference patterns, which are mainly related to cell-frequency allocation. Therefore, frequency management plays a key role in WCDMA network planning. However, the frequency management problem has not been in the center of attention for WCDMA systems so far due to the fact that WCDMA has not yet been implemented in wide range with all possible services. Nevertheless, the arrival of multimedia services will emphasize the critical importance of a smart frequency allocation. In this context, the presence of several frequencies for each operator (typically 2 or 3 in Europe) manifests itself as a fertile dimension of flexibility to be exploited. This paper introduces a novel frequency management methodology based on coupling matrices that reflects the interaction between cells for a given interference pattern. The proposed methodology is implemented as an integral task of the planning tool and has lead to better results than the frequency allocation in the classical hierarchical cell structur

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The Kinetic Basis of Morphogenesis

It has been shown recently (Shalygo, 2014) that stationary and dynamic patterns can arise in the proposed one-component model of the analog (continuous state) kinetic automaton, or kinon for short, defined as a reflexive dynamical system with active transport. This paper presents extensions of the model, which increase further its complexity and tunability, and shows that the extended kinon model can produce spatio-temporal patterns pertaining not only to pattern formation but also to morphogenesis in real physical and biological systems. The possible applicability of the model to morphogenetic engineering and swarm robotics is also discussed.Comment: 8 pages. Submitted to the 13th European Conference on Artificial Life (ECAL-2015) on March 10, 2015. Accepted on April 28, 201

Deep Joint Encryption and Source-Channel Coding: An Image Visual Protection Approach

Joint source and channel coding (JSCC) has achieved great success due to the introduction of deep learning. Compared with traditional separate source channel coding (SSCC) schemes, the advantages of DL based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and the relief of "cliff effect". However, it is difficult to couple encryption-decryption mechanisms with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of the emerging technology. To this end, our paper proposes a novel method called DL based joint encryption and source-channel coding (DJESCC) for images that can successfully protect the visual information of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is using a neural network to conduct image encryption, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJESCC method learns: 1) deep neural networks for image encryption and image decryption, and 2) an effective DJSCC network for image transmission in encrypted domain. Compared with the perceptual image encryption methods with DJSCC transmission, the DJESCC method achieves much better reconstruction performance and is more robust to ciphertext-only attacks.Comment: 12 pages, 13 figure

Fake Malware Generation Using HMM and GAN

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results