Journal articles: '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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YING, YINGZI, and LI MA. "VOLUME CLUTTER ELIMINATION, ROUGH INTERFACE REVERBERATION SUPPRESSION, AND TARGET RESONANCE CONVERGENCE IN HETEROGENEOUS MEDIA USING AN ITERATIVE TIME REVERSAL MIRROR." Journal of Computational Acoustics 18, no. 03 (September 2010): 227–43. http://dx.doi.org/10.1142/s0218396x10004140.

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The presence of the clutter of volume scattering and the echo return from rough interface hinders the detection of target in heterogeneous media. This work investigates the application of an iterative time reversal mirror to mitigate the difficulties. Numerical simulations based on pseudospectral finite-difference time-domain method are performed in one and two layered media. A wideband probe pulse is launched to initiate the process, and the time-reversed echo received at the same position is retransmitted as the renewed input signal for next iteration, and repeat the procedures iteratively. The results illustrate as the number of iteration increases, small volume clutter is eliminated, interface reverberation is suppressed relatively, and the echoes will converge to a time-harmonic waveform that corresponds to an object's dominant resonance mode. The detection of target is achieved by extracting this important acoustic signature.

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Rukhovich, D. D. "Iterative Scheme for Object Detection in Crowded Environments." Programmnaya Ingeneria 12, no. 1 (January 22, 2021): 31–39. http://dx.doi.org/10.17587/prin.12.31-39.

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Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using non-maximum suppression (NMS) in order to select exactly one bounding box per object of interest. This greedy scheme is simple and provides sufficient accuracy for isolated objects but often fails in crowded environments, since one needs to both preserve boxes for different objects and suppress duplicate detections. In this work we develop an alternative iterative scheme, where a new subset of objects is detected at each iteration. Detected boxes from the previous iterations are passed to the network at the following iterations to ensure that the same object would not be detected twice. This iterative scheme can be applied to both one-stage and two-stage object detectors with just minor modifications of the training and inference procedures. We perform extensive experiments with two different baseline detectors on four datasets and show significant improvement over the baseline, leading to state-of-the-art performance on CrowdHuman and WiderPerson datasets.

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Zou, Qifeng, Xuezhi Tan, Mei Liu, and Lin Ma. "Main-Branch Structure Iterative Detection Using Approximate Message Passing for Uplink Large-Scale Multiuser MIMO Systems." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2832584.

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The emerging large-scale/massive multi-input multioutput (MIMO) system combined with orthogonal frequency division multiplexing (OFDM) is considered a key technology for its advantage of improving the spectral efficiency. In this paper, we introduce an iterative detection algorithm for uplink large-scale multiuser MIMO-OFDM communication systems. We design a Main-Branch structure iterative turbo detector using the Approximate Message Passing algorithm simplified by linear approximation (AMP-LA) and using the Mean Square Error (MSE) criterion to calculate the correlation coefficients between main detector and branch detector for the given iteration. The complexity of our method is compared with other detection algorithms. The simulation results show that our scheme can achieve better performance than the conventional detection methods and have the acceptable complexity.

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Li, Zheng, Xiaocheng Wu, Cui Tu, Junfeng Yang, Xiong Hu, and Zhaoai Yan. "Oxygen and Air Density Retrieval Method for Single-Band Stellar Occultation Measurement." Remote Sensing 16, no. 11 (June 3, 2024): 2006. http://dx.doi.org/10.3390/rs16112006.

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The stellar occultation technique is capable of atmospheric trace gas detection using the molecule absorption characteristics of the stellar spectra. In this paper, the non-iterative and iterative retrieval methods for oxygen and air density detection by stellar occultation are investigated. For the single-band average transmission data in the oxygen 761 nm A-band, an onion-peeling algorithm is used to calculate the effective optical depth of each atmospheric layer, and then the optical depth is used to retrieve the oxygen number density. The iteration method introduces atmospheric hydrostatic equilibrium and the ideal gas equation of state, and it achieves a more accurate retrieval of the air density under the condition of a priori temperature deviation. Finally, this paper analyzes the double solution problem in the iteration process and the ideas to improve the problem. This paper provides a theoretical basis for the development of a new type of atmospheric density detection method.

