Technologies, Volume 7, Issue 2 (June 2019) – 17 articles

In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis. Full article

In areas such as health, environment, and energy consumption, there is a need to do better. A common goal in society is to get people to behave in ways that are sustainable for the environment or support a healthier lifestyle. Nudging is a term known from economics and political theory, for influencing decisions and behavior using suggestions, positive reinforcement, and other non-coercive means. With the extensive use of digital devices, nudging within a digital environment (known as digital nudging) has great potential. We introduce smart nudging, where the guidance of user behavior is presented through digital nudges tailored to be relevant to the current situation of each individual user. The ethics of smart nudging and the transparency of nudging is also discussed. We see a smart nudge as a recommendation to the user, followed by information that both motivates and helps the user choose the suggested behavior. This paper describes such nudgy recommendations, the design of a smart nudge, and an architecture for a smart nudging system. We compare smart nudging to traditional models for recommender systems, and we describe and discuss tools (or approaches) for nudge design. We discuss the challenges of designing personalized smart nudges that evolve and adapt according to the user’s reactions to the previous nudging and possible behavioral change of the user. Full article

In this study, micro-computed tomography (CT) is utilized to detect defects of Ti-6Al-4V specimens fabricated by selective laser melting (SLM) and electron beam melting (EBM), which are two popular metal additive manufacturing methods. SLM and EBM specimens were fabricated with random defects at a specific porosity. The capability of micro-CT to evaluate inclusion defects in the SLM and EBM specimens is discussed. The porosity of EBM specimens was analyzed through image processing of CT single slices. An empirical method is also proposed to estimate the porosity of reconstructed models of the CT scan. Full article

In the past year, fifth-generation (5G) wireless technology has seen dramatic growth, spurred on by the continuing demand for faster data communications with lower latency. At the same time, many researchers argue that 5G will be inadequate in a short time, given the explosive growth of machine connectivity, such as the Internet-of-Things (IoT). This has prompted many to question what comes after 5G. The obvious answer is sixth-generation (6G), however, the substance of 6G is still very much undefined, leaving much to the imagination in terms of real-world implementation. What is clear, however, is that the next generation will likely involve the use of terahertz frequency (0.1–10 THz) electromagnetic waves. Here, we review recent research in terahertz wireless communications and technology, focusing on three broad topic classes: the terahertz channel, terahertz devices, and space-based terahertz system considerations. In all of these, we describe the nature of the research, the specific challenges involved, and current research findings. We conclude by providing a brief perspective on the path forward. Full article

Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model. Full article

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s). Full article

This article introduces a novel approach for generating low-sensitive Pareto fronts of analog circuit performances. The main idea consists of taking advantage from the social interaction between particles within a multi-objective particle swarm optimization algorithm by progressively guiding the global leading process towards low sensitive solutions inside the landscape. We show that the proposed approach significantly outperforms already proposed techniques dealing with the generation of sensitivity-aware Pareto fronts, not only in terms of computing time, but also with regards to the number of solutions forming the tradeoff surface. Performances of our approach are highlighted via the design of two analog circuits. Full article

The CO₂-assisted polymer compression method is used herein to prepare porous polymer materials by bonding laminated polymer fiber sheets using a piston in the presence of CO₂. In this work, the CO₂ flow line connections were moved from the pressure vessel to the piston to increase productivity, which makes the pressure vessel free-moving and the processing time of sample introduction and removal seemingly zero. In addition, a numbering-up method suitable for CO₂-assisted polymer compression is proposed and verified based on the variability of the products. The variability of the product was evaluated using porosity, which is one of the most important properties of a porous material. It is found that the CO₂ exhaust process, specific to this method, that uses high-pressure CO₂, causes product variation, which can be successfully suppressed by optimizing the CO₂ exhaust process. Full article

When a new chaotic oscillator is introduced, it must accomplish characteristics like guaranteeing the existence of a positive Lyapunov exponent and a high Kaplan–Yorke dimension. In some cases, the coefficients of a mathematical model can be varied to increase the values of those characteristics but it is not a trivial task because a very huge number of combinations arise and the required computing time can be unreachable. In this manner, we introduced the optimization of the Kaplan–Yorke dimension of chaotic oscillators by applying metaheuristics, e.g., differential evolution (DE) and particle swarm optimization (PSO) algorithms. We showed the equilibrium points and eigenvalues of three chaotic oscillators that are simulated applying ODE45, and the Kaplan–Yorke dimension was evaluated by Wolf’s method. The chaotic time series of the state variables associated to the highest Kaplan–Yorke dimension provided by DE and PSO are used to encrypt a color image to demonstrate that they are useful in implementing a secure chaotic communication system. Finally, the very low correlation between the chaotic channel and the original color image confirmed the usefulness of optimizing Kaplan–Yorke dimension for cryptographic applications. Full article

