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This paper presents a comparative study between two robust estimation approaches: homography matrix-based RANSAC and fundamental matrix-based RANSAC, for outlier elimination in various computer vision applications. The study focuses on the critical task of reliably estimating correspondences across two-view images. The Random Sample Consensus (RANSAC) algorithm is employed to estimate accurate homography and fundamental matrices robustly, even in the presence of outliers. Image datasets are utilized for experimental analysis, including rotations and translations of object. The performance of both methods is compared in terms of accuracy, robustness based on their geometric properties with the different test dataset. Experimental results demonstrate that the homography matrix-based RANSAC method works well with planar movements of the objects, while the fundamental matrix-based RANSAC method performs better with 3D movements of the objects. The paper concludes by discussing the implications of these findings and highlighting the suitability of each approach.
We present an implementation of an improved adder via a spiking neural P system. Our adder processes arbitrary length binary numbers, and thus, is suitable for cryptographic applications. Due to the use of dual-rail logic, the adder is also error tolerant. We present the implementation concept, as well as a simulation model in System-C.
This paper proposes a preliminary microfluidic computing system design for Spiking Neural P systems designed to solve the computational hard problem of Boolean satisfiability SAT by implementing the model studied in our previous work. We have also developed a simulation model for the proposed system and have been doing in silico experiments. An AC voltage applied to facilitated electrodes generates Dielectrophoretic force (DEP) and non-uniform electric field in the microfluidic channels. This DEP serves as the main functioning tool of the proposed biochip to control computation steps.
In this paper, we propose a preliminary microfluidic comput- ing system design for spiking neural P systems. The system is designed particularly to solve the computational hard problem Boolean satisfiabil- ity SAT by implementing the model studied in [10]. We have also devel- oped a computer model for the considering system and have been doing in silico experiments. Dielectrophoretic force (DEP), generated in the microfluidic channels by AC voltage facilitated electrodes, is employed as the main functioning tool of the proposing computing system.
In this paper, we propose a preliminary microfluidic comput- ing system design for spiking neural P systems. The system is designed particularly to solve the computational hard problem Boolean satisfiabil- ity SAT by implementing the model studied in [10]. We have also devel- oped a computer model for the considering system and have been doing in silico experiments. Dielectrophoretic force (DEP), generated in the microfluidic channels by AC voltage facilitated electrodes, is employed as the main functioning tool of the proposing computing system.
