Inertial sensors are used for human motion capture in a wide range of applications. Some kinds of human motion can be tracked by inertial sensors incorporated in smartphones or smartwatches. However, the latter can scarcely be used if misclassification of user activities is highly undesirable. In this case electronics and embedded software engineers should design, implement and verify their own human motion capture embedded systems, and oftentimes they have to do so from scratch. One of the issues the engineers should face is selection of suitable components, primarily accelerometers, gyroscopes and magnetometers, after thorough examination of commercially available items. Among technical characteristics of inertial sensors their sample frequency determines whether the sensor will be able to capture a specific motion kind or not. We propose a novel algorithm that allows the researcher or embedded software engineer to calculate the minimal sample frequency sufficient for tracking a prescribed motion scenario without significant signal losses. The algorithm utilizes the Poisson equation for motion of a triaxial rigid body, the Shoemake’s algorithm for interpolating quaternions on the unit hypersphere, and the frequency analysis of a discrete-time signal. One can use the proposed algorithm as an argument for acceptance or rejection of a gyroscope when selecting hardware components for a human motion tracking system.

The importance of fuzzy numbers to express uncertainty in certain applications, concerned with decision making, is observed in a large number of problems of different kinds. In Decision making problems, the best of available alternatives is chosen to the possible extent. In the process of ordering the alternatives, ranking of fuzzy numbers plays a key role. A large volume of ranking methods, based on different features, have been available in this domain. Owing to the complicated nature of fuzzy numbers, the so far introduced methods suffered setbacks or posed difficulties or showed drawbacks in one context or other. In addition, some methods are lengthy and complicated to apply on concerned problems. In this article, a new ranking procedure based on defuzzification, stemmed from the concepts of geometric mean and height of a fuzzy number, is proposed. Finally, numerical comparisons are made with other existing procedures for testing and validation of proposed method with the support of some standard numerical examples.

In the last few years, Sentiment Analysis regarding customers' reviews in order to comprehend the opinion polarity on social media has received considerable attention. However, the improvement of deep learning for sentiment analysis relating to customer reviews in Arabic language has received less attention. In fact, many users post and jot down their reviews in Arabic daily, so we ought to shed more light on Arabic sentiment analysis. Most likely all previous work depends on conventional classification techniques, such as KNN, Naïve Bayes (NB), etc. But in this work, we implement two deep learning models: Long Short Term Memory (LSTM) and Convolution Neural Networks (CNN), in addition to three traditional techniques: Naïve Bayes, K-Nearest Neighbor (KNN), Decision trees for sentiment analysis and compared the experimental results. Also, we offer a combined model from CNN and Recurrent Neural Network (RNN) architecture where this model collects local features through CNN as the input for RNN for Arabic sentiment analysis of short texts. An appropriate data preparation has been conducted for each utilized dataset. Our Conducted experiments for each dataset against traditional machine learning classifier; KNN, NB, and decision trees and regular deep learning models; CNN and LSTM, has resulted in impressive performance using our proposed combined (CNN-LSTM) model with an average accuracy of 85,83%, 86,88% for HTL and LABR datasets respectively.

The scientist has proven that the birth of neurons in a region of adult rat brain migrates from their birthplace to other parts of the brain. The same process also happens in adult humans. There was no efficient visualization tool to view the functions and structures of the human brain. In this paper, we focus to design a framework to understand more about Alzheimer’s disease and its process of neurons in the human brain. This framework named a hypergraph-based neuron reconstruction framework. It helped to map, the birth and death of neurons with the construction and reconstruction of the hypergraph. This framework also recognizes the structural changes during the life cycle of the neuron. Its performance was evaluated quantitatively with small-world networks and robust connectivity measures.