Parameter estimation of Direction of Arrival (DOA) using deterministic and stochastic computing paradigms is an enabling development for underwater acoustic signal processing beside its applications in the field of seismology, astronomy, earthquake and bio-medicine. In this work, the comparative study between state of the art deterministic and heuristics algorithms is presented for viable DOA estimation for different underwater dynamic objects. A Uniform Linear Array (ULA) of eight hydrophones is used for impinging acoustic waves from far-field targets. In order to evaluate the performance, the viability of innovative statistical indices is utilized to explain. Performance analysis of Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is conducted with standard counterparts including MVDR, MUSIC, ESPRIT and UESPRIT for different number of targets in terms of estimation accuracy, robustness against the number of elements and noise, cumulative distribution function of Root Mean Sqaure Error(RMSE), frequency distribution of the RMSE over the monte carlo trials, the resolution ability and computational complexity in the presence of white Gaussian measurement noise. Crammer Rao Bound (CRB) based analysis is also performed for the validation assessments and results on Monte Carlo simulations depict that the Genetic Algorithm(GA) showed the outperform counterparts on precision, convergence and complexity indices.

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to $O\left(MN\right)$ is achieved by employing $M+N$ CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.

Managing the users multimedia and long-range based demands, the radio over fiber (RoF) mechanism has been introduced recently. RoF mingles the optical and radio communication frameworks to increase mobility and offer high capacity communication networks (CNs). In this paper, a full-duplex RoF-based CN is investigated for the next-generation passive optical network (PON), utilizing wavelength division multiplexing (WDM) technology. The desolations on account of optical and electronic domains (OEDs) are addressed, using dispersion compensation fiber (DCF) and optical and electrical filters, including modulation scheme. The analytical and simulation models are analyzed in terms of phase error (PE), radio frequency (RF), fiber length and input and received powers. The performance of the proposed model is successfully evaluated for 50 km range, −40 to −18 dBm received power, −20 to 0 dBm input power, where below ${10}^{-6}$ bit error rate (BER) is recorded. Thus, this signifies that the presented model exhibits smooth execution against OEDs impairments.

Sequence alignment is a core step in the processing of DNA and RNA sequencing data. In this paper, we present a high performance GPU accelerated set of APIs (GASAL) for pairwise sequence alignment of DNA and RNA sequences. The GASAL APIs provide accelerated kernels for local, global as well as semi-global alignment, allowing the computation of the alignment score, and optionally the start and end positions of the alignment. GASAL outperforms the fastest CPU-optimized SIMD implementations such as SSW and Parasail. It also outperforms NVBIO, NVIDIA's own CUDA library for sequence analysis of high-throughput sequencing data. GASAL uses the unique approach of also performing the sequence packing on GPU, which is over 200× faster than the NVBIO approach. Overall on Tesla K40c GASAL is 10-14× faster than 28 Intel Xeon cores and 3-4× faster than NVBIO with a query length of 100 bases. The APIs are included in an easy to use library to allow integration into various bioinformatics tools.