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The construction of complex engrams requires hippocampal–cortical interactions. These include both direct interactions and ones via often-overlooked subcortical loops. Here, we review the anatomical organization of a hierarchy of parallel 'Papez' loops through the hypothalamus that are homologous in mammals from rats to humans. These hypothalamic loops supplement direct hippocampal–cortical connections with iterative reprocessing paced by theta rhythmicity. We couple existing anatomy and lesion data with theory to propose that recirculation in these loops progressively enhances desired connections, while reducing interference from competing external goals and internal associations. This increases the signal-to-noise ratio in the distributed engrams (neocortical and cerebellar) necessary for complex learning and memory. The hypothalamic nodes provide key motivational input for engram enhancement during consolidation. Construction of complex memory engrams requires subcortex as well as cortex. Hippocampus and prefrontal cortex are connected via parallel 'Papez' circuits, with the mamillary area a key waystation. The parallel circuits have an onion-like nesting, with each circuit connecting a specific processing level of the areas involved. These circuits span from reflexive 'survival circuits', via the supramammillary nuclei, to slow and sophisticated, via the medial mammillary nucleus. Circuits involving cerebellar cortex have a similar organization to those involving neocortex. These parallel distributed circuits allow iterative processing to integrate information related to situation and motivation in the construction of complex distributed engrams and enable their inclusion in the perception–action cycle. [ABSTRACT FROM AUTHOR]

Several application-specific processor design approaches have been proposed and investigated to cope with the emerging flexibility requirements jointly associated with the maximum performance efficiency and minimum implementation area and power consumption. Dynamic scheduling of a set of instructions generally leads to an overhead related to instruction decoding. To mitigate this overhead, other approaches have been proposed using static scheduling of datapath control signals. In this context, No-Instruction-Set-Computer (NISC) concept have been introduced considering that a dedicated processor to a specific application does not need an instruction set especially when it is programmed by its designers and not by its users. In this paper, the hardware architecture design of flexible NISC-based architecture design dedicated for minimum mean-squared error (MMSE) linear detection is presented. The devised design, which is used in iterative turbo-receiver, fulfills the performance requirements of emergent wireless communication standards with throughput reaching that of LTE-Advanced. FPGA hardware implementation of the detector architecture achieves a maximum throughput of 115.8 Mega symbols per second for 2 × 2 and 6.4 Mega symbols per second for 4 × 4 MIMO systems for an operating clock frequency of 202.67 MHz. [ABSTRACT FROM AUTHOR]

Histograms of second-order derivatives are generated from the pixel data of MRI images. The histograms are then used to calculate a factor that is to be used for iterative processing. The factor is intended to limit the number of iterations, with the goal of preventing further loss of detail. The factor uses two conditions that depend on the profiles of the histograms. The methodology uses sample MRI images and versions of these images with Rician noise introduced into them. The noisy images are subjected to iterative noise reduction with a recursive averaging filter. The control tests in the methodology use the ground truth images to limit the number of iterations, with PSNR and SSIM peaks used as the measurements for determining when the iterations stop. The other tests use the proposed termination factor for the limitation. The results of the tests are compared to determine the effectiveness of the termination factor. The proposed termination factor does not cause divergence, but there are still different numbers of iterations in the case of MRI images with image subjects that have discrete regions and details resembling noise. The tests also reveal that differences between the histograms of derivatives and Laplace curves have to be retained in order to prevent loss of information. [ABSTRACT FROM AUTHOR]

The intention is to design the schemes for the enhancements for the enhancement of behaviour of map reduction scheme when it is employed for the iterative processing. The iterative processing has usually employed the place the information is restored episodically to imitate minimal alterations to the input information set. For minimizing the delays in reanalysis of unaltered information announces the schemes which precisely estimate only the information only when the information that has been modified. It integrates the concept of blooms screening. The blooms screening is a space effective information framework which could have a precise possibility verification in case the information is altered or not. The conventional systems process the comprehensive information in a minimal proportion or none of the information is altered. This the time arduous and it guzzles the immense number of CPU clock cycles moreover to process the information has not been altered. For minimizing the consumption of CPU clock cycles the system is designed so that the scheme of implementation employs blooms screening aids enhancing the behaviour of the system nearly to 17% as evaluated to the conventional system. [ABSTRACT FROM AUTHOR]

