Samilly Morau, Leandro Macedo, Eliton Morais
et al.
This paper presents the development of an optoelectronic wearable sensor system for portable monitoring of the movement and physiological parameters of patients. The sensor system is based on a low-cost inertial measurement unit (IMU) and an optical fiber-integrated chest belt for breathing rate monitoring with wireless connection with a gateway connected to the cloud. The sensors also use artificial intelligence algorithms for clustering, classification, and regression of the data. Results show a root mean squared error (RMSE) between the reference data and the proposed breathing rate sensor of 0.6 BPM, whereas RMSEs of 0.037 m/s<sup>2</sup> and 0.27 °/s are obtained for the acceleration and angular velocity analysis, respectively. For the sensor validation under different movement analysis protocols, the balance and Timed up and Go (TUG) tests performed with 12 subjects demonstrate the feasibility of the proposed device for biomechanical and physical therapy protocols’ automatization and assessment. The balance tests were performed in two different conditions, with a wider and narrower base, whereas the TUG tests were made with the combination of cognitive and motor tests. The results demonstrate the influence of the change of base on the balance analysis as well as the dual task effect on the scores during the TUG testing, where the combination between motor and cognitive tests lead to smaller scores on the TUG tests due to the increase of complexity of the task. Therefore, the proposed approach results in a low-cost and fully automated sensor system that can be used in different protocols for physical rehabilitation.
Igor Litvinchev, Andreas Fischer, Tetyana Romanova
et al.
Packing irregular objects composed by generalized spheres is considered. A generalized sphere is defined by an arbitrary norm. For three classes of packing problems, balance, homothetic and sparse packing, the corresponding new (generalized) models are formulated. Non-overlapping and containment conditions for irregular objects composed by generalized spheres are presented. It is demonstrated that these formulations can be stated for any norm. Different geometrical shapes can be treated in the same way by simply selecting a suitable norm. The approach is applied to generalized spheres defined by Lp norms and their compositions. Numerical solutions of small problem instances obtained by the global solver BARON are provided for two-dimensional objects composed by spheres defined in Lp norms to demonstrate the potential of the approach for a wide range of engineering optimization problems.
Mochammad Ariyanto, Chowdhury Mohammad Masum Refat, Kazuyoshi Hirao
et al.
Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%.
Rashid Nafei, Seyed Ali Asghar Razavi, Safiyeh Tahmasebi Limooni
Purpose: The present research aims to examine and offer a model of a cloud-based knowledge-sharing process among the faculty members of Islamic Azad University (IAU).Methods: This research is applied in terms of purpose, and in terms of type of research, it is mixed exploratory, and in terms of research method, it is research-based on grounded theory. For the purpose of the research, all 2800 faculty members of Islamic Azad University (IAU) in the country were studied. To determine the sample size (397 people), a multi-stage sampling method and Cochran's sample size formula were used. In this research, structured interviews and electronic questionnaires were used as research tools in two qualitative and qualitative stages. In order to achieve the objectives of the research, the interview with the experts continued until the theoretical adequacy. The pattern of knowledge-sharing behavior was extracted from the data of the qualitative phase of the research. Also, the research employed structural equations (Partial Least Square Model) to analyze the research data, using smartPLS software.Findings: The research findings indicated that there were six effective components connected with the exploratory pattern of knowledge-sharing behavior, which are given below in order of priority: 1. Principal Component; the pivotal component with the factor of organizational maturity level, 2. Causal Conditions; including the management and organizational factors, 3. Strategies; including the factors affecting the development of job security and the improvement of the level of trust across organizations and organizational communication network, 4. Background Conditions; including organizational culture and technological infrastructures 5. Intervening Conditions, including the enhancement of workforce skills, and 6. Consequences. These include the factors affecting the efficiency of human resources and the development of occupational engagement.Similarly, the research results indicated that both the management factor and the organizational factors, as the dual elements of the component causal conditions, influenced the organizational maturity development process. This is because the T-statistic value given the effectiveness of the management factors and the path coefficient equaled 3.666 and 0.21, respectively, which is indicative of the direct positive effects of the management factors on organizational maturity. The organizational factors affected the maturity process directly and positively since the T-statistic value was 6.334 going beyond the absolute value of 1.96, and the path coefficient was 0.327.Conclusions: Organizations can pave the way for the development of organizational maturity if they are in favorable conditions in terms of support from managers and the coordination between cloud computing and knowledge-sharing systems. The implementation of such systems across organizations will result in improved performance of the faculty members and an increased competitive advantage.
Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification.
The United States of America is a subject of international relations which took the leading position in application of unilateral sanctions, especially extraterritorial restrictive measures. The main target of the US extraterritorial sanctions are legal entities and individuals which are in the jurisdiction of third countries, namely member states of the European Union. The increasing sanction pressure against Iran in the 2nd half of 2010s has brought the problem of American extraterritorial measures in the US-EU relations to the fore. Consequently, the aim of this research is to reveal the contradictions between the USA and the EU due to the strong influence of secondary sanction on the European business and examine the main countermeasures taken by the EU authorities in this regard.
Mohsen Hajizadeh, Mohsen Jajarmizadeh, Ali Mohtashami
Aim: Considering that human resource management practices are one of the main factors to achieve green organizational goals and sustainable performance, the purpose of this article was to investigate green human resource management activities, effective factors, and their consequences in government organizations.Methodology: To achieve the purpose of this article, a qualitative research project was selected and the dimensions, components, and indicators of the green human resource management model were identified using the meta-synthesis method and theme analysis. Then the identified dimensions, components, and indicators were validated using the fuzzy Delphi technique.Findings: The results of the meta-synthesis led to the identification of the dimensions of leadership, technology, budget, and culture as prerequisites for the establishment of green human resource management. The dimensions of recruitment, training, performance appraisal, and compensation for green services were identified as the executive processes of establishing green human resource management. Finally, the dimensions of economic, social, organizational, and environmental consequences were identified as the consequences of the establishment of green human resource management.Conclusion: Considering that green human resource management activities can have positive consequences for the development of Iranian government organizations, this article was conducted to identify the prerequisites, processes, and consequences of establishing green human resource management in government organizations and led to the design of a model in this regard.
Te-Wei Chiang, Cheng-Ying Yang, Gwo-Jen Chiou
et al.
As usual, if many students are attending the lectures, their attendance tracking may become time consuming. Furthermore, there are some possibilities that students could cheat the lecturers of their attendance in the classroom. Therefore, a reliable method to manage the attendance tracking has become a critical issue. This paper aims to propose an attendance tracking system using an Android smartphone equipped with Global Positioning System (GPS) and Near Field Communication (NFC) technologies. Lecturers and students can constantly connect with one another by using smartphones to check and show their attendance automatically if they download and install the software Application (App). Finally, the experimental results have shown that our proposed system can successfully reduce some time for tracking students’ attendance. It also allows users to use their own Android smartphones without purchasing other electronic devices.
Unmanned Aerial Vehicles (UAVs) have benefited from a tremendous increase in popularity over the past decade, which has inspired their application toward many novel and unique use cases. One of them is the use of UAVs in meteorological research, in particular for wind measurement. Research in this field using quadcopter UAVs has shown promising results. However, most of the results in the literature suffer from three main drawbacks. First, experiments are performed as numerical simulations or in wind tunnels. Such results are limited in their validity in real-life conditions. Second, it is almost always assumed that the drone is stationary, which limits measurements spatially. Third, no attempts at estimating vertical wind are made. Overcoming these limitations offer an opportunity to gain significant value from using UAVs for meteorological measurements. We address these shortcomings by proposing a new dynamic model-based approach, that relies on the assumption that thrust can be measured or estimated, while drag can be related to air speed. Moreover, the proposed method is tested on empirical data gathered on a DJI Phantom 4 drone. During hovering, our method leads to precision and accuracy comparable to existing methods that use tilt to estimate the wind. At the same time, the method is able to estimate wind while the drone is moving. This paves the way for new uses of UAVs, such as the measurement of shear wind profiles, knowledge of which is relevant in Atmospheric Boundary Layer (ABL) meteorology. Additionally, since a commercial off-the-shelf drone is used, the methodology can be replicated by others without any need for custom hardware development or modifications.
