Relevant bibliographies by topics / Iterative detection

Academic literature on the topic 'Iterative detection'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Iterative detection"

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Mejri, Rafika, and Taoufik Aguili. "Modeling of Radiating Aperture Using the Iterative Method." Detection 09, no. 03 (2022): 29–36. http://dx.doi.org/10.4236/detection.2022.93003.

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Damnjanovic, Aleksandar D., and Branimir R. Vojcic. "Iterative multiuser detection." Journal of Communications and Networks 3, no. 3 (September 2001): 1–8. http://dx.doi.org/10.1109/jcn.2001.6596792.

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Poor, H. V. "Iterative multiuser detection." IEEE Signal Processing Magazine 21, no. 1 (January 2004): 81–88. http://dx.doi.org/10.1109/msp.2004.1267051.

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Zhang, Jicun, Jiyou Fei, Xueping Song, and Jiawei Feng. "An Improved Louvain Algorithm for Community Detection." Mathematical Problems in Engineering 2021 (November 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/1485592.

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Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.
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Tang, Chuan, Cang Liu, Luechao Yuan, and Zuocheng Xing. "Approximate iteration detection with iterative refinement in massive MIMO systems." IET Communications 11, no. 7 (May 11, 2017): 1152–57. http://dx.doi.org/10.1049/iet-com.2016.0826.

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Delgado Castro, Alejandro, and John E. Szymanski. "Multipitch estimation based on the iterative detection and separation of note events from single-channel polyphonic recordings." Journal of the Acoustical Society of America 154, no. 4 (October 1, 2023): 2625–41. http://dx.doi.org/10.1121/10.0021886.

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Multiple fundamental frequency estimation has been extensively used in applications such as melody extraction, music transcription, instrument identification, and source separation. This paper presents an approach based on the iterative detection and extraction of note events, which are considered to be harmonic sounds characterised by a continuous pitch trajectory. Note events are assumed to be associated with musical notes being played by a single instrument, and their pitch trajectories are iteratively estimated. In every iteration, the pitch contour of the predominant note event is selected from a set of pitch estimates and used to separate its spectral energy from the input mixture in order to obtain a residual signal, which is then used as input in the next iteration. This iterative process stops when the energy of the residual is below a significance threshold. The pitch trajectories of all detected note events are then revised and reassembled to form the final set of pitch estimates for the original audio input. Evaluation of performance is conducted in different scenarios to show the potential of the proposed system, both in terms of its accuracy, and also as an initial stage in other complex tasks, such as note tracking and multipitch streaming.
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Herschel, Melanie, Felix Naumann, Sascha Szott, and Maik Taubert. "Scalable Iterative Graph Duplicate Detection." IEEE Transactions on Knowledge and Data Engineering 24, no. 11 (November 2012): 2094–108. http://dx.doi.org/10.1109/tkde.2011.99.

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Deng, Yuhao, Chengliang Chai, Lei Cao, Nan Tang, Jiayi Wang, Ju Fan, Ye Yuan, and Guoren Wang. "MisDetect: Iterative Mislabel Detection using Early Loss." Proceedings of the VLDB Endowment 17, no. 6 (February 2024): 1159–72. http://dx.doi.org/10.14778/3648160.3648161.

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Supervised machine learning (ML) models trained on data with mislabeled instances often produce inaccurate results due to label errors. Traditional methods of detecting mislabeled instances rely on data proximity, where an instance is considered mislabeled if its label is inconsistent with its neighbors. However, it often performs poorly, because an instance does not always share the same label with its neighbors. ML-based methods instead utilize trained models to differentiate between mislabeled and clean instances. However, these methods struggle to achieve high accuracy, since the models may have already overfitted mislabeled instances. In this paper, we propose a novel framework, MisDetect, that detects mislabeled instances during model training. MisDetect leverages the early loss observation to iteratively identify and remove mislabeled instances. In this process, influence-based verification is applied to enhance the detection accuracy. Moreover, MisDetect automatically determines when the early loss is no longer effective in detecting mislabels such that the iterative detection process should terminate. Finally, for the training instances that MisDetect is still not certain about whether they are mislabeled or not, MisDetect automatically produces some pseudo labels to learn a binary classification model and leverages the generalization ability of the machine learning model to determine their status. Our experiments on 15 datasets show that MisDetect outperforms 10 baseline methods, demonstrating its effectiveness in detecting mislabeled instances.
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Yang, Sen, Zerun Li, Jinhui Wei, and Zuocheng Xing. "Deep learning-aided high-precision data detection for massive MU-MIMO systems." MATEC Web of Conferences 336 (2021): 04007. http://dx.doi.org/10.1051/matecconf/202133604007.

