This paper presents novel reconfigurable semi-systolic array architecture for the Smith-Waterman with an affine gap penalty algorithm to align protein sequences optimized for shorter database sequences. This architecture has been modified to enable hardware reuse rather than replicating processing elements of the semi-systolic array in multiple FPGAs. The proposed hardware architecture and the previously published conventional one are described at the Register Transfer Level (RTL) using VHDL language and implemented using the FPGA technology. The results show that the proposed design has significant higher normalized speedup (up to 125%) over the conventional one for query sequence lengths less than 512 residues. According to the UniProtKB/TrEMBL protein database (release 2015_05) statistics, the largest number of sequences (about 80%) have sequence length less than 512 residues that makes the proposed design outperforms the conventional one in terms of speed and area in this sequence lengths range.

Since their inception almost fifty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems--today, most state-of-the-art speech systems are HMM-based. There have been a number of ways to explain HMMs and to list their capabilities, each of these ways having both advantages and disadvantages. In an effort to better understand what HMMs can do, this tutorial article analyzes HMMs by exploring a definition of HMMs in terms of random variables and conditional independence assumptions. We prefer this definition as it allows us to reason more throughly about the capabilities of HMMs. In particular, it is possible to deduce that there are, in theory at least, no limitations to the class of probability distributions representable by HMMs. This paper concludes that, in search of a model to supersede the HMM (say for ASR), rather than trying to correct for HMM limitations in the general case, new models should be found based on their potential for better parsimony, computational requirements, and noise insensitivity.

In this paper, we consider the protein structure alignment problem, which is a very important problem in molecular biology. Since an outline of protein structure is represented by a sequence of points in three-dimensional space, this problem is defined as the following geometric pattern matching problem: given two point sequences P and Q in three-dimensions and a real number δ > 0, find a maximum-cardinality set of point pairs such that the distance between each pair is at most δ under the condition that any translation and rotation can be applied to P. Since it is very difficult to solve this problem exactly, we consider algorithms that solve it approximately. We propose three algorithms: BASICALIGN, RANDALIGN and FRAGALIGN whose worst case time complexities are O(n8), O((n7/k3) polylog(n)) and O(n4) respectively, where n denotes the size of larger input structure and k denotes the minimum size of the alignment to be obtained. All of these have the following common framework: a series of initial superpositions are computed; for each of such superpositions, a rough alignment is first computed using a dynamic programming technique, and then it is refined through an iterative improvement procedure which also uses dynamic programming; the best alignment among them is selected as an output. The difference among three algorithms lies in the methods of finding initial superpositions. BASICALIGN, RANDALIGN and FRAGALIGN use exhaustive search, random sampling technique and fragment-based search, respectively. We prove guaranteed approximation ratios (in the sense of distances between point pairs) for theoretical versions of BASICALIGN and RANDALIGN. Practical versions of RANDALIGN and FRAGALIGN were implemented and compared with a previous algorithm using real protein structure data. The experimental results show that FRAGALIGN is best among them and it outputs good alignments quickly.