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Appl. Sci., Volume 7, Issue 10 (October 2017)

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Open AccessArticle UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Appl. Sci. 2017, 7(10), 1101; https://doi.org/10.3390/app7101101
Received: 10 October 2017 / Revised: 18 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
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Abstract
Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of
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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics. Full article
(This article belongs to the Section Computer Science and Electrical Engineering)
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Open AccessArticle A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets
Appl. Sci. 2017, 7(10), 1100; https://doi.org/10.3390/app7101100
Received: 4 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
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Abstract
This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in
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This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners’ viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty―including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators―are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush–Kuhn–Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment. Full article
(This article belongs to the Section Energy)
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Open AccessArticle Amorphous Oxide Thin Film Transistors with Nitrogen-Doped Hetero-Structure Channel Layers
Appl. Sci. 2017, 7(10), 1099; https://doi.org/10.3390/app7101099
Received: 7 September 2017 / Accepted: 17 October 2017 / Published: 24 October 2017
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Abstract
The nitrogen-doped amorphous oxide semiconductor (AOS) thinfilm transistors (TFTs) with double-stacked channel layers (DSCL) were prepared and characterized. The DSCL structure was composed of nitrogen-doped amorphous InGaZnO and InZnO films (a-IGZO:N/a-IZO:N or a-IZO:N/a-IGZO:N) and gave the corresponding TFT devices large field-effect mobility due
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The nitrogen-doped amorphous oxide semiconductor (AOS) thinfilm transistors (TFTs) with double-stacked channel layers (DSCL) were prepared and characterized. The DSCL structure was composed of nitrogen-doped amorphous InGaZnO and InZnO films (a-IGZO:N/a-IZO:N or a-IZO:N/a-IGZO:N) and gave the corresponding TFT devices large field-effect mobility due to the presence of double conduction channels. The a-IZO:N/a-IGZO:N TFTs, in particular, showed even better electrical performance (µFE = 15.0 cm2・V−1・s−1, SS = 0.5 V/dec, VTH = 1.5 V, ION/IOFF = 1.1 × 108) and stability (VTH shift of 1.5, −0.5 and −2.5 V for positive bias-stress, negative bias-stress, and thermal stress tests, respectively) than the a-IGZO:N/a-IZO:N TFTs. Based on the X-ray photoemission spectroscopy measurements and energy band analysis, we assumed that the optimized interface trap states, the less ambient gas adsorption, and the better suppression of oxygen vacancies in the a-IZO:N/a-IGZO:N hetero-structures might explain the better behavior of the corresponding TFTs. Full article
(This article belongs to the Special Issue Thin-Film Transistors)
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Open AccessArticle Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
Appl. Sci. 2017, 7(10), 1098; https://doi.org/10.3390/app7101098
Received: 4 September 2017 / Accepted: 18 October 2017 / Published: 24 October 2017
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Abstract
The study of ultrasound contrast agent imaging (USCAI) based on plane waves has recently attracted increasing attention. A series of USCAI techniques have been developed to improve the imaging quality. Most of the existing methods enhance the contrast-to-tissue ratio (CTR) using the time-frequency
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The study of ultrasound contrast agent imaging (USCAI) based on plane waves has recently attracted increasing attention. A series of USCAI techniques have been developed to improve the imaging quality. Most of the existing methods enhance the contrast-to-tissue ratio (CTR) using the time-frequency spectrum differences between the tissue and ultrasound contrast agent (UCA) region. In this paper, a new USCAI method based on bubble region detection was proposed, in which the frequency difference as well as the dissimilarity of tissue and UCA in the spatial domain was taken into account. A bubble wavelet based on the Doinikov model was firstly constructed. Bubble wavelet transformation (BWT) was then applied to strengthen the UCA region and weaken the tissue region. The bubble region was thereafter detected by using the combination of eigenvalue and eigenspace-based coherence factor (ESBCF). The phantom and rabbit in vivo experiment results suggested that our method was capable of suppressing the background interference and strengthening the information of UCA. For the phantom experiment, the imaging CTR was improved by 10.1 dB compared with plane wave imaging based on delay-and-sum (DAS) and by 4.2 dB over imaging based on BWT on average. Furthermore, for the rabbit kidney experiment, the corresponding improvements were 18.0 dB and 3.4 dB, respectively. Full article
(This article belongs to the Special Issue Ultrafast Ultrasound Imaging)
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Open AccessArticle Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
Appl. Sci. 2017, 7(10), 1097; https://doi.org/10.3390/app7101097
Received: 29 July 2017 / Accepted: 18 September 2017 / Published: 24 October 2017
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Abstract
Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is
[...] Read more.
Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising. Full article
(This article belongs to the Special Issue Smart Healthcare) Printed Edition available
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Open AccessArticle A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
Appl. Sci. 2017, 7(10), 1096; https://doi.org/10.3390/app7101096
Received: 4 September 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
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Abstract
The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by
[...] Read more.
