Search

The projected growth rates of mobile communication products has created some very enticing large volume markets for integrated circuit manufacturers. At the same time advancements in semiconductor techniques have allowed communication system designers to design products more suited to these larger volume markets. Both integrated circuit and communication system manufacturers recognize this and are working together to design more highly integrated, smaller, lower cost, and low power products. This paper will concentrate on these markets, advancements, and integrated circuit solutions and what can be expected in the future.

Most of the papers presented at this symposium deal with advanced packaging designs and techniques. This paper, however, will attempt to describe some of the best regressive thinking of packaging design, which we choose to term “illogical packaging design.” Illogical design is not a concept; rather, it may be termed a lack of concept. Occasionally, illogical design is accidental rather than premeditated. The illogic of design stems from many sources and takes many forms. It is our aim to bring to your attention some of the bad design habits existing in industry today. By describing them, the job of eliminating these bad design habits and features should be somewhat easier.

In the past 10 years the interior point methods (IPM) for linear programming have gained extraordinary interest as an alternative to the sparse simplex based methods. This has initiated a fruitful competition between the two types of algorithms which has led to very efficient implementations on both sides. The significant difference between interior point and simplex based methods is reflected not only in the theoretical background but also in the practical implementation. In this paper we give an overview of the most important characteristics of advanced implementations of interior point methods. First, we present the infeasible-primal-dual algorithm which is widely considered the most efficient general purpose IPM. Our discussion includes various algorithmic enhancements of the basic algorithm. The only shortcoming of the “traditional” infeasible-primal-dual algorithm is to detect a possible primal or dual infeasibility of the linear program. We discuss how this problem can be solved with the homogeneous and self-dual model.

In the modern business environment, the capability of an enterprise to generate value from its business knowledge influences in an increasingly important way its competitiveness. Towards this direction, knowledge-based systems can be a very effective tool for enhancing the productivity of knowledge workers by providing them with advanced knowledge processing capabilities. In this paper we describe such a system which utilizes organizational and domain knowledge in order to support consultants in the process of evaluating calls for tender.

The opportunities for multicomponent models have increased recently because of the development of new methodologies including dual energy X-ray absorptiometry (DXA), total body electrical conductivity, neutron activation analysis and bioelectric impedance along with the traditional measures of body water, potassium and density. The need for valid multicomponent body composition approaches has arisen because of the failure of the two-component model, i.e., fat and fat-free body (FFB), to yield accurate estimates of body composition both within and among various populations. Without valid two-component approaches, the field of body composition has lacked a criterion method by which to validate new body composition methods. With the development of valid and precise multicomponent approaches, reference fat-free body compositions can be established for various populations, changes in body composition with growth and aging can be more accurately assessed, and the effect of dietary and exercise programs can be quantified on various body components. The pitfalls of various multicomponent body composition approaches now in use arise from theoretical, methodological and statistical considerations. It is the purpose of this paper to review present limitations in various multicomponent body composition approaches and to develop a set of guidelines to foster valid multicomponent systems.

Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 positive patients using radiography chest X-Ray images. To test the efficiency of the solution, using data sets of publicly available X-Ray images of Corona virus positive cases and negative cases. Images of positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which are providing the good results with classification accuracy within the test set-up. Then GUI based application supports for medical examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be precisely obtaining the Covid-19 Patient analysis through the chest X-ray images and also results may be retrieve within a few seconds.

A wireless wearable sensor on a paper substrate was used to continuously monitor respiratory behavior that can extract and deliver clinically relevant respiratory parameters to a smartphone. Intended to be placed horizontally at the midpoint of the costal margin and the xiphoid process as determined through anatomical analysis and experimental test, the wearable sensor is compact at only 40 × 35 × 6 mm3 in size and 6.5 g weight including a 2.7 g lithium battery. The wearable sensor, consisting of an ultrasound emitter, an ultrasound receiver, wireless transmission system, and associated data acquisition, measures the linear change in circumference at the attachment location by recording and analyzing the changes in ultrasound pressure as the distance between the emitter and the receiver changes. Changes in ultrasound pressure corresponding to linear strain are converted to temporal lung volume data and are wirelessly transmitted to an associated custom-designed smartphone app. Processing the received data, the mobile app is able to display the temporal volume trace and the flow rate vs. volume loop graphs, which are standard plots used to analyze respiration. From the plots, the app is able to extract and display clinically relevant respiration parameters, including forced expiratory volume delivered in the first second of expiration (FEV1) and forced vital capacity (FVC). The sensor was evaluated with eight volunteers, showing a mean difference of the FEV1/FVC ratio as bounded by 0.00-4.25% when compared to the industry-standard spirometer results. By enabling continuous tracking of respiratory behavioral parameters, the wireless wearable sensor helps monitor the progression of chronic respiratory illnesses, including providing warnings to asthma patients and caregivers to pursue necessary medical assistance.

Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction. However, L2BLDA is only proposed to cope with vector input samples. When facing with two-dimensional (2D) inputs, such as images, it will lose some useful information, since it does not consider intrinsic structure of images. In this paper, we extend L2BLDA to a two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance which is measured by the weighted pairwise distances of class means and meanwhile minimizes the matrix-based within-class distance. The weighting constant between the between-class and within-class terms is determined by the involved data that makes the proposed 2DBLDA adaptive. In addition, the criterion of 2DBLDA is equivalent to optimizing an upper bound of the Bhattacharyya error. The construction of 2DBLDA makes it avoid the small sample size problem while also possess robustness, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of the proposed methods.

The contact structure of a population plays an important role in transmission of infection. Many ‘structured models’ capture aspects of the contact pattern through an underlying network or a mixing matrix. An important observation in unstructured models of a disease that confers immunity is that once a fraction $$1-1/{\mathcal {R}}_0$$ 1 - 1 / R 0 has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected individuals, because the disease acts like a targeted vaccine, preferentially immunising higher-risk individuals who play a greater role in transmission. Therefore, a limited ‘first wave’ may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we set out to investigate this more systematically. While networks offer a flexible framework to model contact patterns explicitly, they suffer from several shortcomings: (i) high-fidelity network models require a large amount of data which can be difficult to harvest, and (ii) very few, if any, theoretical contact network models offer the flexibility to tune different contact network properties within the same framework. Therefore, we opt to systematically analyse a number of well-known mean-field models. These are computationally efficient and provide good flexibility in varying contact network properties such as heterogeneity in the number contacts, clustering and household structure or differentiating between local and global contacts. In particular, we consider the question of herd immunity under several scenarios. When modelling interventions as changes in transmission rates, we confirm that in networks with significant degree heterogeneity, the first wave of the epidemic confers herd immunity with significantly fewer infections than equivalent models with less or no degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect may become much more subtle. Indeed, modifying the structure disproportionately can shield highly connected nodes from becoming infected during the first wave and therefore make the second wave more substantial. We strengthen this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. Overall, we find that results regarding (disease-induced) herd immunity levels are strongly dependent on the model, the duration of the lockdown and how the lockdown is implemented in the model.

In this full review paper, the recent emerging trends in Educational Data Science have been reviewed and explored to address the recent topics and contributions in the era of Smart Education. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, frameworks and techniques research projects in the area of Data Science applied to Education, using different approaches such as Information Fusion, Soft Computing, Machine Learning, and Internet of Things, among others. Based on this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area.

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

To handle the massive high-speed internet traffic, free space optics (FSO) or single-mode fiber (SMF) based fiber optic communication is being used everywhere across the world. These technologies are capable of providing huge bandwidth and transmitting the data at very high speed with low energy consumption. FSO is a very convenient technology to quickly expand the legacy network in the adverse geographical areas. However, its link performance is highly dependent of inconsistent weather conditions. SMF based fiber optic link has a very low loss and its performance is almost independent on the weather conditions. Though, the installation and maintenance of fibers are quite complex and costly. Individually, FSO or SMF links have their limitations and have to be integrated to leverage their benefits. In this paper, we integrated FSO/SMF links and compared the performance of the proposed architecture which is capable of providing high-speed dual-rate data transmission. The proposed architecture transmits data over either FSO or SMF or both links simultaneously and has 100% more reliability against any one of the link failures. In case of operational link failure (FSO/SMF), data may be switched to the alternative working link (SMF/FSO), simply by tuning the transmitted signal by 50 GHz. The proposed architecture is also reliable against the optical line terminal transceiver (TRx) failure as each user located in the network can be served by two transceivers (1 Gbps and 10 Gbps). The proposed architecture also supports the wavelength division multiplexing overlay transmission for broadcasting the common signal to all the available users in the networks. The architecture reduces ~ 27% of the energy consumption by utilizing the appropriate link of hybrid architecture and TRx according to weather conditions and traffic load. The integrated architecture looks attractive for providing energy-efficient, high speed, and reliable internet coverage to the areas where there is a difficulty of laying fibers and has frequent fiber faults. The architecture is useful for strengthening and boosting rural and urban development.