Abstract It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new systems to perform large-scale data computations incrementally. MapReduce was recently introduced to provide an efficient approach for handling large-scale data computations. Nevertheless, it turned to be inefficient in supporting the processing of small incremental data. While many previous systems have extended MapReduce to perform iterative or incremental computations, these systems are still inefficient and too expensive to perform large-scale iterative computations on changing data. In this paper, we present a new system called iiHadoop, an extension of Hadoop framework, optimized for incremental iterative computations. iiHadoop accelerates program execution by performing the incremental computations on the small fraction of data that is affected by changes rather than the whole data. In addition, iiHadoop improves the performance by executing iterations asynchronously, and employing locality-aware scheduling for the map and reduce tasks taking into account the incremental and iterative behavior. An evaluation for the proposed iiHadoop framework is presented using examples of iterative algorithms, and the results showed significant performance improvements over comparable existing frameworks.

In the multi-radar tracking system, multi-radar data fusion technology can improve the detection and tracking capabilities of radar. Besides, the spatial registration processing of radar data can effectively calibrate the radar system error and improve the fusion accuracy. In this study, the system error registration for two-station coast radars based on non-cooperative targets is implemented by using the exact maximum likelihood spatial registration algorithm (EML). On the basis of the EML algorithm, this study proposes a selection method for the corresponding targets. The selection method that facilitates the application of the EML algorithm can automatically complete the clipping, alignment and registration of the track information. For the defect that the EML algorithm is easy to fall into the local minimum in the iterative processing, the data inspection method based on the minimum root mean square test is proposed in this study, which can further improve the exactitude of the system error estimation. Compared with direct fusion, the plot fusion accuracy of the two-station coast radars is significantly improved after the spatial registration of the EML algorithm.

High data rates and error-rate performance approaching close to theoretical limits are key trends for evolving digital wireless communication applications. To address the first requirement, multiple-input multiple-output (MIMO) techniques are adopted in emergent wireless communication standards and applications. On the other hand, turbo concept is used to alleviate the destructive effects of the channel and ensure error-rate performance close to theoretical limits. At the receiver side, the incorporation of MIMO techniques and turbo processing leads to increased complexity that has a severe impact on computation speed, power consumption and implementation area. Because of its increased complexity, the detector is considered critical among all receiver components. Low-complexity algorithms are developed at the cost of decreased performance. Minimum mean-squared error (MMSE) solution with iterative detection and decoding shows an acceptable tradeoff. In this paper, the complexity of the MMSE algorithm in turbo detection context is investigated thoroughly. Algorithmic computations are surveyed to extract the characteristics of all involved parameters. Consequently, several decompositions are applied leading to enhanced performance and to a significant reduction of utilized computations. The complexity of the algorithm is evaluated in terms of real-valued operations. The proposed decompositions save an average of 2 9 % and 1 7 % of required operations for 2  ×  2 and 4  ×  4 MIMO systems, respectively. In addition, the hardware implementation designed applying the devised simplifications and decompositions outperforms available state-of-the-art implementations in terms of maximum operating frequency, execution time, and performance. [ABSTRACT FROM AUTHOR]

In the multi-radar tracking system, multi-radar data fusion technology can improve the detection and tracking capabilities of radar. Besides, the spatial registration processing of radar data can effectively calibrate the radar system error and improve the fusion accuracy. In this study, the system error registration for two-station coast radars based on non-cooperative targets is implemented by using the exact maximum likelihood spatial registration algorithm (EML). On the basis of the EML algorithm, this study proposes a selection method for the corresponding targets. The selection method that facilitates the application of the EML algorithm can automatically complete the clipping, alignment and registration of the track information. For the defect that the EML algorithm is easy to fall into the local minimum in the iterative processing, the data inspection method based on the minimum root mean square test is proposed in this study, which can further improve the exactitude of the system error estimation. Compared with direct fusion, the plot fusion accuracy of the two-station coast radars is significantly improved after the spatial registration of the EML algorithm. [ABSTRACT FROM AUTHOR]

