Efficient chaotic-based image cryptosystem with different modes of operation

Multimedia Tools and Applications - This paper proposes a design of 2-D chaotic Baker map for image encryption which utilizes three modes of operations: 1) the cipher block chaining (CBC) mode, 2)...     

Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian’s position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.

     

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’ probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process.     

Biosynthesis of gold nanoparticles using marine bacteria and Box–Behnken design optimization

Gold nanoparticles are exciting materials because of their potential applications in optics, electronics, biomedical, and pharmaceutical fields. In recent years, environmentally friendly, low-cost biosynthesis methods with bio-applicable features have continued to be developed for the synthesis of gold nanoparticles. In the present study, an actinobacterial strain was isolated from the Petrosia ficiformis (Poiret 1798) sponge, which was collected from a marine environment, and the gold nanoparticle synthesis was performed for the first time from the bacteria type belonging to the Citricoccus genus. The synthesis conditions were optimized using the Box–Behnken experimental design, with a statistical method that included three independent variables (temperature, time, and mixture ratio) to affect the synthesis at three levels (+1, 0, and ?1). Accordingly, the conditions proposed for the biosynthesis of gold nanoparticles at the maximum optical density values that are specific for the Citricoccus sp. K1D109 strain were estimated as 35°C temperature, 24?h, and 1/5 mixture ratio (cell-free extract/HAuCl₄?·?3H₂O). When recommended conditions were applied, it was determined that the maximum absorbance of the synthesized gold nanoparticles is 1.258 at 545?nm, and their sizes are in the range of 25–65?nm, according to transmission electron microscopy (TEM) data.     

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.     

A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange

The aim of this study is to construct appropriate portfolios by taking investor’s preferences and risk profile into account in a realistic, flexible and practical manner. In this concern, a fuzzy rule based expert system is developed to support portfolio managers in their middle term investment decisions. The proposed expert system is validated by using the data of 61 stocks that publicly traded in Istanbul Stock Exchange National-100 Index from the years 2002 through 2010. The performance of the proposed system is analyzed in comparison with the benchmark index, Istanbul Stock Exchange National-30 Index, in terms of different risk profiles and investment period lengths. The results reveal that the performance of the proposed expert system is superior relative to the benchmark index in most cases. Additionally, in parallel to our expectations, the performance of the expert system is relatively higher in case of risk-averse investor profile and middle term investment period than the performance observed in the other cases.     

A modified probabilistic neural network for partial volume segmentation in brain MR image.

A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated.     

Synthesis and properties of novel biodegradable poly(ε‐caprolactone)/ poly(ethylene glycol)‐based polyurethane elastomers

Elastomeric polyurethanes with tunable biodegradation properties and suitable for numerous biomedical applications were synthesized via reaction of epoxy‐terminated polyurethanes (EUPs) with 1,6‐hexamethylenediamine as curing agent. The EUPs themselves were prepared from glycidol and isocyanate‐terminated polyurethanes made from poly(ε‐caprolactone) (PCL) or poly(ethylene glycol) (PEG) and 1,6‐hexamethylene diisocyanate. All the polymers were characterized by conventional methods, and their physical, mechanical, thermal, and degradation properties were studied. The results showed that the degradation rate and mechanical properties of the final products can be controlled by the amount of PEG or PCL present in the EUP. Increasing the PEG content causes an increase of hydrolytic degradation rate, and increasing the PCL content improves the mechanical properties of the final products. Evaluation of cytotoxcicity showed nontoxic behavior of the prepared samples, but the cytocompatibility of these polymers needs to be improved. Copyright © 2006 Society of Chemical Industry     

Cold-active enzymes from cold-adapted bacteria

The properties of amylase, lipase and protease, excreted by newly isolated bacteria from snow-covered soil, salmon intestine and crab intestine, have been investigated. One amylase, one lipase, and three proteases have been characterized by shifts in their apparent optimal activities toward low temperatures and by reductions in their activation energy values. The discovered enzymes were rapidly inactivated at temperatures above the optimum (30 to 40°C). These results suggest that the enzymes are cold-active. The best cold-active protease producer, isolated from salmon intestine, has been identified as Flavobacterium balustinum by the analysis of 16S rRNA. The optimal growth temperature of this bacterium was 20°C, but a higher amount of protease activity was present at 10°C.