Structured vector quantizers in image coding

Image data compression is concerned with the minimization of the volume of data used to represent an image. In recent years, image compression algorithms using Vector Quantization (VQ) have been receiving considerable attention. Unstructured vector quantizers, i.e., those with no restriction on the geometrical structure of the codebook, suffer from two basic drawbacks, viz., the codebook search complexity and the large storage requirement. This explains the interest in the structured VQ schemes, such as lattice-based VQ and multi-stage VQ. The objective of this thesis is to devise techniques to reduce the complexity of vector quantizers. In order to reduce the codebook search complexity and memory requirement, a universal Gaussian codebook in a residual VQ or a lattice-based VQ is used. To achieve a better performance, a part of work has been done in the frequency domain. Specifically, in order to retain the high-frequency coefficients in transform coding, two methods are suggested. One is developed for moderate to high rate data compression while the other is effective for low to moderate data rate. In the first part of this thesis, a residual VQ using a low rate optimal VQ in the first-stage and a Gaussian codebook in the other stages are introduced. From rate distortion theory, for most memoryless sources and many Gaussian sources with memory, the quantization error under MSE criterion, for small distortion, is memoryless and Gaussian. For VQ with a realistic rate, the error signal has a non-Gaussian distribution. It is shown that the distribution of locally normalized error signals, however, becomes close to a Gaussian distribution. In the second part, a new two-stage quantizer is proposed. The function of the first stage is to encode the more important low-pass components of the image and that of the second is to do the same for the high-frequency components ignored in the first stage. In one scheme, a high-rate lattice-based vector quantizer is used as the quantizer for both stages. In another scheme, the standard JPEG with a low rate is used as the quantizer of the first stage, and a lattice-based VQ is used for the second stage. The resulting bit rate of the two-stage lattice-based VQ in either scheme is found to be considerably better than that of JPEG for moderate to high bit rates. In the third part of the thesis, a method to retain the high-frequency coefficients is proposed by using a relatively huge codebook obtained by truncating the lattices with a large radius. As a result, a large number of points fall inside the boundary of the codebook, and thus, the images are encoded with high quality and low complexity: To reduce the bit rate, a shorter representation is assigned to the more frequently used lattice points. To index the large number of lattice points which fall inside the boundary, two methods that are based on grouping of the lattice points according to their frequencies of occurrence are proposed. For most of the test images, the proposed methods of retaining high-frequency coefficients is found to outperform JPE

Activity-Based Costing in Supply Chain Cost Management Decision Support Systems

Activity-based costing and management (ABC/M) is an accounting and management approach that enhances the level of understanding about business operation costs, especially the overhead costs. ABC/M generates more reliable and precise cost information compared to those of traditional cost accounting (TCA) systems. The integration of ABC/M in supply chain (SC) mathematical decision support models can elucidate the managerial aspects of ABC/M more as an accounting and management tool. Most of the supply chain (SC) order management decision support systems (DSSs) developed so far are based mainly on the material flow and capacity constraints without considering the profitability factor. This thesis first presents a profitable-to-promise (PTP) multi-objective mixed-integer programming (MIP) model which considers profitability in order to effectively manage order acceptance decisions in supply chains, subject to capacity constraints by using ABC/M. The proposed model fulfills a desirable amount of orders completely and accepts a selective number of orders partially having the objective of minimizing the amount of residual capacity and increasing the profitability simultaneously. Because of the common disadvantages that traditional operations research (OR) approaches have such as, complexity in modeling, impossibility of integrating qualitative factors, and inability of on-time model result analysis, the thesis presents a new generic DSS modeling methodology with system dynamics (SD) and based on ABC/M cost structure. The approach presented results a novel real-time cost monitoring and analysis system. SD is a dynamic simulation approach with learning ability to investigate the status changes in the system that correspond to the system variables’ changes as well as their interactions amongst them. Subsequently, the thesis elaborates on both models by integrating them and introducing them as hybrid (MIP-SD) decision support system. In the hybrid system, MIP model generates the order management policy and SD model monitors the cost behavior of each implemented policy during the implementation process. The main purpose is to show how ABC/M acts as a common cost accounting, information, and managerial approach to synchronize the two mentioned models and to introduce the combination as a hybrid DSS system. In general, the approach provides the order fulfillment optimal mix aligned with the implementation strategy considering the factors such as, minimizing the residual capacity, considering the customer satisfaction level, selling price, the cost of resources incurred for each order fulfillment policy, and the share of each product and/or order from manufacturing overhead costs. Such an approach can assists management to analyzing and foreseeing the consequences and outcome of each order fulfillment strategy chosen besides finding the optimal order fulfillment combination

