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

Cover Story (view full-size image): Conformable sensors enable the fabrication of flexible systems and their seamless integration onto irregular-shaped soft surfaces. By mimicking the flexible nature of sensory receptors in living organisms, these devices aim to open the path towards truly imperceptible systems, such as smart textiles and artificial skins, for the development of context-aware systems. To achieve this, materials and structures for innovative flexible sensors, as well as their integration into complex systems, continue to be in the research spotlight. Here, the current state of flexible sensor technologies is outlined. Special emphasis is given to the materials and structures used to realize strain, temperature, chemical, light, and electropotential sensors. Furthermore, the simulation, conditioning, and application of these sensors is discussed. View this paper
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19 pages, 3545 KiB  
Article
CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue
by Michalis Papakostas, Akilesh Rajavenkatanarayanan and Fillia Makedon
Technologies 2019, 7(2), 46; https://doi.org/10.3390/technologies7020046 - 13 Jun 2019
Cited by 18 | Viewed by 8750
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Multimedia and Cross-modal Retrieval)
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<p>The computerized version of the WCST as offered by PsyToolkit [<a href="#B29-technologies-07-00046" class="html-bibr">29</a>]. A standardized collection of computerized cognitive tests. On the top of the image are the four different possible categories. On the bottom is the stimulus card presented to the user. The user is supposed to match the stimulus card to one of the categories by inferring the correct decision rule after the system’s feedback. In a complete session of the original WCST the user is given a total number of approximately 60 stimulus cards while the total number of categories remains always the same.</p> Full article ">Figure 2
<p>Our implementation of WCST. During a complete game, the user must play all the different cases (i.e., <b>a</b>–<b>d</b>). In V1 the game starts with two possible choices (<b>a</b>) and the choices increase gradually by one until a total number of 5 choices (<b>d</b>) has been reached. In V2 options a, b, c, and d are changing randomly after every 4 rounds under the same decision rule. At the end of V1 and V2 each user has played around 32 rounds of each a, b, c, and d cases.</p> Full article ">Figure 3
<p>The Data Collection Experimental Setup.</p> Full article ">Figure 4
<p>Facial keypoint detection and tracking based on [<a href="#B35-technologies-07-00046" class="html-bibr">35</a>].</p> Full article ">Figure 5
<p>Textual Stimuli Version shown in (<b>a</b>) and Auditory Stimuli in (<b>b</b>).</p> Full article ">Figure 6
<p>Feedback provided by the system after each user choice (Left: Negative - Right: Positive). Visual feedback is accompanied by an appropriate sound that makes the overall interaction richer and more appealing to the user, while at the same time eliminates the possibility of misunderstanding the outcome of his/her choice.</p> Full article ">Figure 7
<p>Self-reported levels of cognitive fatigue during the game. The thicker and denser the line is, the larger the group of users that it represents.</p> Full article ">Figure 8
<p>Analysis of Self-Reported Cognitive Fatigue during V1 and V2 versions of WCST.</p> Full article ">Figure 9
<p>Average number of perseverative errors when playing V1 and V2 versions of WCST.</p> Full article ">Figure 10
<p>Roc Curve Estimated for each Fold after applying the combinatory classifier.</p> Full article ">Figure 11
<p>An overall visualization of the CogBeacon data-collection framework. It must be noted that for the purposes of this study we just considered the raw features as described in <a href="#sec4dot2dot1-technologies-07-00046" class="html-sec">Section 4.2.1</a>. All features that are labeled as <span class="html-italic">Potential Features</span> in the Figure above, aim to highlight the potentials offered by the platform towards analyzing aspects of CF in the future.</p> Full article ">
16 pages, 302 KiB  
Article
Recommendations with a Nudge
by Randi Karlsen and Anders Andersen
Technologies 2019, 7(2), 45; https://doi.org/10.3390/technologies7020045 - 13 Jun 2019
Cited by 43 | Viewed by 14982
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Next Generation of Recommender Systems)
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<p>Designing a smart nudge.</p> Full article ">Figure 2
<p>Architecture of a smart nudging system.</p> Full article ">
12 pages, 7767 KiB  
Article
Micro-CT Evaluation of Defects in Ti-6Al-4V Parts Fabricated by Metal Additive Manufacturing
by Haijun Gong, Venkata Karthik Nadimpalli, Khalid Rafi, Thomas Starr and Brent Stucker
Technologies 2019, 7(2), 44; https://doi.org/10.3390/technologies7020044 - 12 Jun 2019
Cited by 26 | Viewed by 9745
Abstract
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 [...] Read more.
