Investigation of zeolitic imidazolate frameworks–derived carbon nanotubes thin film in solar vapor generation

Journal of Porous Materials - Solar vapor generation is considered a green and practical approach to take advantage of solar energy as a renewable source and provide pure water. However, developing...     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

A novel image encryption based on hash function with only two-round diffusion process

In this paper, a novel algorithm for image encryption based on hash function is proposed. In our algorithm, a 512-bit long external secret key is used as the input value of the salsa20 hash function. First of all, the hash function is modified to generate a key stream which is more suitable for image encryption. Then the final encryption key stream is produced by correlating the key stream and plaintext resulting in both key sensitivity and plaintext sensitivity. This scheme can achieve high sensitivity, high complexity, and high security through only two rounds of diffusion process. In the first round of diffusion process, an original image is partitioned horizontally to an array which consists of 1,024 sections of size 8 × 8. In the second round, the same operation is applied vertically to the transpose of the obtained array. The main idea of the algorithm is to use the average of image data for encryption. To encrypt each section, the average of other sections is employed. The algorithm uses different averages when encrypting different input images (even with the same sequence based on hash function). This, in turn, will significantly increase the resistance of the cryptosystem against known/chosen-plaintext and differential attacks. It is demonstrated that the 2D correlation coefficients (CC), peak signal-to-noise ratio (PSNR), encryption quality (EQ), entropy, mean absolute error (MAE) and decryption quality can satisfy security and performance requirements (CC <0.002177, PSNR <8.4642, EQ >204.8, entropy >7.9974 and MAE >79.35). The number of pixel change rate (NPCR) analysis has revealed that when only one pixel of the plain-image is modified, almost all of the cipher pixels will change (NPCR >99.6125 %) and the unified average changing intensity is high (UACI >33.458 %). Moreover, our proposed algorithm is very sensitive with respect to small changes (e.g., modification of only one bit) in the external secret key (NPCR >99.65 %, UACI >33.55 %). It is shown that this algorithm yields better security performance in comparison to the results obtained from other algorithms.     

Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users

Accurate and reliable position determination is a vital component in Global Positioning System (GPS). GPS positioning errors occur from the cumulative effects of receiver, satellite and atmosphere, and also due to the U.S. military intentionally such as Selective Availability (SA). In order to improve the accuracy of positions provided by GPS additional correction information may be used, such as Differential GPS (DGPS) or other sensors to enhance position reliability. The DGPS has the problem of slow updates. To overcome this limitation, DGPS corrections prediction has been proposed. The ability of Neural Networks (NNs) to discover nonlinear relationships in input data makes them ideal for modeling nonlinear dynamic systems. The Wavelet Neural Network (WNN) employing nonlinear wavelet basis function, which are localized in both the time and frequency space, has been developed as an alternative approach to nonlinear fitting problem. Particle Swarm Optimization (PSO), a global optimization method, is used to train the WNN. In this paper, a WNN trained by a PSO algorithm is proposed for DGPS corrections prediction in single-frequency GPS receivers. Experimental results show the feasibility and effectiveness of the proposed method. The results are analyzed and compared with WNN trained by Back Propagation (BP) algorithm. The experimental results show that WNN, trained by the PSO algorithm, is able to reduce RMS errors to less than 1 m with SA on and 0.6 m with SA off.     

Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory

The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.     

Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer’s experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8-13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.     

Quantitative characterization of carrier injection across metal–organic interfaces using Bardeen theory

We have investigated the contact between a metal and an organic/polymeric (o/p) material and we have introduced a relation for carrier injection using Bardeen theory. A series of narrow barriers is considered in the semiconductor side to account for the localized nature of the carriers in the o/p material. As an application of the model, we have calculated the hopping rate of carriers in terms of the contact parameters. Also, we have discussed the hopping of carriers deep into the organic dielectric. Finally, we have explored the hopping rate in practical contacts between polyfluorene-based polymers and different electrodes.     

Exploring the roles of portfolio steering committees in project portfolio governance

Governing project portfolios engages different actors in organizations including portfolio managers, portfolio officers, top managers, and others who may team up as portfolio steering committees. While portfolio steering committees play a regular role in project portfolio governance, their role is still unclear. Through an in-depth multiple case study in three Danish companies, the roles of portfolio steering committees were explored. The results showed that portfolio committees may play three distinct roles in portfolio governance: a) communication and consolidation role, b) negotiation role, and c) decision making role. The results challenged the notion that portfolio committee meetings are the place and time to make collective decisions on the portfolio, rather, portfolio steering committees might unintentionally become or intentionally be used for other purposes than decision making. The empirical evidence of the study proposes that these three roles might be related to two governance design factors: frequency and duration of committee meetings.