The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject’s data into ’healthy’ and ’pathological’ classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks. Full article
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Open AccessArticle A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
Appl. Sci. 2017, 7(10), 1095; https://doi.org/10.3390/app7101095
Received: 28 August 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 24 October 2017
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Abstract
In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running
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In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR) is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM) to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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Open AccessArticle Effect of ZnO Addition and of Alpha Particle Irradiation on Various Properties of Er3+, Yb3+ Doped Phosphate Glasses
Appl. Sci. 2017, 7(10), 1094; https://doi.org/10.3390/app7101094
Received: 6 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
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Abstract
New Er3+, Yb3+ codoped phosphate glasses with the (98-x) (0.50P2O5-0.40SrO-0.10Na2O) -0.5Er2O3-1.5Yb2O3-xZnO (in mol %) composition were prepared by melting process with up to 10 mol %
[...] Read more.
New Er3+, Yb3+ codoped phosphate glasses with the (98-x) (0.50P2O5-0.40SrO-0.10Na2O) -0.5Er2O3-1.5Yb2O3-xZnO (in mol %) composition were prepared by melting process with up to 10 mol % of ZnO. The impact of the changes in the glass composition on the thermal, optical, structural properties was investigated. Using IR and Raman spectroscopies, we confirmed that the addition of ZnO up to 10 mol % leads to a depolymerization of the network without having a significant impact on the Er3+ and Yb3+ sites. We also discuss the effect of alpha particles irradiation. The glass with 2.5 mol % of ZnO was irradiated with 3 MeV alpha particles and a total fluence of 1012 α/cm2. After irradiation, this glass exhibits surface expansion (measured at ~200 nm, 1.5 months after the irradiation) and an increase in the surface roughness. The alpha particles irradiation is suspected to lead to changes in the spectroscopic properties of the glass. Finally, the photo-response of the glass was found to be reversible. Full article
(This article belongs to the Special Issue Rare-Earth Doping for Optical Applications)
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Open AccessArticle Fracture Initiation of an Inhomogeneous Shale Rock under a Pressurized Supercritical CO2 Jet
Appl. Sci. 2017, 7(10), 1093; https://doi.org/10.3390/app7101093
Received: 24 September 2017 / Revised: 13 October 2017 / Accepted: 16 October 2017 / Published: 23 October 2017
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Abstract
Due to the advantages of good fracture performance and the application of carbon capture and storage (CCS), supercritical carbon dioxide (SC-CO2) is considered a promising alternative for hydraulic fracturing. However, the fracture initiation mechanism and its propagation under pressurized SC-CO2
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Due to the advantages of good fracture performance and the application of carbon capture and storage (CCS), supercritical carbon dioxide (SC-CO2) is considered a promising alternative for hydraulic fracturing. However, the fracture initiation mechanism and its propagation under pressurized SC-CO2 jet are still unknown. To address these problems, a fluid–structure interaction (FSI)-based numerical simulation model along with a user-defined code was used to investigate the fracture initiation in an inhomogeneous shale rock. The mechanism of fracturing under the effect of SC-CO2 jet was explored, and the effects of various influencing factors were analyzed and discussed. The results indicated that higher velocity jets of SC-CO2 not only caused hydraulic-fracturing ring, but also resulted in the increase of stress in the shale rock. It was found that, with the increase of perforation pressure, more cracks initiated at the tip. In contrast, the length of cracks at the root decreased. The length-to-diameter ratio and the aperture ratio distinctly affected the pressurization of SC-CO2 jet, and contributed to the non-linear distribution and various maximum values of the stress in shale rock. The results proved that Weibull probability distribution was appropriate for analysis of the fracture initiation. The studied parameters explain the distribution of weak elements, and they affect the stress field in shale rock. Full article
(This article belongs to the Section Chemistry)
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Open AccessArticle T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM
Appl. Sci. 2017, 7(10), 1092; https://doi.org/10.3390/app7101092
Received: 5 September 2017 / Revised: 9 October 2017 / Accepted: 17 October 2017 / Published: 23 October 2017
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Abstract
With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a
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With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs. Full article
(This article belongs to the Section Mechanical Engineering)
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Open AccessArticle Compact Left-Handed Meta-Atom for S-, C- and Ku-Band Application
Appl. Sci. 2017, 7(10), 1071; https://doi.org/10.3390/app7101071
Received: 10 August 2017 / Accepted: 10 October 2017 / Published: 23 October 2017
Cited by 3 | PDF Full-text (10880 KB) | HTML Full-text | XML Full-text | Correction
Abstract
A new compact left-handed meta-atom for S-, C- and Ku-band applications is presented in this paper. The proposed structure provides a wide bandwidth and exhibits left-handed characteristics at 0°, 90°, 180° and 270° (xy-axes) rotations. Besides, the left-handed characteristics and wide bandwidth of