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
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<p>Micro- Computed Tomography (CT) equipment.</p> Full article ">Figure 2
<p>Single slices and locally reconstructed models of SLM specimens with keyhole defects. (<b>a</b>) <span class="html-italic">V</span> = 360 mm/s, <span class="html-italic">R<sub>D</sub></span> = 6.0%. (<b>b</b>) <span class="html-italic">V</span> = 480 mm/s, <span class="html-italic">R<sub>D</sub></span> = 2.0%. (<b>c</b>) <span class="html-italic">V</span> = 600 mm/s, <span class="html-italic">R<sub>D</sub></span> = 0.5%.</p> Full article ">Figure 3
<p>Single slices and locally reconstructed models of SLM specimens with lack-of-fusion defects. (<b>a</b>) <span class="html-italic">V</span> = 1080 mm/s, <span class="html-italic">R<sub>D</sub></span> = 0.3%. (<b>b</b>) <span class="html-italic">V</span> = 1320 mm/s, <span class="html-italic">R<sub>D</sub></span> = 2.0%. (<b>c</b>) <span class="html-italic">V</span> = 1560 mm/s, <span class="html-italic">R<sub>D</sub></span> = 6.0%.</p> Full article ">Figure 4
<p>Single slices and locally reconstructed models of EBM specimens (varied line offset). (<b>a</b>) <span class="html-italic">LO</span> = 0.18 mm, <span class="html-italic">R<sub>D</sub></span> = 0.7%. (<b>b</b>) <span class="html-italic">LO</span> = 0.24 mm, <span class="html-italic">R<sub>D</sub></span> = 2.0%. (<b>c</b>) <span class="html-italic">LO</span> = 0.30 mm, <span class="html-italic">R<sub>D</sub></span> = 4.0%.</p> Full article ">Figure 5
<p>Single slices and locally reconstructed models of EBM specimens (varied focus offset). (<b>a</b>) <span class="html-italic">FO</span> = 16 mA, <span class="html-italic">R<sub>D</sub></span> = 0.3%. (<b>b</b>) <span class="html-italic">FO</span> = 20 mA, <span class="html-italic">R<sub>D</sub></span> = 3.0%. (<b>c</b>) <span class="html-italic">FO</span> = 24 mA, <span class="html-italic">R<sub>D</sub></span> = 4.5%.</p> Full article ">Figure 6
<p>Optical microscopic image of cross-section surfaces of SLM and EBM samples.</p> Full article ">Figure 7
<p>Image processing of a single slice of CT scanned EBM specimen.</p> Full article ">Figure 8
<p>Schematic of grayscale adjustment to an individual cell (16 pixels).</p> Full article ">Figure 9
<p>Example of EBM single slice image correction and binary image.</p> Full article ">Figure 10
<p>Comparison between <span class="html-italic">R<sub>D</sub></span> and <span class="html-italic">R<sub>I</sub></span> (EBM specimens).</p> Full article ">Figure 11
<p>Comparison between <span class="html-italic">R<sub>D</sub></span> and <span class="html-italic">R<sub>M</sub></span>.</p> Full article ">
18 pages, 547 KiB  
Review
A Perspective on Terahertz Next-Generation Wireless Communications
by John F. O’Hara, Sabit Ekin, Wooyeol Choi and Ickhyun Song
Technologies 2019, 7(2), 43; https://doi.org/10.3390/technologies7020043 - 12 Jun 2019
Cited by 108 | Viewed by 15611
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Terahertz Technologies)
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<p>Terahertz wave atmospheric power attenuation for temperature <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> </mrow> </semantics></math> 20 °C and water vapor density <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mi>W</mi> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math> g/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math> at sea level. Left and right plots shows linear and logarithmic scale, respectively. Left plot indicates two predominant molecular oxygen (O<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>) absorption lines at 60 GHz and 120 GHz.</p> Full article ">Figure 2
<p>Illustration of wireless channel, where the channel impulse response (CIR) is indicated by <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Transfer characteristics of a SiGe HBT following various total radiation doses. Upper family of curves shows collector currents <math display="inline"><semantics> <msub> <mi>I</mi> <mi>C</mi> </msub> </semantics></math> and lower family shows base currents <math display="inline"><semantics> <msub> <mi>I</mi> <mi>B</mi> </msub> </semantics></math>. Used with permission from [<a href="#B108-technologies-07-00043" class="html-bibr">108</a>].</p> Full article ">Figure 4
<p>Dynamic interference in different low-noise amplifiers generated by pulse-laser induced SEE experiment. For the core cascode stages, either forward (F) or inverse (I) mode SiGe HBTs are used. Used with permission from [<a href="#B109-technologies-07-00043" class="html-bibr">109</a>].</p> Full article ">
16 pages, 3035 KiB  
Article
An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach
by Hamed Bolandi, Wolfgang Banzhaf, Nizar Lajnef, Kaveh Barri and Amir H. Alavi
Technologies 2019, 7(2), 42; https://doi.org/10.3390/technologies7020042 - 6 Jun 2019
Cited by 23 | Viewed by 6623
Abstract
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 [...] Read more.
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
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<p>A typical multi-gene GP model.</p> Full article ">Figure 2
<p>The MGGP block diagram.</p> Full article ">Figure 3
<p>MGGP Prediction versus experimental bonding strength of (<b>a</b>) training data (<b>b</b>) testing data, and (<b>c</b>) validation data.</p> Full article ">Figure 4
<p>Statistical properties of the evolved MGGP model on the training data (<b>a</b>) Gene Weights (<b>b</b>) P-Value (low = significant).</p> Full article ">Figure 5
<p>Population of the evolved models in terms of their complexity and fitness.</p> Full article ">Figure 6
<p>(<b>a</b>) RMSE of the MGGP and ACI models. (<b>b</b>) MAE of the MGGP and ACI models.</p> Full article ">Figure 7
<p>Bond strength predictions using the MGGP and ACI models.</p> Full article ">Figure 8
<p>Experimental to MGGP-predicted bond strength rations.</p> Full article ">Figure 9
<p>Experimental to ACI-predicted bond strength rations.</p> Full article ">
19 pages, 2019 KiB  
Article
Factors Affecting the Performance of Recommender Systems in a Smart TV Environment
by Iftikhar Alam, Shah Khusro and Mumtaz Khan
Technologies 2019, 7(2), 41; https://doi.org/10.3390/technologies7020041 - 27 May 2019
Cited by 18 | Viewed by 7893
Abstract
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 [...] Read more.
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 belongs to the Special Issue Next Generation of Recommender Systems)
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<p>General scenario of recommendation process on a smart TV.</p> Full article ">Figure 2
<p>Aggregated prediction techniques in which items are aggregated for group recommendations.</p> Full article ">Figure 3
<p>Aggregated model for preferences merging in which preferences of individuals are aggregated for group recommendations.</p> Full article ">Figure 4
<p>(<b>a</b>) Type of smart TVs; (<b>b</b>) Time spent in front of a smart TV.</p> Full article ">Figure 5
<p>Multiple correspondence analysis (MCA) results for analyzing major activities on a smart TV.</p> Full article ">Figure 6
<p>Box plot, showing high-security concerns by smart TV viewers.</p> Full article ">Figure 7
<p>(<b>a</b>) Primary interaction device for smart TV; (<b>b</b>) Preferred method of interaction.</p> Full article ">Figure 8
<p>(<b>a</b>) Device Registration Via Email; (<b>b</b>) Downloading Apps from App-stores.</p> Full article ">
9 pages, 1893 KiB  
Article
Fast and Efficient Sensitivity Aware Multi-Objective Optimization of Analog Circuits
by Amel Garbaya, Mouna Kotti, Omaya Bellaaj Kchaou, Mourad Fakhfakh, Omar Guillen-Fernandez and Esteban Tlelo-Cuautle
Technologies 2019, 7(2), 40; https://doi.org/10.3390/technologies7020040 - 15 May 2019
Cited by 3 | Viewed by 5532
Abstract
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 [...] Read more.
