Tellabs (United States)

Architectural alternatives for metro/regional networks with 400G transmission based on 100, 200, and 400Gb/s superchannel transmission. Reach results are derived for practical metro/regional networks. The results provide clear direction for node and network architectural decisions.

We report on a successful experimental demonstration of a digital optical performance monitoring (OPM) yielding satisfactory estimation accuracy along with adaptive impairment equalization. No observable penalty is measured when equalizer is driven by monitoring module.

A single-fiber full-duplex hybrid TDM/WDM-PON is demonstrated to operate at 2.5/1.25 Gbps rates, with RSOA ONU. It achieves high power budget (GPON compliant) at a distances higher than 20km, and serves up to 1280 users.

In this paper we present a method for echo detection and echo path delay estimation using a pattern recognition approach. We consider the problem of echo detection as attempting to match a speech pattern in the near-end signal to the far-end signal at a given delay. Employing features and techniques that have been successfully used in speech recognition, we define a spectral similarity function based on cepstral correlation. We show, through experimental results, that the proposed similarity function can reliably detect acoustic echoes and correctly estimate the echo path delay. Further, it is shown that the similarity function can be used in the detection of double-talk conditions. The method presented here is applicable to both electrical (hybrid) network echoes as well as to acoustic echoes.

This paper examines congestion control for explicit rate data networks. The available bit rate (ABR) service category of asynchronous transfer mode (ATM) networks serves as an example system, however, the results of this paper are applicable to other explicit rate systems as well. After a plant model is established, an adaptive control strategy is presented. Several algorithm enhancements are then introduced. These enhancements reduce convergence time, improve queue depth management, and reduce parameter bias. This work differentiates itself from the other contributions in the area of rate-based congestion control in its balanced approach of retaining enough complexity as to afford attractive performance properties, but not so much complexity as to make implementation prohibitively expensive.

Abstract Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.

Abstract Samples of university students from the United States, Eastern cultures, and Western cultures rated thirty‐one classroom instructional behaviors for appropriateness in their culture. U.S. students had significantly higher appropriateness ratings than Eastern students on clarity of presentation, enthusiasm, classroom interaction, and spatial‐behavioral communication behaviors. Western students exceeded Eastern students in all areas except spatial‐behavioral communication behaviors; ratings of non‐U.S. students were predicted by field of study and age.

In this paper we analyze several auditory localization neural models that are based on head related transfer functions (HRTFs). HRTFs represent the combined directional-spectral response of the pinnae head and torso. The role of HRTFs in such modeling has hitherto been underestimated despite substantial experimental evidence to its relevance in spatial hearing, especially in determining direction of highfrequency sound sources. In the first section we suggest a neural model that links the physiology of binaural processing to a neural network that extracts spectral ratios. These ratios correspond to HRTFs ratios and can provide auditory directional cues. Next, we compare several methods of matching HRTFs ratios using discriminative matching measure (DMM). We consider several solutions to the matching problem from a neural signal processing viewpoint. We compare correlation based approaches with DMM optimization approach and with a non-linear approach based on neural back-propagation algorithm. All three models can be implemented by neural networks. Finally, we include experimental results of simulations that are conducted using these methods. Experiments show that the back-propagation based neural network yields the best results in terms of DMM both for narrow-band and broad band excitation. The back-propagation neural network is also superior in matching noisy HRTF ratio vectors. [Neurol Res 2001; 23: 489-500]

In this paper we examine the queuing behavior of a multi-stage multiplexer with fixed length packets flowing through the stages. The system consists of two components: a front end multi-input, multi-output device as a preliminary stage, and a single-server, deterministic service time queue system (multiplexer) as a final stage. We treat arbitrary exogenous arrival patterns and examine sample path characteristics of packet occupancy in the system. Under identical inputs, we compare the multi-stage system to the corresponding single-stage system without the front end. Treating both the infinite buffer (unlimited capacity) and finite buffer (fixed capacity) cases, we prove a two-part MultiStage Multiplexing Theorem. From the first part, we conclude that any type of multi-staging is "sub-optimal." However, from the second part we find that deterministic service time queues-if they need to be installed as front ends for a larger network-actually improve upon or smooth the data traffic for downstream nodes.