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The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.
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Wang, Zhicheng, Rong Li, Zhihao Shao, Mengxin Ma, Jianhui Liang, Weizhao Liu, Jie Wang, and Yongli Liu. "Adaptive Harris corner detection algorithm based on iterative threshold." Modern Physics Letters B 31, no. 15 (May 26, 2017): 1750181. http://dx.doi.org/10.1142/s0217984917501810.

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An adaptive Harris corner detection algorithm based on the iterative threshold is proposed for the problem that the corner detection algorithm must be given a proper threshold when the corner detection algorithm is extracted. In order to avoid the phenomenon of clustering and restrain the pseudo corner, this algorithm realizes the adaptive threshold selection by iteration instead of the threshold value of the Harris corner detection algorithm. Simulation results show that the proposed method achieves good results in terms of threshold setting and feature extraction.
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Dissertations / Theses on the topic "Iterative detection"

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Shaheem, Asri. "Iterative detection for wireless communications." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0223.

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[Truncated abstract] The transmission of digital information over a wireless communication channel gives rise to a number of issues which can detract from the system performance. Propagation effects such as multipath fading and intersymbol interference (ISI) can result in significant performance degradation. Recent developments in the field of iterative detection have led to a number of powerful strategies that can be effective in mitigating the detrimental effects of wireless channels. In this thesis, iterative detection is considered for use in two distinct areas of wireless communications. The first considers the iterative decoding of concatenated block codes over slow flat fading wireless channels, while the second considers the problem of detection for a coded communications system transmitting over highly-dispersive frequency-selective wireless channels. The iterative decoding of concatenated codes over slow flat fading channels with coherent signalling requires knowledge of the fading amplitudes, known as the channel state information (CSI). The CSI is combined with statistical knowledge of the channel to form channel reliability metrics for use in the iterative decoding algorithm. When the CSI is unknown to the receiver, the existing literature suggests the use of simple approximations to the channel reliability metric. However, these works generally consider low rate concatenated codes with strong error correcting capabilities. In some situations, the error correcting capability of the channel code must be traded for other requirements, such as higher spectral efficiency, lower end-to-end latency and lower hardware cost. ... In particular, when the error correcting capabilities of the concatenated code is weak, the conventional metrics are observed to fail, whereas the proposed metrics are shown to perform well regardless of the error correcting capabilities of the code. The effects of ISI caused by a frequency-selective wireless channel environment can also be mitigated using iterative detection. When the channel can be viewed as a finite impulse response (FIR) filter, the state-of-the-art iterative receiver is the maximum a posteriori probability (MAP) based turbo equaliser. However, the complexity of this receiver's MAP equaliser increases exponentially with the length of the FIR channel. Consequently, this scheme is restricted for use in systems where the channel length is relatively short. In this thesis, the use of a channel shortening prefilter in conjunction with the MAP-based turbo equaliser is considered in order to allow its use with arbitrarily long channels. The prefilter shortens the effective channel, thereby reducing the number of equaliser states. A consequence of channel shortening is that residual ISI appears at the input to the turbo equaliser and the noise becomes coloured. In order to account for the ensuing performance loss, two simple enhancements to the scheme are proposed. The first is a feedback path which is used to cancel residual ISI, based on decisions from past iterations. The second is the use of a carefully selected value for the variance of the noise assumed by the MAP-based turbo equaliser. Simulations are performed over a number of highly dispersive channels and it is shown that the proposed enhancements result in considerable performance improvements. Moreover, these performance benefits are achieved with very little additional complexity with respect to the unmodified channel shortened turbo equaliser.
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Moher, Michael L. "Cross-entropy and iterative detection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22171.pdf.

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Moher, Michael L. Carleton University Dissertation Engineering Systems and Computer. "Cross-entropy and iterative detection." Ottawa, 1997.

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El-Hajjar, Mohammed H. "Near-capacity MIMOs using iterative detection." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64487/.