A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements

[EN] Certain industries are characterized by obtaining non-homogeneous units of the same product. However, customers require homogeneity in some attributes between units of the same and different products requesting in their orders. To commit such orders, an estimation of the homogeneous product to be obtained can be used. Unfortunately, estimations of homogenous product quantities can differ considerably from real distributions. This fact could entail the impossibility of accomplishing the delivery of customer orders in the terms previously committed. To solve this, we propose a multi-objective mathematical programming model to reallocate already available homogeneous products in stock and planned production to committed orders. The main contributions of this model are the consideration of the homogeneity requirement between units of different lines of the same order, the allowance of partial deliveries of order lines, and the specification of some relevant attributes of products to accomplish with the customer homogeneity requirement. Different hypotheses are proved through experiments and statistical analyses applied to a ceramic tile company. The epsilon-constraint method is used to obtain an implementable solution for the company. The weighted sum method is used when proving other hypotheses that offer some managerial insights to companies.This work was supported by the Program of Formation of University Professors (FPU) of the Spanish Ministry of Education, Culture and Sport (FPU15/03595), and by the Spanish Ministry of Economy and Competitiveness Project DPI2011-23597.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Peidro Payá, D. (2018). A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Computers & Industrial Engineering. 124:180-194. https://doi.org/10.1016/j.cie.2018.07.025S18019412

Acquisitions et services bibliographiques

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Bacteriologic and serologic diagnosis of group B streptococci in pregnant women, neonates and infants

Group B streptococcus (GBS) is the most important pathogen identified in bacterial cultures in neonatal sepsis, sepecially with early-onset in developed countries (approximately 1-5/1000 deliveries). Neonatal colonization with group B streptococcus results primarily from vertical transmission during the birth process. GBS carrier rate in pregnant women varies from 4.6 to 41 percent in different geographic populations. Contamination of neonates during passage through the birth canal is high (more than 50%). Of the 191 pregnant women screened in this study, 28 (14.7%) were found to be colonized with GBS, by the culture method. Direct CIE and SCA tests on SBM (Selective Broth Medium) containing mixed flora showed that only 11.5% and 18.3% had positive reaction. A total of 530 patients were studied. GBS was isolated from the blood of 4 infants (5.5%, 4 vs 73 positive cultures). Of 181 cultures of CSF only one case was positive for GBS (8.3%) and had meningitis. In another part of experiment, two false positive reactions were found using serum specimen for detection of GBS antigen by CIE. Sensitivity of CIE and SCA both were 75%, specificity, 99.3% and 98.7%. Conclusion: Although specimen collection and microbiologic methods are important factors in identification of women colonized with GBS, there is significant variation in the proportion of women colonization with GBS. This study suggests that GBS is a much less important cause of neonatal sepsis, but further studies are needed to explore these important issues

The Study on the Effect of Changes of Oil Revenues on Real Exchange Rate (in Iran’s Economy)

The Purpose this paper, is to investigate the effect of oil revenues on real exchange rate in Iran’s Economy by using a macroeconomic model, which has been estimated by 3sls regression technique and annual data during 1965 to 2003 .Provided that the government has taken no policies, the simulation results depict that, a rise in oil revenues will increase GDP, domestic price levels, money supply and real import, respectively, but will decrease Non-oil export. By regarding simulation results, we could say that nominal exchange rate will primarily decrease by oil revenues increase both in short run and long run. Afterwards, it will take an increasing trend and eventually, will go back to the initial equilibrium level. We will also observe real exchange rate decrease (increase) via the nominal exchange rate decrease (increase) in short run. However, in the long run, the real exchange rate will decrease by oil revenues boom and before the oil revenues increase will reach a lower level relative to its initial one