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Selected Areas in Communications, IEEE Journal on

Issue 9 • Date Dec 1994

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Displaying Results 1 - 18 of 18
  • Omega network-based ATM switch with neural network-controlled bypass queueing and multiplexing

    Publication Year: 1994 , Page(s): 1471 - 1480
    Cited by:  Papers (13)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (916 KB)  

    Multistage interconnection networks (MINs) have long been studied for use in switching networks. Since they have a unique path between source and destination and the intermediate nodes of the paths are shared, internal blocking can cause very poor throughput. This paper proposes a high throughput ATM switch consisting of an Omega network with a new form of input queues called bypass queues. We also improve the switch throughput by partitioning the Input buffers into disjoint buffer sets and multiplexing several sets of nonblocking cells within a time slot, assuming that the routing switch operates only a couple of times faster than the transmission rate. A neural network model is presented as a controller for cell scheduling and multiplexing in the switch. Our simulation results under uniform traffic show that the proposed approach achieves almost 100% of potential switch throughput View full abstract»

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  • Neural scheduling algorithms for time-multiplex switches

    Publication Year: 1994 , Page(s): 1481 - 1487
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (548 KB)  

    In an N×N time-multiplex switch, transmission conflict arises when two or more input adaptors transmit packets to the same output adaptor simultaneously. To resolve transmission conflict, we propose two neural-based scheduling algorithms which use a large number of simple processing elements to perform scheduling in parallel. The first algorithm uses N2 hysteresis McCulloch-Pitts (1943) neurons to determine conflict-free transmission schedules with maximum throughput. The second algorithm resolves transmission conflict among the first M packets in each input queue. It determines suboptimal transmission schedules using only NM neurons (M<N). M is a design parameter: if M is larger, we can find closer-to-optimal transmission schedules, but we need more neurons. Simulation results show that the first algorithm can find near-global optimal transmission schedules. The second algorithm can give close-to-optimal transmission schedules using only a small M. When N=500 and M=10, the throughput efficiency is already 96.44% while the required number of neurons is reduced from N 2=250000 to NM=5000 View full abstract»

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  • Neural network techniques for adaptive multiuser demodulation

    Publication Year: 1994 , Page(s): 1460 - 1470
    Cited by:  Papers (48)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1020 KB)  

    Adaptive methods for performing multiuser demodulation in a direct-sequence spread-spectrum multiple-access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on suboptimal receivers with moderate complexity. Adaptive algorithms for detection allow for reception when the communication environment is either unknown or changing. Motivated by previous work with radial basis functions (RBF's) for performing equalization, RBF networks that operate with knowledge of only a subset of the system parameters are studied. Although this form of detection has been previously studied (group detection) when the system parameters are known, in this work, neural network techniques are employed to adaptively determine unknown system parameters. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by a RBF network when all of the system parameters are known. The RBF network's performance (with estimated parameters) is compared with the optimal synchronous detector, the decorrelating detector and the single layer perceptron detector. Clustering techniques and adaptive least mean squares methods are investigated to determine the unknown system parameters. This work shows that the adaptive radial basis function network attains near optimal performance and is robust in realistic communication environments View full abstract»

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  • Intelligent control of signal processing algorithms in communications

    Publication Year: 1994 , Page(s): 1553 - 1565
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1296 KB)  

    In future telecommunications networks, an important role will be played by “intelligent control” techniques aimed at selecting and tuning signal processing (SP) algorithms. The authors first define the main problems of automatic control of SP algorithms; then, they propose a knowledge-based approach to carry out such a task. In the planning phase, a restricted set of algorithm sequences are selected. This set is used as a raw plan to be refined and corrected in the execution phase, based on quality tests on progressive results. In particular, the authors focus on the control strategies adopted and describe how expert knowledge is represented and applied to implement such strategies. As an example, the control of low-level image processing is detailed. Results on an image-compression application are also reported View full abstract»

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  • Vector quantization using tree-structured self-organizing feature maps

    Publication Year: 1994 , Page(s): 1594 - 1599
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (656 KB)  

    In this paper, we propose a binary-tree structure neural network model suitable for structured clustering. During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space. This model is used to design tree search vector quantization codebooks for image coding. Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images View full abstract»

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  • Residual vector quantization using a multilayer competitive neural network

    Publication Year: 1994 , Page(s): 1452 - 1459
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (732 KB)  

    This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multilayer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks (weights of the multilayer competitive neural network) is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. This Lagrangian error function includes all the constraints that are imposed by the joint optimization of the codebooks. The proposed procedure seeks a locally optimal solution by iteratively solving the equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both generalized Lloyd algorithm (GLA) and the Kohonen learning algorithm (KLA) were used to design each stage codebook independently, as well as the conventional joint-optimization technique View full abstract»

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  • Least mean p-power error criterion for adaptive FIR filter

    Publication Year: 1994 , Page(s): 1540 - 1547
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB)  

    An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred View full abstract»