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
(This article belongs to the Section Information and Communication Technologies)
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<p>Illustration of the concept of a particle’s move within the swarm.</p> Full article ">Figure 2
<p>Flowchart of MOPSO-CD.</p> Full article ">Figure 3
<p>Flowchart of Case #4.</p> Full article ">Figure 4
<p>A CMOS second-generation current conveyor (CCII) [<a href="#B30-technologies-07-00040" class="html-bibr">30</a>,<a href="#B31-technologies-07-00040" class="html-bibr">31</a>].</p> Full article ">Figure 5
<p>A CMOS voltage follower (VF) [<a href="#B18-technologies-07-00040" class="html-bibr">18</a>].</p> Full article ">Figure 6
<p>CCII: Pareto fronts for Case #0-4.</p> Full article ">Figure 7
<p>VF: Pareto fronts for Case #0-4.</p> Full article ">Figure 8
<p>CCII: Pareto fronts for cases #4 (the proposed approach vs. [<a href="#B15-technologies-07-00040" class="html-bibr">15</a>]).</p> Full article ">Figure 9
<p>VF: Pareto fronts for cases #4 (the proposed approach <span class="html-italic">vs.</span> [<a href="#B15-technologies-07-00040" class="html-bibr">15</a>]).</p> Full article ">Figure 10
<p>Boxplot corresponding to the computation times for 30 runs for both CCII and VF.</p> Full article ">
10 pages, 5197 KiB  
Article
Process Development of CO2-Assisted Polymer Compression for High Productivity: Improving Equipment and the Challenge of Numbering-Up
by Takafumi Aizawa
Technologies 2019, 7(2), 39; https://doi.org/10.3390/technologies7020039 - 8 May 2019
Cited by 7 | Viewed by 5936
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Reviews and Advances in Materials Processing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic illustration of the cross-section of the high-pressure vessel used for CO<sub>2</sub>-assisted polymer compression. B1: Body of the high-pressure vessel, B2: Base of the high-pressure vessel, C: CO<sub>2</sub> cylinder, P: Piston, PC: Laptop computer, S1: Sample, S2: Separator, V1: Intake valve, V2: Exhaust valve, V3: Exhaust valve (optional), and V4: Metering valve (optional).</p> Full article ">Figure 2
<p>Cross-sectional diagram of the piston components. The piston comprises three main components, P1–P3.</p> Full article ">Figure 3
<p>Schematic drawings showing the (<b>a</b>) basic process, (<b>b</b>) parallel numbering-up, and (<b>c</b>) serial numbering-up.</p> Full article ">Figure 4
<p>Scanning electron microscope images of (<b>a</b>) raw material, (<b>b</b>) sample at 4.5 mm press position, and (<b>c</b>) sample at 6.5 mm press position.</p> Full article ">Figure 5
<p>Product porosity at each position for different press positions. Each value is the average thickness based on seven experiments.</p> Full article ">
10 pages, 758 KiB  
Article
Optimizing the Kaplan–Yorke Dimension of Chaotic Oscillators Applying DE and PSO
by Alejandro Silva-Juarez, Gustavo Rodriguez-Gomez, Luis Gerardo de la Fraga, Omar Guillen-Fernandez and Esteban Tlelo-Cuautle
Technologies 2019, 7(2), 38; https://doi.org/10.3390/technologies7020038 - 27 Apr 2019
Cited by 14 | Viewed by 6355
Abstract
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 [...] Read more.
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
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<p>Phase portraits of Equations (<a href="#FD1-technologies-07-00038" class="html-disp-formula">1</a>)–(<a href="#FD3-technologies-07-00038" class="html-disp-formula">3</a>) with the parameters listed in <a href="#technologies-07-00038-t001" class="html-table">Table 1</a>, and simulated with a time-step: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>0.0038</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>0.008</mn> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Encryption process adding chaos to an original image and recovering it through synchronizing two chaotic oscillators, as already shown in [<a href="#B20-technologies-07-00038" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>Original image on the left, encrypted image in the chaotic channel in the center, and the recovered image on the right.</p> Full article ">
13 pages, 1383 KiB  
Article
Efficient Uncertainty Assessment in EM Problems via Dimensionality Reduction of Polynomial-Chaos Expansions
by Christos Salis, Nikolaos Kantartzis and Theodoros Zygiridis
Technologies 2019, 7(2), 37; https://doi.org/10.3390/technologies7020037 - 17 Apr 2019
Cited by 2 | Viewed by 5660
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Modern Circuits and Systems Technologies on Communications)
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<p>Geometric features of the 1D transmission-line problem.</p> Full article ">Figure 2
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the electric field for the first case of the 1D transmission-line problem. PC, Polynomial Chaos.</p> Full article ">Figure 3
<p>Mean elementary effects for each random variable in the first case of the 1D transmission-line problem.</p> Full article ">Figure 4
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the electric field for the second case of the 1D transmission-line problem.</p> Full article ">Figure 5
<p>Geometric features of the 2D problem.</p> Full article ">Figure 6
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the magnetic field for the first case of the second problem.</p> Full article ">Figure 7
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the magnetic field for the second case of the second problem.</p> Full article ">Figure 8
<p>Schematic of the patch-antenna problem.</p> Full article ">Figure 9
<p>Mean elementary effects of the path-antenna problem for the first case.</p> Full article ">Figure 10
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the reflection coefficient for the first case of the path-antenna problem.</p> Full article ">Figure 11
<p>Cumulative distribution function for the first case of the patch-antenna problem.</p> Full article ">Figure 12
<p>(<b>a</b>) Mean value and (<b>b</b>) standard deviation of the reflection coefficient for the second case of the path-antenna problem.</p> Full article ">
15 pages, 2089 KiB  
Article
User Similarity Determination in Social Networks
by Sadia Tariq, Muhammad Saleem and Muhammad Shahbaz
Technologies 2019, 7(2), 36; https://doi.org/10.3390/technologies7020036 - 15 Apr 2019
Cited by 3 | Viewed by 6626
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Next Generation of Recommender Systems)
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<p>Algorithms and kinds of similarity metrics.</p> Full article ">Figure 2
<p>Preferential Attachment (PA) similarity score vectors (SSV) of nodes B and C with neighbors A, B, C, D, respectively.</p> Full article ">Figure 3
<p>Weighted network graph.</p> Full article ">Figure 4
<p>High level view of the proposed model.</p> Full article ">Figure 5
<p>Neighbor based similarity scores for all nodes.</p> Full article ">Figure 6
<p>Similarity for neighbor-based metrics.</p> Full article ">Figure 7
<p>Coefficient similarity for neighbor based metrics.</p> Full article ">Figure 8
<p>Similarity for neighbor based metrics.</p> Full article ">Figure 9
<p>Chart for Pearson’s coefficient.</p> Full article ">
81 pages, 49064 KiB  
Review
Flexible Sensors—From Materials to Applications
by Júlio C. Costa, Filippo Spina, Pasindu Lugoda, Leonardo Garcia-Garcia, Daniel Roggen and Niko Münzenrieder
Technologies 2019, 7(2), 35; https://doi.org/10.3390/technologies7020035 - 9 Apr 2019
Cited by 162 | Viewed by 36378
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Reviews and Advances in Internet of Things Technologies)
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<p>Common materials and respective fabrication methods used for the fabrication of flexible sensors.</p> Full article ">Figure 2
<p>Metals combine conductivity and mechanical stability enabling their easy application in flexible devices. (<bold>a</bold>) Biodegradable sensor using Mg contacts. Reprinted with permission from [<xref ref-type="bibr" rid="B30-technologies-07-00035">30</xref>]. Copyright 2015 American Chemical Society. (<bold>b</bold>) Improvement of the junction resistance of AgNWs [<xref ref-type="bibr" rid="B48-technologies-07-00035">48</xref>]. (<bold>c</bold>) Highly stretchable contacts on Ecoflex™ by sintering of Ag nanoflakes. Reprinted from [<xref ref-type="bibr" rid="B57-technologies-07-00035">57</xref>].</p> Full article ">Figure 3
<p>Summary of the conditions and properties of chemical vapour deposited (CVD) graphene. Reprinted from [<xref ref-type="bibr" rid="B111-technologies-07-00035">111</xref>].</p> Full article ">Figure 4
<p>Examples of fabrication and structures of organic conductors. (<bold>a</bold>) PDMS micropillars and PANI nanofibres pressure sensor, schematic representation (<italic>left</italic>) and cross-sectional SEM image (<italic>right</italic>). Adapted with permission from [<xref ref-type="bibr" rid="B136-technologies-07-00035">136</xref>]. Copyright 2015 American Chemical Society. (<bold>b</bold>) schematic representation of synthetic process of PSS doped PANI/graphene nanocomposites. Adapted from [<xref ref-type="bibr" rid="B125-technologies-07-00035">125</xref>]. (<bold>c</bold>) Schematic illustration of the printing process of PEDOT:PSS, silver nanoparticles (AgNPs) and MWCNT inks on paper. Adapted with permission from [<xref ref-type="bibr" rid="B145-technologies-07-00035">145</xref>]. Copyright 2017 American Chemical Society. (<bold>d</bold>) SWCNTs and PEDOT on PDMS, schematic illustration (<italic>left</italic>) and SEM image (<italic>right</italic>). Adapted with permission from [<xref ref-type="bibr" rid="B154-technologies-07-00035">154</xref>]. Copyright 2017 American Chemical Society. (<bold>e</bold>) SEM image of hydroxilated PEDOT nanotubes with nanorods (<italic>left</italic>) and nanonodules (<italic>right</italic>) as surface structures. Adapted with permission from [<xref ref-type="bibr" rid="B148-technologies-07-00035">148</xref>]. Copyright 2017 American Chemical Society.</p> Full article ">Figure 5
<p>SiNMs are fabricated by patterning a Si layer on top of a buried oxide layer (BOX). Subsequent etching of the BOX layer allows for the release of the SiNMs, which are then transferred using a PDMS stamp. Reprinted with permission from [<xref ref-type="bibr" rid="B219-technologies-07-00035">219</xref>]. Copyright 2018 American Chemical Society.</p> Full article ">Figure 6
<p>Black phosphorus as a novel 2D material for flexible sensors. (<bold>a</bold>) Crystalline structure of single layer BP (phosphorene). Adapted from [<xref ref-type="bibr" rid="B271-technologies-07-00035">271</xref>]. (<bold>b</bold>) TFT based on BP for Gigahertz frequency applications. Reprinted with permission from [<xref ref-type="bibr" rid="B264-technologies-07-00035">264</xref>]. Copyright 2016 American Chemical Society.</p> Full article ">Figure 7
<p>Perovskite thin films are deposited by solution methods and find most of their applications in flexible light sensors. (<bold>a</bold>) Crystal structure of perovskite. Reprinted from [<xref ref-type="bibr" rid="B288-technologies-07-00035">288</xref>]. (<bold>b</bold>) CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> flexible photodetector. Reprinted from [<xref ref-type="bibr" rid="B278-technologies-07-00035">278</xref>]. (<bold>c</bold>) Solution process for the fabrication of CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3−x</sub>Cl<sub>x</sub> arrays. Reprinted from [<xref ref-type="bibr" rid="B279-technologies-07-00035">279</xref>].</p> Full article ">Figure 8
<p>Different types of sensors that use change in resistance and capacitance to indicate strain. (<bold>a</bold>) Extremely stretchable self-healing resistive strain sensor (<italic>right</italic>), and its relative resistance change with regard to strain (<italic>left</italic>) and bending performance (<italic>center</italic>). Adapted from [<xref ref-type="bibr" rid="B116-technologies-07-00035">116</xref>]. (<bold>b</bold>) Capacitive strain sensors in an intelligent glove (<italic>left</italic>) and its characterisation (<italic>center</italic> and <italic>right</italic>). Reprinted with permission from [<xref ref-type="bibr" rid="B101-technologies-07-00035">101</xref>].</p> Full article ">Figure 9
<p>Various examples of performing pressure sensors. (<bold>a</bold>) ZnO-microparticle scanning electron microscopy (SEM) image, morphology and pressure sensor concept with the corresponding sensitivities per applied pressures. Scale bar, 1 <inline-formula><mml:math id="mm1539" display="block"><mml:semantics><mml:mi mathvariant="sans-serif">μ</mml:mi></mml:semantics></mml:math></inline-formula><inline-formula><mml:math id="mm1540" display="block"><mml:semantics><mml:mi mathvariant="normal">m</mml:mi></mml:semantics></mml:math></inline-formula>. Adapted from [<xref ref-type="bibr" rid="B422-technologies-07-00035">422</xref>]. (<bold>b</bold>) Gate suspended, pressure sensitive thin film transistor [<xref ref-type="bibr" rid="B10-technologies-07-00035">10</xref>]. (<bold>c</bold>) Laser-scribed graphene (LSG) pressure sensor with foam-like structure [<xref ref-type="bibr" rid="B418-technologies-07-00035">418</xref>]. (<bold>d</bold>) Multi-functional P(VDF-TrFE) field effect organic transistor using a microstructured dielectric layer. Adapted from [<xref ref-type="bibr" rid="B11-technologies-07-00035">11</xref>]. (<bold>e</bold>) AgNWs/PDMS pressure sensor array using a stacked structure [<xref ref-type="bibr" rid="B432-technologies-07-00035">432</xref>].</p> Full article ">Figure 10
