Bathymetry mapping of static water bodies like lakes is essential for sustainable ecosystem development strategies. However, bathymetry mapping using (i) traditional manual sampling approaches using single-beam echosounder (SBE) has large mapping errors and (ii) performing multi-beam survey is very expensive. Alternatively, performing lawn-mower SBE surveys is time consuming due to limited field-of-view. In order to address the above issues, in this paper, we present an on-the-way sampling approach with Gaussian Process Upper Confidence Bound (GPUCB) algorithm called as OAS-GPUCB that can adaptively sample the lake to minimize the bathymetry error while reducing the distance travelled to achieve a given mapping accuracy. We validate the proposed approach using simulations on actual lake bathymetry maps and also carry out real-world experiments using an Autonomous Surface Vehicle(ASV) with SBE. Further, we compare OAS-GPUCB to lawn-mower, GPUCB, and GPUCB with fixed radius approaches. The results consistently show that the proposed approach can achieve less than 10% bathymetry error while achieving distance reduction of more than 55%compared to the lawn-mower approach, and more than 90% less distance travelled compared to GPUCB and GPUCB with fixed radius approaches. The results shows the general applicability of OAS-GPUCB for bathymetry mapping of water bodies without any prior information maps.

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications . The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission . Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

In multiple hypothesis tracking (MHT) the outcome space is partitioned into a discrete collection of events known as association hypotheses, whose posterior probabilities are calculated. The discrete nature of this problem means that it can be viewed as a classification problem. In this paper we argue that the hypothesis probabilities must obey some bounds from classification theory. These bounds can be used to investigate the correctness of MHT implementations.

The multiple hypothesis tracker (MHT) has historically been considered a gold standard for multi-target tracking. In this paper we show that the key formula for hypothesis probabilities in Reid's MHT can be derived from the modern theory of finite set statistics (FISST) insofar as appropriate assumptions (Poisson models for clutter and undetected targets, no target-death, linear-Gaussian Markov target kinematics) are adhered to.

Without access to GPS and high-quality visual landmarks, many autonomous underwater vehicles (AUV) face a fundamental navigation vs. cost tradeoff: advanced navigation systems that might include an INS, Doppler velocity, or long-baseline acoustics are expensive. Supporting low-cost operations, this work focuses on collaborative positioning for a team of AUV's, given a bathymetric terrain map, and only an altimeter and acoustic modem on each vehicle. The joint localization is performed via decentralized particle filtering, where we extend the usual measurement model to allow received information to modulate the importance function. We investigate the impact on performance of sensor noise, communication interval and number of vehicles. Results are shown for bathymetry maps near St. John's Island, Singapore, and for the Charles River Basin, Boston. In the second case, we ran our algorithm with physical measurements from actual vehicles executing trajectories.

This paper analyzes various receiver schemes for orthogonal frequency division multiplexing (OFDM) transmission in impulsive noise. We consider Rayleigh block-fading and model the noise process by additive white symmetric α-stable noise (AWSαSN). We begin by discussing maximum-likelihood (ML) detection performance of baseband OFDM. Though optimal, the computational cost increases exponentially with the number of carriers. Alternatively, one may evaluate soft-estimates of the transmitted symbol block and employ carrier-wise detection to lower computational complexity. We analyze such schemes under the frameworks of M-estimation and compressed sensing theory. Moving on, we highlight important rules that ensure the passband AWSαSN process is converted to a baseband form suitable for the discussed schemes. Finally, it is shown that linear passband-to-baseband conversion actually reduces the signal-to-noise ratio (SNR) at the receiver and that all these rules may be avoided by applying an estimation scheme directly on the passband samples.

In this paper we analyze design aspects of a single-carrier digital communications receiver in the presence of impulsive noise. We use the additive white symmetric α-stable noise (AWSαSN) to model the channel noise. By introducing passband sampling, efficient constellations and suitable baseband detectors, we show that the uncoded error performance of the conventional (linear) receiver can be enhanced given the real and imaginary components of the transmitted symbol are decoded separately. The performance may be improved further by sacrificing the linearity of the system. Various non-linear estimation and joint-detection schemes are discussed and their error performance analyzed. It is shown that if the receiver bandwidth is large enough, impulsive noise may be effectively countered in a single-carrier communications system.

