Increasing image compression rate using steganography

The goal of image compression is to remove the redundancies for minimizing the number of bits required to represent an image while steganography works by embedding the secret data in redundancies of the image in invisibility manner. Our focus in this paper is the improvement of image compression through steganography. Even if the purposes of digital steganography and data compression are by definition contradictory, we use these techniques jointly to compress an image. Hence, two schemes exploring this idea are suggested. The first scheme combines a steganographic algorithm with the baseline DCT-based JPEG, while the second one uses this steganographic algorithm with the DWT-based JPEG. In this study data compression is performed twice. First, we take advantage of energy compaction using JPEG to reduce redundant data. Second, we embed some bit blocks within its subsequent blocks of the same image with steganography. The embedded bits not only increase file size of the compressed image, but also decrease the file size further more. Experimental results show for this promising technique to have wide potential in image coding.     

SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model

ABSTRACT

The freely available global and near-global digital elevation models (DEMs) have shown great potential for various remote sensing applications. The Shuttle Radar Topography Mission (SRTM) data sets provide the near-global DEM of the Earth’s surface obtained using the interferometry synthetic aperture radar (InSAR). Although free accessibility and generality are the advantages of these data sets, many applications require more detailed and accurate DEMs. In this paper, we proposed a modified and advanced polarimetry-clinometry algorithm for improving SRTM topography model which requires only one set of polarimetric synthetic aperture radar (PolSAR) data. The azimuth and range slope components estimation based on polarization orientation angle (POA) shifts and the intensity-based Lambertian model formed the bases of the proposed method. This method initially compensated for the polarimetry topography effect corresponding to SRTM using the DEM-derived POA. In the second step, using a modified algorithm, POA was obtained from the compensated PolSAR data. The POA shifts by the azimuth and range slopes’ variations based on the polarimetric model. In addition to the polarimetric model, a clinometry model based on the Lambertian scattering model related to the terrain slope was employed. Next, two unknown parameters, i.e. azimuth and range slope values, were estimated in a system of equations by two models from the compensated PolSAR data. Azimuth and range slopes of SRTM were enhanced by PolSAR-derived slopes. Finally, a weighted least-square grid adjustment (WLSG) method was proposed to integrate the enhanced slopes’ map and estimate enhanced heights. The National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA JPL) AIRSAR was utilized to illustrate the potential of the proposed method in SRTM enhancement. Also, the InSAR DEM was employed for evaluation experiments. Results showed that the accuracy of SRTM DEM is improved up to 2.91 m in comparison with InSAR DEM.     

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.     

Morphology, rheology and dynamic mechanical properties of PP/EVA/clay nanocomposites

This paper reports on morphology, rheology and dynamic mechanical properties of polypropylene (PP)/ethylene vinyl acetate (EVA) copolymer/clay nanocomposite system prepared via a single step melt compounding process using a twin screw micro-compounder. Scanning electron microscopic (SEM) investigations revealed that the dispersed phase droplet size was reduced with incorporation of an organo-modified montmorillonite (OMMT). This reduction was more significant in presence of a maleated PP (PP-g-MAH) used as compatibilizer. Phase inversion in the compatibilized blends caused a further decrease in PP droplet size. The OMMT gallery spacing was higher in nanocomposites with EVA as matrix which could be attributed to higher tendency of OMMT nanoparticles towards EVA rather than PP. This enhanced tendency was confirmed by rheological analysis too. Transmission electron microscopy (TEM) results also showed that the majority of OMMT nanoparticles were localized on the interface and within EVA droplets. According to dynamic mechanical analysis, the compatibilized nanocomposites showed higher storage and loss moduli due to better dispersion of OMMT layers. The modulus enhancement of nanocomposites as a function of OMMT volume fraction was modeled by Halpin-Tsai’s-Nielsen expression of modulus for nanocomposites. The results of modeling suggested that the aspect ratio of the intercalated OMMT, in the form of Einstein coefficient (K _E), plays a determining role in the modulus enhancement of nanocomposites.     

Deformation Tracking in 3D Point Clouds Via Statistical Sampling of Direct Cloud-to-Cloud Distances

Dense three-dimensional (3D) point clouds of infrastructure systems, generated from laser scanners or through multi-view photogrammetry, have significant potential as a source of nondestructive evaluation information. The growing maturity of these techniques make them capable of reconstructing photorealistic 3D models with accuracy on the millimeter scale, adequate for inspection and evaluation practices. Manual analysis of these point clouds is often time consuming and labor intensive and does not provide explicit information on structural performance and health conditions, highlighting the need for new techniques to efficiently analyze these models. This paper presents a new 3D point cloud change analysis approach for tracking small movements over time through localized spatial analytics. This technique uses a combination of a direct point-wise distance metric in conjunction with statistical sampling to extract structural deformations. By identifying and tracking these changes, mechanical deformations can be quantified along with the associated strains and stresses. These measurements can then be used to assess both service conditions and remaining system capacity. The results of a series of laboratory experiments designed to test the proposed approach are presented as well. The findings indicate measurement accuracy on the order of +/? 0.2 mm (95% confidence interval), making it suitable for accurate and automatic geometrical analyses and change detection in a variety of infrastructure inspection scenarios. Ongoing work seeks to connect this technique to automated finite element model updating, and to field test the measurement technique.     

A new method for detecting various variants of GoF design patterns using conceptual signatures

Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns’ instances that conform to the standard structure of patterns, and cover more instances of patterns’ variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns’ variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

A nondominated ranked genetic algorithm for bi-objective single machine preemptive scheduling in just-in-time environment

Motivated by just-in-time (JIT) manufacturing, we study the bi-objective scheduling problem of minimizing the total weighted earliness and the number of tardy jobs on a single machine, in which machine idle time and preemption are allowed. The problem is known to be NP-hard. In this paper, we propose a new mathematical model, with nonlinear terms and integer variables which cannot be solved efficiently for medium- and large-sized problems. A method combining the new ranked-based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named nondominated ranking genetic algorithm (NRGA), has been presented to find nondominated solutions in a reasonable time. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The solutions obtained via NRGA are compared against solutions obtained via ε-constraint method in small-sized problems. Experimental results show that the proposed NRGA is competitive in terms of the quality and diversity of solutions in medium- and large-sized problems.     

Interfacially compatibilized LDPE/POE blends reinforced with nanoclay: investigation of morphology,rheology and dynamic mechanical properties

Morphological, melt rheological and dynamic mechanical properties of low-density polyethylene (LDPE)/ethylene–octene copolymer (POE)/organo-montmorillonite (OMMT) nanocomposites, prepared via melt compounding were studied. The XRD traces indicated different levels of intercalated structures for the nanocomposites. Addition of a compatibilizer (PE-g-MA) improved the intercalation process. TEM results revealed existence of clay layers in both phases but they were mainly localized in the elastomeric POE phase. Addition of 5 wt% OMMT to the LDPE/POE blend led to reduction in the size of the elastomer particles confirmed by AFM. The complex viscosity and storage modulus showed little effect of the presence of the clay when no compatibilizer was added. As the extent of exfoliation increased with addition of compatibilizer, the linear viscoelastic behavior of the composites gradually changed specially at low-frequency regions. The interfacially compatibilized nanocomposites with 5 wt% OMMT had the highest melt viscosity and modulus among all the studied nanocomposites and blends. Also, this particular composition showed the best improvement in dynamic storage modulus. The results indicated that clay dispersion and interfacial adhesion, and consequently different properties of LDPE/POE/clay nanocomposites, are greatly affected by addition of compatibilizer.     

Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.