The Plastic Coupled Map Lattice: a Novel Image‐processing Paradigm

Coupled map lattices (CML) can describe many relaxation and optimization algorithms currently used in image processing. We recently introduced the ‘‘plastic‐CML’’ as a paradigm to extract (segment) objects in an image. Here, the image is applied by a set of forces to a metal sheet which is allowed to undergo plastic deformation parallel to the applied forces. In this paper we present an analysis of our ‘‘plastic‐CML’’ in one and two dimensions, deriving the nature and stability of its stationary solutions. We also detail how to use the CML in image processing, how to set the system parameters and present examples of it at work. We conclude that the plastic‐CML is able to segment images with large amounts of noise and large dynamic range of pixel values, and is suitable for a very large scale integration(VLSI) implementation.

Erratum to "Optimization of resin extraction from an Australian arid grass 'Triodia pungens' and its preliminary evaluation as an anti-termite timber coating" [Ind. Crops Prod. 59 (2014) 241–247]

The publisher regrets to inform the readers that the image that is appearing for Fig. 8 is incorrect and that the Supplementary material is missing on the published paper. The correct image for Fig. 8 and the Supplementary files are provided below: Fig. 8. (a) Timber blocks covered by invented plastic container bottom open, (b) timber blocks in the field after trial, (c) and (d) comparison between resin-coated blocks without termite damage and control blocks which were severely damaged by termites.

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Image and video compression play a major role in the world today, allowing the storage and transmission of large multimedia content volumes. However, the processing of this information requires high computational resources, hence the improvement of the computational performance of these compression algorithms is very important. The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based compression algorithm for multimedia contents, namely images, achieving high compression ratios, maintaining good image quality, Rodrigues et al. [2008]. However, in comparison with other existing algorithms, this algorithm takes some time to execute. Therefore, two parallel implementations for GPUs were proposed by Ribeiro [2016] and Silva [2015] in CUDA and OpenCL-GPU, respectively. In this dissertation, to complement the referred work, we propose two parallel versions that run the MMP algorithm in CPU: one resorting to OpenMP and another that converts the existing OpenCL-GPU into OpenCL-CPU. The proposed solutions are able to improve the computational performance of MMP by 3 and 2:7 , respectively. The High Efficiency Video Coding (HEVC/H.265) is the most recent standard for compression of image and video. Its impressive compression performance, makes it a target for many adaptations, particularly for holoscopic image/video processing (or light field). Some of the proposed modifications to encode this new multimedia content are based on geometry-based disparity compensations (SS), developed by Conti et al. [2014], and a Geometric Transformations (GT) module, proposed by Monteiro et al. [2015]. These compression algorithms for holoscopic images based on HEVC present an implementation of specific search for similar micro-images that is more efficient than the one performed by HEVC, but its implementation is considerably slower than HEVC. In order to enable better execution times, we choose to use the OpenCL API as the GPU enabling language in order to increase the module performance. With its most costly setting, we are able to reduce the GT module execution time from 6.9 days to less then 4 hours, effectively attaining a speedup of 45 .

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

A Multilevel Gradient Method for Optimization Problems

I problemi di ottimizzazione di dimensione finita di larga scala spesso derivano dalla discretizzazione di problemi di dimensione infinita. È perciò possibile descrivere il problema di ottimizzazione su più livelli discreti. Lavorando su un livello più basso di quello del problema considerato, si possono calcolare soluzioni approssimate che saranno poi punti di partenza per il problema di ottimizzazione al livello più fine. I metodi multilivello, già ampiamente presenti in letteratura a partire dagli anni Novanta, sfruttano tale caratteristica dei problemi di ottimizzazione per migliorare le prestazioni dei metodi di ottimizzazione standard. L’obiettivo di questa tesi è quello di implementare una variante multilivello del metodo del gradiente (MGM) e di testarlo su due diversi campi: la risoluzione delle Equazioni alle Derivate Parziali la ricostruzione di immagini. In questo elaborato viene illustrata la teoria dello schema multilivello e presentato l’algoritmo di MGM utilizzato nei nostri esperimenti. Sono poi discusse le modalità di utilizzo di MGM per i due problemi sopra presentati. Per il problema PDE, i risultati ottenuti mostrano un ottimo comportamento di MGM rispetto alla implementazione classica ad un livello. I risultati ottenuti per il problema di ricostruzione di immagini, al contrario delle PDEs, evidenziano come MGM sia efficace solo in determinate condizioni.

Solid Lipid Nanoparticles For Hydrophilic Biotech Drugs: Optimization And Cell Viability Studies (caco-2 & Hepg-2 Cell Lines).

