Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Super-resolution: a comprehensive survey

Smart houses are considered a good alternative for the independent life of older persons and persons with disabilities. Numerous intelligent devices, embedded into the home environment, can provide the resident with both movement assistance and 24-h health monitoring. Modern home-installed systems tend to be not only physically versatile in functionality but also emotionally human-friendly, i.e., they may be able to perform their functions without disturbing the user and without causing him/her any pain, inconvenience, or movement restriction, instead possibly providing him/her with comfort and pleasure. Through an extensive survey, this paper analyzes the building blocks of smart houses, with particular attention paid to the health monitoring subsystem as an important component, by addressing the basic requirements of various sensors implemented from both research and clinical perspectives. The paper will then discuss some important issues of the future development of an intelligent residential space with a human-friendly health monitoring functional system.

The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives

https://www.ruor.uottawa.ca/bitstream/10393/10903/1/EC52066.PDF

The teaching of literature.

Clustering forms one of the most visible conceptual and algorithmic framework of developing information granules. In spite of the algorithm being used, the representation of information granules-clusters is predominantly numeric (coming in the form of prototypes, partition matrices, dendrograms, etc.). In this paper, we consider a concept of granular prototypes that generalizes the numeric representation of the clusters and, in this way, helps capture more details about the data structure. By invoking the granulation-degranulation scheme, we design granular prototypes being reflective of the structure of data to a higher extent than the representation that is provided by their numeric counterparts (prototypes). The design is formulated as an optimization problem, which is guided by the coverage criterion, meaning that we maximize the number of data for which their granular realization includes the original data. The granularity of the prototypes themselves is treated as an important design asset; hence, its allocation to the individual prototypes is optimized so that the coverage criterion becomes maximized. With this regard, several schemes of optimal allocation of information granularity are investigated, where interval-valued prototypes are formed around the already produced numeric representatives. Experimental studies are provided in which the design of granular prototypes of interval format is discussed and characterized.

An Optimization of Allocation of Information Granularity in the Interpretation of Data Structures: Toward Granular Fuzzy Clustering

HVAC system optimization—in-building section

There have been many researches about fuzzy model having the approximation property of the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method encapsulating the human expert's knowledge or experience in fuzzy rules and besides it is not easy to find the membership function of fuzzy rule to minimize the output error. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the fuzzy modelling methods that work quite well and are used with various types of fuzzy rules. But in this model, it is the problem to find the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning technique with appropriate measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.

Adaptive network-based fuzzy inference system with pruning

Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models

Evolving Compact and Interpretable Takagi&#8211;Sugeno Fuzzy Models With a New Encoding Scheme

This paper presents the analysis and experimental of the effective normal force and power consumption in a maglev vehicle. A maglev vehicle employs the electromagnets as suspension and linear motors for propulsion. These components give the higher ride comfort but suffered from energy efficiency. Therefore, a vector control-based algorithm and various air gaps are employed to increase the propulsion efficiency and lower the system energy consumption. The effective levitation load according to the levitation gap and normal force of a linear induction motor is calculated by power consumption in a levitation system and compared with finite-element analysis results. The experiment is carried out with the full-scaled train in test line and the efficiency of a propulsion system is also presented.

Analysis and Experimental Evaluation of Normal Force of Linear Induction Motor for Maglev Vehicle

PURPOSE The aim of this study was to compare the short-term surgical and long-term functional outcomes of Billroth I, Billroth II, and Roux-en-Y reconstruction after laparoscopic distal gastrectomy. MATERIALS AND METHODS We retrospectively collected data from 697 patients who underwent laparoscopic distal gastrectomy for operable gastric cancer between January 2009 and December 2012. The patients were classified into three groups according to the reconstruction methods: Billroth I, Billroth II, and Roux-en-Y. The parameters evaluated included patient and tumor characteristics, operative details, and postoperative complications classified according to the Clavien-Dindo classification. Endoscopic findings of the remnant stomach were evaluated according to the residue, gastritis, bile (RGB) classification and the Los Angeles classification 1 year postoperatively. RESULTS Billroth I, Billroth II, and Roux-en-Y were performed in 165 (23.7%), 371 (53.2%), and 161 patients (23.1%), respectively. Operation time was significantly shorter (173.4±44.7 minute, P<0.001) as was time to first flatus (2.8±0.8 days, P=0.009), time to first soft diet was significantly faster (4.3±1.0 days, P<0.001), and postoperative hospital stay was significantly shorter (7.7±4.0 days, P=0.004) in Billroth I in comparison to the other methods. Postoperative complications higher than Clavien-Dindo grade III occurred in 61 patients (8.8%) with no statistically significant differences between groups (P=0.797). Endoscopic findings confirmed that gastric residue, gastritis, bile reflux, and reflux esophagitis were significantly lower in Roux-en-Y (P<0.001) patients. CONCLUSIONS Roux-en-Y reconstruction after laparoscopic distal gastrectomy for middle-third gastric cancer is beneficial in terms of long-term functional outcome, whereas Billroth I reconstruction for distal-third gastric cancer has a superior short-term surgical outcome and postoperative weight change.

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A Comparison of Outcomes of Three Reconstruction Methods after Laparoscopic Distal Gastrectomy

In example-based super-resolution, it is difficult to determine appropriate high-frequency (HF) patches from a training database by using only the information of one input image. In this letter, we utilize the sharpness of high-resolution (HR) patch candidates for the reliable determination of HF patches. For each input patch, we first preselect a sufficient number of HF patch candidates and produce HR patches by adding the candidates to the input patch. After removing the outlier patches, we then reselect several HF patches according to the patch characteristic for producing the final HR image. This reselection procedure is optimized for edge patches and non-edge patches, respectively. Experimental results show that the proposed algorithm provides sharper details compared to the existing algorithms.

Chang-Hyun Kim

Papers

Adaptive network-based fuzzy inference system with pruning

Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme

Analysis and Experimental Evaluation of Normal Force of Linear Induction Motor for Maglev Vehicle

A Comparison of Outcomes of Three Reconstruction Methods after Laparoscopic Distal Gastrectomy

Example-Based Super-Resolution via Structure Analysis of Patches

Chang-Hyun Kim

Papers

Adaptive network-based fuzzy inference system with pruning

Evolving Compact and Interpretable Takagi&#8211;Sugeno Fuzzy Models With a New Encoding Scheme

Analysis and Experimental Evaluation of Normal Force of Linear Induction Motor for Maglev Vehicle

A Comparison of Outcomes of Three Reconstruction Methods after Laparoscopic Distal Gastrectomy

Example-Based Super-Resolution via Structure Analysis of Patches

Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme