Hardware software co-design of the Aho-Corasick algorithm: Scalable for protein identification?

Pattern matching is commonly required in many application areas and bioinformatics is a major area of interest that requires both exact and approximate pattern matching. Much work has been done in this area, yet there is still a significant space for improvement in efficiency, flexibility, and throughput. This paper presents a hardware software co-design of Aho-Corasick algorithm in Nios II soft-processor and a study on its scalability for a pattern matching application. A software only approach is used to compare the throughput and the scalability of the hardware software co-design approach. According to the results we obtained, we conclude that the hardware software co-design implementation shows a maximum of 10 times speed up for pattern size of 1200 peptides compared to the software only implementation. The results also show that the hardware software co-design approach scales well for increasing data size compared to the software only approach

Hardware accelerated protein inference framework

Protein inference plays a vital role in the proteomics study. Two major approaches could be used to handle the problem of protein inference; top-down and bottom-up. This paper presents a framework for protein inference, which uses hardware accelerated protein inference framework for handling the most important step in a bottom-up approach, viz. peptide identification during the assembling process. In our framework, identified peptides and their probabilities are used to predict the most suitable reference protein cluster for a given input amino acid sequence with the probability of identified peptides. The framework is developed on an FPGA where hardware software co-design techniques are used to accelerate the computationally intensive parts of the protein inference process. In the paper we have measured, compared and reported the time taken for the protein inference process in our framework against a pure software implementation

Evidence Based Navigation in Swarms

A low-complexity, evidence based navigation algorithm for swarms of mobile sensors is presented. It can be effectively used in scenarios where a particular event signature is characterized by a mix of weak signal modalities with certain degrees of intensity, distributed in a local region. The method is based on Dempster-Shafer (DS) evidence theory and enables the mobile nodes to process temporally ordered sensor data and accommodate imprecise information from multi-modal sensors on board. Local decisions are made based on fused evidence triggering an attractive beacon, which in turn draws other agents for further detection and tracking. Simulation results are presented for a multi-modal signal signature tracking scenari

Conditioning and updating evidence

A new interpretation of Dempster–Shafer conditional notions based directly upon the mass assignments is provided. The masses of those propositions that may imply the complement of the conditioning proposition are shown to be completely annulled by the conditioning operation; conditioning may then be construed as a re-distribution of the masses of some of these propositions to those that definitely imply the conditioning proposition. A complete characterization of the propositions whose masses are annulled without re-distribution, annulled with re-distribution and enhanced by the re-distribution of masses is provided. A new evidence updating strategy that is composed of a linear combination of the available evidence and the conditional evidence is also proposed. It enables one to account for the ‘integrity’ and ‘inertia’ of the available evidence and its ‘flexibility’ to updating by appropriate selection of the linear combination weights. Several such strategies, including one that has a probabilistic interpretation, are also provided

Mixed Convective Boundary Layer Mhd Flow Along A Vertical Elastic Sheet

The influence of magnetic field on flow and heat transfer of an electrically conducting fluid at an impermeable elastic sheet is analyzed. The governing nonlinear differential equations are solved analytically via homotopy analysis method. To validate the approximate-analytical method, comparisons are made with the available results in the literature for some special cases and the results are found to be in good agreement. The effects of physical parameters on the flow and temperature fields are analyzed graphically. We could obtain the residual errors E2f=1.3×10-4 and E2θ=8.0×10-4 respectively for velocity and temperature fields only with second-order approximations. The velocity power index and the variable thickness parameters have strong effects on the shear stress and the Nusselt number