Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods

Shocks, household consumption, and livelihood diversification: a comparative evidence from panel data in rural Thailand and Vietnam

We examine the roles of land and labor diversification in mitigating the effects of covariate and idiosyncratic shocks in the two middle-income countries Thailand and Vietnam. We use an unbalanced panel dataset of rural households obtained from five survey waves during 2007–2016 (9291 households for Thailand and 9255 households for Vietnam). We employ the System-Generalized Method of Moments estimators to control for endogeneity. Our study finds that (i) rural households in both countries are able to maintain per capita consumption in the face of idiosyncratic shocks but not covariate shocks; (ii) labor diversification in Thailand and land diversification in Vietnam are used as ex-post coping strategies against covariate shocks but their shock-mitigating roles are insignificant; and (iii) land diversification in Thailand and labor diversification in Vietnam are helpful in improving per capita consumption when households face covariate shocks. Our findings suggest that facilitating access to credit, enhancing farm mechanization, and improving road quality in Thailand as well as promoting the development of local rural nonfarm sectors in Vietnam would benefit rural households in dealing with covariate shocks. © 2022, The Author(s)

Robust Subspace Tracking Algorithms in Signal Processing: A Brief Survey

Principal component analysis (PCA) and subspace estimation (SE) are popular data analysis tools and used in a wide range of applications. The main interest in PCA/SE is for dimensionality reduction and low-rank approximation purposes. The emergence of big data streams have led to several essential issues for performing PCA/SE. Among them are (i) the size of such data streams increases over time, (ii) the underlying models may be time-dependent, and (iii) problem of dealing with the uncertainty and incompleteness in data. A robust variant of PCA/SE for such data streams, namely robust online PCA or robust subspace tracking (RST), has been introduced as a good alternative. The main goal of this paper is to provide a brief survey on recent RST algorithms in signal processing. Particularly, we begin this survey by introducing the basic ideas of the RST problem. Then, different aspects of RST are reviewed with respect to different kinds of non-Gaussian noises and sparse constraints. Our own contributions on this topic are also highlighted

Three-Way Tensor Decompositions: A Generalized Minimum Noise Subspace Based Approach

Tensor decomposition has recently become a popular method of multi-dimensional data analysis in various applications. The main interest in tensor decomposition is for dimensionality reduction, approximation or subspace purposes. However, the emergence of “big data” now gives rise to increased computational complexity for performing tensor decomposition. In this paper, motivated by the advantages of the generalized minimum noise subspace (GMNS) method, recently proposed for array processing, we proposed two algorithms for principal subspace analysis (PSA) and two algorithms for tensor decomposition using parallel factor analysis (PARAFAC) and higher-order singular value decomposition (HOSVD). The proposed decomposition algorithms can preserve several desired properties of PARAFAC and HOSVD while substantially reducing the computational complexity. Performance comparisons of PSA and tensor decomposition of our proposed algorithms against the state-of-the-art ones were studied via numerical experiments. Experimental results indicated that the proposed algorithms are of practical values

NUMERICAL SIMULATION OF THE BULK FORMING PROCESS TO MANUFACTURE COUPLING DETAILS FROM TUBE BILLET

Currently, most coupling details in the actuators are made by traditional methods such as bulk forming from block billet and then metal cutting. Such manufacturing methods often lead to material wastes due to cutting a large amount of excess processing. To save material and improve production efficiency, tube billet would be selected for bulk forming. However, when tube billet is used for bulk forming, it should be carefully calculated to avoid instability and folding defects in workpiece. This article presents the research on the forming process of the coupling details using numerical simulation and based on the obtained results, the suitable geometric size of tube billet for the forming operation in closed die can be determined

Synthesis and characterization of polythiophenes from hydrazone derivatives sidegroups

Polythiophenes with various substituted hydrazone side groups were synthesized using a chemical oxidative coupling reaction. Analyses of IR and NMR spectra confirmed the expected structure of new synthesized polymers and confirmed suitability of the suggested synthetic route. Surface properties, morphology and thermal stability of the prepared polymers were studied by SEM and TGA methods. Two derivatives were found to have a good solubility in several water-miscible solvents. They can be used as active materials in electrochromic and electronic devices

Improved Time and Frequency Synchronization Algorithm for 802.11a Wireless Standard based on the SIGNAL Field

International audienceTime and frequency synchronization in the IEEE 802.11a OFDM (Orthogonal Frequency Division Multiplexing) wireless communication system is addressed in this paper. Usually synchronization algorithms rely only on training sequences specified by the standard. To enhance the synchronization between stations, we propose to extract known information by both the transmitter and the receiver at the IEEE 802.11a physical layer to be then exploited by the receiver in addition to the training sequences. Indeed the parts of the identified SIGNAL field are either known or predictable from the RtS (Request to Send) control frame when the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism is triggered jointly to bit-rate adaptation algorithms to the channel. Moreover the received RtS control frame allows the receiver to estimate the channel before time synchronization stage improving then the performance of the proposed synchronization algorithm. Simulation results show that the performance of the proposed synchronization algorithm is improved as compared to existing algorithms

Multihop Decode-and-Forward Relay Networks: Secrecy Analysis and Relay Position Optimization

Relay communication has advantages over direct transmission in terms of secrecy capacity. In this paper, the performance of secrecy, offered by multihop decode-and-forward relaying, is investigated and compared to its counterpart in direct transmission. Three key performance measures are derived over Rayleigh fading channels: probability of non-zero secrecy capacity, secure outage probability and secrecy capacity, which are valid for an arbitrary number of hops. Based on the tractable form of the probability of non-zero secrecy capacity, the optimization problem of trusty relay replacement is also studied. Numerical results indicate that a proper relay replacement can increase the network security without extra network resources. The correctness of analytical results is confirmed by using a MATLAB-based independent simulation model