Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.     

An overheating‐tolerant space vector modulation algorithm for multilevel inverters

As promising and suitable candidates for high‐power applications, multilevel inverters have become one of the most promising solutions in various applications. However, power switching devices are highly subjected to thermal overheating, which leads to shortened lifetime and reduced reliability. Thermal overheating may result from the degradation of power switching devices due to continuous overloading and power cycling. In addition, degradation and faults in the cooling system of power switching devices may cause thermal stresses as well. A new overheating‐tolerant space vector modulation algorithm is proposed in this paper to alleviate thermal stresses from overheated power switching device. The proposed algorithm relies on using the redundancy property between switching states in multilevel inverters to continuously evaluate a cost function of the junction temperature of the overheated device for all possible switching sequence sets and then selects the optimal switching sequence. In addition, the proposed algorithm preserves the DC‐link capacitor voltage without reduction in the output current of the inverter. The proposed algorithm is general, which can be applied to n‐level inverters. Both simulation and experimental results reveal the efficient performance of the proposed algorithm using a three‐level T‐type inverter as a case study. Accordingly, the lifetime and reliability of the inverter are enhanced considerably using the proposed algorithm. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model

Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Electrical properties of multi walled carbon nanotubes/ poly(vinylidene fluoride/trifluoroethylene) nanocomposites

Poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE)/multi-walled carbon nanotube (MWCNT) nanocomposites were prepared by the method of solution mixing/casting. The dispersity of the MWCNTs in the PVDF-TrFE matrix was investigated using transmission electron microscopy (TEM), revealing that MWCNT are well distributed in the PVDF matrix. Both individual and agglomerations of MWCNT’s were evident. The electrical properties were characterized by ac conductivity measurements. The conductivity was found to obey a percolation-like power law with a percolation threshold below 0.30 wt. %. The electrical conductivity of the neat PVDF-TrFE could be enhanced by seven orders of magnitude, with the addition of only 0.3 wt. % MWCNTs, suggesting the formation of a well-conducting network by the MWCNT’s throughout the insulating polymer matrix. The intercluster polarization and anomalous diffusion models were used to explain the dielectric behaviors of the composites near the percolation threshold, and the analyses of ac conductivity and dielectric constant imply that the intercluster polarization is more applicable to our systems.     

A unified framework for tree search decoding: rediscovering the sequential decoder

We consider receiver design for coded transmission over linear Gaussian channels. We restrict ourselves to the class of lattice codes and formulate the joint detection and decoding problem as a closest lattice point search (CLPS). Here, a tree search framework for solving the CLPS is adopted. In our framework, the CLPS algorithm is decomposed into the preprocessing and tree search stages. The role of the preprocessing stage is to expose the tree structure in a form matched to the search stage. We argue that the forward and feedback (matrix) filters of the minimum mean-square error decision feedback equalizer (MMSE-DFE) are instrumental for solving the joint detection and decoding problem in a single search stage. It is further shown that MMSE-DFE filtering allows for solving underdetermined linear systems and using lattice reduction methods to diminish complexity, at the expense of a marginal performance loss. For the search stage, we present a generic method, based on the branch and bound (BB) algorithm, and show that it encompasses all existing sphere decoders as special cases. The proposed generic algorithm further allows for an interesting classification of tree search decoders, sheds more light on the structural properties of all known sphere decoders, and inspires the design of more efficient decoders. In particular, an efficient decoding algorithm that resembles the well-known Fano sequential decoder is identified. The excellent performance-complexity tradeoff achieved by the proposed MMSE-DFE Fano decoder is established via simulation results and analytical arguments in several multiple-input multiple-output (MIMO) and intersymbol interference (ISI) scenarios.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Lignan contents in sesame seeds and products

Sesame seed (Sesamum indicum L.) is a rich source of furofuran lignans with a wide range of potential biological activities. The major lignans in sesame seeds are the oil‐soluble sesamin and sesamolin, as well as glucosides of sesaminol and sesamolinol that reside in the defatted sesame flour. Upon refining of sesame oil, acid‐catalyzed transformation of sesamin to episesamin and of sesamolin to epimeric sesaminols takes place, making the profile of refined sesame oils different from that of virgin oils. In this study, the total lignan content of 14 sesame seeds ranged between 405 and 1178 mg/100 g and the total lignan content in 14 different products, including tahini, ranged between 11 and 763 mg/100 g. The content of sesamin and sesamolin in ten commercial virgin and roasted sesame oils was in the range of 444–1601 mg/100 g oil. In five refined sesame oils, sesamin ranged between 118 and 401 mg/100 g seed, episesamin between 12 and 206 mg/100 g seed, and the total contents of sesaminol epimers between 5 and 35 mg/100 g seed, and no sesamolin was found. Thus, there is a great variation in the types and amounts of lignans in sesame seeds, seed products and oils. This knowledge is important for nutritionists working on resolving the connection between diet and health. Since the consumption of sesame seed products is increasing steadily in Europe and USA, it is important to include sesame seed lignans in databases and studies pertinent to the nutritional significance of antioxidants and phytoestrogens. It is also important to differentiate between virgin, roasted and refined sesame oils.     

Analysis and characterization of comparison shopping behavior in the mobile handset domain

In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.