<p>Various approaches in fabricating temperature sensors, from arrays to fingerprint temperature detectors. (<bold>a</bold>) PEDOT:PSS temperature sensor integrated with a fingerprint sensor array and performance chart. Adapted from [<xref ref-type="bibr" rid="B462-technologies-07-00035">462</xref>]. (<bold>b</bold>) temperature sensor array with performance characterisation and temperature distribution. Adapted from [<xref ref-type="bibr" rid="B341-technologies-07-00035">341</xref>]. (<bold>c</bold>) Polyethylene terephthalate (PET) temperature sensor, performance and potential application. Adapted from [<xref ref-type="bibr" rid="B337-technologies-07-00035">337</xref>].</p> Full article ">Figure 11
<p>Various types of humidity sensors, from capacitive to quartz crystal microbalance sensors. (<bold>a</bold>) Humidity-reactive PTFE sensor using an interdigitated configuration, application on a curved surface and performance comparison before and after NaOH treatment. Adapted from [<xref ref-type="bibr" rid="B485-technologies-07-00035">485</xref>]. (<bold>b</bold>) GO hydrophilic quartz crystal microbalance humidity sensor and performance characterisation. Adapted from [<xref ref-type="bibr" rid="B490-technologies-07-00035">490</xref>].</p> Full article ">Figure 12
<p>Various typologies of chemical sensors. (<bold>a</bold>) Photo-induced room temperature gas sensor using an IGZO thin film transistor [<xref ref-type="bibr" rid="B36-technologies-07-00035">36</xref>]. (<bold>b</bold>) CNTs field effect transistor for enzymatic acetylcholinesterase detection [<xref ref-type="bibr" rid="B506-technologies-07-00035">506</xref>]. (<bold>c</bold>) <inline-formula><mml:math id="mm1541" display="block"><mml:semantics><mml:mrow><mml:mi mathvariant="normal">S</mml:mi><mml:mi mathvariant="normal">n</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> flexible sensor, inkjet fabricated and performance with two channel width configurations [<xref ref-type="bibr" rid="B507-technologies-07-00035">507</xref>].</p> Full article ">Figure 13
<p>Various types of electropotential electrodes and a sensor acquisition system. (<bold>a</bold>) Washable textile electrode for ECG monitoring [<xref ref-type="bibr" rid="B355-technologies-07-00035">355</xref>]. (<bold>b</bold>) Tape-free electronic tattoo which performs ECG sensing [<xref ref-type="bibr" rid="B539-technologies-07-00035">539</xref>]. (<bold>c</bold>) Signal acquisition electronic system where a flexible amplifier array comprised of organic transistors (<italic>left</italic>), electrodes positioned on the rats heart (<italic>middle</italic>), electropotential signals from the rats heart (<italic>right</italic>). Adapted from [<xref ref-type="bibr" rid="B540-technologies-07-00035">540</xref>]. (<bold>d</bold>) A flexible capacitive electrode for ECG sensing (<italic>left</italic>) and the electropotential signals obtained from the electrode (<italic>right</italic>). Adapted from [<xref ref-type="bibr" rid="B541-technologies-07-00035">541</xref>].</p> Full article ">Figure 14
<p>FEA simulations for the optimisation of flexible sensors. (<bold>a</bold>) Strain distribution analysis of a PDMS substrate, bending and twisting 180<inline-formula><mml:math id="mm1542" display="block"><mml:semantics><mml:msup><mml:mrow/><mml:mo>°</mml:mo></mml:msup></mml:semantics></mml:math></inline-formula> (<italic>top</italic>), and circular and hexagonal pillars under biaxial strain (20%) (<italic>bottom</italic>), reprinted with permission from [<xref ref-type="bibr" rid="B22-technologies-07-00035">22</xref>]. (<bold>b</bold>) FEA simulation of an Eddy current sensor, distribution of magnetic fields (<italic>top</italic>) and Eddy currents (<italic>bottom</italic>). Adapted with permission from [<xref ref-type="bibr" rid="B574-technologies-07-00035">574</xref>]. (<bold>c</bold>) Comparison between experimental analysis (<italic>top left</italic>) and numerical model (<italic>top right</italic>) of a sensor under bending at 5 <inline-formula><mml:math id="mm1543" display="block"><mml:semantics><mml:mi mathvariant="normal">m</mml:mi></mml:semantics></mml:math></inline-formula><inline-formula><mml:math id="mm1544" display="block"><mml:semantics><mml:mi mathvariant="normal">m</mml:mi></mml:semantics></mml:math></inline-formula>, and comparison between biaxial stretch (30%) tests (<italic>bottom left</italic>) and simulation (<italic>bottom right</italic>). Reprinted with permission from [<xref ref-type="bibr" rid="B451-technologies-07-00035">451</xref>].</p> Full article ">Figure 15
<p>Flexible circuits allow for the on-site processing of the data acquired by flexible sensors. (<bold>a</bold>) On-site conditioned flexible magnetic sensor comprised of a differential GMR in a Wheatstone configuration, a fully flexible a-IGZO differential amplifier and a TFT acting as a power amplifier [<xref ref-type="bibr" rid="B23-technologies-07-00035">23</xref>]. (<bold>b</bold>) a-IGZO 4:1 multiplexer. Reprinted with permission from [<xref ref-type="bibr" rid="B24-technologies-07-00035">24</xref>]. (<bold>c</bold>) Wireless power transmission on a flexible substrate at a frequency of 125 <inline-formula><mml:math id="mm1545" display="block"><mml:semantics><mml:mi mathvariant="normal">k</mml:mi></mml:semantics></mml:math></inline-formula><inline-formula><mml:math id="mm1546" display="block"><mml:semantics><mml:mi mathvariant="normal">Hz</mml:mi></mml:semantics></mml:math></inline-formula>. Reprinted with permission from [<xref ref-type="bibr" rid="B44-technologies-07-00035">44</xref>]. Circuit schematic including the inductive link and rectification circuitry (<italic>left</italic>). Rectifier circuit consisting of n-type a-IGZO and p-type NiO (<italic>center</italic>). Input and output signals at the emitter and receiver coils, respectively, as well as after rectification (<italic>right</italic>).</p> Full article ">Figure 16
