An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Variable toroidal ferrite phase shifter

Careful design of the magnetic bias circuit used in ferrite phase shifters can help to reduce space, weight and energy consumption. Low reluctance circuits must avoid air-gaps and this can be achieved using toroidal-shaped ferrite inserts positioned inside a rectangular waveguide. Here, these ferrite inserts are fabricated using a viscous plastic processing method that avoids machining and produces a continuous magnetic circuit. Finite-element methods are used to initially model the magnetostatic solution for the bias circuit before being used to analyse the microwave performance of a double toroid phase shifter. A closedform technique is introduced to model the dielectric slab waveguide impedance transformer. The final insertion loss was ,1 dB over the 9.5?10.3 GHz band and return loss of 20 dB was achievable. The phase shift calculation agrees to within 10% of the measured values.     

Fuzzy logic-based pre-classifier for tropical wood species recognition system

Classifying tropical wood species poses a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. The problem of wood recognition is compounded with the nonlinearities of the features among the similar wood species. Besides that, large wood databases presented a problem of large processing time especially for online wood recognition system. In view of these problems, we propose the use of fuzzy logic-based pre-classifier as a means of treating uncertainty to improve the classification accuracy of tropical wood recognition system. The pre-classifier serve as a clustering mechanism for the large database simplifying the classification process making it more efficient. The use of the fuzzy logic-based pre-classifier has managed to increase the accuracy of the wood recognition system by 4 % and reduce the processing time for training and testing by more than 75 % and 26 % respectively.     

Erratum to: Conductivity Sensors Based System Development and Application to Investigate the Interfacial Behaviour between Supersonic Steam Jet and Water

Instruments and Experimental Techniques - An Erratum to this paper has been published: https://doi.org/10.1134/S0020441221050249     

Catalytic steam reforming of tar for enhancing hydrogen production from biomass gasification: a review

Presently, the global search for alternative renewable energy sources is rising due to the depletion of fossil fuel and rising greenhouse gas (GHG) emissions. Among alternatives, hydrogen (H₂) produced from biomass gasification is considered a green energy sector, due to its environmentally friendly, sustainable, and renewable characteristics. However, tar formation along with syngas is a severe impediment to biomass conversion efficiency, which results in process-related problems. Typically, tar consists of various hydrocarbons (HCs), which are also sources for syngas. Hence, catalytic steam reforming is an effective technique to address tar formation and improve H₂ production from biomass gasification. Of the various classes in existence, supported metal catalysts are considered the most promising. This paper focuses on the current researching status, prospects, and challenges of steam reforming of gasified biomass tar. Besides, it includes recent developments in tar compositional analysis, supported metal catalysts, along with the reactions and process conditions for catalytic steam reforming. Moreover, it discusses alternatives such as dry and autothermal reforming of tar.     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Variable Global Optimization min-sum (VGOMS) algorithm of decode-and-forward protocol for the relay node in the cooperative channel

Wireless Networks - The excessive delay due to the complexity of the decoding process at the relay node results in additional delays at the destination which is a significant issue in cooperative...     

Transfer Learning of BCI Using CUR Algorithm

The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject’s EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression.

     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

Determination of synthetic phenolic antioxidants in food items using reversed-phase HPLC

A HPLC with gradient elution method for the determination of the synthetic phenolic antioxidants (SPAs) propyl gallate (PG), tertiary butyl hydroquinone (TBHQ), butylated hydroxyanisole (BHA), and butylated hydroxytoluene (BHT) in food items is described. A C18 column served as the stationary phase; the gradient elution was formed by acetonitrile and water:acetic acid (1%). The UV detector was set at 280 nm. Under the recommended conditions, separation of the four SPAs was achieved in less than 8 min. Analytical characteristics of the HPLC method such as limit of detection, linear range, and reproducibility were evaluated. Extraction parameters were optimized for the recoveries of the SPAs in different types of food items (cooking oil, margarine and butter, and cheese). Before the HPLC separation, the SPAs were extracted with methanol/acetonitrile (1:1, v/v) and were subjected to vortex/ultrasonic treatment. The extracts were next kept in a freezer (∼2 h) to precipitate co-extracted components. Recoveries of the SPAs when spiked to cooking oil, margarine, butter and cheese at 50 and 200 mg l⁻¹ were in the ranges 93.3–108.3% for PG, 85.3–108.3% for TBHQ, 96.7–101.2% for BHA and 73.9–94.6% for BHT. The method was applied to the determination of SPAs in 38 food items (16 cooking oils, ten margarine, six butter and six cheese samples). The levels of SPAs in positive samples are all below the legal limits of Malaysia.