Motivated by 5G application requirements that challenge the use of orthogonal frequency division multiplexing (OFDM), non-orthogonal multi-carriers are being investigated. Unlike OFDM that takes advantage of orthogonal pilot observation, in non-orthogonal waveforms, pilots are contaminated by interference from multiple dimensions, i.e., inter-subsymbol-, inter-carrier-, and inter-antenna-interference, when multiple-input-multiple-output (MIMO) is also part of the transmission. Employing cyclic-prefix (CP) in multi-carrier systems not only protects the signal from inter-symbol-interference but also allows circular interpretations of the channel, which simplifies the estimation and equalization techniques. Nevertheless, the CP information is usually discarded at the receiver side. In this paper, by considering the fact that non-orthogonal waveforms suffer from multiple dimensions of interference, we derive a MIMO linear-minimum-mean-squared-error (LMMSE)-based parallel-interference-cancellation (PIC) method for joint channel estimation and equalization of non-orthogonal waveforms. Unlike the common practice, by properly localizing the pilots in time domain, we also use the pilots’ information from CP. We apply our proposed algorithm to a flexible non-orthogonal waveform known as generalized frequency division multiplexing (GFDM). Taking advantage of block-circularity of GFDM, we investigate the complexity aspects for such CP-aided LMMSE-PIC channel estimation. Through simulation results, we show that using CP information of pilots for GFDM gains up to 2.4-dB better frame error rate performance than an OFDM signal. [ABSTRACT FROM AUTHOR]

We propose an iterative crosstalk cancellation scheme based on the generalized minimal residual (GMRES) algorithm for large-scale digital subscriber line (DSL) systems. The proposed scheme does not require channel inversion and stores fewer vectors for crosstalk cancellation. We analyze the convergence of the GMRES algorithm and derive computable bounds on the residual error and signal-to-noise ratio in terms of system parameters at each iteration for upstream DSL systems. We show that the GMRES algorithm typically requires a single iteration for very large vectored systems to achieve crosstalk-free performance for the very high-speed DSL (VDSL) frequencies and only a few more in the highest frequency bands of the G.fast spectrum. This yields significant complexity savings and reduction in memory storage, compared to the zero forcing scheme under certain conditions. [ABSTRACT FROM AUTHOR]

Helicopter‐borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter‐borne ROSAR wavenumber‐domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA‐based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well‐focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter‐borne ROSAR imaging. [ABSTRACT FROM AUTHOR]

Driven by 5G requirements, research on alternatives to the popular cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) waveform recently arose. In particular, non-orthogonal circularly filtered waveforms such as generalized frequency division multiplexing (GFDM) were proposed due to flexibility and robustness. Applying multiple-input multiple-output (MIMO) techniques for future wireless networks are unquestionable and thereby compulsory for any alternative waveform. Despite advancements in accurate MIMO detection algorithms for GFDM, compared with CP-OFDM their complexity still exhibited a higher order of magnitude, impeding an energy-efficient implementation. In this paper, we propose a low-complexity formulation for iterative minimum mean squared error with parallel interference cancellation (MMSE-PIC) detection for non-orthogonal waveforms with localized inter-carrier interference, where we focus on the application to MIMO-GFDM. The proposal achieves complexity similar to CP-OFDM and we evaluate its performance under realistic channel conditions with imperfect channel state information, where we obtain up to 2-dB gain of GFDM compared with OFDM. We confirm our findings by analyzing the measured extrinsic information transfer charts and show that the proposal achieves the performance of optimal maximum likelihood detection. The results point out the MMSE-PIC algorithm as a viable technique for iterative MIMO receiver implementations for non-orthogonal waveforms. [ABSTRACT FROM AUTHOR]

