Comprehensive review and future research directions on using various lanthanum-based adsorbents for selective phosphate removal

Clean Technologies and Environmental Policy - Due to the specific affinity of lanthanum (La) toward phosphate over a wide pH range, La compounds such as lanthanum oxide (LO), lanthanum hydroxide...     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.     

User identification using deep learning and human activity mobile sensor data

International Journal of Information Security - The ownership of user actions in computer and mobile applications is an important concern, especially when using shared devices. User identification...     

Utility-based adaptive radio resource allocation in OFDM wireless networks with traffic prioritization

In this letter, a joint transmit scheduling and dynamic sub-carrier and power allocation method is proposed to exploit multi-user diversity in downlink packet transmission in an OFDM wireless network with mixed real-time and non-real-time traffic patterns. To balance efficiency and fairness and to satisfy the QoS requirements of real-time users, we utilize a utility-based framework and propose a polynomial-time heuristic algorithm to solve the formulated optimization problem. The distinguishing feature of the proposed method is that it gives in one shot, the transmission scheduling, the sub-carriers assigned to each user, and the power allocated to each sub-carrier, based on a fair and efficient framework while satisfying the delay requirements of real-time users.     

Towards a framework for large-scale multimedia data storage and processing on Hadoop platform

Cloud computing techniques take the form of distributed computing by utilizing multiple computers to execute computing simultaneously on the service side. To process the increasing quantity of multimedia data, numerous large-scale multimedia data storage computing techniques in the cloud computing have been developed. Of all the techniques, Hadoop plays a key role in the cloud computing. Hadoop, a computing cluster formed by low-priced hardware, can conduct the parallel computing of petabytes of multimedia data. Hadoop features high-reliability, high-efficiency, and high-scalability. The numerous large-scale multimedia data computing techniques include not only the key core techniques, Hadoop and MapReduce, but also the data collection techniques, such as File Transfer Protocol and Flume. In addition, distributed system configuration allocation, automatic installation, and monitoring platform building and management techniques are all included. As a result, only with the integration of all the techniques, a reliable large-scale multimedia data platform can be offered. In this paper, we introduce how cloud computing can make a breakthrough by proposing a multimedia social network dataset on Hadoop platform and implementing a prototype version. Detailed specifications and design issues are discussed as well. An important finding of this article is that we can save more time if we conduct the multimedia social networking analysis using Cloud Hadoop Platform rather than using a single computer. The advantages of cloud computing over the traditional data processing practices are fully demonstrated in this article. The applicable framework designs and the tools available for the large-scale data processing are also proposed. We show the experimental multimedia data including data sizes and processing time.     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

Efficient and practical resource block allocation for LTE-based D2D network via graph coloring

In this paper, we construct a practical framework for efficiently allocating long term evolution (LTE) resource blocks (RB) among the users in a device-to-device (D2D) network. For such network that presumably operates under the LTE cellular network, our aim is to improve the overall throughput of D2D connections using opportunistic or fairness-based approach. Taking the practical considerations into account, our proposed framework allows a number of connections to share a single RB whenever possible, thus utilizing the radio resources. To do so, our solution first identifies a superior set of the interference-free D2D reuse groups via graph modeling and graph coloring approach. In particular, we model a D2D network with a two-overlapping disk graph for which a suitable coloring algorithm is proposed and its performance bound is calculated. Once the reuse groups are known, our solution optimizes the RB allocation among these groups based on their reported channel condition as well as the scheduling criterion, whether it is fairness-based or opportunistic. Through numerical experiments, we show that our solution can significantly improve the throughput performance of a D2D network.     

Morphological,thermal, rheological,and mechanical properties of polypropylene-nanoclay composites prepared from masterbatch in a twin screw extruder

A commercial homopolymer polypropylene was melt blended with commercial nanoclay masterbatch at different concentrations of nanoclay using twin screw extruder (TSE). The influence of three different concentrations (5, 10, and 15 wt%) of the nanoclay on the morphological, thermal, rheological, and mechanical properties was investigated. The morphology of the nanocomposites was characterized using Scanning Electron Microscope (SEM), whereas, the thermal behavior (e.g., melting and crystallization) was characterized using Differential Scanning Calorimetry (DSC). The melt rheology and dynamic mechanical properties were analyzed using a torsional rheometer. Additionally, the tensile properties were characterized as well. The morphological analysis showed that the nanoclay was well distributed in the PP matrix as indicated by the SEM micrographs. The DSC results showed that the presence of nanoclay in the PP matrix increased the degree of crystallinity of PP-nanoclay composites, which reached a maximum at 5 wt% of nanoclay concentration. However, the melting temperature of the PP-nanoclay composites was not affected by the presence of nanoclay particles. In addition, rheological analysis showed that the melt response gradually changed from pseudo-liquid like to pseudo-solid like as the nanoclay concentration increased. Moreover, the storage modulus (G′) increased by increasing nanoclay content. Furthermore, tensile test results showed that the addition of nanoclay leads to a significant enhancement in the mechanical properties of the PP nanocomposites.