A Deep Learning Approach to Drone Monitoring

A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public

Carbon Trading with Blockchain

Blockchain has the potential to accelerate the deployment of emissions trading systems (ETS) worldwide and improve upon the efficiency of existing systems. In this paper, we present a model for a permissioned blockchain implementation based on the successful European Union (EU) ETS and discuss its potential advantages over existing technology. We propose an ETS model that is both backwards compatible and future-proof, characterised by interconnectedness, transparency, tamper-resistance and high liquidity. Further, we identify key challenges to implementation of a blockchain ETS, as well as areas of future work required to enable a fully-decentralised blockchain ETS

Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility Maximization under Imperfect Channel-State Information

We consider the problem of simultaneous user-scheduling, power-allocation, and rate-selection in an OFDMA downlink, with the goal of maximizing expected sum-utility under a sum-power constraint. In doing so, we consider a family of generic goodput-based utilities that facilitate, e.g., throughput-based pricing, quality-of-service enforcement, and/or the treatment of practical modulation-and-coding schemes (MCS). Since perfect knowledge of channel state information (CSI) may be difficult to maintain at the base-station, especially when the number of users and/or subchannels is large, we consider scheduling and resource allocation under imperfect CSI, where the channel state is described by a generic probability distribution. First, we consider the "continuous" case where multiple users and/or code rates can time-share a single OFDMA subchannel and time slot. This yields a non-convex optimization problem that we convert into a convex optimization problem and solve exactly using a dual optimization approach. Second, we consider the "discrete" case where only a single user and code rate is allowed per OFDMA subchannel per time slot. For the mixed-integer optimization problem that arises, we discuss the connections it has with the continuous case and show that it can solved exactly in some situations. For the other situations, we present a bound on the optimality gap. For both cases, we provide algorithmic implementations of the obtained solution. Finally, we study, numerically, the performance of the proposed algorithms under various degrees of CSI uncertainty, utilities, and OFDMA system configurations. In addition, we demonstrate advantages relative to existing state-of-the-art algorithms

Centrality dependence of the multiplicity and transverse momentum distributions at RHIC and LHC and the percolation of strings

The dependence of the multiplicity and the transverse momentum distribution on the number of collisions are studied for central and peripheral Au-Au collisions at SPS, RHIC and LHC energies in the framework of percolation of strings. A scaling law relating the multiplicity to the mean transverse momentum is obtained. Our results are in overall agreement with the SPS and RHIC data, obtaining a suppression on $p_T$ distribution even for $p_T$ larger than 1 GeV/c.Comment: Contribution to QM2002, espcrc1.st

Studies in matter antimatter separation and in the origin of lunar magnetism

Antimatter experiments of the University of Santa Clara are investigated. Topics reported include: (1) planetary geology, (2) lunar Apollo magnetometer experiments, and (3) Roche limit of a solid body

Event-by-Event Search for Charged Neutral Fluctuations in Pb - Pb Collisions at 158-A-GeV

Results from the analysis of data obtained from the WA98 experiment at the CERN SPS have been presented. Some events have been filtered which show photon excess in limited $\eta-\phi$ zones within the overlap region of the charged particle and photon multiplicity detectors.Comment: 6 pages, 4 figure

Interferometry of direct photons in Pb+Pb collisions at 158 AGeV

We present final results from the WA98 experiment which provide first measurements of Bose-Einstein correlations of direct photons in ultrarelativistic heavy ion collisions. Invariant interferometric radii were extracted in the range $100<K_T<300$ MeV/c and compared to interferometric radii of charged pions. The yield of direct photons for $100<p_T<300$ MeV/c was extracted from the correlation strength parameter and compared to the yield of direct photons measured in WA98 at higher $p_T$ with the statistical subtraction method, and to predictions of a fireball model.Comment: 4 pages, 3 figures, proceedings for Quark Matter 200

Investigation of high pt_{t} events in Nucleus-Nucleus collisions using the Hijing event generator

In recent years lot of interest has been observed in the nucleus-nucleus collisions at RHIC energies in phenomena related to high $p_{t}$ physics \cite{ref1}. The suppression of high $p_{t}$ particles and disappearance of back-to-back jets compared to the scaling with number of binary nucleon-nucleon collisions indicates that a nearly perfect liquid is produced in these collisions. Results on self shadowing of high $p_{t}$ events are presented using hadron multiplicity associated to high $p_{t}$ and unbiased events in nucleus-nucleus collisions \cite{ref2} obtained from the hijing event generator.Comment: 4 pages, 3 figures, Proceedings of the poster presented at Quark Matter 200

Change detection in categorical evolving data streams

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream