Computational analyses of the growing corpus of three-dimensional (3D) structures of proteins have revealed a limited set of recurrent substructural themes, termed super-secondary structures. Knowledge of super-secondary structures is important for the study of protein evolution and for the modeling of proteins with unknown structures. Characterizing a comprehensive dictionary of these super-secondary structures has been an unanswered computational challenge in protein structural studies. This paper presents an unsupervised method for learning such a comprehensive dictionary using the statistical framework of lossless compression on a database comprised of concise geometric representations of protein 3D folding patterns. The best dictionary is defined as the one that yields the most compression of the database. Here we describe the inference methodology and the statistical models used to estimate the encoding lengths. An interactive website for this dictionary is available at http://lcb.infotech.monash.edu.au/proteinConcepts/scop100/dictionary.html.

We consider the problem of estimating the distance, or range, between two locations by measuring the phase of a sinusoidal signal transmitted between the locations. This method is only capable of unambiguously measuring range within an interval of length equal to the wavelength of the signal. To address this problem signals of multiple different wavelengths can be transmitted. The range can then be measured within an interval of length equal to the least common multiple of these wavelengths. Estimation of the range requires solution of a problem from computational number theory called the closest lattice point problem. Algorithms to solve this problem require a basis for this lattice. Constructing a basis is non-trivial and an explicit construction has only been given in the case that the wavelengths can be scaled to pairwise relatively prime integers. In this paper we present an explicit construction of a basis without this assumption on the wavelengths. This is important because the accuracy of the range estimator depends upon the wavelengths. Simulations indicate that significant improvement in accuracy can be achieved by using wavelengths that cannot be scaled to pairwise relatively prime integers.

We analyze and optimize the secrecy performance of artificial noise (AN) in multi-input single-output wiretap channels with multiple antennas at the transmitter and a single antenna at the receiver and the eavesdropper. We consider two transmission schemes: 1) an on-off transmission scheme with a constant secrecy rate for all transmission periods, and 2) an adaptive transmission scheme with a varying secrecy rate during each transmission period. For the on-off transmission scheme, an easy-to-compute expression is derived for the hybrid outage probability, which allows us to evaluate the transmission outage probability and the secrecy outage probability. For the adaptive transmission scheme where transmission outage does not occur, we derive a closed-form expression for the secrecy outage probability. Using these expressions, we determine the optimal power allocation between the information signal and the AN signal and also determine the optimal secrecy rate such that the effective secrecy throughput is maximized for both transmission schemes. We show that the maximum effective secrecy throughput requires more power to be allocated to the AN signal when the quality of the transmitter-receiver channel or the transmitter-eavesdropper channel improves. We also show that both transmission schemes achieve a higher maximum effective secrecy throughput while incurring a lower secrecy outage probability than existing schemes.

Stochastic routing offers a promising substitute to deterministic routing in wireless sensor networks, mainly due to its randomized nature. Still, most of the stochastic routing algorithms are heuristic, while the optimality for a given degree of randomness is difficult to find. In this paper, we propose a theoretical framework to find the optimal stochastic routing policy that maximizes the packet delivery ratio for a given degree of randomness in routing. Our model consists of directed acyclic networks with unreliable communication links. The degree of randomness is quantified by the global entropy of routing. By employing the Lagrangian approach, a simple iterative algorithm to find the optimal stochastic routing policy is proposed. This can be used as a benchmark for performance comparison of stochastic routing algorithms.

We analyze and optimize the use of artificial noise (AN) for a predefined secrecy rate in wiretap channels with a multi-antenna transmitter, a single-antenna receiver, and a single-antenna eavesdropper. We derive a new closed-form expression for the secrecy outage probability that is independent of the channel realization. Based on this expression, we first optimize the power allocation between the information signal and the AN signal such that the secrecy outage probability is minimized. We then optimize jointly the power allocation and secrecy rate such that the secrecy throughput is maximized. As demonstrated by our analysis, the minimum secrecy outage probability requires more power to be allocated to the AN signal when the quality of the main channel quality or the eavesdropper's channel improves.

New insights into the role of artificial noise in securing communication in a Gaussian multi-antenna fading wiretap channel are presented. An appropriate secrecy-outage-based optimization framework is developed for the Multiple-Input Single-Output Single-Eavesdropper (MISOSE) case to measure the performance of artificial noise. It is assumed that only the legitimate receiver's instantaneous channel state information and the average statistics of the eavesdropper's channel are available at the transmitter. The optimization is based on maximizing the effective secret-message rate constrained by a given maximum secrecy outage criterion. Under this framework, a fundamental investigation is conducted into whether it is worthwhile for the transmitter to allocate any of its available power for artificial noise. By numerically solving the optimization problem, it is demonstrated that there are: (i) scenarios where artificial noise does indeed give significant gains in the secret-message rate, and (ii) scenarios where any amount of power allocation to artificial noise is wasteful in view of the overall performance.