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  • Convergence properties of an adaptive lattice filter exploiting nonlinear dynamics of a biochemical reaction system

    Publication Year: 1994 , Page(s): 1524 - 1529
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (452 KB)  

    An adaptive lattice filter (ALF) which computes the PARCOR coefficients through a cyclic enzyme system has recently been developed by the author. Using nonlinear dynamics of the cyclic enzyme system, the ALF becomes robust against impulsive noise, and the stability of the estimated AR model can be ensured. The convergence properties of the ALF are studied. First, a theoretical expression for the asymptotic error variance of the PARCOR coefficient is derived. Simulation results are presented, and the theoretical and simulated values show a very good match. Next, the convergence speed of the proposed ALF is compared with that of the simplified ALF. The step sizes are then determined by using the above theoretical expression such that both ALF's achieve the same error variance in steady states. The results show that the proposed ALF has excellent convergence properties than the simplified one View full abstract»

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  • Quantizer neuron model and neuroprocessor-named quantizer neuron chip

    Publication Year: 1994 , Page(s): 1503 - 1509
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (684 KB)  

    A quantizer neuron model and a hardware implementation of the model is described. A quantizer neuron model and a multifunctional layered network (MFLN) with quantizer neurons is proposed and applied to a character recognition system. Each layer of MFLN has a specific function defined by quantizer input, and the weights between neurons are set dynamically according to quantizer inputs. The learning speed of MFLN is extremely fast in comparison with conventional multilayered perceptrons using back propagation, and the structure of MFLN is suitable for supplemental learning with extraneous learning data sets. We tested the learning speed and compared it with three other network models: RCE networks, LVQ3, and multilayered neural network with back propagation. According to the simulation, we also developed a quantizer neuron chip (QNC) using two newly developed schemes. QNC simulates MFLN and has 4736 neurons and 2000000 synaptic weights. The processing speed of the chip achieved 20300000000 connections per second (GCPS) for recognition and 20 000 000 connection updates per second (MCUPS) for learning. QNC is implemented in a 1.2 μm double-metal CMOS-process sea of gates and contains 27 000 gates on a 10.99×10.93 mm2 die. The neuroboard, which consists of a main board with a QNC and a memory board for synaptic weights of the neurons, can be connected to a host personal computer and can be used for image or character recognition and learning. The quantizer neuron model, the quantizer neuron chip, and the neuroboard with QNC can realize adaptive learning or filtering View full abstract»

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  • Markov monitoring with unknown states

    Publication Year: 1994 , Page(s): 1600 - 1612
    Cited by:  Papers (20)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1048 KB)  

    Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is difficult if not impossible to model all the possible fault states of the system in advance. In this paper a method is described for extending the Markov monitoring approach to allow for unknown or novel states which cannot be accounted for when the model is being designed. The method is described and evaluated on data from one of the Jet Propulsion Laboratory's Deep Space Network antennas. The experimental results indicate that the method is both practical and effective, allowing both discrimination between known states and detection of previously unknown fault conditions View full abstract»

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  • Linear prediction of subband signals

    Publication Year: 1994 , Page(s): 1576 - 1583
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (548 KB)  

    The performance of linear prediction of fullband and subband signals is described in terms of the respective prediction gain. The subband prediction gain is characterized in terms of the fullband signal power spectral density and the frequency response of the subband filters. For Gaussian fullband signals, the asymptotic subband prediction gain can never be larger than the asymptotic fullband prediction gain. Simulation results compare fixed and adaptive fullband and subband prediction gains for Gaussian sources and speech. For speech, the subband prediction gain can exceed the fullband prediction gain View full abstract»

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  • A subband adaptive filter allowing maximally decimation

    Publication Year: 1994 , Page(s): 1548 - 1552
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    Conventional subband adaptive filters (ADF) using filter banks have shown degradation in performance because of the nonideal nature of filter banks. For this problem, the authors propose an alias free subband structure for adaptive filtering using polyphase frequency sampling filter (FSF) banks. As a preliminary, they make it clear that the conventional polyphase discrete Fourier transform (DFT) bank is equivalent to a class of the FSF bank. Then, they propose a new class of ADF using the FSF banks based on the frequency sampling theorem. As a result, the proposed technique enables subband adaptive filtering without the degradation effect of both the aliasing and cross terms, even if one chooses critical subsampling View full abstract»

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  • Knowledge model based approach in recognition of on-line Chinese characters

    Publication Year: 1994 , Page(s): 1566 - 1575
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (912 KB)  

    A knowledge model-based OCR system is presented for the recognition of on-line connected stroke Chinese characters. In the approach, segment attributes are first extracted to characterize the segment sequence of an unknown character. Next, radical recognition based on model matching is adopted as the coarse classification to reduce the number of candidate characters before detailed matching. Finally, a deviation modeling method is proposed to recognize not only regular writing characters but also characters with stroke-order and stroke-number deviations. The effectiveness of the approach is verified by experiments on the recognition of on-line Chinese characters View full abstract»

