Journal d'informatique et de biologie des systèmes

This paper proposes the edge-directed unsharp masking sharpening (EDUMS) method and demonstrates the method’s ability to provide single-image resolution enhancement. The EDUMS method implements an efficient single-image, super-resolution process by synergizing edge-directed information and unsharp masking sharpening. This study designs a simple and robust edge information detection process for unsharp masking sharpening that is used to produce a clear edge structure and vivid details of high-resolution images with minimal ringing and jaggy artifacts. This method is a non-iterative process that is computationally efficient for use in a real-time integrated circuit design implementation.

Spectrum is a natural resource of communication path and it is main entity of wireless communication. At present maintain the spectrum secrecy is a big problem because of exponential growth of users and devices of wireless communication. Spectrum have two types of user first one is primary i.e. licenced and other one is secondary i.e. unlicensed user. Secondary user doesn’t need any license to operate but primary user needs to license to operate in a fix geographical area with a fixes time duration. The entire time spectrum is underutilized. Spectrum has limited frequencies so we can’t increase spectrum frequencies but we try to improve spectrum efficiency by the help of different technologies and methodologies. Cognitive radio, Software defined Radio and Spectrum sharing technique play an important role to improve spectrum efficiency but interference, false alarm and low detection is a problem that can reduce by MIMO technology. In this paper we discuss about cognitive radio network and Software Defined Radio technology to improve the performance of wireless communication and MIMO technology to reduce interference and false alarm and improve probability of detection to build future generation of wireless communication.

Viruses are the most abundant biological entities and infectious agents present in almost every ecosystem on the planet. Yet our understanding of how viral-mediated gene transfer and metabolic reprogramming influence the evolutionary history of their hosts and microbial communities remains poor at best. At the same time, identifying and modeling the community dynamics of viruses from the environment through conventional plaque assays is complicated because less than one percent of microbial hosts have been cultivated in vitro. Computational methods in metagenomics and phage isolation techniques have limitations in identifying the uncultured hosts of most viruses. Moreover, the model system-based measurements derived from such techniques rarely reflect the network properties of natural microbial communities. To address these problems, development of high-throughput, massively parallel sequencing approaches that do not rely on cultivation to identify specific virus-host relations such as single-cell genomic sequencing (SCGS) has become critical. SCGS has advanced our capacity to understand the genomic and transcriptomic diversity that occurs during viral-host interactions in an individual uncultured host. Here, we review the major technological and biological challenges and the breakthroughs achieved, describe the remaining challenges, and provide a glimpse into the recent advancements.

Security Testing is the essential method of any information System and this method is used to detect flaws in the security measures in an Information System which protect the data from an unauthorized access. Passing through the Security testing method does not ensure that flaws are not present in the System. Python is a new emerging Programming language. This research paper looks into the tool named Scapy which is based on Python language; lists out some vital commands, explanation with examples and uses of Security Testing. This research paper, being introductory one tries to give a brief description and understandable usage of the security Testing tool.

Hydrogen is a good candidate for the next generation fuel with a high energy density and an environment friendly behavior in the energy production phase. Micro-organism based biological production of hydrogen currently suffers low hydrogen production yields because the living cells must sustain different cellular activities other than the hydrogen production to survive. To circumvent this, teams have explored the synthetic assembly of enzymes in-vitro in cell-free systems with specific functions. Such a synthetic cell-free system was recently devised by combining 13 different enzymes to synthesize hydrogen from cellulose or cellobiose with better yield than microorganism-based systems. We used methods based on differential equations calculations to investigate how the initial conditions and the kinetic parameters of the enzymes influenced the productivity of a such system and, through simulations, identified those conditions that would optimize hydrogen production starting with cellobiose as substrate. Further, if the kinetic parameters of the component enzymes of such a system are not known, we showed how, using artificial neural network, it is possible to identify alternative models that account for the rate of production of hydrogen. This work demonstrates how modeling can help in designing and characterizing cell-free systems in synthetic biology. A web-based simulator implementing our differential equations based model is provided freely as a service for noncommercial usage at http://www.bo-protscience.fr/h2.

Many changes have been made in sensor fields which are different for different applications and there are many more which are under development. It is under research to develop sensor nodes which utilize low power and are of low cost. In this paper we have overviewed different data centric protocols and their up gradation. After this there is comparison in some of the latest data centric protocols on different performance metrics that affect the application or wireless sensor network.

Artificial neural networks are often understood as a good way to imitate mind through the web structure of neurons in brain, but the very high complexity of human brain prevents to consider neural networks as good models for human mind;anyway neural networks are good devices for computation in parallel. The difference between feed-forward and feedback neural networks is introduced; the Hopfield network and the multi-layers Perceptron are discussed. In a very weak isomorphism (not similitude) between brain and neural networks, an artificial form of short term memory and of acknowledgement, in Elman neural networks, is proposed.