Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.     

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access (NOMA) is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. NOMA allows multiple terminals to share the same resource unit at the same time. The receiver usually needs to configure successive interference cancellation (SIC). The receiver eliminates co-channel interference (CCI) between users and it can significantly improve the system throughput. In order to meet the demands of users and improve fairness among them, this paper proposes a new power allocation scheme. The objective is to maximize user fairness by deploying the least fairness in multiplexed users. However, the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker (KKT) constraints. Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.     

Sensory lexicon and relationships among brown colour,saltiness perception and sensory liking evaluated by regular users and culinary chefs: a case of soy sauces

This study was performed to develop lexicon to describe soy sauces using regular users (consumers) vs. culinary chefs, to perform product grouping using a free-sorting task and to determine relationship among physicochemical properties (colour, %NaCl) and consumer perception of soy sauces. Both panels identified eighteen terms to describe soy sauces, of which eleven terms (light, medium and dark brown colour, weak, medium and strong saltiness, sweetness, saltiness–sweetness, tasty/umami, and soy sauce and fermented aroma) were similar. For culinary chefs, three distinctive soy sauce groups based on brown colour (light, medium and dark brown) were identified, while two groups (light and medium dark brown) for regular users. From free-sorting maps, both panels associated brown colour intensity with saltiness expectation/perception. From PCA biplots, both panels shared some similarities for sensory liking (colour, saltiness, flavour, overall liking) for soy sauces. This study suggested that brown colour intensities affected saltiness expectation/perception and were used for product grouping for soy sauces.     

The effects of freeze, dry, and wet grinding processes on rice flour properties and their energy consumption

This study attempted to replace the wet grinding process of rice with a freeze grinding process. The freeze grinding process involved soaking the rice samples in liquid nitrogen before grinding in a dry grinding machine. Three different types of grinders (hammer mill, roller mill, and pin mill) were used in both the freeze and the dry grinding processes. Wet grinding resulted in significantly (P < 0.05) smaller average particle size and a lower percentage of damaged starch than the alternative methods of grinding. Freeze grinding, especially using the hammer mill significantly reduced both the average particle size and the damaged starch content. Moreover, freeze grinding produced a higher yield after sieving in comparison with dry grinding using an identical grinder. In particular, freeze grinding with the hammer mill gave a significantly higher yield after sieving than dry grinding with the hammer mill. The wet grinding process had the significantly highest specific energy consumption (13,868 kJ/kg) due to the large consumption of electrical energy by the many machines in the process. The energy consumption of freeze grinding was similar to dry grinding. Consequently, the freeze grinding process was a viable alternative to the traditional wet grinding process.     

Fabrication of core–shell structured macrocapsules by electro‐coextrusion with agar–hydrocolloid mixtures for precooked food applications: textural and release characteristics

Textural and release characteristics of three types of core–shell macrocapsules, fabricated by electro‐coextrusion with agar only, an agar/fish gelatin (80:20) mixture or an agar/κ‐carrageenan (80:20) mixture at a total hydrocolloid concentration of 2% (w/v) and different applied voltages (0–8 kV), were investigated. The capsule diameter (1.1–1.6 mm) and shell thickness (15.1–38.4 μm) decreased with increasing applied voltage. Mixing agar with fish gelatin or κ‐carrageenan reduced slightly the capsule hardness and breaking energy; however, the capsule integrity was solid enough for safe handling. The release of core (olive oil containing a dye) in boiling water followed first‐order kinetics and was retarded significantly when κ‐carrageenan was mixed with agar but accelerated with fish gelatin addition (only 2.5–3.4 min for the release of 80% of the core). The agar/fish gelatin macrocapsules can be applied in precooked foods, because they are mechanically stable and can rapidly release the core upon contact with hot water.     

Optimization of Channel Estimation Using ELMx-based in Massive MIMO

In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated topics today. This is essential in technology to overcome traditional performance barriers. The better the channel estimation, the more accurate it is. This paper investigated machine learning (ML) for channel estimation. ML channel estimations based on the Extreme Learning Machine (ELMx) group are also implemented. These estimations, known as the ELMx group, include Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, it was compared with LS and MMSE. The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate. Additionally, this paper has proven complexity for verified computational times. The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.     

Is Construction Labor Productivity Really Declining?

Macroeconomics data suggest that labor productivity declined significantly in the construction industry during the 1979–1998 period. However, microeconomic studies indicate the contrary. This paper critically examines the construction labor productivity macroeconomic data in the United States from 1979 to 1998 to determine their validity and reliability. Data collection, distribution, manipulation, analysis, and interpretation are reviewed and problems are identified. The paper also presents a comparison of construction and manufacturing labor productivity during this period. The main conclusion of the study is that the raw data used to calculate construction productivity values at the macroeconomic level and their further manipulation and interpretation present so many problems that the results should be deemed unreliable. The uncertainty generated in the process of computing these values is such that it cannot be determined if labor productivity has actually increased, decreased, or remained constant in the construction industry for the 1979–1998 period.     

Wideband beam and steering using a rectangular array of planar monopoles

This article presents the design of a wideband rectangular array of planar monopoles, which is able to steer its beam and s over a wide frequency band using real-valued weights. These weights can be realized in practice by amplifiers or attenuators leading to a low cost development of a wideband array antenna with beam and steering capability. The weights are determined by applying an inverse discrete Fourier transform to an assumed radiation pattern. This wideband beam and forming concept is verified by full electromagnetic simulations which take into account mutual coupling effects between the array elements.     

Investigations into a wideband spatial beamformer employing a rectangular array of planar monopoles

This paper presents a rectangular array antenna with a suitable signal-processing algorithm that is able to steer the beam in azimuth over a wide frequency band. In the previous approach, which was reported in the literature, an inverse discrete Fourier transform technique was proposed for obtaining the signal weighting coefficients. This approach was demonstrated for large arrays in which the physical parameters of the antenna elements were not considered. In this paper, a modified signal-weighting algorithm that works for arbitrary-size arrays is described. Its validity is demonstrated in examples of moderate-size arrays with real antenna elements. It is shown that in some cases, the original beam-forming algorithm fails, while the new algorithm is able to form the desired radiation pattern over a wide frequency band. The performance of the new algorithm is assessed for two cases when the mutual coupling between array elements is both neglected and taken into account.