It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new systems to perform large-scale data computations incrementally. MapReduce was recently introduced to provide an efficient approach for handling large-scale data computations. Nevertheless, it turned to be inefficient in supporting the processing of small incremental data. While many previous systems have extended MapReduce to perform iterative or incremental computations, these systems are still inefficient and too expensive to perform large-scale iterative computations on changing data. In this paper, we present a new system called iiHadoop, an extension of Hadoop framework, optimized for incremental iterative computations. iiHadoop accelerates program execution by performing the incremental computations on the small fraction of data that is affected by changes rather than the whole data. In addition, iiHadoop improves the performance by executing iterations asynchronously, and employing locality-aware scheduling for the map and reduce tasks taking into account the incremental and iterative behavior. An evaluation for the proposed iiHadoop framework is presented using examples of iterative algorithms, and the results showed significant performance improvements over comparable existing frameworks. [ABSTRACT FROM AUTHOR]

Accessioning has long been one of our profession's least visible, discussed, and critically examined activities. This article analyzes our scholarly literature on the subject, revealing an absent theoretical framework and a marked over-reliance on static assumptions about what accessioning entails, to the detriment of both archivists and our collections. The case study that follows describes the creation of an accessioning program at New York University and posits a model of accessioning situated in the context of holistic archival administration, based on both theory and practice, and informed by access-driven workflows and iterative processing strategies. [ABSTRACT FROM AUTHOR]

The application of error control coding (ECC) algorithms to iterative signal cancelation systems is examined. It is shown that in such “turbo” equalization, or cancelation systems, the role of ECC codes extends beyond the mere goal of minimizing error rates. In an iterative cancelation system, the soft-output ECC algorithm has to guarantee convergence of the iterative process, as well as to establish target BERs at that convergence point. Bounds for the output symbol error variance of a general ECC are presented, and ECC systems are examined which approach these bounds in the low SNIR regime and therefore can tolerate the highest level of interference. Guidelines on how to evaluate ECC codes for use in cancelation systems are developed, and near-optimal code choices are discussed. It is shown that such iterative “turbo” equalizers with suitable ECC codes can achieve the capacity of the channel in the case of isotropic random interference, such as arises in certain multiple access and multiple-input multiple-output systems. [ABSTRACT FROM PUBLISHER]

The Nickel-based superalloy 718 is a precipitation-hardened material, especially by the ordered and coherent phases γ" (Ni 3 Nb) and γ″ (Ni 3 (Al, Ti)). In addition to these phases, precipitation of carbides and δ-phase is possible. This material has high strength, creep, and corrosion resistance at temperatures up to 650 °C. However, despite the alloy's outstanding performance under harsh environments, research effort has been put into optimizing its properties and increasing the operation temperature. Manipulation of microstructural parameters such as grain boundary character distribution (GBCD), microtexture, second phase fraction, etc. is frequently betaken in order ot achieve better properties. In this context, this work aimed to assess the influence of Thermomechanical Processing on the recrystallization texture of the material and its influence on the alloy's mechanical behavior. The material was submitted to four iterative processing routes, composed of deformation-heat treatment cycles. The alloy's microtexture data were obtained through the Electron Backscattered Diffraction technique. Data processing was performed using the MTEX toolbox, where Orientation Density Functions were obtained for microtexture analysis. It was observed that the deformation imposed during thermomechanical processing significantly affects the recrystallization texture of the material so that smaller deformations induce the formation of typical recrystallization texture components; on the other hand, high degrees of deformation lead to a retained deformation texture after annealing. Mechanical properties were evaluated by depicting the influence of different strengthening mechanisms and δ-phase fraction on yield strength and ductility, respectively. It was observed that the main strengthening parameters for the alloy were grain boundaries and intrinsic resistance (Pierls-Nabarro and solid solution), meanwhile ductility was greatly influenced by δ-phase and ∑3n boundaries fractions. • Low deformation cycles induced to gradual microtexture weakening of the alloys. • Increasing δ-phase content led to pinning effect and retained deformation texture. • Grain boundaries and lattice friction stress are the main strenghthening mechanisms for the studied conditions. • High δ-phase fraction leads deleterious effect on ductility. [ABSTRACT FROM AUTHOR]

We consider factor-graph-based soft self-iterative equalization in wireless multipath channels. Since factor graphs are able to characterize multipath channels to per-path level, the corresponding soft self-iterative equalizer possesses reduced computational complexity in sparse multipath channels. The performance of the considered self-iterative equalizer is analyzed in both single-antenna and multiple-antenna multipath channels. When factor graphs of multipath channels have no cycles or mild cycle conditions, the considered self-iterative equalizer can converge to optimum performance after a few iterations; but it may suffer local convergence in channels with severe cycle conditions.