Insulin was used as model protein to developed innovative Solid Lipid Nanoparticles (SLNs) for the delivery of hydrophilic biotech drugs, with potential use in medicinal chemistry. SLNs were prepared by double emulsion with the purpose of promoting stability and enhancing the protein bioavailability. Softisan(®)100 was selected as solid lipid matrix. The surfactants (Tween(®)80, Span(®)80 and Lipoid(®)S75) and insulin were chosen applying a 2(2) factorial design with triplicate of central point, evaluating the influence of dependents variables as polydispersity index (PI), mean particle size (z-AVE), zeta potential (ZP) and encapsulation efficiency (EE) by factorial design using the ANOVA test. Therefore, thermodynamic stability, polymorphism and matrix crystallinity were checked by Differential Scanning Calorimetry (DSC) and Wide Angle X-ray Diffraction (WAXD), whereas the effect of toxicity of SLNs was check in HepG2 and Caco-2 cells. Results showed a mean particle size (z-AVE) width between 294.6 nm and 627.0 nm, a PI in the range of 0.425-0.750, ZP about -3 mV, and the EE between 38.39% and 81.20%. After tempering the bulk lipid (mimicking the end process of production), the lipid showed amorphous characteristics, with a melting point of ca. 30 °C. The toxicity of SLNs was evaluated in two distinct cell lines (HEPG-2 and Caco-2), showing to be dependent on the concentration of particles in HEPG-2 cells, while no toxicity in was reported in Caco-2 cells. SLNs were stable for 24 h in in vitro human serum albumin (HSA) solution. The resulting SLNs fabricated by double emulsion may provide a promising approach for administration of protein therapeutics and antigens.

Electrochemical Oxidation Of Landfill Leachate In A Flow Reactor: Optimization Using Response Surface Methodology.

Response surface methodology based on Box-Behnken (BBD) design was successfully applied to the optimization in the operating conditions of the electrochemical oxidation of sanitary landfill leachate aimed for making this method feasible for scale up. Landfill leachate was treated in continuous batch-recirculation system, where a dimensional stable anode (DSA(©)) coated with Ti/TiO2 and RuO2 film oxide were used. The effects of three variables, current density (milliampere per square centimeter), time of treatment (minutes), and supporting electrolyte dosage (moles per liter) upon the total organic carbon removal were evaluated. Optimized conditions were obtained for the highest desirability at 244.11 mA/cm(2), 41.78 min, and 0.07 mol/L of NaCl and 242.84 mA/cm(2), 37.07 min, and 0.07 mol/L of Na2SO4. Under the optimal conditions, 54.99 % of chemical oxygen demand (COD) and 71.07 ammonia nitrogen (NH3-N) removal was achieved with NaCl and 45.50 of COD and 62.13 NH3-N with Na2SO4. A new kinetic model predicted obtained from the relation between BBD and the kinetic model was suggested.

Tradução, adaptação cultural e validação do questionário Body image After Breast Cancer para a língua portuguesa do Brasil

Universidade Estadual de Campinas . Faculdade de Educação Física

Tradução, adaptação transcultural e validação do "Body Image Avoidance Questionnaire (BIAQ)" e do "Body Checking Questionnaire (BCQ)" para lingua portuguesa no Brasil

Universidade Estadual de Campinas . Faculdade de Educação Física

Juvenile idiopathic arthritis with involvement of the temporomandibular joint: the role of image examinations

PURPOSE: Juvenile idiopathic arthritis (JIA) has unknown etiology, and the involvement of the temporomandibular joint (TMJ) is rare in the early phase of the disease. The present article describes the use of computed tomography (CT) and magnetic resonance (MRI) images for the diagnosis of affected TMJ in JIA. CASE DESCRIPTION: A 12-year-old, female, Caucasian patient, with systemic rheumathoid arthritis and involvement of multiple joints was referred to the Imaging Center for TMJ assessment. The patient reported TMJ pain and limited opening of the mouth. The helical CT examination of the TMJ region showed asymmetric mandibular condyles, erosion of the right condyle and osteophyte-like formation. The MRI examination showed erosion of the right mandibular condyle, osteophytes, displacement without reduction and disruption of the articular disc. CONCLUSION: The disorders of the TMJ as a consequence of JIA must be carefully assessed by modern imaging methods such as CT and MRI. CT is very useful for the evaluation of discrete bone changes, which are not identified by conventional radiographs in the early phase of JIA. MRI allows the evaluation of soft tissues, the identification of acute articular inflammation and the differentiation between pannus and synovial hypertrophy.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Spectral Region Optimization for Raman-Based Optical Biopsy of Inflammatory Lesions

