Carrier recovery techniques on satellite mobile channels

An analytical method and a stored channel model were used to evaluate error performance of uncoded quadrature phase shift keying (QPSK) and M-ary phase shift keying (MPSK) trellis coded modulation (TCM) over shadowed satellite mobile channels in the presence of phase jitter for various carrier recovery techniques

Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

An interior point algorithm for minimum sum-of-squares clustering

Copyright @ 2000 SIAM PublicationsAn exact algorithm is proposed for minimum sum-of-squares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean m-space into a given number of clusters in order to minimize the sum of squared distances from all points to the centroid of the cluster to which they belong. This problem is expressed as a constrained hyperbolic program in 0-1 variables. The resolution method combines an interior point algorithm, i.e., a weighted analytic center column generation method, with branch-and-bound. The auxiliary problem of determining the entering column (i.e., the oracle) is an unconstrained hyperbolic program in 0-1 variables with a quadratic numerator and linear denominator. It is solved through a sequence of unconstrained quadratic programs in 0-1 variables. To accelerate resolution, variable neighborhood search heuristics are used both to get a good initial solution and to solve quickly the auxiliary problem as long as global optimality is not reached. Estimated bounds for the dual variables are deduced from the heuristic solution and used in the resolution process as a trust region. Proved minimum sum-of-squares partitions are determined for the rst time for several fairly large data sets from the literature, including Fisher's 150 iris.This research was supported by the Fonds National de la Recherche Scientifique Suisse, NSERC-Canada, and FCAR-Quebec

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Does South Africa Have the Potential and Capacity to Grow at 7 Per Cent?: A Labour Market Perspective

Facing the challenge to adjust, the question is to what extent South African markets, specifically labour and investment markets, are flexible enough to enhance its global competitiveness, without having to revert to inward domestic protectionism. In investigating the level of flexibility in this regard, we need to determine the adjustment potential or capacity of the South African economy. However, modelling potential output and/or capacity is problematic. Building on previous research, this paper’s estimation of potential output for South Africa is based on a structural production function relationship with the maximum level of output consistent with stable inflation, supported by a full-scale macro-econometric model which is primarily supply-side driven, with capacity utilisation (or the output gap) as one of the key drivers of economic activity. The extent to which capacity is utilised in the economy is determined (defined) by the actual output (gross domestic product) relative to the potential of the economy to generate gross domestic product. Following this approach, South Africa’s potential employment needs to be determined. Does the entire labour force of working age have the potential and necessary skills to fill the available vacancies in the job market? On the contrary, our belief is that there exist certain constraints/rigidities in the labour market, which reduce the ranks of the potentially employable. In order to capture this effect, we assume that some “equilibrium or natural rate of unemployment” exists. Therefore, we presuppose a NAWRU − a natural rate of unemployment consistent with stable wage inflation. Ideally speaking, the NAWRU of an economy should be stable and not trending. However, the estimate we obtain for the NAWRU of the South African economy is increasing at a steady rate, suggesting severe structural problems in the economy, in particular, the labour market. Using this calculated NAWRU, we obtain estimates for potential output based on the structural production function approach. Our results indicate that the capacity of the South African economy is lower than conventionally expected. This reveals the essence of the impediments on the South African economy, primarily due to the sizeable constraint posed by rising labour market disequilibrium.capacity utilisation, potential output, NAWRU, macro-econometric model

Random Topologies and the emergence of cooperation: the role of short-cuts

We study in detail the role of short-cuts in promoting the emergence of cooperation in a network of agents playing the Prisoner's Dilemma Game (PDG). We introduce a model whose topology interpolates between the one-dimensional euclidean lattice (a ring) and the complete graph by changing the value of one parameter (the probability p to add a link between two nodes not already connected in the euclidean configuration). We show that there is a region of values of p in which cooperation is largely enhanced, whilst for smaller values of p only a few cooperators are present in the final state, and for p \rightarrow 1- cooperation is totally suppressed. We present analytical arguments that provide a very plausible interpretation of the simulation results, thus unveiling the mechanism by which short-cuts contribute to promote (or suppress) cooperation

Space, signs and symbolic power in Chinese criminal courtrooms

Parallel Session: LThe right to a fair trial as a fundamental human right is now widely accepted by the international community (McConville, 2011). While the notion of a fair trial is typically associated with procedural safeguards that are expressly provided in law, such as the right to counsel, the right to defending oneself, fairness can be reflected in architectural or spatial dimensions (Tait, 2011). Courtroom design, apart from achieving its main functional objectives, is the embodiment of institutional values of “trust, hope and faith in ...postprin