Applications in wireless digital communication field are becoming increasingly complex and diverse. Circuits and systems adopted in this application domain must not only consider performance and implementation constraints but also the requirement of flexibility. The combination of flexibility and the ever increasing performance requirements demands design approach that provides better ways of controlling and managing hardware resources. An application-specific instruction-set processor (ASIP) design approach is a key trend in designing flexible architectures. The ASIP concept implies dynamic scheduling of a set of instructions that generally leads to an overhead related to instruction decoding. The no-instruction-set-computer (NISC) concept has been introduced to reduce this overhead through the adoption of static scheduling. In this brief, the NISC approach is explored through a case-study design of universal demapper for multiple wireless standards. The proposed design has common main architectural choices as a state-of-the-art ASIP for comparison purpose. The obtained results illustrate a significant improvement in execution time and implementation area while using identical computational resources and supporting same flexibility parameters. [ABSTRACT FROM PUBLISHER]

This paper proposes a semianalytical performance prediction method for an iterative receiver that processes signals arriving through a frequency-selective multiple-input multiple-output radio channel. Earlier results provide a distribution of the signal-to-interference-plus-noise ratio at the output of a frequency domain soft interference cancellation minimum mean square error equalizer, which is used to approximate the distribution of the log-likelihood ratios (LLRs) of the demapped bits. Mutual information transfer charts establish a bijective relationship between the variance of the LLRs and the mutual information. We assume a 3rd Generation Partnership Project compliant turbo code, whose transfer charts are simulated in advance. By approximating the LLR variance distribution of the demapped equalizer output, we can evaluate the probability of an intersection between an equalizer chart associated with a random channel realization and a fixed decoder chart. This probability provides the frame error rate. Our proposed prediction method allows horizontally and vertically encoded spatial multiplexing schemes. The vertically encoded scheme is complemented by the inclusion of the Chase combining hybrid automatic repeat request protocol. The proposed method allows reasonable receive antenna correlation. [ABSTRACT FROM AUTHOR]

Distributed optimal power flow (OPF) is a challenging non-linear, non-convex problem of central importance to the future power grid. Although many approaches are currently available in the literature, these require some form of central coordination to properly work. In this paper a fully distributed and robust algorithm for OPF is proposed which does not require any form of central coordination. The algorithm is based upon the alternating direction multiplier method (ADMM) in a form recently proposed by the author, which, in turn, builds upon the work of Schizas The approach is customized as a region-based optimization procedure, and it is tested in meaningful scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, a graph-based soft iterative receiver (GSIR) for fast fading channels is investigated. Soft channel estimation as well as soft-output data detection are jointly accomplished via Bayesian inference over a general factor graph. The key feature is a transfer node which enables information flow from one channel node to adjacent channel nodes. The performance characteristics of this receiver is investigated via an EXIT chart analysis and simulation results. Particular emphasis is on a proper channel code design. The algorithm is universally applicable to arbitrary (bijective and non-bijective) modulation formats and can easily be extended to multi-dimensional channel estimation. [ABSTRACT FROM PUBLISHER]

