A survey on low-power wide area networks for IoT applications

Telecommunication Systems - We are on the entry of the exponential advancement of the internet-of-things (IoT) due to the quick development of internet-connected smart-objects. As the number of...     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Static assessment of plain/notched polylactide (PLA) 3D‐printed with different infill levels: Equivalent homogenised material concept and Theory of Critical Distances

A novel approach based on the equivalent homogenised material concept and the theory of critical distances is formulated to perform static assessment of plain/notched objects of polylactide (PLA) when this polymer is additively manufactured with different infill levels. The key idea is that the internal net structure resulting from the 3D‐printing process can be modelled by keeping treating the material as linear elastic, continuum, homogenous, and isotropic, with the effect of the internal voids being taken into account in terms of change in mechanical/strength properties. This idea is initially used to assess the detrimental effect of the manufacturing voids on the static strength of the plain (ie, unnotched) material. This is done by addressing this problem in a Kitagawa‐Takahashi setting via the Theory of Critical Distances. Subsequently, this approach is extended to the static assessment of notched components of 3D‐printed PLA; ie, it is used to take into account simultaneously the effect of both manufacturing voids and macroscopic geometrical features. The accuracy and reliability of this design methodology were checked against a large number of experimental data generated by testing, under axial loading, plain specimens, as well as notched samples (including open notches) of PLA. These specimens were manufactured by making the infill level vary in the rage 10% to 90%. This validation exercise allowed us to demonstrate that the proposed approach is highly accurate, returning estimates falling within an error interval of ±20%. This remarkable level of accuracy strongly supports the idea that static assessment of 3D‐printed materials with complex geometries and manufactured with different infill levels can be performed by simply post‐processing conventional linear elastic finite element (FE) solid models, ie, without the need for modelling explicitly the detrimental effect of the manufacturing voids.     

Finite volume analysis of laminated composite plates

A numerical procedure for analysis of general laminated plates under transverse load is developed utilizing the Mindlin plate theory, the finite volume discretization, and a segregated solution algorithm. The force and moment balance equations with the laminate constitutive relations are written in the form of a generic transport equation. In order to obtain discrete counterparts of the governing equations, the plate is subdivided into N control volumes by a Cartesian numerical mesh. As a result, five sets of N linear equations with N unknowns are obtained and solved using the conjugate gradient method with preconditioning. For the method validation, a number of test cases are designed to cover thick and thin laminated plates with aspect ratio (width to thickness) from 4 to 100. Simply supported orthotropic, symmetric cross‐ply, and angle‐ply laminated plates under uniform and sinusoidal pressure loads are solved, and results are compared with available analytical solutions. The shear correction factor of 5/6 is utilized throughout the procedure, which is consistent with test cases used in the reviewed literature. Comparisons of the finite volume method results for maximum deflections at the center of the plate and the Navier solutions obtained for aspect ratios 10, 20, and 100 shows a very good agreement. Copyright © 2016 John Wiley & Sons, Ltd.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Novel Analytical Thermal Performance Rate Analysis in ZnO-SAE50 Nanolubricant: Nonlinear Mathematical Model

The investigation of local thermal transport rate in the nanolubricants is significant. These lubricants are broadly used in environmental pollution, mechanical engineering and in the paint industry due to high thermal performance rate. Therefore, thermal transport in ZnO-SAE50 nanolubricant under the impacts of heat generation/absorption is conducted. The colloidal suspension is flowing between parallel stretching disks in which the lower disk is positioned at z = 0 and upper disk apart from distance d. The problem is transformed in dimensionless version via described similarity transforms. In the next stage, an analytical technique (VPM) is implemented for the solution purpose. The graphical results against multiple flow parameters were furnished over the region of interest and explained comprehensively. It is imperative to mention that the results are plotted for ZnO-SAE50 and conventional liquid as well. Further, rapid motion of the fluid is perceived against high Reynolds and γ parameters. The wall shear stresses at the upper end rises for multiple Reynolds and γ while; decrement is detected at the lower end. The significant contribution of an internal heat source is noted for thermal performance rate at the upper end. Foremost, the local heat transport rate declines at the lower disk. By altering Reynolds number, prompt heat transfer rate is gained at the upper disk and increasing behavior of the local heat transport rate is slow at the lower disk. From the study, it is concluded that the nanolubricants have high thermal characteristics. Therefore, such fluids are reliable to use in above stated areas.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.