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Yang, Yanbo. "Signal detection algorithms for massive MIMO system." Applied and Computational Engineering 49, no. 1 (March 22, 2024): 21–30. http://dx.doi.org/10.54254/2755-2721/49/20241052.

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As 5G communication networks are maturing, we have higher and higher requirements for the detection of communication signals. In this paper, for the Massive MIMO system signal detection problem, we mainly summarize the detection algorithms that can be used to replace the traditional ZF and MMSE, so as to avoid large-scale matrix inverse and reduce the computational complexity. It mainly includes the general iterative method, typically represented by SSOR, which makes the transmit signal matrix constantly close to the ideal value by iterating; the other is the level expansion class solution method, which takes the order expansion of the level as the initial value of the iteration to accelerate the convergence rate of the algorithm, typically represented by the MLI algorithm. However, today where the demand for communication is gradually increasing and the number of users is constantly getting larger, the performance of the above algorithms may degrade seriously, so the AI signal detection algorithm is a good alternative, which learns autonomously through deep neural networks, including model-driven and data-driven schemes.

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Gong, Tingkai, Yanbin Yuan, Xiaohui Yuan, Xiyang Wang, Xiaotao Wu, and Yuanzheng Li. "Iterative asymmetric multiscale morphology and its application to fault detection for rolling element bearing." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 2 (January 6, 2017): 316–30. http://dx.doi.org/10.1177/0954406216679690.

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The impulsive signals produced by bearing faults are usually modulated in amplitude. Multiscale morphology is suited to demodulate the signal because of its powerful demodulation ability. However, when the structuring element scales are increased gradually, the multiscale morphology method using closing and/or opening allows the low-amplitude impulses to be eliminated. Therefore, iterative asymmetric multiscale morphology is explored in this paper to handle the problem. Firstly, a modified difference filter is developed based on closing and opening to conduct iterative morphology operation, and then a type of asymmetric-multiscale is designed to set the structuring element scales of the modified difference filter filter for demodulating the fault signal with amplitude modulation well. Meanwhile, iterative morphology is conducted to enhance the impulsive features, and kurtosis acts as the iteration stop condition. The effectiveness of the proposed method is evaluated by both simulation experiment and the vibration signals of rolling element bearings with an inner race, an outer, and a rolling element faults. In comparisons with the conventional multiscale morphology, the results demonstrate that the iterative asymmetric multiscale morphology method has better diagnosis for the bearing faults.

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Li, Xiangshun, Di Wei, Cheng Lei, Zhiang Li, and Wenlin Wang. "Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis." Mathematical Problems in Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/1729612.

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Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.

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Qi, Zhuang, Dazhi Jiang, and Xiaming Chen. "Iterative gradient descent for outlier detection." International Journal of Wavelets, Multiresolution and Information Processing 19, no. 04 (February 22, 2021): 2150004. http://dx.doi.org/10.1142/s0219691321500041.

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In linear regression, outliers have a serious effect on the estimation of regression model parameters and the prediction of final results, so outlier detection is one of the key steps in data analysis. In this paper, we use a mean shift model and then we apply the penalty function to penalize the mean shift parameters, which is conducive to get a sparse parameter vector. We choose Sorted L1 regularization (SLOPE), which provides a convex loss function, and shows good statistical properties in parameter selection. We apply an iterative process which using gradient descent method and parameter selection at each step. Our algorithm has higher computational efficiency since the calculation of inverse matrix is avoided. Finally, we use Cross-Validation rules (CV) and Bayesian Information Criterion (BIC) criteria to fine tune the parameters, which helps our program identify outliers and obtain more robust regression coefficients. Compared with other methods, the experimental results show that our program has a fantastic performance in all aspects of outlier detection.

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Spalvieri, Arnaldo, Mateo Vanoy Marin, Sergio Bianchi, and Michelangelo Ricciulli. "Iterative Feedforward Demodulation and Threshold Detection." IEEE Photonics Technology Letters 33, no. 11 (June 1, 2021): 573–76. http://dx.doi.org/10.1109/lpt.2021.3076414.