Iterative processing is widely adopted nowadays in modern wireless receivers for advanced channel codes like turbo and LDPC codes. Extension of this principle with an additional iterative feedback loop to the demapping function has proven to provide substantial error performance gain. However, the adoption of iterative demodulation with turbo decoding is constrained by the additional implied implementation complexity, heavily impacting latency and power consumption. In this paper, we analyze the convergence speed of these combined two iterative processes in order to determine the exact required number of iterations at each level. Extrinsic information transfer (EXIT) charts are used for a thorough analysis at different modulation orders and code rates. An original iteration scheduling is proposed reducing two demapping iterations with reasonable performance loss of less than 0.15 dB. Analyzing and normalizing the computational and memory access complexity, which directly impact latency and power consumption, demonstrates the considerable gains of the proposed scheduling and the promising contributions of the proposed analysis. [ABSTRACT FROM PUBLISHER]

Abstract: The combined use of available spectral and spatial information for object detection, which has been promoted by the advent of high spatial resolution hyperspectral imaging devices, now seems essential for many application domains (characterization of urban areas, agriculture, etc.). The proposed approach called “butterfly” is focusing on this issue and realizes a spectral–spatial cooperation scheme to split images into spectrally homogeneous adjoining regions (segmentation). The main idea of the method is to extract spatial and spectral features simultaneously. For achieving this goal, it establishes some correspondences between the spatial and the spectral concepts, in order to run alternately in the two spaces. Thus, the notion of partition specific to the spatial space is associated with the notion of classes in the spectral space. In parallel, the concept of latent variable owing to the spectral space is associated with the notion of image plans in the spatial space. The proposed scheme is therefore to update the features specific to each space (i.e. partition, classes, latent variables and plans) by the knowledge of the features in the complementary space and this recursively. An implementation of this generic scheme using a split and merge strategy is given. Experimental results are presented for a synthetic image and two real hyperspectral images with different spatial resolution. Results on the set of real images are also compared to those obtained with conventional approaches. [Copyright &y& Elsevier]

The mitigation of 2-D intersymbol interference (ISI) has recently emerged as an important problem in the field of storage technology and wireless communications. Several solutions based on message passing have been proposed to perform equalization on 2-D channels with ISI. We present an algorithm based on Gaussian belief propagation (GaBP). We show that the proposed algorithm has interesting features compared with the corresponding message passing solutions available in the literature. First, the complexity of the proposed equalizer is independent of the size of the constellation used for modulation. Second, since the complexity is not exponential in the size of the state space, the computational burden is reduced for channels with long memory. As an application, we consider decentralized intercell interference mitigation in uplink cellular networks, where base stations communicate with their neighbors to recover their own users' signals. [ABSTRACT FROM PUBLISHER]

Transmission power allocation in single-carrier multiple-input multiple-output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean-squared error (MMSE) equalization is considered. A novel framework for transmission power minimization subject to equalizer convergence constraints, referred as convergence constrained power allocation (CCPA) method, is proposed based on extrinsic information transfer (EXIT) chart analysis. The proposed method decouples the spatial interference between the streams using singular value decomposition (SVD), and minimizes the transmission power while achieving the target mutual information for each stream after iterations at the receiver. We show that the transmission power optimization can be formulated as a convex optimization problem. Three CCPA methods, one approximately optimal, and other two heuristic methods inspired by the Lagrange duality are derived. The numerical results demonstrate that the proposed scheme outperforms the existing linear precoding schemes. Moreover, the proposed heuristic schemes can achieve performance close with that of the approximately optimal method in terms of the equalizer convergence properties as well as transmission power. [ABSTRACT FROM PUBLISHER]

For turbo-coded differential phase-shift-keying (DPSK) systems under correlated flat-fading channels, a serial iterative structure of soft-input-soft-output (SISO) forward/backward MAP (FB-MAP) detection and SISO decoder is proposed to reduce the computational complexity of MAP detection without performance degradation. The simulation results show that the FB-MAP detection scheme offers the same or better bit error rate (BER) performance with lower computational complexity when compared with the a posteriori probability (APP) demodulator, the SISO MAP detection, and the SISO multiple-symbol differential sphere decoder (MSDSD) scheme. Although the computational complexity is higher than the iterative filtered multiple differential detection (IF-MDD) scheme, the proposed detection scheme can have better BER performance. [ABSTRACT FROM AUTHOR]

