In this covid19 pandemic the number of people gathering at public places and festivals are restricted and maintaining social distancing is practiced throughout the world. Managing the crowd is always a challenging task. It requires monitoring technology. In this paper, we develop a device that detects and provide human count and detects people who are not maintaining social distancing . The work depicted above was finished using a Raspberry Pi 3 board with OpenCV-Python. This method can effectively manage crowds.

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.

A new method of Electrocardiogram (ECG) features extraction is proposed in this paper. The purpose of this study is to detect the main characteristics of the signal: P, Q, R, S, and T, then localize and extract its intervals and segments. To do so we first detect peaks, onsets and offsets of the signal's waveform by calculating the slope change (SC) coefficients and consequently, the peaks of the signal are determined. The SC coefficients are based on the calculation of the integral of two-scale signals with opposite signs. The simulation results of our algorithm applied on recordings of MIT-BIH arrhythmia electrocardiogram database show that the proposed method delineates the electrocardiogram waveforms and segments with high precision.

Respiratory rate measurement is important under different types of health issues. The need for technological developments for measuring respiratory rate has become imperative for healthcare professionals. The paper presents an approach to respiratory monitoring, with the aim to improve the accuracy and efficacy of the data monitored. We use multiple types of sensors on various locations on the body to continuously transmit real-time data, which is processed to calculate the respiration rate. Variations in the respiration rate will help us identify the current health condition of the patient also for diagnosis and further medical treatment. The software tools such as Keil μVision IDE, Mbed Studio IDE, Energia IDE are used to compile and build the system architecture and display information. EasyEDA is used to provide pin map details and complete architecture information.

One of the most recent challenges in communication system and network system is the privacy and security of information and communication session. Blockchain is one of technologies that use in sensing application in different important environments such as healthcare. In healthcare the patient privacy should be protected use high security system. Key agreement protocol based on lattice ensure the authentication and high protection against different types of attack especially impersonation and man in the middle attack where the latticebased protocol is quantum-withstand protocol. Proposed improved framework using lattice based key agreement protocol for application of block chain, with security analysis of many literatures that proposed different protocols has been presented with comparative study. The resultant new framework based on lattice overcome the latency limitation of block chain in the old framework and lowered the computation cost that depend on Elliptic curve Diffie-Hellman. Also, it ensures high privacy and protection of patient’s information.

Additions were proposed to the method of organizing the information security (IS) event management process of companies. Unlike existing solutions, the algorithm of the "Event handling" subprocess was detailed. This detailing is a complex, which includes the IS event processing substage. In addition, the proposed detailing of the "Event Handling" subprocess allows for covering the entire life cycle of an IS event. The performed research allows in practice to fill in potential gaps in information when creating a company's ISMS. An additional advantage of the proposed solution is the possibility of using this sub-process as an independent one. The proposed approach makes it possible to simplify the procedure for managing the information security of a company as a whole, as well as potentially reduce the costs of its construction for small companies and enterprises. Also, this subprocess can be considered as an independent information security management process, for example, for a company's CIS. The proposed solutions and additions, in contrast to similar studies, are characterized by invariance with respect to the methods of implementing the company's IS infrastructure solutions, and in particular its CIS. This ultimately allows, without changing the methodological tools, to scale this approach and adapt it to the ISMS of various companies.

In this article, a 3D printed rectangular dielectric resonator antenna which is capable of polarization reconfiguration has been designed. Dielectric resonator is composed of environment friendly and biodegradable material, which is Polylactic Acid. In the proposed model, the polarization can be switch from a linear to a circular by changing the state of a switch, electonically. The antenna switch between two different polarizations: Linear polarization during OFF STATE and Lefthand circular polarization during ON STATE. The proposed 3D printed dielectric resonator antenna is designed to operate in Cband of microwave spectrum, with a broad effective bandwidth (overlapped impedance bandwidths of both states) of 14.542% with centre frequency at 5.845GHz and peak gain 5.5dBi. Further, validated simulated results with experiment and both results are in good agreement.

