Testing the RPI data for consistency with the theory of the cost-of-living index

This paper tests the published section level price and weight data used in the compilation of the UK Retail Prices Index for consistency with the theory of the cost-of-living index. We use a nonparametric test of theoretical consistency and bootstrap statistical methods to estimate the probability of consistency

Testing the RPI data for consistency with the theory of the cost-of-living index

This paper tests the published section level price and weight dataused in the compilation of the UK Retail Prices Index for consistencywith the theory of the cost-of-living index. We use a nonparametric testof theoretical consistency and bootstrap statistical methods to estimatethe probability of consistency.

Generalized Inpainting Method for Hyperspectral Image Acquisition

A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters. Such an agile imaging technique comes at the cost of a reduced spatial resolution and the need for a demosaicing procedure on its interleaved data. In this work, we address both issues and propose an approach inspired by recent developments in compressed sensing and analysis sparse models. We formulate our superresolution and demosaicing task as a 3-D generalized inpainting problem. Interestingly, the target spatial resolution can be adjusted for mitigating the compression level of our sensing. The reconstruction procedure uses a fast greedy method called Pseudo-inverse IHT. We also show on simulations that a random arrangement of the spectral filters on the sensor is preferable to regular mosaic layout as it improves the quality of the reconstruction. The efficiency of our technique is demonstrated through numerical experiments on both synthetic and real data as acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding, sparse models, CMOS, Fabry-P\'ero

Size dependence of the photoinduced magnetism and long-range ordering in Prussian blue analog nanoparticles of rubidium cobalt hexacyanoferrate

Nanoparticles of rubidium cobalt hexacyanoferrate (Rb$_j$Co$_k$[Fe(CN)$_6$]$_l \cdot n$H$_2$O) were synthesized using different concentrations of the polyvinylpyrrolidone (PVP) to produce four different batches of particles with characteristic diameters ranging from 3 to 13 nm. Upon illumination with white light at 5 K, the magnetization of these particles increases. The long-range ferrimagnetic ordering temperatures and the coercive fields evolve with nanoparticle size. At 2 K, particles with diameters less than approximately 10 nm provide a Curie-like magnetic signal.Comment: 10 pages, 6 figures in text, expanded text and dat

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Improved rate-adaptive codes for distributed video coding

The research work is partially funded by the STEPS Malta.This scholarship is partly financed by the European Union - European Social Fund (ESF 1.25).Distributed Video Coding (DVC) is a coding paradigm which shifts the major computational intensive tasks from the encoder to the decoder. Temporal correlation is exploited at the decoder by predicting the Wyner-Ziv (WZ) frames from the adjacent key frames. Compression is then achieved by transmitting just the parity information required to correct the predicted frame and recover the original frame. This paper proposes an algorithm which identifies most of the unreliable bits in the predicted bit planes, by considering the discrepancies in the previously decoded bit plane. The design of the used Low Density Parity Check (LDPC) codes is then biased to provide better protection to the unreliable bits. Simulation results show that, for the same target quality, the proposed scheme can reduce the WZ bit rates by up to 7% compared to traditional schemes.peer-reviewe

Improved Wyner-Ziv video coding efficiency using bit plane prediction

The research work is partially funded by STEPS-Malta and partially by the European Union - ESF 1.25.Distributed Video Coding (DVC) is a coding paradigm where video statistics are exploited, partially or totally, at the decoder. The performance of such a codec depends on the accuracy of the soft-input information estimated at the decoder, which is affected by the quality of the side information (SI) and the dependency model. This paper studies the discrepancies between the bit planes of the Wyner-Ziv (WZ) frames and the corresponding bit planes of the SI. The relationship between these discrepancies is then exploited to predict the locations where the bit plane of the SI is expected to differ from that of the original WZ frame. This information is then used to derive more accurate soft-input values that achieve better compression efficiencies. Simulation results demonstrate that a WZ bit-rate reduction of 9.4% is achieved for a given video quality.peer-reviewe