The limiting density of a nonlinear system

The RC filter-hard limiter-RC filter nonlinear system is the subject of this paper. Because of computational difficulties implicated in the analysis of the above system, only its response to the zero mean Gaussian system input has been analytically investigated. An approximate output density has also been found for nonzero mean Gaussian, while verified to be “close” to the real one for finite means. In the present paper, a close form of the system output density is obtained when the input mean tends to infinity. For that, ε-upcrossing methods were used.     

Robust Sequential Algorithms for the Detection of Changes in Data Generating Processes

We consider the case where data sequences may be generated by either one of a number of non-parametrically defined processes and where the data generating process may change at any point in time. The objective is to effectively track the latter changes, where each acting process is essentially represented by a whole class of parametrically defined processes. We present, analyze and evaluate robust sequential algorithms which attain the objective for a variety of scenarios. Our robust algorithms consist of appropriate modifications of previously presented parametric sequential algorithms, to mainly resist the occurrence of occasional data outliers in terms of dramatic performance deterioration.     

An optimal policy for competitive processing of high- and low-priority arrivals

We consider two mutually independent traffic streams whose arrivals are stored in an infinite capacity buffer. The two arrival traffics are modelled by renewal processes and are competing for service by a single processor, where one of the processes requires a strict upper bound on the total delay per arrival. It is assumed that the processing per arrival, for both processes, is a constant; the value of this constant is different for each of the two traffic streams. Given the above model, we consider policies that operate in a non-interruptive mode. We first find the optimal scheduling policy, which attains throughput one and minimizes the expected delays of both traffic streams. The delays induced by the latter policy are analysed via a methodology based on the regenerative theorem. The theoretical results are supported by numerical examples.     

Feedforward neural structures in binary hypothesis testing

Two feedforward neural structures intended for binary hypothesis testing are considered. The first structure, FFS1, is a tandem structure, while the second structure, FFS2, involves cumulative feedforward feedback. Both parametric and robust designs for the two structures are considered and analyzed in terms of induced false alarm and power probabilities. The inferiority of the FFS1 is rigorously proved in terms of the rate with which the induced power probability increases with respect to the number of the neural elements. Asymptotic results are presented, as well as numerical results, with emphasis on the Gaussian and location parameter nominal hypotheses model. Learning algorithms for the parameter involved in the robust network designs are discussed as well     

A class of scheduling policies for mixed data with renewal arrivalprocesses

Two mutually independent traffic streams whose arrivals are modeled as renewal processes are considered. The arrivals are stored in an infinite capacity buffer. One of the processes requires a strict upper bound on the total delay per arrival, and it is assumed that the processing time per arrival for both processes is a constant. Given the above model, the class of nonpreemptive scheduling policies which satisfy the delay constraint while maintaining the system stability is found. The delays induced by the latter policy are analyzed via a methodology based on the regenerative theorem. A numerical example is given in which the policy that minimizes the expected delays for both traffic streams is also considered     

On the Relation Between the Finite and the Infinite Population Models for a Class of RAA's

We examine the relation between the finite and the infinite population models for a class of random access algorithms. The algorithms in the class are a combination of random access and reservation techniques, they are synchronous, and they are studied under the condition that each of the users can monitor the channel feedback continuously (full feedback sensing). For any finite number of independent and identical users in the system, and any i.i.d. arrival process per user, the algorithms are stable, provided that the total input rate is less than one. However, as the population size increases, the stability of an algorithm in the class is determined by its throughput in the presence of the infinite population model for all practical purposes.     

Semidynamic and dynamic transmission algorithms for multiplexing voice with data

The design of hybrid transmission algorithms for the multiplexing of voice and data over a common digital channel is of interest to various communication networks, including cellular radio and high-speed topologies. In environments where the characteristics of the voice and data traffics may vary dynamically, the issue is the deployment of hybrid multiplexing algorithms (HMAs) which satisfy the constraints imposed by the voice traffic, while they simultaneously attain high channel utilization and induce low implementation overhead. In this paper, we propose, evaluate, and compare two HMAs: a semidynamic and a dynamic. The former induces lower implementation overhead than the latter, but it is applicable only to environments where the rate of the voice traffic may vary relatively slowly and its statistics are parametrically known. The semidynamic HMA induces frame structures, where the capacity allocation per frame, for the voice versus data traffic, is dictated by a superimposed traffic monitoring algorithm. The dynamic HMA, on the other hand, assigns each channel slot to voice versus data packets dynamically; it requires no statistical knowledge about the voice traffic, at the expense of significantly increased implementation overhead.     

A window random access algorithm for environments with capture

The authors propose and analyze a stable random access algorithm which is a modification of the two-cell algorithm proposed by M. Paterakis (1984). The authors assume ternary feedback broadcast per slot, and in the presence of capture, identification of the captured packet by all the users in the system. It is also assumed that a successfully received signal transmission cannot be distinguished from a capture in the presence of multiple transmission. The performance of the algorithm is compared to an appropriate modification of J.I Capetanakis' (1979) dynamic algorithm. The proposed algorithm, and in contrast to the latter, it can be easily modified to operate in limited feedback sensing environments     

A protocol for cellular radio signaling channels carrying data andhigh-priority accessing requests

In cellular radio, accessing requests are transmitted through signaling channels in the form of minipackets. The accessing requests are generated by a variety of mobile users that impose different constraints. The authors consider data and high-priority public-safety cellular users and isolate a single channel that is shared by the mixed user population. They devise and analyze a mixed protocol that satisfies the constraints of the high-priority public-safety users, while maintaining the data traffic with satisfactory delays. For the high-priority users, a well-defined and finite but bursty population and a number of different priorities are considered. The population of data users is assumed time-varying and not well-defined, and is modeled as limit Poisson and in the analysis of the protocol