Ricardo Tejeida

Supply Chain Management from a Systems Science Perspective

Supply chain management (SCM) is going to be the main management process for production systems in the xxi century. This management process will take care of the flow of materials, information, purchased parts, personnel and financial needs supplied from different vendors, sometimes geographically too far from the main production plant. The industry of domestic appliances is a good example of the supply chain management. Before SCM a production system designed their products itself and manufacture all the subassemblies and components and gave after sale service during and after warranty period. After SCM the new production systems “comakership” several aspects of the production process, for example hermetic compressors for fridges, plastic parts and motors for washing machines, electrical components, etc. SCM provides different management principles to help in the designed planning and controlling the network of suppliers in order to synchronize the variability of customer’s demand with the variability of capacity of suppliers. One management principle is called Asbhy’s law:” the variability of the manager system should be more than or equal to the variability of the managed system”. In order to speak correctly about SCM let see how is the official definition expressed by the Association for Operations Management in their APICS Dictionary (Blackstone, 2008): SCM is “The design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measurement performance globally”. The previous definition emphasizing the main functions of production systems management as follows: the design of the supply chain when it is going to be a new corporation, the planning of operational and strategic activities, the scheduling and execution of the production planning, the control and solution of conflicts and the monitoring and auditing of the production processes . The financial management to create net value to all stakeholders: owners , employers, employees, society and environment. In the following section of this chapter, it is going to be described in more detail each one of the manufacturing functions of Supply Chain (SC), considering a systems approach based on the five components of the Viable System Model (VSM) by Beer (1985). Supported by the popular business/industrial information system called Enterprise Resources Planning (ERP).

FabricVision: System of Error Detection in the Manufacture of Garments

A computer vision system is implemented to detect errors in the cutting stage within the manufacturing process of garments in the textile industry. It provides solution to errors within the process that cannot be easily detected by any employee, in addition to significantly increase the speed of quality review. In the textile industry as in many others, quality control is required in manufactured products and this has been carried out manually by means of visual inspection by employees over the years. For this reason, the objective of this project is to design a quality control system using computer vision to identify errors in the cutting stage within the garment manufacturing process to increase the productivity of textile processes by reducing costs.

BRIQA: Framework for the Blind and Referenced Visual Image Quality Assessment

Our proposal is to present a Blind and Referenced Image Quality Assessment or BRIQA. Thus, the main proposal of this paper is to propose an Interface, which contains not only a Full-Referenced Image Quality Assessment (IQA) but also a No- Referenced or Blind IQA applying perceptual concepts by means of Contrast Band-Pass Filtering (CBPF). Then, this proposal consists in contrast a degraded input image with the filtered versions of several distances by a CBPF, which computes some of the Human Visual System (HVS) variables. If BRIQA detects only one input, it performs a Blind Image Quality Assessment, on the contrary if BRIQA detects two inputs, it considers that a Referenced Image Quality Assessment will be computed. Thus, we first define a Full-Reference IQA and then a No-Reference IQA, which correlation is important when is contrasted with the psychophysical results performed by several observers. BRIQA weights the Peak Signal-to-Noise Ratio by using an algorithm that estimates some properties of the Human Visual System. Then, we compare BRIQA algorithm not only with the mainstream estimator in IQA, PSNR, but also state-of-the-art IQA algorithms, such as Structural SIMilarity (SSIM), Mean Structural SIMilarity (MSSIM), Visual Information Fidelity (VIF), etc. Our experiments show that the correlation of BRIQA correlated with PSNR is important, but this proposal does not need imperatively the reference image in order to estimate the quality of the recovered image.

Chapter 11 – ρGBbBShift: Method for introducing perceptual criteria to region of interest coding

This work describes perceptual generalized bitplane-by-bitplane shift (ρGBbBShift) a perceptual method for coding of region of interest (ROI) and it is based on Moreno et al. (2013) [1]. Then, this article introduces perceptual criteria to the GBbBShift method when bit planes of ROI and background areas are shifted. This additional feature is intended for balancing perceptual importance of some coefficients regardless of their numerical importance. Perceptual criteria are applied using a contrast band-pass filtering, which is a low-level computational model that reproduces color perception in the human visual system. Results show that there is no perceptual difference at ROI between the MaxShift method and ρGBbBShift and, at the same time, perceptual quality of the entire image is improved when using ρGBbBShift. Furthermore, when ρGBbBShift method is applied to Hi-SET coder and it is compared against MaxShift method applied to both the JPEG2000 standard and the Hi-SET, the images coded by the combination ρGBbBShift-Hi-SET get the best results when the overall perceptual image quality is estimated. The ρpGBbBShift method is a generalized algorithm that can be applied to other Wavelet-based image compression algorithms such as JPEG2000, SPIHT, or SPECK.

XSET: Image coder based on contrast band-pass filtering

Abstract Noise is fatal to image compression performance because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Some noise, in addition to some numerical redundancy, is removed during the quantization process, but in some circumstances the removed information is easily perceived by the observer, leading to annoying visual artifacts. Perceptual quantization reduces unperceivable details and thus improves both visual impression and transmission properties. In this work, we apply perceptual criteria in order to define a perceptual forward and inverse quantizer. It is based on the CBPF, a low-level computational model that reproduces color perception in the Human Visual System. Our approach consists in performing a local quantization of wavelet transform coefficients using some of the human visual system behavior properties. It is performed applying a local weight for every coefficient. The CBPF allows recovering these weights from the quantized data, which avoids the storing and transmission of these weights. We apply this perceptual quantizer to the H i -SET coder. The comparison between JPEG2000 coder and the combination of H i -SET with the proposed perceptual quantizer (XSET) is shown. The latter produces images with lower PSNR than the former, but they have the same or even better visual quality when measured with well-known image quality metrics such as MSSIM, UQI, or VIF, for instance. Hence, XSET obtain more compressed (i.e., lower bit-rate) images at the same perceptual image quality than JPEG2000.