Consolidated Markets, Brand Competition, and Orange Juice Prices

This paper examines how consolidation in the marketing system affects prices for orange juice. We isolated the pricing behavior of brand marketers, wholesalers, and retailers by observing the retail prices for specific orange juice products, including leading national brands and private label brands, in 54 U.S. markets over a 1-year period. The data provided little compelling evidence that consolidated markets engaged in non-competitive pricing behavior. Increased brand competition, particularly between private labels and leading national brands, did, however, appear to lower average market prices.consumer demographics, national brands, orange juice, price behavior, private labels, wholesaler concentration, retailer concentration, Demand and Price Analysis, Industrial Organization,

Disocclusion Hole-Filling in DIBR-Synthesized Images using Multi-Scale Template Matching

Transmitting texture and depth images of captured camera view(s) of a 3D scene enables a receiver to synthesize novel virtual viewpoint images via Depth-Image-Based Rendering (DIBR). However, a DIBR-synthesized image often contains disocclusion holes, which are spatial regions in the virtual view image that were occluded by foreground objects in the captured camera view(s). In this paper, we propose to complete these disocclusion holes by exploiting the self-similarity characteristic of natural images via nonlocal template-matching (TM). Specifically, we first define self-similarity as nonlocal recurrences of pixel patches within the same image across different scales--one characterization of self-similarity in a given image is the scale range in which these patch recurrences take place. Then, at encoder we segment an image into multiple depth layers using available per-pixel depth values, and characterize self-similarity in each layer with a scale range; scale ranges for all layers are transmitted as side information to the decoder. At decoder, disocclusion holes are completed via TM on a per-layer basis by searching for similar patches within the designated scale range. Experimental results show that our method improves the quality of rendered images over previous disocclusion hole-filling algorithms by up to 3.9dB in PSNR

Analysis of weighted networks

The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a new and simple proof of the max-flow/min-cut theorem.Comment: 9 pages, 3 figure

Enhanced retinal image registration accuracy using expectation maximisation and variable bin-sized mutual information

While retinal images (RI) assist in the diagnosis of various eye conditions and diseases such as glaucoma and diabetic retinopathy, their innate features including low contrast homogeneous and nonuniformly illuminated regions, present a particular challenge for retinal image registration (RIR). Recently, the hybrid similarity measure, Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) has been proposed for RIR. This paper investigates incorporating various fixed and adaptive bin size selection strategies to estimate the probability distribution in the mutual information (MI) stage of EMPCA-MI, and analyses their corresponding effect upon RIR performance. Experimental results using a clinical mono-modal RI dataset confirms that adaptive bin size selection consistently provides both lower RIR errors and superior robustness compared to the empirically determined fixed bin sizes

A new cross-layer dynamic spectrum access architecture for TV White Space cognitive radio applications

As evermore applications and services are developed for wireless devices, the dramatic growth in user data traffic has led to the legacy channels becoming congested with the corresponding imperative of requiring more spectra. This has motivated both regulatory bodies and commercial companies to investigate strategies to increase the efficiency of the existing spectrum. With the emergence of cognitive radio technology, and the transference of TV channels from analogue to digital platforms, a unique opportunity to exploit spectrum by mobile digital service providers has emerged, commonly referred to as TV White Space (TVWS). One of the challenges in utilising TVWS spectrum is reliable primary user (PU) detection which is essential as any unlicensed secondary user has no knowledge of the PU and thereby can generate interference. This paper addresses the issue of PU detection by introducing a new dynamic spectrum access algorithm that exploits the unique properties of how digital TV (DTV) frequencies are deployed. A fuzzy logic inference model based on an enhanced detection algorithm (EDA) is used to resolve the inherent uncertain nature of DTV signals. Simulation results confirm EDA significantly improves the detection probability of a TVWS channel compared to existing PU detection techniques, while providing consistently low false positive detections. The paper also analyses the impact of the hidden node problem on EDA by modelling representative buildings and proposes a novel solution

Improving distributed video coding side information by intelligently combining macro-blocks from multiple algorithms

The performance of distributed video coding (DVC) greatly relies on the quality of Side information (SI). This paper investigates a novel way of producing SI by intelligently combining macroblocks (MB) produced by two SI generation algorithms, namely higher-order piecewise temporal trajectory interpolation (HOPTTI) and adaptive overlapped block motion compensation (AOBMC). The two algorithms address the problem differently. HOPTTI attempts to improve the motion estimation using higher order trajectory interpolation while AOMBC addresses the blocking and overlapping problem caused by inaccurate block matching. By judiciously selecting when to incorporate AOBMC with HOPTTI, it would give a peak signal-to-noise ratio (PSNR) improvement in SI quality. Two switching mechanisms, which exploit the spatial-temporal correlation at the macro-block level, have been investigated and the RST-based intelligent mode switching (IMS) algorithm is found to produce enhanced SI quality. Experimental results show that the basic mode switching algorithm gives a PSNR improvement of up to 1.8dB in SI quality compared to using only HOPTTI. The more intelligent RST-based switching provides a further PSNR enhancement of up to 1.1dB for certain test sequences

Cognitive Radio and TV White Space (TVWS) Applications

As more user applications emerge for wireless devices, the corresponding amount of traffic is rapidly expanding, with the corollary that ever-greater spectrum capacity is required. Service providers are experiencing deployment blockages due to insufficient bandwidth being available to accommodate such devices. TV White Space (TVWS) represents an opportunity to supplement existing licensed spectrum by exploiting unlicensed resources. TVWS spectrum has materialised from the unused TV channels in the switchover from analogue to digital platforms. The main obstacles to TVWS adoption are reliable detection of primary users (PU) i.e., TV operators and consumers, allied with specifically, the hidden node problem. This chapter presents a new Generalised Enhanced Detection Algorithm (GEDA) that exploits the unique way Digital Terrestrial TV (DTT) channels are deployed in different geographical areas. GEDA effectively transforms an energy detector into a feature sensor to achieve significant improvements in detection probability of a DTT PU. Furthermore, by framing a novel margin strategy utilising a keep out contour, the hidden node issue is resolved, and a viable secondary user sensing solution formulated. Experimental results for a cognitive radio TVWS model have formalised both the bandwidth and throughput gains secured by TVWS users with this new paradigm

Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method