Ubiquitous Applications Research Articles

A Review of Ubiquitous Language Learning Environment

<p>In today’s era of ubiquitous learning research, various studies have investigated the transformation of ubiquitous learning in multiple aspects. However, most of these research mainly focus on the study of ubiquitous learning environment. Limited review is on the configuration and components of ubiquitous learning focusing on the interaction process in language learning. Hence, this research performed literature analysis for ubiquitous language learning environment whereby it intends at providing specific information about ubiquitous learning concepts and the criteria that needed in the future research focusing on language learning. It begins by briefly describing the concept of ubiquitous learning environment, language learning skills and the importance of vocabulary learning and then on adult language learning. Finally, ubiquitous learning applications for multiple types of language learning are also compared and discussed to further enhance the understanding of ubiquitous language learning environment. Therefore, it is important to have a medium that can be used to improve language learning process to be more interactive. The result will also become a reference point to assist development of ubiquitous language learning applications.</p>

A Secure Crypto Base Authentication and Communication Suite in Wireless Body Area Network (WBAN) for IoT Applications

To monitor the functions of human body and their surroundings Wireless Body Area Network (WBAN) is used, which are based on low powered and light weight wireless sensors devices. WBAN highly supports numerous applications but this study will focus on the security of ubiquitous healthcare applications. In E-health research monitoring the critical data in terms of security has become a major challenge as WBAN deals with various threats day by day. Therefore the design of secure and reasonably resource optimal algorithms with a robust key generation and management scheme is today’s need. There must be only authorized user’s who can have access to patient related data; otherwise it can be exploited by anyone. This proposed study is aiming to formulate the two security suite for WBAN, which comprises on KBS keys, KAISC and Hash algorithm three improved versions of key management procedures and authentication procedure respectively. Firstly the KBS Keys and improved Hashing suite which is an independent and adaptive key management and authentication scheme for improving the security of WBANs will be used, and secondly KAISC will be used for inter-sensor communication and key management security scheme. All above mentioned procedures will be suitably blend with the encryption and decryption process which will securely send the patient’s critical data to the base station and further to the concerned doctor. The novelty of work is that the proposed methodology is not only simple but also advanced and much secured procedure of key generation and management that will be further validated by the performance analysis. This technique will be beneficial for the continuous monitoring of patient’s critical data in remote areas also.

Improving modulation bandwidth of c-plane GaN-based light-emitting diodes by an ultra-thin quantum wells design.

The GaN-based light emitting diodes (LEDs) have a great potential for visible light communication (VLC) due to their ubiquitous application in general lighting, but the modulation bandwidth of conventional c-plane LEDs is limited by carrier recombination rate in InGaN quantum wells (QWs) due to the polarization-field-induced quantum confined Stark effect (QCSE). Furthermore, the high modulation bandwidth on c-plane sapphire substrates can only be achieved at high current densities. Here, blue LEDs with ultra-thin InGaN QWs (1nm) and GaN barriers (3nm) are grown on c-plane sapphire substrate to suppress QCSE and extend the cut-off frequency from 214 MHz for conventional LEDs to 536 MHz at a current density of 2.5 kA/cm2, which is comparable to devices grown on semi-polar substrates.

Non-Invasive Measurement of Cognitive Load and Stress Based on the Reflected Stress-Induced Vascular Response Index

Measuring cognitive load and stress is crucial for ubiquitous human--computer interaction applications to dynamically understand and respond to the mental status of users, such as in smart healthcare, smart driving, and robotics. Various quantitative methods have been employed for this purpose, such as physiological and behavioral methods. However, the sensitivity, reliability, and usability are not satisfactory in many of the current methods, so they are not ideal for ubiquitous applications. In this study, we employed a reflected photoplethysmogram-based stress-induced vascular response index, i.e., the reflected sVRI (sVRI-r), to non-invasively measure the cognitive load and stress. This method has high usability as well as good sensitivity and reliability compared with the previously proposed transmitted sVRI (sVRI-t). We developed the basic methodology and detailed algorithm framework to validate the sVRI-r measurements, and it was implemented by employing two light sources, i.e., infrared light and green light. Compared with the simultaneously recorded blood pressure, heart rate variation, and sVRI-t, our findings demonstrated the greater potential of the sVRI-r for use as a sensitive, reliable, and usable parameter, as well as suggesting its potential integration with ubiquitous touch interactions for dynamic cognition and stress-sensing scenarios.