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A new compact left-handed meta-atom for S-, C- and Ku-band applications is presented in this paper. The proposed structure provides a wide bandwidth and exhibits left-handed characteristics at 0°, 90°, 180° and 270° (xy-axes) rotations. Besides, the left-handed characteristics and wide bandwidth of 1 × 2, 2 × 2, 3 × 3 and 4 × 4 arrays are also investigated at the above-mentioned rotation angles. In this study, the meta-atom is designed by creating splits at the outer and inner square-shaped ring resonators, and a metal arm is placed at the middle of the inner ring resonator. The arm is also connected to the upper and lower portions of the inner ring resonator, and later, the design appears as an I-shaped split ring resonator. The commercially available, finite integration technique (FIT)-based electromagnetic simulator CST Microwave Studio is used for design and simulation purposes. The measured data comply well with the simulated data of the unit cell for 1 × 2, 2 × 2, 3 × 3 and 4 × 4 arrays at every rotation angle. Owing to the effective medium ratio (EMR) of 8.50 at 0° and 180° rotations, the proposed meta-atom structure is compact in size. Moreover, due to the quality factor of 82, the designed meta-atom is flexible for high-performance antenna, filter and sensor applications. Therefore, the meta-atom integrated antenna shows multi frequency bands with the highest peak gain of 5 dBi, which is used as the long distance radio communication frequency. Full article
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Open AccessArticle Hybrid Prediction Model of the Temperature Field of a Motorized Spindle
Appl. Sci. 2017, 7(10), 1091; https://doi.org/10.3390/app7101091
Received: 26 September 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 22 October 2017
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Abstract
The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve
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The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle Characterization and Use of Construction and Demolition Waste from South of Brazil in the Production of Foamed Concrete Blocks
Appl. Sci. 2017, 7(10), 1090; https://doi.org/10.3390/app7101090
Received: 19 September 2017 / Revised: 14 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
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Abstract
The main objective of this study was to evaluate the use of construction and demolition waste (CDW) from the Passo Fundo region of Rio Grande do Sul (RS), Brazil, in the development of aerated foamed concrete. This waste had not yet been characterized
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The main objective of this study was to evaluate the use of construction and demolition waste (CDW) from the Passo Fundo region of Rio Grande do Sul (RS), Brazil, in the development of aerated foamed concrete. This waste had not yet been characterized or even reused. CDW was processed (sieved only), characterized, and used as an aggregate, completely substituting natural sand. The influence of CDW granulometry and the amount of foam upon compressive strength, wet and dry bulk density, water absorption, and the air voids of concrete blocks were determined. Results showed that CDW has regular characteristics for the development of aerated foamed concrete. Compressive strength and density decreased as the amount of foam increased, while water absorption and air voids also increased. Also, CDW that was classified as coarse showed higher compressive strength. On average, CDW medium-sized particles had a higher air void content, while water absorption showed little variation with respect to granulometry. CDW residue from the region of study can be used as aggregate for the development of aerated foamed concrete. However, it must characterized before being used to guarantee the quality of the final product. Full article
(This article belongs to the Section Materials)
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Open AccessArticle A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
Appl. Sci. 2017, 7(10), 1089; https://doi.org/10.3390/app7101089
Received: 13 September 2017 / Accepted: 17 October 2017 / Published: 20 October 2017
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Abstract
The digital time delay integration (digital TDI) technology of the complementary metal-oxide-semiconductor (CMOS) image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high
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The digital time delay integration (digital TDI) technology of the complementary metal-oxide-semiconductor (CMOS) image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE) algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI) capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients. Full article
(This article belongs to the Section Optics and Lasers)
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Open AccessFeature PaperArticle Research on the Mechanical, Thermal, Induction Heating and Healing Properties of Steel Slag/Steel Fibers Composite Asphalt Mixture
Appl. Sci. 2017, 7(10), 1088; https://doi.org/10.3390/app7101088
Received: 13 September 2017 / Revised: 4 October 2017 / Accepted: 4 October 2017 / Published: 20 October 2017
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Abstract
In this paper, steel slag/steel fiber composite asphalt mixture were prepared. The effects of the addition of steel slag and/or steel fibers on the mechanical, thermal, induction heating and healing properties of asphalt mixture were investigated. The results showed that adding steel slag
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In this paper, steel slag/steel fiber composite asphalt mixture were prepared. The effects of the addition of steel slag and/or steel fibers on the mechanical, thermal, induction heating and healing properties of asphalt mixture were investigated. The results showed that adding steel slag and/or steel fibers improves the water stability, particle loss resistance and fracture energy of asphalt mixtures. The addition of steel fibers increased the thermal conductivity and thermal diffusion of the asphalt mixture, and steel slag showed a reverse effect. Steel slag asphalt mixture cooled more slowly than steel fiber asphalt mixture, which is beneficial to crack healing of asphalt mixture. The composite of steel fibers and steel slag can enhance the induction heating speed, heating homogeneity and thus enhance the induction healing ratio of asphalt mixture. It is concluded that steel slag/steel fibers composite asphalt mixture achieves good mechanical and induction healing properties. Full article
(This article belongs to the Special Issue Self-Healing Asphalt)
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