This paper presents a maximum a posteriori probability (MAP) detector, based on a forward-only algorithm that can achieve high through puts. The MAP algorithm is optimal in terms of bit error rate (BER) performance and, with Turbo processing, can approach performance close to the channel capacity limit. The implementation benefits from optimizations performed at both algorithm and circuit level. The proposed detector utilizes a deep-pipelined architecture implemented in skew-tolerant domino and experimentally measured results verify the detector can achieve throughputs greater than 750 Mb/s while consuming 2.4 W. The 16-state EEPR4 channel detector is implemented in a 0.13 µm CMOS technology and has a core area of 7.1 mm². [ABSTRACT FROM AUTHOR]

The present article deals with the analysis of grain-boundary character distribution (GBCD) and microstructural characteristics after iterative processing of austenitic stainless steel, AISI 316L. The steel was subjected to iterative cold reduction and subsequent annealings. After an initial decrease in the fraction of Σ3 boundaries, the number of these increases in subsequent steps. The results relate the importance of iterative processing and the mechanism of obtaining a higher fraction of Σ3 boundaries. [ABSTRACT FROM AUTHOR]

In this correspondence, we investigate the decision-feedback detection for multiple-input multiple-output intersymbol-interference (MIMO-ISI) channels. First, a novel constrained symbol-by-symbol decision-feedback detector (DFD) is proposed, in which a constraint on the feedback filter provides robustness against error propagation and outperforms the conventional DFD. However, we find that an error floor is observed at high signal-to-noise ratios (SNRs) if a fixed constraint is used. To resolve this problem, we then propose an iterative symbol-by-symbol DFD in which an adaptive constraint is implicitly used to update the DFD's coefficients. Simulation results show that the error-floor problem is overcome and that the performance becomes satisfactory at high SNRs through iterations. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a new noise suppression technique that executes iterative processing and sets parameters that are suited for that processing in the spectral subtraction method, which is a noise reduction technique for noise-added speech. Iterative processing is a technique in which speech enhancement processing is executed by considering the estimated speech that is obtained when noise reduction processing is executed once as the input signal again so that a reduction of the residual noise is anticipated. A further reduction of residual noise for which speech degradation is controlled can be achieved by adjusting the parameters for each iteration. The authors also simultaneously propose a technique for maintaining the real-time nature of spectral subtraction when the proposed technique is executed. They use actual speech to which white noise, automobile noise, and crowd babble noise is added to compare the characteristics of the two proposed methods with the conventional spectral subtraction method and its improvements. The authors verified according to objective and subjective evaluations that each proposed technique showed superior results in all noisy environments. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(4): 39 –51, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20242 [ABSTRACT FROM AUTHOR]

We consider a communication scenario in which a message is received in the presence of partial-time Gaussian jamming and additive white Gaussian noise. We consider a quasi-static channel, in which the amplitude and phase are constant over each packet transmission. The receiver does not know the amplitude and phase of the incoming signal, which symbols are jammed, or even the statistics of the jammer, such as the jamming power and jamming probability. In this scenario, the receiver must accurately estimate the parameters of the channel and the jamming to achieve good performance. We apply the expectation-maximization (EM) algorithm to iteratively approximate the maximum-likelihood (ML) estimator for all of the parameters. We find that the overall performance of the EM algorithm is very sensitive to the initial estimates, so we propose a new initial estimator that offers good performance. The EM algorithm approach requires pilot symbols to resolve a phase ambiguity. Thus, we also present a blind estimation algorithm to avoid the reduction in overall code rate from the use of pilot symbols. [ABSTRACT FROM AUTHOR]

This paper deals with joint data detection, synchronization and channel parameter estimation for direct-sequence code-division multiple-access systems over frequency-selective channels. In low-signal-to-noise ratio environments, conventional data-aided (DA) estimation algorithms may require an unacceptably large number of pilot symbols in order to obtain sufficiently accurate estimates of the channel and synchronization parameters. Especially, frequency offset estimation results in a significant loss in spectral efficiency. In this contribution, we consider several code-aided estimation schemes which can be incorporated in an iterative turbo detection scheme. We consider the expectation maximization algorithm, as well as the space alternating generalized expectation maximization algorithm as tools to develop code-aided estimation algorithms for a variety of scenarios. We pay special attention to the issue of computational complexity, and propose some complexity-reducing approximations. We show through computer simulations that the proposed code-aided estimation techniques considerably outperform their conventional DA counterparts. [ABSTRACT FROM AUTHOR]