Technology integration within instructional practices is an essential element in the preparation of teachers. However, expectations that a single course or hopes that technology infusion will spontaneously occur are not enough to prepare teacher candidates to integrate technology in meaningful ways. In the absence of a required educational technology course for all initial licensure candidates, an educator preparation program in the Midwest sought creative solutions to meet accreditation and clinical placement expectations regarding candidate preparation to integrate technology. This report from the field discusses the iterative process to develop a comprehensive micro-credentialing system aligned with the ISTE standards for educators. The micro-credentials provide candidates opportunities to apply and model technology use within courses and throughout their program. Feedback from stakeholders indicated both possibilities and potential challenges in the adoption of the system. This feedback has led to further development of the micro-credentialing system.

Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.

This paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition. Most existing CNN methods facilitate classifiers to learn discriminating patterns to identify existing categories trained with large samples. However, learning to recognize novel categories from a few examples is a challenging task. To address this, we propose the Residual Compact Network to train a deep neural network to learn hierarchical nonlinear transformations to project image pairs into the same latent feature space, under which the distance of each positive pair is reduced. To better use the commonality of class-level features for category recognition, we develop a batch compact loss to form robust feature representations relevant to a category. The proposed methods are evaluated on several datasets. Experimental evaluations show that our proposed method achieves acceptable results in Few-shot learning.

The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help physicians to consider patients’ needs in healthcare system designing and offering service, which leads to an increase in patient satisfaction. Therefore, healthcare is becoming a growing contesting market. With this significant growth in healthcare systems, such challenges as huge data volume, response time, latency, and security vulnerability are raised. Therefore, fog computing, as a well-known distributed architecture, could help to solve such challenges. In fog computing architecture, processing components are placed between the end devices and cloud components, and they execute applications. This architecture is suitable for such applications as healthcare systems that need a real-time response and low latency. In this paper, a systematic review of available approaches in the field of fog-based healthcare systems is proposed; the challenges of its application in healthcare are explored, classified, and discussed. First, the fog computing approaches in healthcare are categorized into three main classes: communication, application, and resource/service. Then, they are discussed and compared based on their tools, evaluation methods, and evaluation metrics. Finally, based on observations, some open issues and challenges are highlighted for further studies in fog-based healthcare.

A wearable C-shaped antenna based on a fabric material operating at 2.4 GHz frequency is proposed for use in flexible/wearable IoT medical systems. The wearable IoT device plays a key role in medical applications, and the antenna is a key part of it. Loading the presented antenna on the body models showed a frequency detuned with the gain and efficiency reduced from 1. 28 to −9 dB and 90% to 10%. In addition, the SAR did not meet the safety health requirement defined by the FCC or ICNIRP standards. Therefore, an “Artificial Magnetic Conductor” structure (AMC) is added to the C-shaped antenna to overcome these problems. The AMC acts as shielding material between the human skin and the presented antenna because of its 0° reflection phase, which mimics the action of the Perfect Magnetic Conductor (PMC). The overall size of the proposed design was 54 × 54 × 3.9 mm3. Numerical and experimental findings indicated that integrating the AMC structures with a C-shaped antenna was robust for body deformation and load. The C-shaped antenna worked equally well with the AMC, whether positioned in free space or on the chest or the arm of the human body. The integrated antenna with AMC structures has excellent performances. The gain and efficiency without loading on the chest were 6.49 dB and 84%, respectively. While for loaded on the chest were 6.21 dB and 81%, respectively. It also decreased the back radiation and raised the Front to Back Ration (FBR) by 13.8 dB. SAR levels have been reduced by more than 90% between the FCC and ICNIRP standards compared to the C-shaped antenna alone, which does not comply with the standards. As a result, the C-shaped integration with AMC structures is highly suitable for assembly in any wearable system. Supplementary Information The online version contains supplementary material available at 10.1007/s11276-021-02770-4.

Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time.

Face recognition is one of the most common biometric authentication methods as its feasibility while convenient use. Recently, the COVID-19 pandemic is dramatically spreading throughout the world, which seriously leads to negative impacts on people's health and economy. Wearing masks in public settings is an effective way to prevent viruses from spreading. However, masked face recognition is a highly challenging task due to the lack of facial feature information. In this paper, we propose a method that takes advantage of the combination of deep learning and Local Binary Pattern (LBP) features to recognize the masked face by utilizing RetinaFace, a joint extra-supervised and self-supervised multi-task learning face detector that can deal with various scales of faces, as a fast yet effective encoder. In addition, we extract local binary pattern features from masked face's eye, forehead and eyebow areas and combine them with features learnt from RetinaFace into a unified framework for recognizing masked faces. In addition, we collected a dataset named COMASK20 from 300 subjects at our institution. In the experiment, we compared our proposed system with several state of the art face recognition methods on the published Essex dataset and our self-collected dataset COMASK20. With the recognition results of 87% f1-score on the COMASK20 dataset and 98% f1-score on the Essex dataset, these demonstrated that our proposed system outperforms Dlib and InsightFace, which has shown the effectiveness and suitability of the proposed method. The COMASK20 dataset is available on https://github.com/tuminguyen/COMASK20 for research purposes.

Entropy has been widely applied in system identification in the last decade. In this paper, a novel stochastic gradient algorithm based on minimum Shannon entropy is proposed. Though needing less computation than the mean square error algorithm, the traditional stochastic gradient algorithm converges relatively slowly. To make the convergence faster, a multi-error method and a forgetting factor are integrated into the algorithm. The scalar error is replaced by a vector error with stacked errors. Further, a simple step size method is proposed and a forgetting factor is adopted to adjust the step size. The proposed algorithm is utilized to estimate the parameters of an ARX model with random impulse noise. Several numerical solutions and case study indicate that the proposed algorithm can obtain more accurate estimates than the traditional gradient algorithm and has a faster convergence speed.

Timely and effective distribution of relief materials is one of the most important aspects when fighting with a natural or a man-made disaster. Due to the sudden and urgent nature of most disasters, it is hard to make the exact prediction on the demand information. Meanwhile, timely delivery is also a problem. In this paper, taking the COVID-19 epidemic as an example, we propose an integrated method to fulfill both the demand estimation and the relief material distribution. We assume the relief supply is directed by government, so it is possible to arrange experts to evaluate the situation from aspects and coordinate supplies of different sources. The first part of the integrated method is a fuzzy decision-making process. The demand degrees on relief materials are estimated by extending COPRAS under interval 2-tuple linguistic environment. The second part includes the demand degrees as one of the inputs, conducts a hybrid distribution model to decide the allocation and routing. The key point of hybrid distribution is that each demand point could be visited by different vehicles and each vehicle could visit different demand points. Our method can also be extended to include both relief materials and medical staffs. A real-life case study of Wuhan, China is provided to illustrate the presented method.

The main objective of this paper is to review and analysis of the state of the art regarding triage applications (apps) for health emergencies. This research is based on a systematic review of the literature in scientific databases from 2010 to early 2021, following a prism methodology. In addition, a Google Play Store search of the triage apps found in the literature was performed for further evaluation. A total of 26 relevant papers were obtained for this study, of which 13 apps were identified. After searching for each of these apps in the Google Play Store platform, only 2 of them were obtained, and these were subsequently evaluated together with another app obtained from the link provided in the corresponding paper. In the analysis carried out, it was detected that from 2019 onwards there has been an increase in research interest in this area, since the papers obtained from this year onwards represent 38.5% of the relevant papers. This increase may be caused by the need for early selection of the most serious patients in such difficult times for the health service. According to the review carried out, an increase in mobile app research focused on Emergency Triage and a decrease in app studies for triage catastrophe have been identified. In this study it was also observed that despite the existence of many researches in this sense, only 3 apps contained in them are accessible. "TRIAGIST" does not allow the entry of an unidentified user, "Major Trauma Triage Tool" presents negative comments from users who have used it and "ESITriage" lacks updates to improve its performance.

