Real-time processing and compression of DNA microarray images.

In this paper, we present a pipeline architecture specifically designed to process and compress DNA microarray images. Many of the pixilated image generation methods produce one row of the image at a time. This property is fully exploited by the proposed pipeline that takes in one row of the produced image at each clock pulse and performs the necessary image processing steps on it. This will remove the present need for sluggish software routines that are considered a major bottleneck in the microarray technology. Moreover, two different structures are proposed for compressing DNA microarray images. The proposed architecture is proved to be highly modular, scalable, and suited for a standard cell VLSI implementation.     

An adaptive LSB matching steganography based on octonary complexity measure

Adaptive steganography methods tend to increase the security against attacks. Most of adaptive methods use LSB flipping (LSB-F) for embedding part of their algorithms. LSB-F is very much vulnerable against simple steganalysis methods but it allows the adaptive algorithms to be extractable at the receiver side. Use of LSB matching (LSB-M) could increase the security but extraction of data at the receiver is difficult or, in occasions, impossible. There are numerous attacks against LSB-M. In this paper we are proposing an adaptive algorithm which, unlike most adaptive methods, uses LSB-M as its embedding method. The proposed method uses a complexity measure based on a local neighborhood analysis for determination of secure locations of an image. Comparable adaptive methods that use LSB-M suffer from possible changes in the complexity of pixels when embedding is performed. The proposed algorithm is such that when a pixel is categorized as complex at the transmitter and is embedded the receiver will identify it as complex too, and data is correctly retrieved. Better performance of the algorithm is shown by obtaining higher PSNR values for the embedded images with respect to comparable adaptive algorithms. The security of the algorithm against numerous attacks is shown to be higher than LSB-M. Also, it is compared with a recent adaptive method and is proved to be advantageous for most embedding rates.     

Real time fractal image coder based on characteristic vector matching

Fractal coding algorithm has many applications including image compression. In this paper a classification scheme is presented which allows the hardware implementation of the fractal coder. High speed and low power consumption are the goal of the suggested design. The introduced method is based on binary classification of domain and range blocks. The proposed technique increases the processing speed and reduces the power consumption while the qualities of the reconstructed images are comparable with those of the available software techniques. In order to show the functionality of the proposed algorithm, the architecture was implemented on a FPGA chip. The application of the proposed hardware is shown in image compression. The resulted compression ratios, PSNR error, gate count, compression speed and power consumption are compared with the existing designs. Other applications of the proposed design are feasible in certain fields such as mass–volume database coding and also in video coder’s block matching schemes.     

Visual sensor network lifetime maximization by prioritized scheduling of nodes

Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable.The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations.     

Effects of rarefaction,viscous dissipation and rotation mode on the first and second law analyses of rarefied gaseous slip flows confined between a rotating shaft and its concentric housing

A parametric analytical study is carried out to scrutinize the mechanism of fluid flow, heat transfer and entropy generation in a low-speed rarefied gaseous flow confined between a shaft and its concentric housing, i.e., the cylindrical Couette flow. In the first law analysis, closed form solutions for the radial temperature profiles are obtained by incorporating the calculated velocity distribution into the energy equation. The derivations for three thermal cases, which are founded on imposing different thermal conditions, namely, the Uniform Heat Flux (UHF) and the Constant Wall Temperature (CWT) boundary conditions, are presented. In the second law analysis, the contributions of thermal diffusion and fluid friction irreversibility to the total entropy generation in the micro domain are illustrated, and the relevant expressions for the Bejan number and the entropy generation number as well as the average entropy generation rate are derived. Finally, the variations of major variables with influential parameters such as the Knudsen number, the Brinkman number and rotation mode are investigated to elucidate the associated effects of rarefaction phenomenon, viscous dissipation and geometric condition on the characteristics of the flow.     

The morphology and corrosion resistance of electrodeposited Co‐TiO2 nanocomposite coatings

Co‐TiO₂ nanocomposite coatings with various contents of TiO₂ nanoparticles were prepared by electrodeposition in Co sulfate plating bath containing TiO₂ nanoparticles. The influence of the TiO₂ nanoparticles concentration in the bath, of the current density and of sodium dodecyle sulfate (SDS) as anionic surfactant on the morphology, composition, texture, roughness, and microhardness of the coatings was investigated. The morphology and composition of coatings were studied by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The phase structure of coatings was analyzed by X‐ray diffraction (XRD). The results showed that the maximum codeposition of TiO₂ nanoparticles in Co matrix was around 4.5 vol% obtained in 60 g/L TiO₂ in the bath, 30 mA/cm² and 0.15 g/L SDS. The microhardness of coatings was increased up to 504 Hv by increasing TiO₂ concentration in the bath to 60 g/L TiO₂. The electrochemistry tests including potentiodynamic polarization and impedance spectroscopy revealed that by addition of TiO₂ into Co matrix, the corrosion current density, polarization resistance, and charge transfer resistance of Co‐TiO₂ coating were increased compared with Co coating.     

New approach to bidding strategies of generating companies in day ahead energy market

In the restructured power systems, generating companies (Genco) are responsible for selling their product in the energy market. In this condition, the question is how much and for what price must each Genco generate to maximize its profit. Therefore, this paper intends to propose a rational method to answer this question. In the proposed methodology, the hourly forecasted market clearing price (FMCP) is used as a reference to model the possible and probable price strategies of Gencos. The forecasted price is the basis of the bidding strategies of each Genco, which can be achieved by solving a bi-level optimization problem using GAMS (general algebraic modeling system) language. The first level, called upper sub-problem is used to maximize the individual Genco’s payoffs for obtaining the optimal offered quantity of Gencos. The second one, hereafter called the lower sub-problem uses the results of the upper sub-problem and minimizes the consumer’s payment with regard to the technical and network constraints, which leads to the awarded generation of the Gencos. Similar to the other game problems, the Nash equilibrium strategies are the optimum bidding strategies of Gencos. A six bus system is employed to illustrate the application of the proposed method and to show its high precision and capabilities.