Lifting-based wavelet domain adaptive Wiener filter for image enhancement

A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR.     

Regional chemotherapy of liver tumors

In the absence of adequate autogenous vein for tibial artery bypass in limb salvage surgery, the use of prosthetic grafts with a distal anastomotic vein cuff or patch has shown promising results. Here, we describe how the Florester Internal Vessel Occluder (Meadox UK, Bedfordshire, UK) can facilitate the construction of a distal anastomotic vein cuff.     

Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Kinetics and phase evolution during carbothermal synthesis of titanium carbide from ultrafine titania/carbon mixture

The kinetics and phase evolution of the TiC formation process by carbothermal reduction of ultrafine titania/carbon mixture were investigated using thermogravimetric analysis (TGA), X-ray diffraction (XRD) and oxygen analysis. Titania (TiO2) first lowered its oxidation state to Ti3O5 via an unidentified phase (possibly one of the Magneli phases). Then Ti3O5 was further reduced to Ti2O3, followed by the formation of titanium oxycarbide (TiCxOy) phase and its purification toward high purity TiC thereafter. Ti2O3 was the oxide phase with the lowest oxidation state before forming TiCxOy phase. In the isothermal TGA trace, the formation of Ti3O5 showed a diffusion-controlled process; possibly carbon diffusion limited the solid state reaction. The formation of Ti2O3 and TiCxOy was interpreted to be associated with CO gas-assisted reduction reaction, based on constant reaction rate for each process. The activation energy for the formation of Ti2O3 (from Ti3O5) and TiCxOy (from Ti2O3) phase were calculated to be 415.6 and 264.3 kJ mol-1, respectively. The TiC powder synthesized at 1550 °C for 4 h in flowing argon atmosphere showed fine particle size (0.3–0.6 m) with oxygen content of 0.7 wt % and lattice parameter of 0.4328 nm while interparticle agglomeration was moderate. © 1998 Chapman & Hall.     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

Multi-directional blending for heterogeneous objects

This paper presents a novel multi-directional blending method for heterogeneous object design. Contrary to earlier studies, this paper introduces material blending through multiple features with different heterogeneous material composition. Feature-based method is used to represent and design heterogeneous objects with multi-directional material composition. The Voronoi diagram of multiple curves is constructed to generate bisector of the geometric domain. Then, metamorphosis from the bounding curve to multiple internal curves is performed using dynamic programming based optimization approach in two steps. First, optimum curve matching between internal curves and enclosing Voronoi cells is obtained. Then, an optimum ruling line alignment and insertion technique between the Voronoi diagram and the bounding curve is developed. Metamorphosis through complex concavities is also achieved. Finally, multi-directional material composition is mapped based on a set of relations.     

A fuzzy assessment framework to select among transportation investment projects in Turkey

Selecting the best transportation investment project (TIP) is often a difficult task, since many social, environmental and economic criteria have to be considered simultaneously. Evaluating a set of different projects, especially the best set of alternatives, portfolios, is even more complex. Pursuing the goal of selecting the best TIP portfolio, we propose a fuzzy assessment method to aid the selection process of a multi-criterion project by utilizing the concept of entropy and interval normalization procedure in a fuzzy analytic hierarchy process (F-AHP). Then, regarding this informative phase, we propose a fuzzy linear programming model to select the best TIP portfolio under uncertain cost pressure. A real case study is conducted to illustrate the efficiency of the proposed method.     

Complex assembly variant design in agile manufacturing. Part I: System architecture and assembly modeling methodology

In the distributed and horizontally integrated manufacturing environment found in agile manufacturing, there is a great demand for new product development methods that are capable of generating new customized assembly designs based on mature component designs that might be dispersed at geographically distributed partner sites. To cater for this demand, this paper addresses the methodology for complex assembly variant design in agile manufacturing. It consists in fundamental research in two parts: (i) assembly modeling; and (ii) assembly variant design methodology. This paper, the first of a two-part series, presents the assembly variant design system architecture and the assembly modeling methodology. First, a complementary assembly modeling concept is proposed with two kinds of assembly models, the hierarchical assembly model and the relational assembly model. The first explicitly captures the hierarchical and functional relationships between constituent components whereas the second explicitly captures the mating relationships at the form-feature-level. These models are complementary in the sense that each of them models only a specific aspect of assembly-related information but together they include the required assembly-related information. They are further specialized to accommodate the features of assembly variant design. As a result, two kinds of assembly models, the assembly variants model and the assembly mating graph are generated. These assembly models serve as the basis for assembly variant design which is discussed in the companion paper.