In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates.

In this paper a new security technique aiming to ensure safe and reliable communications between different nodes on an automotive Controller Area Network (CAN) is presented. The proposed method relies on a robust authentication code using Blake-3 as a hash algorithm within an adapted structure that includes a monitor node. A prototype is implemented and run effectively to perform hardware simulations of real case-based security problems of automotive embedded CAN systems. As a result, data transfer can take place on a newly enhanced CAN bus according to the standard protocol without being intercepted nor tampered with by unauthorized parties thereby highlighting the effectiveness of the proposed technique.

Health informatics is characterized by the need to securely store, process and transmit large amounts of sensitive medical data while ensuring interoperability with other systems. Among many standards used in such systems there are two which have gained interest in recent years and cover most of those needs: openEHR and HL7 FHIR. In this paper, both standards are discussed and compared with each other. The architecture of both systems, the similarities and differences, methods of data modeling and ensuring interoperability were presented.

In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.

A double balanced passive mixer-based receiver operating in the 3-5 GHz UWB for medical applications is described in this paper. The receiver front-end circuit is composed of an inductorless low noise amplifier (LNA) followed by a fully differential voltage-driven double-balanced passive mixer. A duty cycle of 25% was chosen to eliminate overlap between LO signals, thereby improving receiver linearity. The LNA realizes a gain of 25.3 dB and a noise figure of 2.9 dB. The proposed receiver achieves an IIP3 of 3.14 dBm, an IIP2 of 17.5 dBm and an input return loss (S11) below -12.5dB. Designed in 0.18μm CMOS technology, the proposed mixer consumes 0.72pW from a 1.8V power supply. The designed receiver demonstrated a good ports isolation performance with LO_IF isolation of 60dB and RF_IF isolation of 78dB.

Electromagnetic theoretical concepts, which are represented mathematically, are usually challenging to grasp by students. In this study, we explore an interactive technology-based teaching tool to develop further students’ mastery of electromagnetic concepts through learning development and visualization of electromagnetic problems. This visualization of the problems will help students analyse, evaluate, and draw conclusions of the impact of electromagnetic-related problems in real-life. The simulation tool in this study is based on a MATLAB® toolbox package, in which partial-differential equations (PDE) solver is the core engine. In this paper, we will also provide a step-by-step guide on the use of such an interactive computer-aided tool so that it can be a great self-guide tool for beginners in the field of physics and a first-level introductory course in electromagnetism. This study will focus mainly on one classical electrostatic problem that is a challenge to students to visualize, analyze and evaluate. Based on students feedback by the end of the course, 80% of students’ population are more comfortable with the introduced interactive learning tool.

This study expands on prior studies on wireless telecommunication generations by examining the technological differences and evolutional triggers that characterise each Generation (from 1G to 5G). Based on a systematic literature review approach, this study examines fifty (50) articles to enhance our understanding of wireless generation evolution. Specifically, this study analyses i) the triggers that necessitated the evolution of wireless telecommunication generations and ii) makes a case regarding why it is imperative to look beyond the fifth Generation (5G) network technologies. The authors propose areas for future research.

Localization is one of the oldest mathematical and technical problems that have been at the forefront of research and development for decades. In a wireless sensor network (WSN), nodes are not able to recognize their position. To solve this problem, studies have been done on algorithms to achieve accurate estimation of nodes in WSNs. In this paper, we present an improvement of a localization algorithm namely Gaussian mixture semi-definite programming (GM-SDP-2). GMSDP is based on the received signal strength (RSS) to achieve a maximum likelihood location estimator. The improvement lies in the placement of anchors through the Fuzzy C-Means clustering method where the cluster centers represent the anchors’ positions. The simulation of the algorithm is done in Matlab and is based on two evaluation metrics, namely normalized root-mean-squared error (RMSE) and cumulative distribution function (CDF). Simulation results show that our improved algorithm achieves better performance compared to those using a predetermined placement of anchors.