Wavelet analyses and comparative denoised signals of meteorological factors of the namibian atmosphere

With population growth and climate change increasing the exposure of communities and assets to extreme hydrological events, such as floods and droughts, it is crucial to make accurate and timely early warning, climate adaptation and water management information available that can help minimize the loss of the limited quantities of water in arid regions. Trend estimation using wavelets has continued to attract ubiquitous applications in various fields including atmospheric and water studies. While Fourier analysis employs big waves, wavelet analysis uses small waves. Since wavelets localise features in a signal data to different scales, we can therefore preserve vital signal features while removing noise in the signal. Hence the basic notion of wavelet denoising or wavelet thresholding is that the wavelet transform results in a sparse representation for many real-world signals, meaning that the wavelet transform concentrates signal features in a few large-magnitude wavelet coefficients. Since small wavelet coefficients are typically noise, we can “shrink” or remove them without affecting the signal quality.This paper employs wavelet analyses of some selected meteorological factors of the Namibian atmosphere to investigate the noise in the data collected from a number of Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) stations, for the years 2012 to 2015. The results show some significant levels of noise around the thresholds of various optimal time series peaks of the data. Hence the denoised data serve to represent fairly refined representations of data for the given period. Moreover, the results of comparing the optimal game model solutions for the original and denoised data specifically identified some weather stations that are “humidity noise stable” and “temperature noise stable”. Thus, these suggest that despite the noise in the respective meteorological data for these stations, the effects of the noise in the data appear to be manageable or optimally controllable, within the given period.

Computational screening and molecular design of anthracene-based semiconductors

Abstract Anthracene derivatives constitute the active elements in high performance organic semiconductors and in order to find ubiquitous electronics applications, the investigation of new materials with high stability and mobilities is critical. Despite the versatility of substituent groups in anthracene and the extensive research works on anthracene derivatives, the exploration and discovery of high-performance anthracene derivatives can be impeded by the difficulty in molecular designing and synthesis techniques. Theoretical modeling is a powerful instrument and a variety of modeling methods with different accuracy levels have been reported. However, there is no systematic research work on theoretical prediction of the fundamental properties of anthracene derivatives so far. Here, density functional theory calculations are performed on anthracene derivatives to systematically address the prediction accuracy in energy levels as compared to our experimental data and it is shown that the PBE0/6-311G(d,p) is a more cost-effective method. Based on the influence of diverse substituent groups on energy levels, we present a strategy to design high-performance anthracene derivatives with specific energy levels, which are predicted to be excellent candidates for charge transfer materials. Therefore, the strategy may offer a guide for tailoring materials' functional properties via modifications of the molecular units before materials synthesis and open up new possibilities for the discovery of rising organic semiconductors.

Instantaneous Semiconductor-to-Conductor Transformation of a Transparent Oxide Semiconductor a-InGaZnO at 45 °C.

The emphasis on ubiquitous technology means that future technological applications will depend heavily on transparent conducting materials. To facilitate truly ubiquitous applications, transparent conductors should be fabricated at low temperatures (<50 °C). Here, we demonstrate an instantaneous (<100 ns) and low-temperature (<45 °C at the substrate) method, excimer laser irradiation, for the transformation of an a-InGaZnO semiconductor into a transparent highly conductive oxide with performance rivaling traditional and emerging transparent conductors. Our analysis shows that the instantaneous and substantial conductivity enhancement is due to the generation of a large amount of oxygen vacancies in a-InGaZnO after irradiation. The method's combination of low temperature, extremely rapid process, and applicability to other materials will create a new class of transparent conductors for the high-throughput roll-to-roll fabrication of future flexible devices.

Oil-Palm Plantation Identification from Satellite Images Using Google Earth Engine

Oil<strong>-</strong>palm plantation is a crucial determinant for land-use planning and agricultural studies<strong>. </strong>Remote sensing techniques have<strong> </strong>elevated limitations of the on<strong>-</strong>site survey as computerized imaging is much efficient and economical. This paper presents a ubiquitous application of Gabor analysis for<strong> </strong>extracting oil<strong>-</strong>palm plantation from satellite images<strong>. </strong>The proposed system was built on<strong> </strong>the cloud-based Google Earth Engine<strong>. </strong>Herein, THEOS images were convoluted with Gabor kernels, and both K-Means and SVM then learned their responses<strong> </strong>for comparison. Experimental results showed that SVM could better identify the plantation areas with precision, recall, and accuracy of 92.98<strong>%</strong>, 88<strong>.</strong>96<strong>%</strong>,<strong> </strong>and 94<strong>.</strong>24<strong>%</strong> respectively.