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In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to provide a diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing with SP. Furthermore, in order to attain additional performance gains, the DSTS system invokes iterative detection, where the outer code is constituted by a Recursive Systematic Convolutional (RSC) code, while the inner code is a SP demapper in one of the prototype systems investigated, while the other scheme employs a Unity Rate Code (URC) as its inner code in order to eliminate the error floor exhibited by the system dispensing with URC. EXIT charts are used to analyse the convergence behaviour of the iteratively detected schemes and a novel technique is proposed for computing the maximum achievable rate of the system based on EXIT charts. Explicitly, the four-antenna-aided DSTSSP system employing no URC precoding attains a coding gain of 12 dB at a BER of 10−5 and performs within 1.82 dB from the maximum achievable rate limit. By contrast, the URC aided precoded system operates within 0.92 dB from the same limit. On the other hand, in order to maximise the DSTS system’s throughput, an adaptive DSTSSP scheme is proposed that exploits the advantages of differential encoding, iterative decoding as well as SP modulation. The achievable integrity and bit rate enhancements of the system are determined by the following factors: the specific MIMO configuration used for transmitting data from the four antennas, the spreading factor used and the RSC encoder’s code rate. Additionally, multi-functional MIMO techniques are designed to provide diversity gains, multiplexing gains and beamforming gains by combining the benefits of space-time codes, VBLAST and beamforming. First, a system employing Nt=4 transmit Antenna Arrays (AA) with LAA number of elements per AA and Nr=4 receive antennas is proposed, which is referred to as a Layered Steered Space-Time Code (LSSTC). Three iteratively detected near-capacity LSSTC-SP receiver structures are proposed, which differ in the number of inner iterations employed between the inner decoder and the SP demapper as well as in the choice of the outer code, which is either an RSC code or an Irregular Convolutional Code (IrCC). The three systems are capable of operating within 0.9, 0.4 and 0.6 dB from the maximum achievable rate limit of the system. A comparison between the three iteratively-detected schemes reveals that a carefully designed two-stage iterative detection scheme is capable of operating sufficiently close to capacity at a lower complexity, when compared to a three-stage system employing a RSC or a two-stage system using an IrCC as an outer code. On the other hand, in order to allow the LSSTC scheme to employ less receive antennas than transmit antennas, while still accommodating multiple users, a Layered Steered Space-Time Spreading (LSSTS) scheme is proposed that combines the benefits of space-time spreading, V-BLAST, beamforming and generalised MC DS-CDMA. Furthermore, iteratively detected LSSTS schemes are presented and an LLR post-processing technique is proposed in order to improve the attainable performance of the iteratively detected LSSTS system. Finally, a distributed turbo coding scheme is proposed that combines the benefits of turbo coding and cooperative communication, where iterative detection is employed by exchanging extrinsic information between the decoders of different single-antenna-aided users. Specifically, the effect of the errors induced in the first phase of cooperation, where the two users exchange their data, on the performance of the uplink in studied, while considering different fading channel characteristics.
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Xu, Danfeng. "Iterative coded multiuser detection using LDPC codes." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27939.

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Multiuser detection (MUD) has been regarded as an effective technique for combating cochannel interference (CCI) in time-division multiple access (TDMA) systems and multiple access interference (MAI) in code-division multiple access (CDMA) systems. An optimal multiuser detector for coded multiuser systems is usually practically infeasible due to the associated complexity. An iterative receiver consisting of a soft-input soft-output (SISO) multiuser detector and a bank of SISO single user decoders can provide a system performance which approaches to that of single user system after a few iterations. In this thesis, MUD and LDPC decoding are combined to improve the multiuser receiver performance. The soft output of the LDPC decoder is fed back to the multiuser detector to improve the detection. This leads to decision variables that have a smaller MAI component. These decision variables are then returned to the decoder and the decoding process benefits from the improvement to the decision variables. The process can be repeated many times. The resulting iterative multiuser receiver is designed based on the soft parallel interference cancellation (PIC) algorithm. For the interference reconstruction, the LDPC decoder is improved to produce the log-likelihood ratios (LLR) of the information bits as well as the parity bits. A sub-optimal approach is proposed to output the LLR of the parity bits with very low complexity. Thanks to the powerful error-correction ability of the LDPC decoder, the LDPC multiuser receiver can achieve a satisfactory convergence, and substantially outperforms non-iterative receivers. Three types of SISO multiuser detectors are provided. They are: Soft Interference Cancellation (SIC) detector, SISO decorrelating detector and SISO minimum mean square error (MMSE) detector. The resulting system performance converges very quickly. The comparison of these three types of detectors is also shown in this thesis.
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Valenti, Matthew C. "Iterative Detection and Decoding for Wireless Communications." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28290.

