Adaptive compressed sensing using sparse measurement matrices

Compressed sensing methods using sparse measure- ment matrices and iterative message-passing recovery procedures are recently investigated due to their low computational complex- ity and excellent performance. The design and analysis of this class of methods is inspired by a large volume of work on sparse- graph codes such as Low-Density Parity-Check (LDPC) codes and the iterative Belief-Propagation (BP) decoding algorithms. In par- ticular, we focus on a class of compressed sensing methods emerg- ing from the Sudocodes scheme that follow similar ideas used in a class of sparse-graph codes called rateless codes. We are inter- ested in the design and analysis of adaptive Sudocodes methods and this paper provides initial steps in this direction

Channel-Adapted Quantum Error Correction

Abstract—Error correction procedures are considered which are designed specifically for the amplitude damping channel. Amplitude damping errors are analyzed in the stabilizer formalism. This analysis allows a generalization of the [4; 1] “approximate ” amplitude damping code. This generalization is presented as a class of [2(M +1);M] codes; quantum circuits for encoding and recovery operations are presented. A [7; 3] amplitude damping code based on the classical Hamming code is presented. All of these are stabilizer codes whose encoding and recovery operations can be completely described with Clifford group operations. Finally, optimization options are described in which recovery operations may be further adapted according to the damping probability. Index Terms—Amplitude damping channel, quantum error correction, stabilizer codes. I

Segmenting the heterogeneity of tourist preferences using a latent class model combined with the EM algorithm

An important component of conjoint analysis is market segmentation where the main objective is to address the heterogeneity of consumer preferences. Latent class methodology is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and a-priori segmentation. The main benefit of Latent class models is that they simultaneously estimate market segment membership and parameter estimates for each derived market segment. In this paper we present two latent class models. The first model is a latent class metric model using mixtures of multivariate conditional normal distributions to analyze rating data. The second is a latent class multinomial logit model used to analyze choice data. The EM algorithm is employed to maximize the likelihood in both models. The application focuses on tourists’ preference and choice behaviour when assessing package tours. A number of demographic and product related explanatory variables are used to generate segments that are accessible and actionable. A Monte Carlo study is also presented in this paper. This study examines how the number of hypothetical subjects, number of specified segments and number of predictors affect the performance of the latent class metric conjoint model with respect to parameter recovery and segment membership recovery.peer-reviewe

Wear and Friction Modeling on Lifeboat Launch Systems

The RNLI provides search and rescue cover along the UK and RoI coast using a variety of lifeboats and launch techniques. In locations where there is no natural harbour it is necessary to use a slipway to launch the lifeboat into the sea. Lifeboat slipway stations consist of an initial section where the boat is held on rollers followed by an inclined keelway lined with low friction composite materials, the lifeboat is released from the top of the slipway and proceeds under its own weight into the water. The lifeboat is later recovered using a winch line. It is common to manually apply grease to the composite slipway lining before each launch and recovery in order to ensure sufficiently low friction for successful operation. With the introduction of the Tamar class lifeboat it is necessary to upgrade existing boathouses and standardise slipway operational procedures to ensure consistent operation. The higher contact pressures associated with the new lifeboat have led to issues of high friction and wear on the composite slipway linings and the manual application of grease to reduce friction is to be restricted due to environmental impact and cost factors. This paper presents a multidisciplinary approach to modelling slipway panel wear and friction using tribometer testing in conjunction with finite element analysis and slipway condition surveys to incorporate common real-world effects such as panel misalignments. Finally, it is shown that a freshwater lubrication system is effective, reducing cost and environmental impacts while maintaining good friction and wear performance

Adaptive Compressed Sensing for Support Recovery of Structured Sparse Sets

This paper investigates the problem of recovering the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately recovering such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.Comment: to appear in IEEE Transactions on Information Theor

Analysis of RSVP-TE graceful restart

GMPLS is viewed as an attractive intelligent control plane for different network technologies and graceful restart is a key technique in ensuring this control plane is resilient and able to recover adequately from faults. This paper analyses the graceful restart mechanism proposed for a key GMPLS protocol, RSVP-TE. A novel analytical model, which may be readily adapted to study other protocols, is developed. This model allows the efficacy of graceful restart to be evaluated in a number of scenarios. It is found that, unsurprisingly, increasing control message loss and increasing the number of data plane connections both increased the time to complete recovery. It was also discovered that a threshold exists beyond which a relatively small change in the control message loss probability causes a disproportionately large increase in the time to complete recovery. The interesting findings in this work suggest that the performance of graceful restart is worthy of further investigation, with emphasis being placed on exploring procedures to optimise the performance of graceful restart