<p>Various examples of artificial skins (e-skin) and potential applications. (<bold>a</bold>) Skin-inspired conformable matrix network for multifunctional sensing with potential applications in humanoid robotics, new prosthetics, human–machine interfaces (HMI), and health-monitoring technologies [<xref ref-type="bibr" rid="B495-technologies-07-00035">495</xref>]. (<bold>b</bold>) Stacked concept of conformal artificial skins. Adapted from [<xref ref-type="bibr" rid="B618-technologies-07-00035">618</xref>]. (<bold>c</bold>) Multi-modal, pressure and temperature sensing artificial skin with health care and artificial intelligence applications. Adapted from [<xref ref-type="bibr" rid="B619-technologies-07-00035">619</xref>]. (<bold>d</bold>) Description of a stacked layers e-skin [<xref ref-type="bibr" rid="B495-technologies-07-00035">495</xref>]. (<bold>e</bold>) A chameleon-inspired, colour changing artificial e-skin with tactile sensing capabilities with many potential applications in wearable devices, artificial prosthetics, health monitoring and smart robots [<xref ref-type="bibr" rid="B415-technologies-07-00035">415</xref>].</p> Full article ">Figure 17
<p>Bio-monitoring, diagnosis and hazards prevention. (<bold>a</bold>) Biomimetic application of flexible electronics for regenerative neuronal cuff implants in biomedical applications [<xref ref-type="bibr" rid="B180-technologies-07-00035">180</xref>]. (<bold>b</bold>) Flexible graphene wearable electrodes for ECG sensing [<xref ref-type="bibr" rid="B357-technologies-07-00035">357</xref>]. (<bold>c</bold>) Flexible polymer transistors for health monitoring applications. Reprinted with permission from [<xref ref-type="bibr" rid="B193-technologies-07-00035">193</xref>]. (<bold>d</bold>) Contact lens capable of detecting the glucose levels in tears in patients affected by diabetes. Adapted from [<xref ref-type="bibr" rid="B320-technologies-07-00035">320</xref>]. (<bold>e</bold>) Unobtrusive ambulatory and wearable EEG sensor [<xref ref-type="bibr" rid="B544-technologies-07-00035">544</xref>]. (<bold>f</bold>) Electronic nose (e-nose) capable of detecting hazardous volatile gases [<xref ref-type="bibr" rid="B512-technologies-07-00035">512</xref>].</p> Full article ">Figure 18
<p>Smart textiles and applications. (<bold>a</bold>) Temperature sensing yarns embroidered into a textile for health monitoring [<xref ref-type="bibr" rid="B444-technologies-07-00035">444</xref>]. (<bold>b</bold>) Optoelectronic near-infrared spectroscopy (NIRS) smart textile which gauges blood oxygenation levels with applications in health care [<xref ref-type="bibr" rid="B439-technologies-07-00035">439</xref>]. (<bold>c</bold>) A temperature sensing sock with applications in fitness and health care. Adapted from [<xref ref-type="bibr" rid="B445-technologies-07-00035">445</xref>]. (<bold>d</bold>) Health-monitoring textile with embedded photodiodes. Adapted from [<xref ref-type="bibr" rid="B630-technologies-07-00035">630</xref>]. (<bold>e</bold>) Tactile-sensing fabric with applications in human-computer interaction (HCI), smartphones and Internet of Things (IoT) devices. Adapted from [<xref ref-type="bibr" rid="B354-technologies-07-00035">354</xref>].</p> Full article ">
22 pages, 1246 KiB  
Review
Integration of Biometrics and Steganography: A Comprehensive Review
by Ian McAteer, Ahmed Ibrahim, Guanglou Zheng, Wencheng Yang and Craig Valli
Technologies 2019, 7(2), 34; https://doi.org/10.3390/technologies7020034 - 8 Apr 2019
Cited by 21 | Viewed by 10675
Abstract
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 [...] Read more.
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
(This article belongs to the Section Information and Communication Technologies)
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<p>The defence-in-depth security model protects assets behind multiple defensive layers, each layer utilising a different strategy, so that if one layer is breached, overall security of the system is not compromised (Adapted from [<a href="#B6-technologies-07-00034" class="html-bibr">6</a>]).</p> Full article ">Figure 2
<p>Fingerprint authentication involves image acquisition, image processing, feature extraction, and subsequent comparison to registered fingerprint features stored in a template database [<a href="#B17-technologies-07-00034" class="html-bibr">17</a>].</p> Full article ">Figure 3
<p>A face-recognition system (FRS) has seven main modules, consisting of enrolment, detection, normalisation, feature extraction, template storage, feature matching, and decision-making stages [<a href="#B19-technologies-07-00034" class="html-bibr">19</a>].</p> Full article ">Figure 4
<p>An iris recognition system will usually consist of eight modules, consisting of acquisition, preprocessing, normalisation, enhancement, feature extraction, template storage, feature matching, and decision-making stages [<a href="#B21-technologies-07-00034" class="html-bibr">21</a>].</p> Full article ">Figure 5
<p>Keyboard dynamics involves an enrolment stage in which a support vector machine (SVM) is used for the learning step and the output is stored in a template database. A verification stage compares the results of the SVM algorithm for a new biometric capture with stored templates. If the decision shows agreement, the data from the new biometric capture replaces the stored template to cater for changes in behavioural characteristics over time [<a href="#B39-technologies-07-00034" class="html-bibr">39</a>].</p> Full article ">Figure 6
<p>Biometric authentication system attack points [<a href="#B44-technologies-07-00034" class="html-bibr">44</a>].</p> Full article ">Figure 7
<p>Discrete cosine transform (DCT)-based data-hiding using the JPEG compression model. A cover image is divided into 8 × 8-sized non-overlapping blocks, each block is applied to DCT in a raster scan order, and the transformed DCT coefficients are quantised using a quantization table. As a result of this process secret data can be embedded [<a href="#B53-technologies-07-00034" class="html-bibr">53</a>].</p> Full article ">Figure 8
<p>Discrete wavelet transform (DWT)-based data-hiding. A cover image is decomposed by a row and column operations into two low frequency (L) and high frequency (H) components. After image decomposition, the embedding algorithm is performed on the sub bands [<a href="#B53-technologies-07-00034" class="html-bibr">53</a>].</p> Full article ">Figure 9