Anna I. Belozubova, Konstantin G. Kogos, Philipp V. Lebedev
Attention to covert channels has largely increased due to the documents published by E. Snowden, which described the software and hardware embedding that implement undeclared features of covert information transfer in the Huawei and Juniper network equipment, Apple mobile phones and computers with the Windows XP operating system. A covert channel can be built using any information technology, but often attackers build covert channels in IP networks, since they are widespread, have a high information transfer rate, and ubiquitous information security measures, such as traffic encryption, do not affect the possibility of covert transmission of information via some types of such channels. The limitation of their bandwidth is a promising direction for countering information leakage via network covert channels. This study considers network timing covert channels, provides a method for assessing their capacity, proposes and investigates a way to counter information leakage via such covert channels by introducing noise having added delays before sending packets. The values of the delays are distributed in two different ways: uniformly and according to distribution with a decreasing probability density function. As an experiment, the temporal characteristics of IP traffic from a host in the internal network to a public service were obtained, which were used to verify the obtained methods for assessing the covert channel capacity. A distinctive feature of the calculations is an illustration of the possibility to minimize the load on the communication channel in the context of introducing a countermeasure method, as well as taking into consideration the fact that the intruder's ability to observe the current conditions in the network allows him to adjust the parameters of information transfer to the load in the communication channel, thereby maintaining the maximum possible covert channel bandwidth.
We have shown that when sharply focusing a linearly polarized optical vortex with topological charge 2, in the near-axis region of the focal plane, not only does a reverse energy flow (the negative on-axis projection of the Poynting vector) occur, but also the right-handed circular polariza-tion of light. Moreover, due to spin-orbital angular momentum conversion, the on-axis polarization vector and the transverse energy flow rotate around the optical axis in the same direction (counter-clockwise). If an absorbing spherical microparticle is put in the focus on the optical axis, it will rotate around the axis and around its center of mass counterclockwise. Numerical simulation results confirms the theoretical predictions.
We consider the statistical properties of different mosaic filters. We demonstrate that in Pitiev's morphology, the measure of shape complexity is directly related to the shape simplicity measure based on morphological correlation coefficient (MCC). Based on MCC, we introduce the normalized morphological simplification index (NMSI). Using NMSI, we show that the simpler the mosaic shape, the more shape simplification is provided by the corresponding Pyt'ev projector. For the examples of mean and median mosaic filters, we address the problem of different operator comparison. In this context we introduce the concept of statistically simplifying morphological operators. Morphological correlation of mosaic shape and diffusion mosaic operator is considered. We prove that the NMSI for the diffusion mosaic operator is not related to the complexity for the corresponding diffusion shape kernel. Thus, a principal qualitative difference in the relationship between relational and operator models for diffuse and projective mosaic linear filters is demonstrated.
Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.
In this paper we propose a novel approach for face anti-spoofing called Extended Division Directional Ternary Co-relation Pattern (EDDTCP). The EDDTCP encodes co-relation of ternary edges based on the centre pixel gray values with its immediate directional neighbour and its next immediate average directional neighbour, which is calculated by using the average of cornered neighbours with directional neighbours. The proposed method is robust against presentation attacks by extracting the spatial information in all directions. Three Experiments were performed by using all the four texture descriptors (LBP, LTP, LGS and EDDTCP) and the results are compared. The proposed face anti-spoofing method performs better than LBP, LTP and LGS.