Reliability Challenges for SiC Power Devices in Systems and the Impact on Reliability Testing

The reliability of SiC devices remains to be a field of hectic activity because it is one of the obstacles for the ubiquitous application of SiC devices. Without decades of field experience, reliability testing, especially accelerated testing, is the only way to obtain information on reliability during the projected lifespan of the devices. For silicon devices, such tests exist and they are canonized in internationally recognized test standards. For SiC devices, these standards have to be revised and/or supplemented with tests to capture SiC specific degradation mechanisms. On the one hand, this requires a detailed knowledge about the mechanisms but on the other hand, this also requires the mission profile of the devices. In fact, it is not the mission profile of the device that determines its reliability but the mission profile of the chip. This contribution reviews the standard silicon tests useful for SiC devices and looks into additional, SiC specific tests that have been proposed but not yet been recognized as standards.

Surface and Defect Chemistry of Oxide Materials.

Oxides and oxide-derived materials find ubiquitous applications in many industrial applications. These materials usually contain point defects, the concentration and type depending on the synthesis and operating conditions. Many of the functional properties are determined or strongly affected by the presence of these point defects. Despite steady advances in experimental techniques it is still difficult to unambiguously identify point defects and resulting atomic-scale mechanisms from experiment alone. Atomic-scale computational approaches such as density functional theory (DFT) provide an alternative approach that ideally complements experimental investigations. The resulting fundamental understanding of defect-induced mechanisms combined with the knowledge of how to tailor a point-defect profile to induce new functionality opens exciting new avenues to engineer novel material properties. This account discusses some case studies of defect-induced functionality in the area of oxide electronics and photocatalysis, giving a general flavour of our current understanding of the role of defects in these applications.

How American Nurses Association Code of Ethics informs genetic/genomic nursing.

Members of the Ethics and Public Policy Committee of the International Society of Nurses in Genetics prepared this article to assist nurses in interpreting the American Nurses Association (2015) Code of Ethics for Nurses with Interpretive Statements (Code) within the context of genetics/genomics. The Code explicates the nursing profession's norms and responsibilities in managing ethical issues. The nearly ubiquitous application of genetic/genomic technologies in healthcare poses unique ethical challenges for nursing. Therefore, authors conducted literature searches that drew from various professional resources to elucidate implications of the code in genetic/genomic nursing practice, education, research, and public policy. We contend that the revised Code coupled with the application of genomic technologies to healthcare creates moral obligations for nurses to continually refresh their knowledge and capacities to translate genetic/genomic research into evidence-based practice, assure the ethical conduct of scientific inquiry, and continually develop or revise national/international guidelines that protect the rights of individuals and populations within the context of genetics/genomics. Thus, nurses have an ethical responsibility to remain knowledgeable about advances in genetics/genomics and incorporate emergent evidence into their work.

IPOJO flow: a declarative service workflow architecture for ubiquitous cloud applications

The growth of innovative services backed up by various sensors and devices provides an unprecedented potential for ubiquitous computing applications and systems. However, in order to benefit from the recent developments, the current service middleware technology needs a catch-up of being able to fully support interactions among the services. OSGi is considered as a viable service framework solution due to its ability to deal with the dynamism inherent with ubiquitous cloud environments. iPOJO has also emerged as a service component model that simplifies the development of OSGi applications. However, the technology runs short of providing adequate support to foster declarative service compositions of realistic interaction topologies. Noticing this deficiency, we propose an iPOJO component-based service workflow architecture, named iPOJO Flow, where component services can easily be composed together to form realistic, complicated applications. Along with the architectural design, the paper also introduces a new DSL to specify service workflow topologies in a declarative way. The effectiveness of our proposed approach is validated through a prototype demonstration, comparative design analysis, and performance experiments.

Ultrathin Metallic Nanowire-Based Architectures as High-Performing Electrocatalysts.

Fuel cells (FCs) convert chemical energy into electricity through electrochemical reactions. They maintain desirable functional advantages that render them as attractive candidates for renewable energy alternatives. However, the high cost and general scarcity of conventional FC catalysts largely limit the ubiquitous application of this device configuration. For example, under current consumption requirements, there is an insufficient global reserve of Pt to provide for the needs of an effective FC for every car produced. Therefore, it is absolutely necessary in the future to replace Pt either completely or in part with far more plentiful, abundant, cheaper, and potentially less toxic first row transition metals, because the high cost-to-benefit ratio of conventional catalysts is and will continue to be a major limiting factor preventing mass commercialization. We and other groups have explored a number of nanowire-based catalytic architectures, which are either Pt-free or with reduced Pt content, as an energy efficient solution with improved performance metrics versus conventional, currently commercially available Pt nanoparticles that are already well established in the community. Specifically, in this Perspective, we highlight strategies aimed at the rational modification of not only the physical structure but also the chemical composition as a means of developing superior electrocatalysts for a number of small-molecule-based anodic oxidation and cathodic reduction reactions, which underlie the overall FC behavior. In particular, we focus on efforts to precisely, synergistically, and simultaneously tune not only the size, morphology, architectural motif, surface chemistry, and chemical composition of the as-generated catalysts but also the nature of the underlying support so as to controllably improve performance metrics of the hydrogen oxidation reaction, the methanol oxidation reaction, the ethanol oxidation reaction, and the formic acid oxidation reaction, in addition to the oxygen reduction reaction.