<p>Object-oriented embedding. A proposed skin-based steganography system for hiding medical data in a face image: original image (<b>A</b>), skin blob of the segmented skin area (<b>B</b>), eyes’ centroid detection (<b>C</b>), eye regions (<b>D</b>), distance transformation based on face features (<b>E</b>), construction of ellipses (<b>F</b>), CT scan image (<b>G</b>), CT scan encrypted (<b>H</b>) and stego-image carrying the embedded CT image (<b>I</b>) [<a href="#B58-technologies-07-00034" class="html-bibr">58</a>].</p> Full article ">Figure 10
<p>Distribution of biometric feature types.</p> Full article ">Figure 11
<p>Distribution of steganographic methods.</p> Full article ">Figure 12
<p>Distribution of other methods/applications.</p> Full article ">
16 pages, 805 KiB  
Communication
A Pipeline for Rapid Post-Crisis Twitter Data Acquisition, Filtering and Visualization
by Mayank Kejriwal and Yao Gu
Technologies 2019, 7(2), 33; https://doi.org/10.3390/technologies7020033 - 2 Apr 2019
Cited by 7 | Viewed by 7334
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Multimedia and Cross-modal Retrieval)
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<p>A workflow-level illustration of the data acquisition, filtering and labeling pipeline. The ‘Input Vectors’ are also used in a system called HashViz to visually explore the data, as subsequently described.</p> Full article ">Figure 2
<p>An illustration of pre-processing for a real-world tweet.</p> Full article ">Figure 3
<p>A linear classification plot based on our Las Vegas case study experiments. The blue points represent tweets relevant to the Las Vegas crisis (positive tweets), while the red points are irrelevant to the crisis (negative tweets). Note that the active learning method would not have access to this information and would pick the points closest to the line as the ‘uncertain’ data for the next iteration. The figure expresses the intuition that there is a higher density of mis-classified points closer to the line than further away.</p> Full article ">Figure 4
<p>Active learning results for the Las Vegas shooting case study. AL stands for Active Learning, and P and R are meant to indicate the Precision and Recall metrics respectively.</p> Full article ">Figure 5
<p>Interactively visualizing hashtags using unsupervised text embeddings and t-SNE dimensionality reduction. The units and numbers on the axes are without semantics, and are for visualization purposes only.</p> Full article ">Figure 6
<p>As the user interacts with the plot using simple actions like scrolling and zooming, more details start emerging in an unsupervised fashion.</p> Full article ">
28 pages, 7498 KiB  
Review
Nematic Liquid Crystal Composite Materials for DC and RF Switching
by Mohiuddin Munna, Farhana Anwar and Ronald A. Coutu, Jr.
Technologies 2019, 7(2), 32; https://doi.org/10.3390/technologies7020032 - 2 Apr 2019
Cited by 18 | Viewed by 12203
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Microswitching Technologies)
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<p>The types of nanoparticles commonly used as a dopant in liquid crystal matrices for altering electro-optical properties.</p> Full article ">Figure 2
<p>The typical anisotropic behavior of a liquid crystal: (<b>a</b>) The temperature dependence of dielectric anisotropy in liquid crystals and (<b>b</b>) the frequency dependence of the dielectric constants.</p> Full article ">Figure 3
<p>The cell capacitance and conductance for pure Liquid Crystal (LC) materials as function of the magnitude and frequency of applied voltage; (<b>a</b>) Capacitance modulation; (<b>b</b>) Conductance modulation [<a href="#B23-technologies-07-00032" class="html-bibr">23</a>].</p> Full article ">Figure 4
<p>The electrical conductivity (σ) of a carbon nanotube-doped liquid crystal (LC-CNT) composite versus the weight fraction (wt.%) of carbon nanotubes (CNTs) [<a href="#B39-technologies-07-00032" class="html-bibr">39</a>].</p> Full article ">Figure 5
<p>The electrical conductivity (σ) versus temperature (here, S-N = Solid-to-Nematic transition and N-I = Nematic-to-Isotropic transition); (<b>a</b>) C = 1 wt.%, f = 1 kHz, U = 1 V. The inset shows thet conductivity and temperature evolution with time t; (<b>b</b>) The hysteresis behavior of an LC-CNT composite electrical conductivity in a heating–cooling cycle at different CNT doping concentrations [<a href="#B39-technologies-07-00032" class="html-bibr">39</a>].</p> Full article ">Figure 6
<p>Controlling the orientation of a Multiwalled Carbon Nanotube (MWCNT) by an electric field: The two dashed lines are the copper electrodes to apply the electric field perpendicular to the surface microgrooves (for an initial alignment parallel to them). When the electric filed is above 1.8 Vµm<sup>−1</sup>, the CNTs reorient along the field direction [<a href="#B14-technologies-07-00032" class="html-bibr">14</a>].</p> Full article ">Figure 7
<p>The electric-field-induced reorientation processes of CNTs in LC-CNT composites: (<b>a</b>) From a planar to homeotropic transition for a dielectrically positive LC-CNT dispersion and (<b>b</b>) from a homeotropic to planar transition for a dielectrically negative LC-CNT dispersion.</p> Full article ">Figure 8
<p>The nematic–isotropic transition temperature (T<sub>NI</sub>) versus weight percent of MWCNT: This phase diagram shows chimney type characteristics (OM = Optical Microscopy, DSC = Differential Scanning Calorimetry) [<a href="#B52-technologies-07-00032" class="html-bibr">52</a>].</p> Full article ">Figure 9
<p>(<b>a</b>) Psudonematic domain (PND) formation around a CNT in an LC matrix; (<b>b</b>) PND in the isotropic phase; and (<b>c</b>) PND alignment along the electric field.</p> Full article ">Figure 10
<p>The field-induced resistivity modulation in 3.3 µm cells for (i) pure 5CB in the isotropic phase at T = 40 °C (<b>the blue circle</b>); (ii) CNT (0.005 wt. %) in ethanol at room temperature (<b>the green triangle</b>), and (iii) 5CB/CNT (0.005 wt. %) in the isotropic phase at T = 40 °C (<b>red square</b>) [<a href="#B57-technologies-07-00032" class="html-bibr">57</a>].</p> Full article ">Figure 11
<p>The electrical conductance, σ, as a function of the applied rms (root mean square) field E (f = 1000 Hz) in the nematic phase (T = 50 °C) for the pure LC and LC/graphene (GP) composite (GP concentration 2.29 × 10<sup>4</sup> wt. %.) The reorientation of the graphene flakes with the nematic director is shown in the inset schematics [<a href="#B62-technologies-07-00032" class="html-bibr">62</a>].</p> Full article ">Figure 12