Today’s digital era has undertaken most of the responsibilities of public and private sectors, not only the industries or big organizations dependent on the internet but individual’s household needs also lying on it. To make the data transmission/reception confidential and secure for both internet users and internet service providers, a large number of researches have been done in this field. It has proved that cryptography is the best solution for solving this purpose. Mostly, digital images are continuously transferring on the network rather than texts. Enciphering a digital image is a much bulkier and a complex task. It has been evident from many types of research that chaotic logistic map-based equations provide a great level of randomness. Hence Chaotic logistic maps-based image encryption techniques (also called chaos techniques) were implemented to obtain highly random cipher images. On the other hand, time consumption must be as low as it can be possible to sustain real-time communication. Presently, the advanced encryption schemes based on quantum technology have enhanced efficiency and security because of having a large key-space and less time complexity along with randomness. The quantum-chaos based encryption is done by utilizing uncertainty principles of quantum mechanics on logistic maps. This paper is an effort to compare chaos and quantum chaos-based image encryption schemes. MATLAB 2016a software is used for the execution and the comparison is made based on various security attack analyses. Based on the study and experimental results, the quantum chaos techniques used for bit plane scrambling provides better results in terms of effectiveness, efficiency, and trustworthy that can be adopted for highly secured image encryption.

Face age progression, goals to alter the individual's face from a given face image to predict the future appearance of that image. In today's world that demands more security and a touchless unique identification system, face aging attains tremendous attention. The existing face age progression approaches have the key problem of unnatural modifications of facial attributes due to insufficient prior knowledge of input images and nearly visual artifacts in the generated output. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. AttentionGAN uses two separate subnets in a generator. One subnet for generating multiple attention masks and the other for generating multiple content masks. Then attention mask is multiplied with the corresponding content mask along with an input image to finally achieve the desired results. Further, the regex filtering process is performed to separates the synthesized face images from the output of AttentionGAN. Then image sharpening with edge enhancement is done to give high-quality input to SRGAN, which further generates the super-resolution face aged images. Thus, presents more detailed information in an image because of its high quality. Moreover, the experimental results are obtained from five publicly available datasets: UTKFace, CACD, FGNET, IMDB-WIKI, and CelebA. The proposed work is evaluated with quantitative and qualitative methods, produces synthesized face aged images with a 0.001% error rate, and is also evaluated with the comparison to prior methods. The paper focuses on the various practical applications of super-resolution face aging using Generative Adversarial Networks (GANs).

In the century of automation, which is digitized, and more and more technology is used, automatic systems' replacement of old manual systems makes people's lives easier. Nowadays, people have made the Internet an integral part of humans' daily lives unless they are insecure. The Internet of Things (IoT) secures a platform that authorizes devices and sensors to be remotely detected, connected, and controlled over the Internet. Due to the developments in sensor technologies, the production of tiny and low-cost sensors has increased. Many sensors, such as temperature, pressure, vibration, sound, light, can be used in the IoT. As a result of the development of these sensors with new generations, the power of the IoT technology increases, and accordingly, the revolution of IoT applications are developing rapidly. Therefore, their security issues and threats are challenging topics. In this paper, the benefits and open issues, threats, limitations of IoT applications are presented. The assessment shows that the most influential factor for evaluating IoT applications is the cost that is used in 79% of all articles, then the real-time-ness that is used in 64%, and security and error are used in 57% of all reviewed articles.