This paper presents an investigation of employing transmit antenna selection for the MIMO wiretap channel. Instantaneous channel state information of the intended receiver and the eavesdropper are assumed to be available at the transmitter. We consider that the eavesdropper employs optimum combining, and investigate the two cases the intended receiver employs (a) optimum combining, (b) sub-optimal combining. We derive analytical expressions of the average secrecy capacities for these two cases in the high-SNR regime. We compare the performance of the proposed antenna selection scheme to a precoding scheme based on generalized singular value decomposition. Our investigations show that the average secrecy capacities increase with the number of antennas when the eavesdropper is at low SNR, but decrease when the eavesdropper is at high SNR.

This paper analyzes the impact of error propagation on the achievable throughput and throughput-delay tradeoff in wireless networks. It addresses the particular class of multihop routing schemes for parallel unicast that achieve a throughput scaling of Θ(n-1/2) per node in a network of n nodes. It is shown that in the finite-block-length case, necessitated by finite decoding memory at the nodes, the guaranteed per-node throughput in the network cannot scale better than o (n-r) per node for any r > 0. This bound on the guaranteed per-node throughput is tighter than the O (1/n) bound shown previously. Instead of focusing on the probability of error for each link, which is intractable, an approach of bounding mutual information is employed to show tight results on the achievable throughput and throughput-delay tradeoffs. It is shown that for multihop transmission protocols, error propagation leads to significant changes in the tradeoff between the throughput T(n) and the delay D(n), compared to previous results. The best known scaling behavior is only D(n) = Θ (n (log n) T(n)) under maximum throughput scaling, where the block length required scales as Ω (log n). When decoding memory at nodes is constrained to be O (log log n), the achievable tradeoff worsens to D(n) = Θ (n (log n)2T(n)).

A bound on the maximum information transmission rate through a cascade of Gaussian links is presented. The network model consists of a source node attempting to send a message drawn from a finite alphabet to a sink, through a cascade of Additive White Gaussian Noise links each having an input power constraint. Intermediate nodes are allowed to perform arbitrary encoding/decoding operations, but the block length and the encoding rate are fixed. The bound presented in this paper is fundamental and depends only on the design parameters namely, the network size, block length, transmission rate, and signal-to-noise ratio.

This paper presents an investigation of employing transmit antenna selection under secrecy constraints. The transmitter is equipped with multiple antennas while the intended receiver and the eavesdropper have a single antenna each. Instantaneous channel state information of the intended receiver and the eavesdropper are assumed to be available at the transmitter. We derive an analytical expression for the average secrecy rate, in terms of the number of available transmit antennas and the average signal-to-noise ratios at the intended receiver and the eavesdropper. We compare the performance of the antenna selection scheme, as revealed by our analysis, to a precoding scheme based on Generalized Singular Value Decomposition (GSVD) and characterize the loss in the secrecy capacity. In addition, we also derive an expression for the average secrecy capacity for the high SNR regime. From our investigation of the proposed system, we find that the increase of the average secrecy rate with the number of transmit antennas is sub-logarithmic.

Consider an intermittently-connected mobile ad-hoc network with a single source/destination aided by n mobile relay nodes each of which has a finite storage buffer. In this paper we develop, for the first time, an analysis of the steady-state performance of multihop routing in such a network with a general mobility model and characterize it in terms of throughput and transmission-cost overhead. We investigate whether multihop routing has any potential for improvement over two hop routing. We show that analytical models for performance under multihop can be obtained by employing queuing-theoretic techniques and embedded-Markov-chain identification. The solution offered is in the form of non-linear steady-state equations which can be efficiently solved iteratively. The key outcome of this work is that multihop can indeed improve upon two-hop routing in the finite-buffer regime, by means of mitigating the reduction in throughput caused by limited storage (leading to blocking/saturation of buffers). However, the improvement in throughput diminishes as the buffer size grows, and comes at the cost of additional relay-to-relay transmissions.

We consider the root lattice$A_{n}$and derive explicit recursive formulas for the moments of its Voronoi cell. These formulas enable accurate prediction of the error probability of lattice codes constructed from$A_{n}$.

New results on the achievable trade-off between the per-node throughput T(n) and the average delay D(n) in a static wireless network of n nodes are presented for physical link models. For links modeled by channels with additive white Gaussian noise with power-law attenuation, a trade-off of only D(n) = ⊖ (n (log n) T(n)) is guaranteed for T(n) = ⊖(n-½). This follows from showing that there is significant information loss in the network due to error propagation, unless the length of the channel code employed is sufficiently high. Constraining the block length to be bounded yields worse trade-offs: only ⊖(n (log n)2T(n)) is guaranteed for optimal throughput, provided there is rich fading diversity.