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Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or more Markov processes; the output of one or more of the Markov processes is observed through noise; based on the observations, the original data set is estimated. This dissertation specifically describes the process of encoding and decoding turbo codes. In addition, a more general discussion of iterative decoding is presented. Then, several new applications of iterative decoding are proposed and investigated through computer simulation. The new applications solve two categories of problems: the detection of turbo codes over time-varying channels, and the distributed detection of coded multiple-access signals. Because turbo codes operate at low signal-to-noise ratios, the process of phase estimation and tracking becomes difficult to perform. Additionally, the turbo decoding algorithm requires precise estimates of the channel gain and noise variance. The first significant contribution of this dissertation is a study of several methods of channel estimation suitable specifically for turbo coded systems. The second significant contribution of this dissertation is a proposed method for jointly detecting coded multiple-access signals using observations from several locations, such as spatially separated base stations. The proposed system architecture draws from the concepts of macrodiversity combining, multiuser detection, and iterative decoding. Simulation results show that when the system is applied to the time division multiple-access cellular uplink, a significant improvement in system capacity results.
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Marsland, Ian D. "Iterative noncoherent detection of differentially encoded M-PSK." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0016/NQ46386.pdf.

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Wong, Eddy. "Iterative decoding of coded GMSK with discriminator detection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63037.pdf.

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Wu, Zining. "Coding and iterative detection for magnetic recording channels /." Boston, Mass. [u.a.] : Kluwer Academic Publ, 2000. http://www.loc.gov/catdir/enhancements/fy0820/99049501-d.html.

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Balasubramanyam, Ramkumar. "Adaptive iterative multiuser detection for wireless communication systems." Thesis, University of Greenwich, 2008. http://gala.gre.ac.uk/8203/.

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Wireless multi-user communication systems that operate in a low signal to interference noise ratio (SINR) region are studied in this thesis. This thesis examines a class of wireless communication systems that employs an adaptive receiver for multi-user symbol detection that operates in a low SINR (< 5 dB) region. Since the knowledge of channel-parameter estimates is unavailable at the receiver, a pilot (training) sequence is applied in the communication system, to learn the channel state information (CSI) at the receiver. In studying the classical view of a DFE, the mean square error (MSE) behaviour follows the bit error rate (BER) performance. Certain original results are obtained using the classical adaptive DFE to achieve minimum MSE, employing the least mean square (LMS) algorithm. The results thus obtained for an uncoded adaptive receiver system are applied to a coded system, transmitting either recursive systematic code (RSC) or turbo-code through a spread-spectrum multiuser multiple-path channel, which are referred to as two-stage and three-stage systems respectively in this thesis. The following claims are made based on the findings of this thesis: 1. It is known that a receiver implementing DFE can mitigate symbol-interference completely at high SINR. An adaptive LMS DFE realizes this by adapting the forward and backward filter coefficients with respective step-size constants. The classical approach to realizing interference mitigation was to set the forward and backward adaptation constants as the same. While this approach has provided interference mitigation at high SINR, it has been shown in this thesis that such an approach does not yield complete interference mitigation, even at high SINR. Instead, using different step-size constants at the backward and forward step-size constants provides the required optimality. 2. A decision feedback detector (DFD) mitigates the effects of interference on the information symbols that are transmitted through this communication channel. This thesis shows that an adaptive (LMS) DFD, using unequal compared to equal step-size constants to update the forward and backward filter coefficients, has a steady-state MSE improvement for an uncoded frequency selective communication channel. This thesis shows that, when the knowledge of CSI is not assumed to be known at a wireless receiver, a three-stage receiver has a BER performance improvement and operates at a lower SINR, without any additional computational complexity compared to a two-stage receiver.
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Books on the topic "Iterative detection"

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. Iterative Detection. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6.

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Chugg, Keith M. Iterative detection: Adaptivity, complexity reduction, and applications. Boston, Mass: Kluwer Academic Publishers, 2001.

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Achilleas, Anastasopoulos, and Chen Xiaopeng, eds. Iterative detection: Adaptivity, complexity reduction, and applications. Boston, Mass: Kluwer Academic Publishers, 2001.

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Chugg, Keith M. Iterative Detection: Adaptivity, Complexity Reduction, and Applications. Boston, MA: Springer US, 2001.

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Wong, Eddy. Iterative decoding of coded GMSK with discriminator detection. Ottawa: National Library of Canada, 2001.

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Wu, Zining. Coding and Iterative Detection for Magnetic Recording Channels. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4565-1.