We tackle the problem of channel estimation in environments that exhibit both sparse, time-varying impulse responses and impulsive noise with Symmetric alpha-Stable (SαS) statistics. Two novel frameworks are proposed for designing online adaptive algorithms that exploit channel sparseness and achieve robust performance against impulses. The first framework generates recursive least-squares (RLS)-type algorithms based on a differentiable cost function that combines robust nonlinear methods with sparse-promoting L0norm regularization. The second framework employs the natural gradient (NG) and incorporates non-linear methods for the channel prediction error as well as the L0norm of the channel taps. From these frameworks, we derive linear and quadratic complexity algorithms. The improved performance of the proposed RLS-type and NG-type algorithms relative to conventional robust algorithms, such as the recursive least M-estimate (RLM) algorithm and the recursive least p-norm (RLP) algorithm, is validated by using extensive computer simulations as well as signal analysis from an underwater acoustic communications experiment. In addition, we discovered that RLM is not robust under specific SαS noise conditions, contrary to the claim in\thinspace. Finally, our results also demonstrate the clear superiority of the NG-type algorithms over their RLS-type counterparts.

Impulsive noise is a non-Gaussian heavy-tailed random process that is encountered in various communication scenarios. If not catered for, a single impulse will corrupt several symbols in an OFDM block. In this paper we analyze the maximum-likelihood (ML) detection error performance of uncoded OFDM. Results are presented for two different models with emphasis on binary and quadrature phase-shift keying (BPSK/QPSK) constellations. As the number of carriers increases, the error performance actually tends towards the Gaussian noise error curve irrespective of the noise impulsiveness. These results provide benchmarks to validate error performance of various schemes in impulsive noise.

The performance of conventional digital communication schemes in the presence of additive white Gaussian noise (AWGN) has been widely studied and optimized. The efficiency of these systems, however, is severely hampered if the channel noise is impulsive. Impulsive noise is non-Gaussian in nature and is modeled well by random processes based on heavy-tailed symmetric α-stable (SαS) distributions. If the noise samples are identical and mutually independent, the additive white SαS noise (AWSαSN) model is used to simulate the channel. As performance is conventionally analyzed at the baseband level, we investigate characteristics of complex baseband noise derived from passband AWSαSN. The baseband noise samples are shown to be mutually independent with identical distribution. The bivariate distribution of each complex noise sample takes on a star-like geometrical configuration. We also investigate the baseband scale parameter as a function of the noise impulsiveness and system parameters.

The conventional additive white Gaussian noise (AWGN) model adequately simulates many noisy environments that hamper the performance of practical digital communication systems. However if the channel noise is impulsive, the approximation this model provides reduces significantly. The AWGN channel may then be replaced by the more general additive white symmetric α-stable noise (AWSαSN) model. When converted to its complex baseband form, the resulting noise for the non-Gaussian AWSαSN case is radically different from its Gaussian counterpart. In this paper we investigate the properties of baseband noise for the general AWSαSN case using conventional passband-to-baseband conversion schemes. The converted noise is generally not isotropic and furthermore the real and imaginary components may be dependent. By varying certain physical parameters we may attain different non-isotropic distributions. Using the variable geometry offered by these distributions, efficient placement of signal points on the constellation map for the quadrature phase-shift keying (QPSK) scheme is proposed. It is shown that efficient placement of signal points significantly improve the uncoded error performance of the system. We plot the bit error rate (BER) and symbol error rate (SER) curves against a signal-to-noise ratio (SNR) measure for a few selected rotated versions of the QPSK scheme.

Algorithms developed with a Gaussian noise assumption perform poorly in impulsive noise, such as that described by the symmetric alpha-stable (SalphaS) distribution. We investigate the performance of antipodal signaling and Viterbi decoding of convolutional codes in SalphaS noise. We demonstrate that the p-norm branch metric is robust in SalphaS noise.

Abstract Summary: Modelling and simulation of complex cellular transactions involve development of platforms that understand diverse mathematical representations and are capable of handling large backend computations. Grid Cellware, an integrated modelling and simulation tool, has been developed to precisely address these niche requirements of the modelling community. Grid Cellware implements various pathway simulation algorithms along with adaptive Swarm algorithm for parameter estimation. For enchanced computational productivity Grid Cellware uses grid technology with Globus as the middleware. Availability: https://www.bii-sg.org/sbg/cellware Contact: [email protected] Supplementary information: User Manual is available as PDF file. Examples are attached to expedite mastering of the software.

UNLABELLED: The intracellular environment of a cell hosts a wide variety of enzymatic reactions, diffusion events, molecular binding, polymerization and metabolic channeling. To transform these biological events into a computational framework, distinct modeling strategies are required. While currently no tool is capable of capturing all these events, progress is being made to create an integrated environment for the modeling community. To address this niche requirement, Cellware has been developed to offer a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. AVAILABILITY: The software is available for free and can be downloaded from http://www.bii.a-star.edu.sg/sbg/cellware