India's health infrastructure is under pressure since the daily COVID-19 cases have crossed the milestone of 4 Lakhs cases per day which surpass the previous years' peak. Patients with mild symptoms have been advised for home treatment since most of the hospitals are running out of bed. In this situation delivering healthcare to people has become revolutionizing due to the rapid advancement of embedded systems, communication, and informatics technologies. Integration of different health sensors, handheld devices, and internet can be a great potential for significant improvement of the quality of remote healthcare. This paper discusses the use of MySignals HW shield which is a hardware development platform for medical devices to build e-health monitoring system. Wearable health monitoring system prototype has been developed in this work. To conduct experiments, health vitals such as body temperature, ECG, oxygen saturation level, and pulse rate from 5 volunteers have been measured, collected, and stored in a cloud database using the system prototype. To evaluate the performance of the prototype, transmission delay has been recorded in both wired (Ethernet) and wireless (Wi-Fi) communication modes. It is observed that it takes 2.71 ms and 5.18 ms of time to collect and store the health vitals to the cloud database in wired and wireless mode respectively. Comparing the collected health vitals with the normal range of health vitals, no abnormality is found in all volunteer's health. Finally, a framework for contactless monitoring of COVID-affected patients is proposed. Contactless monitoring of health vitals can reduce the chance of community spread.

Internet of Multimedia Things (IoMT), a special subset of Internet of Things (IoT), is a novel paradigm which is progressively increasing and gaining in popularity, allowing many multimedia content based applications such as Wireless Multimedia Sensor Networks (WMSNs). The standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), originally designed for transmitting scalar data traffic of IoT applications, should be customized in order to deal with IoMT applications which pose new challenges and requirements in terms of the Quality of Service (QoS) and the user’s Quality of Experience (QoE). For this purpose and thanks to the flexibility and adaptability of RPL, we propose, in this paper, a multipath version of RPL, not defined in the RPL specification RFC 6550, named MP-RPL. MP-RPL leverages the multiparent feature offered by RPL in order to construct multiples end-to-end paths of different qualities based on radio link quality measurements. It is intended to improve the video traffic delivery for IoMT based WMSN applications by simultaneously using the built paths while taking into account the video traffic differentiation as per priority levels. Compared to the traditional single-path RPL, the results obtained from simulations when considering the influence of video characteristics, show that our proposal provides feasible and acceptable QoS and QoE performance metrics for multimedia applications, while maintaining RPL compliance.

Information technology outsourcing (ITO) is a USD multi-trillion industry. There is growing competition among ITO service providers to improve their service deliveries. Natural language processing (NLP) is a technique, which can be leveraged to gain a competitive advantage in the ITO industry. This paper explores the information security implications of using NLP in ITO. First, it explores the use of NLP to enhance information security risk management (ISRM) in ITO. Then, it delves into the information security risks (ISRs) that may arise from the use of NLP in ITO. Finally, it proposes possible ISRM approaches to address those ISRs in ITO from the use of NLP. The study follows a qualitative approach using the case study method. Nine participants from three organisations (an ITO client, service provider and sub-contractor) engaged in an ITO relationship in the ICT industry were interviewed through a semi-structured questionnaire. The research findings were verified through a focus group. Case study scenarios are provided for a clear understanding of the findings. To the best of our knowledge, it is the first study to investigate the information security implications of the use of NLP in ITO. Refereed/Peer-reviewed

Across so many industries, non-destructive evaluation has proven its worth time and again through quality and safety assurance of valuable assets. Yet, over time, it became underappreciated in business decisions. In most cases, the data gathered by NDT is used for quality assurance assessments resulting in binary decisions. And we seem to miss out on value of the information content of NDE which goes way deeper and can help other stakeholders: such as engineering, management, inspectors, service providers, and even regulators. Some of those groups might not even be aware of the benefits of NDE data and its digitalization. Unfortunately, the NDE industry typically makes the data access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be addressed now by the NDE industry. The confluence of NDE and Industry 4.0, dubbed as NDE 4.0, provides a unique opportunity for the NDE/NDT Industry to not only readjust the value perception but to gain new customer groups through a broad set of value creation activities across the ecosystem. The integration of NDE into the Cyber-Physical Loop (including IIoT and Digital Twin) is the chance for the NDE industry to now shift the perception from a cost center to a value center. This paper provides an overview of the NDE ecosystem, key value streams, cyber-physical loops that create value, and a number of use cases for various stakeholders in the ecosystem.