In order to determine an individual's DNA sequence, sequencing platforms often employ shotgun sequencing where multiple identical copies of the DNA strand of interest are randomly fragmented and then the nucleotide content of the short fragments is determined. Assembly of the long DNA strand from short fragments is a computationally challenging task that has attracted significant amount of attention in recent years. We formulate reference-guided assembly as the inference problem on a bipartite graph and solve it using a message-passing algorithm. The message-passing algorithm does not need to rely on the quality score information which expresses reliability of the short reads. To assess the performance of the proposed methodology, we derive an expression for the probability of error of a genie-aided MAP consensus scheme. Simulation results on a Neisseria meningitidis data set demonstrate that the proposed message-passing algorithm performs close to the idealistic MAP consensus scheme.

We develop distributed algorithms for efficient spectrum access strategies in cognitive radio relay networks. In our setup, primary users permit secondary users access to the resource (spectrum) as long as they consent to aiding the primary users as relays in addition to transmitting their own data. Given a pool of primary and secondary users, we desire to optimize overall network utility by determining the best configuration/pairing of secondary users with primary users. This optimization can be stated in a form similar to the maximum weighted matching problem. Given such formulation, we develop an algorithm based on affinity propagation technique that is completely distributed in its structure. We demonstrate the convergence of the developed algorithm and show that it performs close to the optimal centralized scheme.

Performance of DNA sequencing-by-synthesis systems is fundamentally limited by the stochastic nature of the under lying biochemical process. We develop a novel graphical representation of sequencing-by-synthesis which allows for computationally efficient base-calling via message-passing algorithms. Computational studies indicate that the proposed message-passing technique outperforms existing base-calling methods.

This paper applies affinity propagation (AP) to develop distributed solutions for routing over networks. AP is a message passing algorithm for unsupervised learning. This paper demonstrates that AP can be generalized and applied to a wide class of problems in networking. In particular, AP can be used to develop distributed routing mechanisms for networks. Simulation results demonstrate that the proposed schemes compare favorably with the existing methods.

Estimation of the amounts of target molecules in real-time affinity-based biosensors is studied. The problem is mapped to inferring the parameters of a temporally sampled diffusion process. To solve it, we rely on a sequential Monte Carlo algorithm which generates particles using transition density of the diffusion process. The transition density is not available in a closed form and is thus approximated using Hermite polynomial expansion. Simulations and experimental results demonstrate effectiveness of the proposed scheme, and show that it outperforms competing techniques.

We consider the state estimation problem in distributed nonlinear systems with bandwidth constraints. In particular, we focus on the sensor networks with limited communication between the sensor nodes and the fusion center. Two practical bandwidth-saving methods are considered: (1) recursive filtering with quantized innovations, and (2) compressive sampling of sparse signals. For both scenarios, Rao-Blackwellized unscented Kalman filter (RBUKF) based methods are developed. The simulation results demonstrate that the proposed algorithms closely track the original signal.

This paper considers compressed sensing (CS) of time varying signals with quantized innovations (QI). Recently, an MMSE optimal Kalman like particle filter (KLPF) for systems with QI was proposed in. We first present a low complexity sequential implementation of the KLPF algorithm for multiple observations, and then adapt the algorithm to the CS scenario. Three algorithms (SKLPF1, SKLPF2 and SKLPF3) are presented and their performance is compared to the full innovation Kalman filter CS (FIKFCS). The simulation results demonstrate that SKLPF1 and SKLPF2 achieve performance comparable to that of the FIKFCS even for the single-bit quantization scheme.

The wireless standard IEEE 802.15.4 is emerging as a key enabling technology for different wireless sensor network applications. IEEE802.15.4 supports a low data rate wireless sensor network with low power requirement. In this paper, we discuss the performance analysis of IEEE 802.15.4 MAC assuming star topology in the beacon enabled mode. We obtain analytical expressions for channel sensing probabilities and saturation throughput during the contention access period. We have included the packet discard analysis and validated the analytical expressions for these parameters using ns-2 simulation. Further, we also discuss the performance analysis of the MAC when the contention access mechanism of IEEE 802.15.4 is modified to have only one channel sensing. Finally, we discuss adaptation of the frame length for the case with one channel sensing.

UWB is an emerging technology inviting major advances in wireless communication, networking, radar and positioning systems. In this paper, we present novel algorithms for the multiuser detection based on minimum mean square error (MMSE) for a DS-UWB multiuser communication system. The algorithms exploit the inherent multipath diversity and also mitigate the effects of both inter symbol interference(ISI) and multiuser interference (MUI). Simulation results show that the given algorithms perform better than the other known detectors in literature. We also provide closed form expression for the BER results of the above scheme