János Levendovszky

Enhancing the performance of medical implant communication systems through cooperative diversity

Battery-operated medical implants—such as pacemakers or cardioverter-defibrillators—have already been widely used in practical telemedicine and telecare applications. However, no solution has yet been found to mitigate the effect of the fading that the in-body to off-body communication channel is subject to. In this paper, we reveal and assess the potential of cooperative diversity to combat fading—hence to improve system performance—in medical implant communication systems. In the particular cooperative communication scenario we consider, multiple cooperating receiver units are installed across the room accommodating the patient with a medical implant inside his/her body. Our investigations have shown that the application of cooperative diversity is a promising approach to enhance the performance of medical implant communication systems in various aspects such as implant lifetime and communication link reliability.

A Novel Reliability Based Routing Protocol for Power Aware Communications in Wireless Sensor Networks

In this paper a Rayleigh fading model based reliability-centric routing algorithm is proposed for Wireless Sensor Networks (WSNs). The proposed scheme is optimized with respect to minimal power consumption to improve longevity as well as to ensure reliable packet transmission to the Base Station (BS). Reliability is guaranteed by selecting path over which the probability of correct packet reception of the transmitted packet will exceed a predefined threshold at the BS. It will be pointed out that reliable and power efficient packet forwarding over WSN can be mapped into a constrained optimization problem. This optimization is then reduced to a shortest path problem with specific link metrics solved in polynomial time.

Novel Load Balancing Algorithms Ensuring Uniform Packet Loss Probabilities for WSN

In this paper, we develop optimal scheduling mechanisms for packet forwarding in Wireless Sensor Network, where clusterheads are gathering information with a predefined Quality of Service. The objective is to ensure balanced energy consumption and to minimize the packet loss probability, subject to time constraints (i.e. different nodes must send all their packets within a given time interval). Novel solutions of scheduling are developed by combinatorial optimization, and by quadratic programming methods. In our approach, the scheduling of packet forwarding is broken down to a discrete quadratic optimization problem and the optimum is sought by a Hopfield Neural Network yielding the solution in polynomial time. The scheduling provided by the Hopfield Neural Network indeed guarantees uniform packet loss probabilities for all the nodes and saves the energy of the clusterheads. In this way, the longevity of the network can be increased.

Adaptive statistical algorithms in network reliability analysis

The paper is concerned with introducing novel algorithms, such as adaptive approximation and deterministic radial basis function (RBF) method, for calculating the average loss (AL). Different approximators are trained to approximate the loss function and, after a short learning period, AL can be evaluated analytically with fast calculations. An improvement of the Li-Silvester (LS) method is also presented which yields a sharper lower bound on AL. The efficiency of the new methods are proven by theoretical analysis as well as demonstrated by excessive simulations.

Real-time call admission control for packet-switched networking by cellular neural networks

In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line.

Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.

Fading-aware reliable and energy efficient routing in wireless sensor networks

In this paper, we introduce two fading-aware reliability based routing algorithms for wireless sensor networks (WSNs) with lossy radio links. The proposed algorithms are able to find optimal multi-hop paths in polynomial complexity, over lossy links, which are modeled by using standard fading models (e.g. Rayleigh and Rice fading). These algorithms minimize the energy consumption and ensure reliable packet transmission to the base station (BS) at the same time. A reliable path is defined in terms of successful packet transfer to the BS despite the lossy links. More precisely, the probability of correct reception of the packet at the BS must exceed a predefined threshold. The first algorithm minimizes the total energy consumption sending a packet over the selected path to the BS. On the other hand, the second algorithm selects a path which maximizes the minimum remaining energy on the node closest to exhaustion and, as a result, balances the energy consumption yielding high longevity. In both cases, reliable and energy efficient packet forwarding in WSN can be reduced to a constrained optimization problem. By using a specific link metrics, these problems can then be mapped into shortest path problems solved in polynomial time. Thus the obtained results ensure the selection of reliable paths which also guarantee minimum energy consumption in real time.

Optimizing Sparse Mean Reverting Portfolios

In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, we identify the parameters of the underlying VAR(1) model of asset prices and then the quantities of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according to two approaches: (i) maximizing the predictability by solving the generalized eigenvalue problem or (ii) maximizing the mean return. The optimization itself is carried out by stochastic search algorithms and Feed Forward Neural Networks (FFNNs). The presented solutions satisfy the cardinality constraint thus providing sparse portfolios to minimize the transaction costs and to maximize interpretability of the results. The performance has been tested on historical data (SWAP rates, SP 500, and FOREX). The proposed trading algorithms have achieved 29.57% yearly return on average, on the examined data sets. The algorithms prove to be suitable for high frequency, intraday trading as they can handle financial data up to the arrival rate of every second

Blind adaptive stochastic neural network for multiuser detection

In this paper some blind adaptive methods are introduced for multiuser detection (MUD). The detector architecture contains a channel identifier followed by a stochastic Hopfield (1985) net. Blind channel identification is proposed to be carried out by either the Kohonen (see Self-Organizing Maps, Springer, 2000) algorithm or by a novel adaptive decorrelation technique. Based on the estimated channel parameters the stochastic Hopfield net implements a near optimal decision. Besides describing the related algorithms, the paper contains extensive simulations to evaluate the performance of the proposed detector structures.

Nonparametric decision algorithms for CAC in ATM networks

Abstract When performing call admission control (CAC) in ATM networks, the users are requested to declare their traffic descriptors on the basis of which the aggregated load is estimated. If this estimated load does not exceed the available node capacity, then calls are to be accepted and otherwise rejected. In the process of CAC, it is crucial that the network manager obtains correct information about the traffic characteristics declared by the users. Otherwise, the quality of service (QoS) can be severely deteriorated by accepting calls based on erroneous traffic descriptors. As traffic descriptors are usually obtained by measurements or statistical estimation, they are subject to errors or changes in time. Consequently, CAC must be viewed as a decision whether to accept or to refuse a certain traffic configuration, where the underlying traffic parameters are to be estimated from the samples of the aggregate traffic. This casts CAC as a nonparametric estimation problem first investigated by Gibbens et al. [R.J. Gibbens, F.P. Kelly, P.B. Key, IEEE J. Selected Areas Commun. 13 (6) (1995)]. As a result, when implementing a CAC algorithm, one is faced with the challenges of: (i) developing a good estimate of the tail of the aggregate traffic assuming that the true values of the traffic descriptors are given; and (ii) addressing the problem that the traffic descriptors themselves are random variables. In the latter case the tail estimation must be combined with a hypothesis testing method. To meet these challenges, in Section 1 of this paper a number of tail estimation techniques are listed which are based on statistical inequalities. Then a decision theoretic approach will be developed to perform CAC in the case of unknown traffic descriptors. This approach includes both parametric and nonparametric techniques. In developing these methods, the results of Gibbens et al. regarding the parametric case will be extended from homogeneous to heterogeneous traffic. The nonparametric decision algorithm will be implemented by a feedforward neural network which yields an asymptotically optimal Bayesian CAC function. More specifically, a two-layer neural network processes the samples of the aggregate traffic and yields an “accept” or “reject” decision at the output. Applying a special encoding scheme in the course of training, the outputs of the network will estimate the a posteriori probabilities needed for the Bayesian decision. In this way, CAC is performed as a decision function without the knowledge of the a priori distribution of the traffic descriptors. The paper contains the proof of this statement with some applications and simulation results regarding the different CAC algorithms.