Comparison of chemical procedures for Re-isotopic measurements by N-TIMS

Precise and accurate measurements of the isotopic composition of rhenium (Re) in geologic samples are of paramount importance for the successful application of the Re-Os geochronometer. Two methods for chemical isolation of Re are used in the geochronology community – column chemistry using anion exchange resin and solvent-solvent extraction using a sodium hydroxide-acetone mixture. Despite their ubiquitous application, a systematic comparison of these two methods is missing. Here we compare Re-isotopic data on a variety of samples (sulfides, hydrocarbons, shales, and in-house standard solutions) that were processed by both chemical methods. Measured 185Re/187Re ratios for both methods have similar precision and overlap within the propagated long-term analytical reproducibility of ~1‰ for samples that yield moderate to strong intensities (>50 mV on the weaker Re mass). In contrast, measured 185Re/187Re ratios for most samples that yield less intense Re signals (<50 mV on the weaker Re mass) disagree by ~3–6‰ between the two methods, exceeding the propagated analytical uncertainty of ~3‰ for low-intensity measurements. A systematic investigation of samples with natural Re isotopic composition (shale, molybdenite, and standard solutions) shows that use of anion exchange resin (column chemistry) may cause elevated 185Re/187Re ratios. This effect is more pronounced for low-Re shales (<1 ng total Re processed) and becomes nearly or completely insignificant for high-Re samples measured over a wide range of signal intensities. Standard solutions processed through column chemistry, however, preserve their natural Re isotopic composition. Together, these data suggest that complex sample matrices leading to sample-derived impurities passed through column chemistry may cause isobaric interferences that deviate measured 185Re/187Re ratios in geologic samples. In contrast, the acetone-NaOH method produces more consistent Re isotopic results across the same range of sample types, Re loads and signal intensities. Thus, we recommend use of the acetone-NaOH solvent-solvent extraction procedure for low-Re samples, and provide a practical way to lower the typically higher Re blank supplied by NaOH.

Automatic Radio Map Adaptation for Indoor Localization Using Smartphones

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across six months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB, outperforming existing approaches. Moreover, AcMu provides 2 $\times$ improvement on localization accuracy by maintaining an up-to-date radio map.

In-ovo exposed carbon black nanoparticles altered mRNA gene transcripts of antioxidants, proinflammatory and apoptotic pathways in the brain of chicken embryos

With ubiquitous applications of nanotechnology, there are increasing probabilities of exposure to manufactured nanoparticles (NPs), which might be posing emerging health concerns on the next generation. Recent data suggest that generation of reactive oxygen species may play an integral role in the carbon black nanoparticles (CBNPs)-induced oxidative injury; however, the exact molecular mechanism has not been clarified. Hence, the role of oxidative stress, inflammation and apoptosis pathways in the CBNPs-induced neuronal toxicity following in-ovo exposure of chicken embryo was elucidated. Specific pathogen-free fertilized Sasso eggs were inoculated with 4.8, 9.5 and 14 μg CBNPs/egg at the 3rd day of incubation alongside vehicle controls. In a concentration-dependent manner, CBNPs inoculation induced oxidative stress, which was ascertained by enhancement of lipid peroxides and diminishing total antioxidant capacity and glutathione levels, and catalase activity in brain tissues. mRNA transcript levels of antioxidant genes showed up-regulation of heme oxygenase-1 and superoxide dismutase-1, with marked down-regulation of glutathione S-transferase-α. Additionally, the pro-inflammatory genes; nuclear factor-κB1 was up-regulated, while interferon-γ was down-regulated. There is also a clear down-regulation in apoptotic markers caspase-8, caspase-3, cytochrome c and B-cell CLL/lymphoma 2 at the different concentrations, while caspase-2 is up-regulated only at higher concentration. Collectively, these results show that CBNPs exposure-mediated overproduction of the free radicals, particularly at higher concentration contributes to inflammation and subsequent cellular apoptosis at the gene expression level, thus unveiling possible molecular relationship between CBNPs and genes linked to the oxidant, inflammatory and apoptotic responses.