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  • Fuzzy rule-based signal processing and its application to image restoration

    Publication Year: 1994 , Page(s): 1495 - 1502
    Cited by:  Papers (14)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (736 KB)  

    A novel signal processing technique based on fuzzy rules is proposed for estimating nonstationary signals, such as image signals, contaminated with additive random noises. In this filter, fuzzy rules concerning the relationship between signal characteristics and filter design are utilized to set the filter parameters, taking the local characteristics of the signal into consideration. The fuzzy rules are found to be quite effective, since the rules to set the filter parameters are usually expressed in an ambiguous style. The high performance of this filter is demonstrated in noise reduction of a 1-D test signal and a natural image with various training signals View full abstract»

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  • Adaptive interference suppression for CDMA overlay systems

    Publication Year: 1994 , Page(s): 1510 - 1523
    Cited by:  Papers (12)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1052 KB)  

    It has been proposed that CDMA systems can be assigned to spectral bands which are presently occupied by narrowband users to further increase spectral capacity. Such CDMA overlay systems could provide new options for efficient utilization of the spectrum with minimal disruption to existing narrowband users, especially if adaptive interference suppression techniques are utilized in the spread spectrum receiver. Previous studies have defined the SNR improvement ratio which can be achieved for tone interferers and for narrowband interferers for which the center frequency of the interference is at the carrier frequency of the CDMA signal. In this paper the bit-error-rate (BER) performance of the mobile-to-base link of a CDMA system for a single narrowband user which occupies a significant portion of the CDMA bandwidth is evaluated. It is shown that the narrowband model used in previous studies does not apply in this case, especially for the large, effective, bandwidths which are characteristic of the interferers in the overlay system. The dependence of the BER on the filter order, the bandwidth of the interference, and its center frequency relative to the CDMA carrier frequency are defined. Additionally the increase in BER for a digital implementation of the adaptive suppression filter relative to the optimal Wiener filter is characterized with respect to the adaptive time constant and the quantization errors due to finite wordlength. It is shown that these implementation errors can be made negligible compared to the errors which are characteristic of the optimal Wiener filter. Analytic results are validated by simulation for typical system parameters View full abstract»

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  • A constrained joint source/channel coder design

    Publication Year: 1994 , Page(s): 1584 - 1593
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (928 KB)  

    The design of joint source/channel coders in situations where there is residual redundancy at the output of the source coder is examined. It has previously been shown that this residual redundancy can be used to provide error protection without a channel coder. In this paper, this approach is extended to conventional source coder/convolutional coder combinations. A family of nonbinary encoders is developed which more efficiently use the residual redundancy in the source coder output. It is shown through simulation results that the proposed systems outperform conventional source-channel coder pairs with gains of greater than 9 dB in the reconstruction SNR at high probability of error View full abstract»

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  • A new IIR adaptive echo canceler: GIVE

    Publication Year: 1994 , Page(s): 1530 - 1539
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB)  

    A novel IIR adaptive gradient instrumental variable echo canceler (GIVE) is presented. Its features include adaptive controllability during double-talk periods in acoustic conference systems; guarantee of global convergence; low computational cost (the same order as the IIR LMS algorithm of the equation error method); and flexible structures (parallel or series-parallel structures). We also show a convergence analysis for gradient adaptive algorithms including GIVE. Based on this analysis, the optimum stepsize for GIVE and three suboptimum algorithms are proposed to accelerate convergence and reduce misadjustment. In addition, a simple method that guarantees the stability of IIR filters and a configuration of GIVE applicable to closed loop systems are presented. These proposals are extensively studied by computer simulations View full abstract»

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  • Fuzzy neural control of voice cells in ATM networks

    Publication Year: 1994 , Page(s): 1488 - 1494
    Cited by:  Papers (31)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    This paper presents the design of a fuzzy controller for managing cells generated by voice sources in asynchronous transfer mode (ATM) networks. Typical voice cells, characterized by a high degree of burstiness, complicate any attempt to use classical control theory in the design of an ATM cell rate controller. The fuzzy control approach presented in this paper overcomes this limitation by appealing to the linguistic ability of fuzzy set theory and logic to handle the complexity. Specifically, the cell rate control problem is linguistically stated but treated mathematically via fuzzy set manipulation. In particular, the ATM voice cell controller being proposed is an improved and intelligent implementation of the leaky bucket cell rate control mechanism extensively studied in the literature. This intelligent implementation of the leaky bucket mechanism uses a channel utilization feedback via the QoS parameters to improve its performance. This ATM fuzzy controller takes the form of an organized set of linguistic rules quantitatively expressed and manipulated by means of fuzzy set theory and fuzzy logic. The fuzzy control rules are stored in fuzzy associative memory to permit parallel executions View full abstract»

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Aims & Scope

IEEE Journal on Selected Areas in Communications focuses on all telecommunications, including telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation.

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Editor-in-Chief
Muriel Médard
MIT