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Wu, Zining. Coding and iterative detection for magnetic recording channels. Boston: Kluwer Academic, 2000.

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Wu, Zining. Coding and Iterative Detection for Magnetic Recording Channels. Boston, MA: Springer US, 2000.

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1952-, Hanzo Lajos, ed. Near-capacity multi functional MIMO systems: Sphere-packing, iterative detection, and cooperation. Chichester, West Sussex, U.K: Wiley, 2009.

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Coding and Iterative Detection for Magnetic Recording Channels. Springer, 2011.

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Book chapters on the topic "Iterative detection"

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Overview of Non-Iterative Detection." In Iterative Detection, 1–76. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_1.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Principles of Iterative Detection." In Iterative Detection, 77–191. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_2.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Iterative Detection for Complexity Reduction." In Iterative Detection, 193–238. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_3.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Adaptive Iterative Detection." In Iterative Detection, 239–71. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_4.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Applications in Two Dimensional Systems." In Iterative Detection, 273–313. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_5.

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Beerel, Peter A. "Implementation Issues: A Turbo Decoder Design Case Study." In Iterative Detection, 315–40. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_6.

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Lehti, Patrick, and Peter Fankhauser. "Probabilistic Iterative Duplicate Detection." In Lecture Notes in Computer Science, 1225–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575801_19.

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Dörpinghaus, Meik. "Iterative Code-Aided Synchronized Detection." In On the Achievable Rate of Stationary Fading Channels, 101–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19780-2_6.

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Toyoda, Kenta, and Kazuhiro Hotta. "Abnormal Detection by Iterative Reconstruction." In Advances in Visual Computing, 443–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50832-0_43.

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Bai, Lin, Jinho Choi, and Quan Yu. "Iterative Channel Estimation and Detection." In Low Complexity MIMO Receivers, 215–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04984-7_9.

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Conference papers on the topic "Iterative detection"

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Hao, Dapeng, and Peter Adam Hoeher. "Superposition modulation with reliability-based hybrid detection." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613858.

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Vehkapera, Mikko, Keigo Takeuchi, Ralf R. Muller, and Toshiyuki Tanaka. "Iterative channel estimation, detection, and decoding in large CDMA systems." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613863.

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Li, Bing, Baoming Bai, and Mengyu Huang. "A robust noncoherent iterative detection algorithm for serially concatenated CPM." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613893.

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Qi, Wei, Wei Li, and Qian Chen. "Iterative Saliency Detection." In 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2020. http://dx.doi.org/10.1109/aemcse50948.2020.00078.

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Wang, Yulei, Bai Xue, Lin Wang, Hsiao-Chi Li, Li-Chien Lee, Chunyan Yu, Meiping Song, Sen Li, and Chein-I. Chang. "Iterative anomaly detection." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127021.

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Raouafi, Fathi, Taoufik Majoul, and Meriem Jaidane. "Turbo codes behavior over near impulsive noisy channels: Audio watermark detection case." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613830.

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Chen, Danshan, and Alister G. Burr. "Adaptive linear precoding for iterative maximum likelihood detection in multi-antenna systems." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613850.

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Senanayake, Bathiya, and C. Reed Mark. "Multi-dimensional EXIT analysis for iterative multi-user detection with unequal power allocation." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613811.

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Yang, Jianxiao, Charbel Abdel Nour, and Charlotte Langlais. "Joint factor graph detection for LDPC and STBC coded MIMO systems: A new framework." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613820.

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Mishne, Gal, and Israel Cohen. "Iterative diffusion-based anomaly detection." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952443.

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Reports on the topic "Iterative detection"

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Song, H. C., Karim Sabra, W. A. Kuperman, and W. S. Hodgkiss. Multi-Static Detection and Localization of Buried Targets using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada494990.

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Kuperman, W. A., Karim Sabra, and Philippe Roux. Multi-Static Detection and Localization of Buried Targets Using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612231.

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Kuperman, W. A., and Karim Sabra. Multi-Static Detection and Localization of Buried Targets using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada541152.

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Wen, Qingsong, Minzhen Ren, and Xiaoli Ma. Fixed-point Design of the Lattice-reduction-aided Iterative Detection and Decoding Receiver for Coded MIMO Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada586964.

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Hamlet, Benjamin, Ryan Prescott, John Burns, and Steven Kubica. IDC Re-Engineering Phase 2 Iteration E2 Draft Component Interface Specification: Signal Detection Control. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1761997.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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Abstract:
In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.
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