Non-Orthogonal Multiple Access for Ubiquitous Wireless Sensor Networks

Ubiquitous wireless sensor networks (UWSNs) have become a critical technology for enabling smart cities and other ubiquitous monitoring applications. Their deployment, however, can be seriously hampered by the spectrum available to the sheer number of sensors for communication. To support the communication needs of UWSNs without requiring more spectrum resources, the power-domain non-orthogonal multiple access (NOMA) technique originally proposed for 5th Generation (5G) cellular networks is investigated for UWSNs for the first time in this paper. However, unlike 5G networks that operate in the licensed spectrum, UWSNs mostly operate in unlicensed spectrum where sensors also experience cross-technology interferences from other devices sharing the same spectrum. In this paper, we model the interferences from various sources at the sensors using stochastic geometry framework. To evaluate the performance, we derive a theorem and present new closed form expression for the outage probability of the sensors in a downlink scenario under interference limited environment. In addition, diversity analysis for the ordered NOMA users is performed. Based on the derived outage probability, we evaluate the average link throughput and energy consumption efficiency of NOMA against conventional orthogonal multiple access (OMA) technique in UWSNs. Further, the required computational complexity for the NOMA users is presented.

I Walk, Therefore I Am: Continuous User Authentication with Plantar Biometrics

The comprehensive evolution of information communication technologies on mobile sensing objects has led to the provision of versatile ubiquitous network services embedded with specific- purpose modern sensors and intelligent wearable devices. The universal Internet connectivity of such smart objects has brought about a new era of ubiquitous application development for the Internet of Things. Meanwhile, security has become critically important. In the past decade, academia and industry have dedicated great efforts to the design of continuous authentication for multi-modal networks. Multiform authentication bio-tokens have been introduced for continuous entity identification and verification. With the rapid growth and universality of wearable devices, in this article we target continuous authentication for the IoT-based environment with users possessing wearable healthcare (and wellness) related smart objects. To present the state of the art, we provide a comprehensive review of continuous authentication in recent years. Critical characteristics of new biometrics are then introduced. Second, we present a wearable plantar bio-feature extractor constructed via commercial pressure sensors and the Raspberry PI platform. The prototype is adopted to retrieve user plantar bio-data as the raw (and training) data in the proposed authentication system. Third, we apply machinelearning- based techniques to derive a user's plantar bio-features as authentication tokens in the system to support continual (and real-time) entity verification in the background without the user's notice.

A complex event processing framework for an adaptive language learning system

Ubiquitous learning applications and worldwide educational websites such as MOOC (Massive Open Online Courses) are rapidly producing large volume of user data. Current delayed analysis processing in adaptive language learning systems is difficult to cope with the high-speed and high-volume data streams. To overcome this problem, we introduce a complex event processing (CEP) framework for an Adaptive Language Learning System. The system consists of an event adapter sub-system that can process various inputs such as voice, video, text and other interaction events. The event adapter extracts relevant data to support the operational events module, the learning activity events module and the learner knowledge space events module. These three modules in the event hierarchies provide support to the learner adaptation and learner visual analytics modules. In this study, we conduct three simulations to evaluate the initialization time, delay time and throughput of the proposed system. Each of the experiments simulates 1000 learners and 1000 rules and generates 10 events per second. The results indicate the CEP framework is efficient with a processing delay of less than 1.2μs and throughput of 80,000 events per second. We conclude by discussing the study’s implications and suggest ideas for future research.

RDeepSense

Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications. Although existing studies have demonstrated the effectiveness and feasibility of running deep neural network inference operations on mobile and embedded devices, they overlooked the reliability of mobile computing models. Reliability measurements such as predictive uncertainty estimations are key factors for improving the decision accuracy and user experience. In this work, we propose RDeepSense, the first deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices. RDeepSense enables the predictive uncertainty by adopting a tunable proper scoring rule as the training criterion and dropout as the implicit Bayesian approximation, which theoretically proves its correctness. To reduce the computational complexity, RDeepSense employs efficient dropout and predictive distribution estimation instead of the model ensemble or sampling-based method for inference operations. We evaluate RDeepSense with four mobile sensing applications using Intel Edison devices. Results show that RDeepSense can reduce around 90% of the energy consumption while producing superior uncertainty estimations and preserving at least the same model accuracy compared with other state-of-the-art methods.