A Non-Parametric Approach to Estimating Ambient Noise Levels in the Presence of Bursty Interference

We propose a new transmitter-side approach for estimating the contribution to the packet error rate that is due to background noise, distinct from the contribution due to interference bursts. The technique relies solely on an existing data-ack handshake and the transmitters ability to adjust or monitor the packet size. One immediate application of this information is in rate adaptation. Experimental measurements with microwave oven interference are presented to demonstrate the practical utility of the proposed technique

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC

Coordinated Radio, Electron, and Waves Experiment (CREWE) for the NASA Comet Rendezvous and Asteroid Flyby (CRAF) instrument

The Coordinated Radio, Electron, and Waves Experiment (CREWE) was designed to determine density, bulk velocity and temperature of the electrons for the NASA Comet Rendezvous and Asteroid Flyby Spacecraft, to define the MHD-SW IMF flow configuration; to clarify the role of impact ionization processes, to comment on the importance of anomalous ionization phenomena (via wave particle processes), to quantify the importance of wave turbulence in the cometary interaction, to establish the importance of photoionization via the presence of characteristic lines in a structured energy spectrum, to infer the presence and grain size of significant ambient dust column density, to search for the theoretically suggested 'impenetrable' contact surface, and to quantify the flow of heat (in the likelihood that no surface exists) that will penetrate very deep into the atmosphere supplying a good deal of heat via impact and charge exchange ionization. This final report provides an instrument description, instrument test plans, list of deliverables/schedule, flight and support equipment and software schedule, CREWE accommodation issues, resource requirements, status of major contracts, an explanation of the non-NASA funded efforts, status of EIP and IM plan, descope options, and Brinton questions

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Seismological data acquisition and signal processing using wavelets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed

Machine Learning based RF Transmitter Characterization in the Presence of Adversaries

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the RF domain. In the presence of adversaries, malicious activities such as jamming, and spoofing are inevitable which render most machine learning techniques ineffective. To facilitate learning in such settings, we propose an adversarial learning-based approach to detect unauthorized exploitation of RF spectrum. First, we show the applicability of existing machine learning algorithms in the RF domain. We design and implement three recurrent neural networks using different types of cell models for fingerprinting RF transmitters. Next, we focus on securing transmissions on dynamic spectrum access network where primary user emulation (PUE) attacks can pose a significant threat. We present a generative adversarial net (GAN) based solution to counter such PUE attacks. Ultimately, we propose recurrent neural network models which are able to accurately predict the primary users\u27 activities in DSA networks so that the secondary users can opportunistically access the shared spectrum. We implement the proposed learning models on testbeds consisting of Universal Software Radio Peripherals (USRPs) working as Software Defined Radios (SDRs). Results reveal significant accuracy gains in accurately characterizing RF transmitters- thereby demonstrating the potential of our models for real world deployments

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Quantum electromechanics of a hypersonic crystal

Radiation pressure within engineered structures has recently been used to couple the motion of nanomechanical objects with high sensitivity to optical and microwave electromagnetic fields. Here, we demonstrate a form of electromechanical crystal for coupling microwave photons and hypersonic phonons by embedding the vacuum-gap capacitor of a superconducting resonator within a phononic crystal acoustic cavity. Utilizing a two-photon resonance condition for efficient microwave pumping and a phononic bandgap shield to eliminate acoustic radiation, we demonstrate large cooperative coupling ($C \approx 30$) between a pair of electrical resonances at $10$GHz and an acoustic resonance at $0.425$GHz. Electrical read-out of the phonon occupancy shows that the hypersonic acoustic mode has an intrinsic energy decay time of $2.3$ms and thermalizes close to its quantum ground-state of motion (occupancy $1.5$) at a fridge temperature of $10$mK. Such an electromechanical transducer is envisioned as part of a hybrid quantum circuit architecture, capable of interfacing to both superconducting qubits and optical photons.Comment: 16 pages, 12 figures, 8 appendice

Early visual encoding of Musca domestica

Fly vision has often been considered to be quite poor, both temporally and spatially, as it is limited by numerous different factors (i.e. number of sampling units, lens dimensions, photoreceptors’ slow integration time, ambient light level as well as flies’ own speed when in motion) (Mallock, 1894; Fermi and Richardt, 1963; Srinivasan and Bernard, 1975; Warrant and McIntyre, 1992; Land, 1997; Warrant, 1999). Some studies have challenged these views and found that flies have evolved to partially overcome these constraints (i.e. via acute zones, head/thorax and body movements) (van Hateren and Schilstra, 1999; Hornstein et al., 2000; Burton, Tatler and Laughlin, 2001; Burton and Laughlin, 2003). One recent example from Juusola et al. (2017) showed that Drosophila photoreceptors contract to light and these photomechanical contractions coupled with refractory sampling enable the fly to overcome motion blur even to objects smaller than their optical limit. Following on from this work, my aim was to test whether different aspects of a fast-flying housefly (Musca domestica) would also have enhanced spatial and temporal vision beyond our current understanding. If slow-flying Drosophila with its optically poorer vision has evolved to compensate for its limitations, then in theory we should see similar, or better, improvements in a faster flying fly such as Musca. Additionally, working with Musca created the opportunity to investigate any presence of sexual dimorphism, as males have "love spots", which Drosophila males lack (GonzalezBellido, Wardill and Juusola, 2011; Perry and Desplan, 2016). My work focussed on examining via in vivo intracellular recordings visual encoding of Musca photoreceptors (R1-R6) and what happens to that information when passed downstream to large monopolar cells (LMCs, L1-L3). In total, this examination resulted in three separate studies: (i) early temporal encoding during body saccades, (ii) R1- R6 and L1-L3 cells' response properties during light adaptation and its impact on underlying quantum bumps (QBs) and (iii) hyperacuity of photoreceptors and LMCs. I found that temporal encoding of Musca early vision was better than previously thought, especially in male flies. Additionally, both photoreceptors' and LMCs’ signalling performance to different stimulus statistics improved when brightening mean light levels. However, when looking at spatial encoding, both male and female photoreceptors were in general not able to resolve details finer than their optical limit i.e. they were not hyperacute. LMCs may have this ability but further investigations are required