This paper proposes a new RAKE receiver incorporated with a bidirectional iterative intersymbol interference (ISI) canceller in order to reinforce multipath robustness of high-rate direct-sequence spread-spectrum complementary code keying (DSSS/CCK) systems. The proposed RAKE receiver first combines multipath signal components through a channel matched filter (CMF) and removes postcursor-ISI by employing a code. word decision feedback equalizer (DFE). Then, a CCK codeword detector tentatively determines the current CCK codeword symbol and reuses it to subtract precursor-ISI from the previous symbol. Therefore, the ultimate symbol decision is made using the delayed signal with both postcursor-ISI and precursor-ISI cancelled. The detection performance can be more improved through an iterative refinement processing between the postcursor and the precursor components. Simulation results exhibit a significantly improved error rate performance of the proposed receiver compared with that of the legacy RAKE receiver employing only a postcursor DFE. The additional cost for realization of the proposed receiver is one symbol decision delay and reuse complexity of the DFE and the codeword detector. [ABSTRACT FROM AUTHOR]

In this paper, we consider iterative space-time multiuser detection and channel parameter estimation in a bit-interleaved coded modulation scheme for asynchronous direct-sequence code division multiple access (DS-CDMA) transmission over frequency selective, slowly fading channels. Accurate estimation of the channel parameters is critical as it has great impact on the overall BER performance. We present an iterative space-time multiuser (STMU) turbo detection and estimation scheme, based on space alternating generalized expectation maximization (SAGE) algorithm. This algorithm operates on the coded symbols by exchanging soft information between the detector and the estimator. We show through computer simulations that the proposed low complexity STMU receiver considerably outperforms conventional estimation schemes and achieves excellent performance, both in terms of BER and estimation error variance. Finally, we will consider different mapping strategies and investigate their impact on the performance and complexity of the estimator. [ABSTRACT FROM AUTHOR]

Presented in this paper is a pipelined 285-MHz maximum a posteriori probability (MAP) decoder IC. The 8.7-mm² IC is implemented in a 1.8-V 0.18-μm CMOS technology and consumes 330 mW at maximum frequency. The MAP decoder chip features a block-interleaved pipelined architecture, which enables the pipelining of the add-compare-select kernels. Measured results indicate that a turbo decoder based on the presented MAP decoder core can achieve: 1) a decoding throughput of 27.6 Mb/s with an energy-efficiency of 2.36 nJ/b/iter; 2) the highest clock frequency compared to existing 0.18-μm designs with the smallest area; and 3) comparable throughput with an area reduction of 3-4.3 ⊗ with reference to a look-ahead based high-speed design (Radix-4 design), and a parallel architecture. [ABSTRACT FROM AUTHOR]

Studies linear complexity detection with iterative processing for space-time coded systems by researchers from Montreal, Quebec. Examination on wireless data transmission over multi-input, multi-output (MIMO) channels when only the receiver has perfect channel state information; Derivation of the linear-complexity space-time detector in the context of the turbo-code-based space-time transmitter and iterative processing; Elucidation that the proposed receivers suitable for uplink transmission perform at a level within 2 to 3 decibel of the ergonomic channel capacity on MIMO channels.

We consider factor-graph-based soft self-iterative equalization in wireless multipath channels. Since factor graphs are able to characterize multipath channels to per-path level, the corresponding soft self-iterative equalizer possesses reduced computational complexity in sparse multipath channels. The performance of the considered self-iterative equalizer is analyzed in both single-antenna and multiple-antenna multipath channels. When factor graphs of multipath channels have no cycles or mild cycle conditions, the considered self-iterative equalizer can converge to optimum performance after a few iterations; but it may suffer local convergence in channels with severe cycle conditions.