Objective: The biochemical alterations between inflammatory fibrous hyperplasia (IFH) and normal tissues of buccal mucosa were probed by using the FT-Raman spectroscopy technique. The aim was to find the minimal set of Raman bands that would furnish the best discrimination. Background: Raman-based optical biopsy is a widely recognized potential technique for noninvasive real-time diagnosis. However, few studies had been devoted to the discrimination of very common subtle or early pathologic states as inflammatory processes that are always present on, for example, cancer lesion borders. Methods: Seventy spectra of IFH from 14 patients were compared with 30 spectra of normal tissues from six patients. The statistical analysis was performed with principal components analysis and soft independent modeling class analogy cross-validated, leave-one-out methods. Results: Bands close to 574, 1,100, 1,250 to 1,350, and 1,500 cm(-1) (mainly amino acids and collagen bands) showed the main intragroup variations that are due to the acanthosis process in the IFH epithelium. The 1,200 (C-C aromatic/DNA), 1,350 (CH(2) bending/collagen 1), and 1,730 cm(-1) (collagen III) regions presented the main intergroup variations. This finding was interpreted as originating in an extracellular matrix-degeneration process occurring in the inflammatory tissues. The statistical analysis results indicated that the best discrimination capability (sensitivity of 95% and specificity of 100%) was found by using the 530-580 cm(-1) spectral region. Conclusions: The existence of this narrow spectral window enabling normal and inflammatory diagnosis also had useful implications for an in vivo dispersive Raman setup for clinical applications.

Optimization of the Enzymatic Interesterification of Milk Fat and Canola Oil Blends Using Immobilized Rhizopus oryzae Lipase by Response Surface Methodology

Blends of milk fat and canola oil (MF:CNO) were enzymatically interesterified (EIE) by Rhizopus oryzne lipase immobilized on polysiloxane-polyvinyl alcohol (SiO(2)-PVA) composite, in a solvent-free system. A central composite design (CCD) was used to optimize the reaction, considering the effects of different mass fractions of binary blends of MF:CNO (50:50, 65:35 and 80:20) and temperatures (45, 55 and 65 degrees C) on the composition and texture properties of the interesterified products, taking the interesterification degree (ID) and consistency (at 10 degrees C) as response variables. For the ID variable both mass fraction of milk fat in the blend and temperature were found to be significant, while for the consistency only mass fraction of milk fat was significant. Empiric models for ID and consistency were obtained that allowed establishing the best interesterification conditions: blend with 65 % of milk fat and 35 %, of canola oil, and temperature of 45 degrees C. Under these conditions, the ID was 19.77 %) and the consistency at 10 degrees C was 56 290 Pa. The potential of this eco-friendly process demonstrated that a product could be obtained with the desirable milk fat flavour and better spreadability under refrigerated conditions.

Risk optimization of a steel frame communications tower subject to tornado winds

The structural engineering community in Brazil faces new challenges with the recent occurrence of high intensity tornados. Satellite surveillance data shows that the area covering the south-east of Brazil, Uruguay and some of Argentina is one of the world most tornado-prone areas, second only to the infamous tornado alley in central United States. The design of structures subject to tornado winds is a typical example of decision making in the presence of uncertainty. Structural design involves finding a good balance between the competing goals of safety and economy. This paper presents a methodology to find the optimum balance between these goals in the presence of uncertainty. In this paper, reliability-based risk optimization is used to find the optimal safety coefficient that minimizes the total expected cost of a steel frame communications tower, subject to extreme storm and tornado wind loads. The technique is not new, but it is applied to a practical problem of increasing interest to Brazilian structural engineers. The problem is formulated in the partial safety factor format used in current design codes, with all additional partial factor introduced to serve as optimization variable. The expected cost of failure (or risk) is defined as the product of a. limit state exceedance probability by a limit state exceedance cost. These costs include costs of repairing, rebuilding, and paying compensation for injury and loss of life. The total expected failure cost is the sum of individual expected costs over all failure modes. The steel frame communications, tower subject of this study has become very common in Brazil due to increasing mobile phone coverage. The study shows that optimum reliability is strongly dependent on the cost (or consequences) of failure. Since failure consequences depend oil actual tower location, it turn,,; out that different optimum designs should be used in different locations. Failure consequences are also different for the different parties involved in the design, construction and operation of the tower. Hence, it is important that risk is well understood by the parties involved, so that proper contracts call be made. The investigation shows that when non-structural terms dominate design costs (e.g, in residential or office buildings) it is not too costly to over-design; this observation is in agreement with the observed practice for non-optimized structural systems. In this situation, is much easier to loose money by under-design. When by under-design. When structural material cost is a significant part of design cost (e.g. concrete dam or bridge), one is likely to lose significantmoney by over-design. In this situation, a cost-risk-benefit optimization analysis is highly recommended. Finally, the study also shows that under time-varying loads like tornados, the optimum reliability is strongly dependent on the selected design life.

Multidisciplinary optimization of a catamaran's hydrofoil design with dynamic response surface and distributive processing

This paper presents a rational approach to the design of a catamaran's hydrofoil applied within a modern context of multidisciplinary optimization. The approach used includes the use of response surfaces represented by neural networks and a distributed programming environment that increases the optimization speed. A rational approach to the problem simplifies the complex optimization model; when combined with the distributed dynamic training used for the response surfaces, this model increases the efficiency of the process. The results achieved using this approach have justified this publication.