The problem of computing the throughput capacity of general unicast in a Delay-Tolerant Network under the finite-buffer regime is addressed in this paper. A sparse mobile wireless network deployed on an M times M square-grid is considered, wherein m unique mobile source/destination pairs communicate with the aid of n mobile relay nodes using the store, carry, and forward paradigm. Each mobile relay node is equipped with a finite storage. Thus, several source/destination pairs contend for limited network resources. Under this setup, the throughput achieved per source-destination pair at steady-state node-mobility is analyzed, incorporating practical considerations such as node-to-node contention and finite communication range, for a class of two-hop relay protocols. In addition, two different approaches for buffer management are considered. The paper shows that, using a novel approach based on embedded Markov-chains, accurate analysis of the throughput can be achieved for the above model, as it is validated by simulations. A significant conclusion of this work is that considerable throughput improvements can be achieved by judicially managing the relay-node buffer-space.

Consider a mobile network wherein nodes are confined to move and communicate in a given area. The network is assumed to be sparse, wherein a direct communication path from a source node via multiple hops to a destination node almost never exists. The nodes resort to storing, carrying, and forwarding packets when a contact occurs, as a means of communication. This paper investigates the question of computing the throughput capacity of the resulting network, in other words, the rate at which a source node can send packets to a destination node using the other nodes in the network as relays. It proposes an accurate generalized framework valid for any mobility model that exhibits stationarity. The framework uses the embedded Markov-Chain approach using which the capacity of such a network can be accurately determined by computing certain well-defined characteristic parameters from the mobility model. Constraints posed by limited node storage and contention between nodes for the wireless channel are also considered in order to obtain a realistic model for the throughput. The paper also illustrates the proposed framework under two specific cases: the random walk, random waypoint, and restricted random waypoint mobility models, and validates the same using simulations.

Modeling the fundamental limits of communication in intermittently connected mobile wireless scenarios is an important problem in understanding such networks. Real-world deployments such as delay tolerant networks (DTNs) exhibit such intermittent connectivity. In addition, the effect of finite buffers in such communication paradigms requires investigation. This paper deals with the problem of modeling the throughput and message delivery delay in networks modeled by nodes performing a random walk on a grid-graph. In addition to finite buffer effects, features such as channel access constraints are introduced in the model , presenting a fairly realistic networking scenario. By means of identifying simple, finite-state embedded Markov chains in such mobile systems, closed-form solutions for the problems addressed may be obtained.

Energy efficiency is a very critical consideration in the design of low cost sensor networks which typically have fairly low node battery life. This raises the need for providing periodic sleep cycles for the radios in the sensor nodes. Keeping sensors in sleep state also implies that node to sink communication incurs certain delays and there exists a threshold on the duty cycling for the communication delay to be bounded, giving rise to an upperbound on the lifetime of the network i.e., the time until at least one node in the network is able to communicate its sensed data to the sink. This paper aims at establishing tight analytical bounds on the sleeping probabilities of nodes and on the achievable lifetime of wireless sensor networks in a very generic setting. Bounds on the sleeping probability need to be satisfied for proper network functionality. Further, an energy efficient deployment scheme is suggested wherein the battery power depletion is fairly uniformly deployed throughout the network. This scheme makes use of the availability of low power auxiliary channel listening radio. With this scheme, we shown that an improvement in lifetime by a factor of O(√n overlog n) over uniform distribution of nodes is achievable, where n is the number of nodes in the network. We also show that the throughput capacity of the network is also improved by the same factor. We show also that the maximum lifetime of the network is bounded above by O(n3/2 over √log n). Further, the accuracy of our analysis is verified by the simulation results presented.

Temporal and spatial correlation in the sensed data in wireless distributed sensor networks gives room for better energy efficiency in the network. Several data aggregation schemes have been suggested in the literature. However a clear-cut solution which quantitatively describes most energy-efficient routing scheme is still lacking. In this paper, we propose a novel, generalized clustering-based aggregation scheme, called "annular slicing-based clustering (ASC)" and show that by varying the cluster size and the distribution of clusters in the deployment area, one can approach the most energy-efficient aggregation scheme. Analytical expressions for the optimal cluster size and distribution have been arrived at, for a specific correlation model and a cost function based on the Euclidean distance traversed by the transmitted data. With the help of numerical simulation, it has been found that the proposed aggregation technique can achieve optimality over a wide range of correlation