Patrick Greussay

Learn More
The relevance of biological materials and processes to computing-alias bioputing-has been explored for decades. These materials include DNA, RNA and proteins, while the processes include transcription, translation, signal transduction and regulation. Recently, the use of bacteria themselves as living computers has been explored but this use generally falls(More)
We propose a critical improvement of the LEACH (Low-Energy Adaptive Clustering Hierarchy) routing protocol for the optimization of the energy consumption as well as memory occupation of Wireless Sensor Network (WSN). Our protocol LEACH-M uses a cascade of clustering algorithms, every step of which chooses the next one. We expect at best an improvement of 5%(More)
In recent years, Mobile sensor networks (MSNs) are significantly developed in various areas of applications. The most important feature of MSN is that the sensor nodes are small size, limited processing, low power and can able to change their location. In mobility, life time of the sensor nodes is the most critical parameter. Designing the energy efficient(More)
We propose a case study where a familiar but very complex and intrinsically woven bio-computing system--the blood clotting cascade--is specified using methods from software design known as object-oriented design (OOD). The specifications involve definition and inheritance of classes and methods and use design techniques from the most widely used(More)
The relevance of certain biological materials and processes to computing or bioputing has been explored for decades. These materials include DNA, RNA, enzymes and other proteins whilst the processes include transcription and translation (as well as the control of these processes by protein and by small RNA) and signal transduction. Recently, other(More)
—This paper presents a method for the extraction of blood vessels from fundus images. The proposed method is an unsupervised learning method which can automatically segment retinal blood vessels based on an adaptive random sampling algorithm. This algorithm consists in taking an adequate number of random samples in fundus images, and all the samples are(More)