We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated. Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.

We consider the problem of power controlled minimum frame length scheduling for TDMA wireless networks. Given a set of one-hop transmission requests, our objective is to schedule them in a minimum number of time slots, so that each slot schedule is free of self-interferences and meets desired SINR constraints. Additionally, the transmit power vector corresponding to each slot schedule should be minimal. We consider two different versions of the problem, a per-slot version and a per-frame version, and develop mixed integer linear programming models which can be used for solving the problems optimally. In addition, we propose a heuristic algorithm for the per-slot version.

We consider the "at least" version of the generalized minimum spanning tree problem (L-GMST). Unlike the MST, the L-GMST is known to be NP-hard. In this paper, we propose an ant colony system based solution approach for the L-GMST. A key feature of our algorithm is its use of ants of different behavioral characteristics, which are adapted over time. Computational results on datasets used in earlier literature indicate that our algorithm provides similar or better results for most of them.

We consider the problem of minimum power bidirectional topology optimization in wireless networks with sectored antennas. We first develop a mixed integer linear programming model for optimal solution of the problem with sectored antennas. Subsequently, we discuss a centralized heuristic algorithm which is based on Kruskal's algorithm for the minimum spanning tree problem. However, unlike Kruskal's algorithm which chooses minimum cost edges from a set of edge weights, our algorithm uses an incremental cost mechanism to select edges. This incremental cost mechanism is motivated by the inherently broadcast nature of the wireless medium. We also discuss a simple branch exchange heuristic which can be used to improve the topology generated by the Kruskal-like algorithm. Simulation results indicate that reasonably good solutions can be obtained using the proposed heuristic algorithms.

In this paper the problem of minimum power multicasting in wireless networks with sectored antennas is considered. For omnidirectional antenna systems, a transmission from node i to node j will also reach all nodes which are closer to i than j. Depending on the network geometry, this strategy can be highly power efficient In ideal sectored antenna systems, however, this phenomenon is sector specific; i.e., only those nodes which are located in the same sector as j will receive the transmission implicitly. Though this might seem an apparent disadvantage, the higher gains associated with directional antennas (as opposed to omni-directional antennas) allow for reduced transmission powers without sacrificing the signal-to-interference ratio at the receiver. In this paper, we first develop a mixed integer linear programming model for optimal solution of the minimum power multicast problem with sectored antennas. Subsequently, we discuss a biologically inspired algorithm for solving the problem to near-optimality at a very reasonable computation time. Experiments on randomly generated 10, 20 and 30-node networks indicate that near-optimal solutions can be obtained using the proposed algorithm.

We consider the problem of maximizing the time-to-first-failure, defined as the time till the first node in the network runs out of battery energy, in energy constrained broadcast wireless networks. We discuss a greedy algorithm and prove that it solves the problem optimally for a broadcast application, in polynomial time, provided the complete power matrix and the battery residual capacities are known.

Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For omni-directional wireless broadcast to a node, all nodes closer will also be reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. It has been shown that this problem is NP-complete. In this paper, we present the r-shrink procedure, a heuristic for improving the solutions obtained using fast sub-optimal algorithms. Specifically, we focus on the low-complexity BIP algorithm and Prim's minimum spanning tree algorithm and show through extensive simulations that better solutions are obtained almost always, with considerably lower tree power, if the proposed procedure is used to improve the trees generated using these algorithms.

We present an outline of reconfigurable processor technologies and design methods with emphasis on an object-oriented approach and on both full and partial dynamic reconfiguration. A specific, broadly applicable architecture for implementing a software reconfigurable network processor for wireless communication applications is presented; a prototype of which is currently operating in the laboratory. This architecture, with its associated object oriented design methods and partial reconfiguration techniques, enables rapid prototyping and rapid implementation of communications and navigation signal processing functions, provides long-life communications infrastructure and results in dynamic operation within networks with heterogeneous nodes, as well as compatibility with other networks. This work builds upon numerous advances in the commercial industry, as well as military software radio developments, to space-based radios and network processing. The development of such radios and the network processor presented here requires the correct combination of processing methods ("objects") to be defined and appropriate dynamic reconfiguration techniques to be developed as a function of system goals and operating parameters.

The use of radial basis function (RBF) networks and least squares algorithms for acquisition and fine tracking of NASA's 70-m-deep space network antennas is described and evaluated. We demonstrate that such a network, trained using the computationally efficient orthogonal least squares algorithm and working in conjunction with an array feed compensation system, can point a 70-m-deep space antenna with root mean square (rms) errors of 0.1-0.5 millidegrees (mdeg) under a wide range of signal-to-noise ratios and antenna elevations. This pointing accuracy is significantly better than the 0.8 mdeg benchmark for communications at Ka-band frequencies (32 GHz). Continuous adaptation strategies for the RBF network were also implemented to compensate for antenna aging, thermal gradients, and other factors leading to time-varying changes in the antenna structure, resulting in dramatic improvements in system performance. The systems described here are currently in testing phases at NASA's Goldstone Deep Space Network (DSN) and were evaluated using Ka-band telemetry from the Cassini spacecraft.

A method for optimal adaptive setting of pulse-position-modulation pulse detection thresholds, which minimizes the total probability of error for the dynamically fading optical free space channel, is presented. The threshold's adaptive setting, in response to varying channel conditions, results in orders of magnitude improvement in probability of error, as compared to use of a fixed threshold. The adaptive threshold system itself is based on a robust channel identification system that uses average signal strengths to estimate the degree of fade and total attenuation in the channel, and a radial basis function network for estimating pulse spreads, all with excellent accuracy.

The use of radial basis function networks for fine pointing NASA's 70-meter deep space network antennas is described and evaluated. We demonstrate that such a network, working in conjunction with the array feed compensation system, and trained using the computationally efficient orthogonal least-squares algorithm, can point a 70-meter deep space antenna with RMS errors of less than 0.3 millidegree under good signal-to-noise ratio conditions, achieving significantly higher accuracies than the 0.8 millidegree benchmark for communications at Ka-band frequencies of 32 GHz.

EURAL-NETWORK technology has reached a degree of maturity as evidenced by an ever-increasing number of applications. Our experience, however, is that most practitioners of neural networks are familiar with only a handful of cases where neural-network technology has been reduced to practice. The objective of this special issue is presentation of some specific cases of ongoing everyday use of neural networks. Specifically excluded are neural-network applications still in the exploratory stage. While publication of extraordinary exploratory applications papers is within the scope of the IEEE TRANSACTIONS ON NEURAL NETWORKS, this special issue deals only with neural networks used on a regular basis. At minimum, the system must be at the beta test stage. Of the 53 papers received for the special issue, the 14 herein were chosen. In some important cases, papers solicited for submission were unfortunately withheld because developers or licensees wished to not disclose proprietary technology. Nevertheless, the spectrum of the everyday neural-network applications reported herein is a veritable smorgasbord of variety. Applications are reported in telecommunications, control of Publisher Item Identifier S 1045-9227(97)05726-3. steel plants, plasma etching, pattern recognition of cataloged parts, credit card fraud detection, space robot tuning, electric utility load forecasting, railway maintenance, power system security assessment, scanning electron microscope image characterization, cold mill prediction, economic forecasting and, not least, assessment of wine bottle cork quality. This sampling of applications in everyday use is in no way complete. It gives, however, a taste of the impact of neural technology in society. Indeed, impact is the metric by which all technology is ultimately measured.