<p>The memory efficiency M as a function of the weight concentration of carbon nanotubes for the different liquid crystals mixtures: (1) p-ethoxy-benzylidene-p-n-butylaniline- (EBBA) based series and (2) MLC6608-based series [<a href="#B64-technologies-07-00032" class="html-bibr">64</a>].</p> Full article ">Figure 13
<p>In-plane electrode configurations in an LC Cell: The black rectangle shows the metal electrodes, and the blue lines are equipotential lines generated by interdigital electrodes for in-plane switching (IPS) and fringe field switching (FFS) cells.</p> Full article ">Figure 14
<p>(<b>a</b>) In-plane gold electrodes having a 1-mm width and separated by 50 µm in a glass wafer; (<b>b</b>) two pairs of electrodes in a cross configuration to inspect the conductivity in mutually perpendicular directions. The inset shows the gap in between the electrodes.</p> Full article ">Figure 15
<p>(<b>a</b>) A dual frequency nematic liquid crystals (DFNLC) mixture at the planar orientation: This mixture is composed of two categories of nematic liquid crystals (NLC), one having transverse (orange arrow) and other having longitudinal (blue arrow) dipole moments; (<b>b</b>) the bistable switching of a DFNLC/CNT composite with two applied frequencies for the ON state and the OFF state.</p> Full article ">Figure 16
<p>The temperature dependence of electrical conductivity in LC-CNT composites (in the nematic to isotropic phases) (<b>a</b>) along (σ<sub>‖</sub>) and (<b>b</b>) perpendicular (σ<sub>⊥</sub>) to the director for the pure compound and LC-gold nanoparticle (GNP) composites with different doping concentrations [<a href="#B49-technologies-07-00032" class="html-bibr">49</a>].</p> Full article ">Figure 17
<p>(<b>a</b>) The conductivity of a 1% GNP in HAT6 as a function of temperature; (<b>b</b>) the I-V sweep of a thin film of 5% GNP in HAT6 in the isotropic phase at 105 °C [<a href="#B93-technologies-07-00032" class="html-bibr">93</a>].</p> Full article ">Figure 18
<p>The self-assembly and reorientation of quantum dots (QD) in a nematic LC matrix by electric field. (The LC molecules are colored blue, and the CdS QDs are sphere with purple color.)</p> Full article ">Figure 19
<p>A schematic of two device structures of a coplanar waveguide (CPW) RF device.</p> Full article ">Figure 20
<p>(<b>a</b>) Capacitance values as a function of cell distances and areas; (<b>b</b>) an LC composite-based RF device model ignoring the parasitic effects.</p> Full article ">Figure 21
<p>The transmission (insertion loss and isolation, denoted by S<sub>21</sub>) characteristic of the proposed RF device; (<b>a</b>) For on state resistance, R<sub>on</sub> = 50 Ω and of state capacitance, C<sub>off</sub> = 0.5 pF; (<b>b</b>) For on state resistance, R<sub>on</sub> = 5 Ω and of state capacitance, C<sub>off</sub> = 0.05 pF.</p> Full article ">Figure 22
<p>The transmission (return loss denoted by S<sub>11</sub>) characteristic of the proposed RF device. (<b>a</b>) For on state resistance, R<sub>on</sub> = 50 Ω and of state capacitance, C<sub>off</sub> = 0.5 pF; (<b>b</b>) For on state resistance, R<sub>on</sub> = 5 Ω and of state capacitance, C<sub>off</sub> = 0.05 pF.</p> Full article ">
10 pages, 583 KiB  
Communication
Text Input in Virtual Reality: A Preliminary Evaluation of the Drum-Like VR Keyboard
by Costas Boletsis and Stian Kongsvik
Technologies 2019, 7(2), 31; https://doi.org/10.3390/technologies7020031 - 2 Apr 2019
Cited by 39 | Viewed by 11826
Abstract
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 [...] Read more.
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
(This article belongs to the Section Information and Communication Technologies)
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<p>The drum-like VR keyboard as implemented in this study (video demonstration: <a href="http://boletsis.net/vrtext/drum/" target="_blank">http://boletsis.net/vrtext/drum/</a>).</p> Full article ">
16 pages, 7036 KiB  
Article
A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network
by Muhammad Fayaz, Habib Shah, Ali Mohammad Aseere, Wali Khan Mashwani and Abdul Salam Shah
Technologies 2019, 7(2), 30; https://doi.org/10.3390/technologies7020030 - 1 Apr 2019
Cited by 50 | Viewed by 8137
Abstract
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 [...] Read more.
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
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<p>Proposed methodology.</p> Full article ">Figure 2
<p>Visualization of two days’ hourly energy consumed data collected from Building-IV.</p> Full article ">Figure 3
<p>Data collection.</p> Full article ">Figure 4
<p>Structure of model M1 for four inputs.</p> Full article ">Figure 5
<p>Structure of model M2 for eight inputs.</p> Full article ">Figure 6
<p>Artificial neural network (ANN) configuration applied on original data.</p> Full article ">Figure 7
<p>ANN configuration applied to normalized data.</p> Full article ">Figure 8
<p>ANN configuration applied to data with statistical moments.</p> Full article ">Figure 9
<p>One day hourly energy consumption prediction using feed forward back propagation neural network (FFBPNN) on simple data.</p> Full article ">Figure 10
<p>Two days of hourly energy consumption prediction using FFBPNN on simple data.</p> Full article ">Figure 11
<p>Five days of hourly energy consumption prediction using FFBPNN on simple data.</p> Full article ">Figure 12
<p>One week hourly energy consumption prediction using FFBPNN on simple data.</p> Full article ">Figure 13
<p>One day hourly energy consumption prediction using FFBPNN on normalized data.</p> Full article ">Figure 14
<p>Two days of hourly energy consumption prediction using FFBPNN on normalized data.</p> Full article ">Figure 15
<p>Five days of hourly energy consumption prediction using FFBPNN on normalized data.</p> Full article ">Figure 16
<p>One-week hourly energy consumption prediction using FFBPNN on normalized data.</p> Full article ">Figure 17
<p>One day hourly energy consumption prediction using FFBPNN on statistical moments data.</p> Full article ">Figure 18
<p>Two days hourly energy consumption prediction using FFBPNN on statistical moments data.</p> Full article ">Figure 19
<p>Five days hourly energy consumption prediction using FFBPNN on statistical moments data.</p> Full article ">Figure 20
<p>One week hourly energy consumption prediction using FFBPNN on statistical moments data.</p> Full article ">
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