The uncertainties in various Electromagnetic (EM) problems may present a significant effect on the properties of the involved field components, and thus, they must be taken into consideration. However, there are cases when a number of stochastic inputs may feature a low influence on the variability of the outputs of interest. Having this in mind, a dimensionality reduction of the Polynomial Chaos (PC) technique is performed, by firstly applying a sensitivity analysis method to the stochastic inputs of multi-dimensional random problems. Therefore, the computational cost of the PC method is reduced, making it more efficient, as only a trivial accuracy loss is observed. We demonstrate numerical results about EM wave propagation in two test cases and a patch antenna problem. Comparisons with the Monte Carlo and the standard PC techniques prove that satisfying outcomes can be extracted with the proposed dimensionality-reduction technique. Full article

Online social networks have provided a promising communication platform for an activity inherently dear to the human heart, to find friends. People are recommended to each other as potential future friends by comparing their profiles which require numerical quantifiers to determine the extent of user similarity. From similarity-based methods to artificial intelligent machine learning methods, several metrics enable us to characterize social networks from different perspectives. This research focuses on the collaborative employment of neighbor based and graphical distance-based similarity measurement methods with text classification tools such as the feature matrix and feature vector. Likeminded nodes are predicted accurately and effectively as compared to other methods. Full article

Flexible sensors have the potential to be seamlessly applied to soft and irregularly shaped surfaces such as the human skin or textile fabrics. This benefits conformability dependant applications including smart tattoos, artificial skins and soft robotics. Consequently, materials and structures for innovative flexible sensors, as well as their integration into systems, continue to be in the spotlight of research. This review outlines the current state of flexible sensor technologies and the impact of material developments on this field. Special attention is given to strain, temperature, chemical, light and electropotential sensors, as well as their respective applications. Full article

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards. Full article

Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerged as a prolific research area in the last decade. Although several benchmarks are now available, especially on portals like CrisisLex, an important, practical problem that has not been addressed thus far is the rapid acquisition, benchmarking and visual exploration of data from free, publicly available streams like the Twitter API in the immediate aftermath of a crisis. In this paper, we present such a pipeline for facilitating immediate post-crisis data collection, curation and relevance filtering from the Twitter API. The pipeline is minimally supervised, alleviating the need for feature engineering by including a judicious mix of data preprocessing and fast text embeddings, along with an active learning framework. We illustrate the utility of the pipeline by describing a recent case study wherein it was used to collect and analyze millions of tweets in the immediate aftermath of the Las Vegas shootings in 2017. Full article

Liquid Crystals (LCs) are widely used in display devices, electro-optic modulators, and optical switches. A field-induced electrical conductivity modulation in pure liquid crystals is very low which makes it less preferable for direct current (DC) and radio-frequency (RF) switching applications. According to the literature, a conductivity enhancement is possible by nanoparticle doping. Considering this aspect, we reviewed published works focused on an electric field-induced conductivity modulation in carbon nanotube-doped liquid crystal composites (LC-CNT composites). A two to four order of magnitude switching in electrical conductivity is observed by several groups. Both in-plane and out-of-plane device configurations are used. In plane configurations are preferable for micro-device fabrication. In this review article, we discussed published works reporting the elastic and molecular interaction of a carbon nanotube (CNT) with LC molecules, temperature and CNT concentration effects on electrical conductivity, local heating, and phase transition behavior during switching. Reversibility and switching speed are the two most important performance parameters of a switching device. It was found that dual frequency nematic liquid crystals (DFNLC) show a faster switching with a good reversibility, but the switching ratio is only two order of magnitudes. A better way to ensure reversibility with a large switching magnitude is to use two pairs of in-plane electrodes in a cross configuration. For completeness and comparison purposes, we briefly reviewed other nanoparticle- (i.e., Au and Ag) doped LC composite’s conductivity behavior as well. Finally, based on the reported works reviewed in this article on field induced conductivity modulation, we proposed a novel idea of RF switching by LC composite materials. To support the idea, we simulated an LC composite-based RF device considering a simple analytical model. Our RF analysis suggests that a device made with an LC-CNT composite could show an acceptable performance. Several technological challenges needed to be addressed for a physical realization and are also discussed briefly. Full article

The drum-like virtual reality (VR) keyboard is a contemporary, controller-based interface for text input in VR that uses a drum set metaphor. The controllers are used as sticks which, through downward movements, “press” the keys of the virtual keyboard. In this work, a preliminary feasibility study of the drum-like VR keyboard is described, focusing on the text entry rate and accuracy as well as its usability and the user experience it offers. Seventeen participants evaluated the drum-like VR keyboard by having a typing session and completing a usability and a user experience questionnaire. The interface achieved a good usability score, positive experiential feedback around its entertaining and immersive qualities, a satisfying text entry rate (24.61 words-per-minute), as well as moderate-to-high total error rate (7.2%) that can probably be further improved in future studies. The work provides strong indications that the drum-like VR keyboard can be an effective and entertaining way to type in VR. Full article

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data. Full article