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Dinan, Mohamad H., Nemanja Stefan Perovic, and Mark F. Flanagan. "Sparse Layered MIMO With Iterative Detection." IEEE Transactions on Communications 70, no. 3 (March 2022): 2042–56. http://dx.doi.org/10.1109/tcomm.2021.3130679.

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Anastasopoulos, Achilleas, Keith M. Chugg, Giulio Colavolpe, Gianluigi Ferrari, and Riccardo Raheli. "Iterative Detection for Channels With Memory." Proceedings of the IEEE 95, no. 6 (June 2007): 1272–94. http://dx.doi.org/10.1109/jproc.2007.896511.

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Thelen, Andrea, Jens Bongartz, Dominik Giel, Susanne Frey, and Peter Hering. "Iterative focus detection in hologram tomography." Journal of the Optical Society of America A 22, no. 6 (June 1, 2005): 1176. http://dx.doi.org/10.1364/josaa.22.001176.

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Honig, M. L., G. K. Woodward, and Y. Sun. "Adaptive Iterative Multiuser Decision Feedback Detection." IEEE Transactions on Wireless Communications 3, no. 2 (March 2004): 477–85. http://dx.doi.org/10.1109/twc.2003.821173.

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Gurcan, Mustafa K., Dhineth Weliwitegoda, and Girish Chandra. "Improved equalization and joint iterative detection." Journal of the Franklin Institute 349, no. 1 (February 2012): 234–59. http://dx.doi.org/10.1016/j.jfranklin.2011.10.018.

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Deng, Xiaojuan, Feifei Zuo, and Hongwei Li. "Cracks Detection Using Iterative Phase Congruency." Journal of Mathematical Imaging and Vision 60, no. 7 (February 14, 2018): 1065–80. http://dx.doi.org/10.1007/s10851-018-0796-y.

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Malik, Parveen, and Kannan Karthik. "Iterative content adaptable purple fringe detection." Signal, Image and Video Processing 12, no. 1 (July 18, 2017): 181–88. http://dx.doi.org/10.1007/s11760-017-1144-1.

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Wang, Qingbo, Gaoqi Dou, Jiali Liu, Jun Gao, and Ran Deng. "Coded OFDM-IM with iterative detection☆." AEU - International Journal of Electronics and Communications 126 (November 2020): 153331. http://dx.doi.org/10.1016/j.aeue.2020.153331.

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Nguyen, Van K., and Langford B. White. "Iterative Multiuser Detection with Parameter Estimation." Digital Signal Processing 12, no. 2-3 (January 2002): 145–58. http://dx.doi.org/10.1006/dspr.2002.0436.

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Chen, Ying, Yue Tang, Bin Jiang, Yinan Zhao, Jianrong Bao, and Xianghong Tang. "Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems." Sensors 23, no. 24 (December 13, 2023): 9798. http://dx.doi.org/10.3390/s23249798.

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To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

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Patton, Jeffrey M., Ying Cheng, Maxwell Hong, and Qi Diao. "Detection and Treatment of Careless Responses to Improve Item Parameter Estimation." Journal of Educational and Behavioral Statistics 44, no. 3 (February 3, 2019): 309–41. http://dx.doi.org/10.3102/1076998618825116.

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In psychological and survey research, the prevalence and serious consequences of careless responses from unmotivated participants are well known. In this study, we propose to iteratively detect careless responders and cleanse the data by removing their responses. The careless responders are detected using person-fit statistics. In two simulation studies, the iterative procedure leads to nearly perfect power in detecting extremely careless responders and much higher power than the noniterative procedure in detecting moderately careless responders. Meanwhile, the false-positive error rate is close to the nominal level. In addition, item parameter estimation is much improved by iteratively cleansing the calibration sample. The bias in item discrimination and location parameter estimates is substantially reduced. The standard error estimates, which are spuriously small in the presence of careless responses, are corrected by the iterative cleansing procedure. An empirical example is also presented to illustrate the proposed procedure. These results suggest that the proposed procedure is a promising way to improve item parameter estimation for tests of 20 items or longer when data are contaminated by careless responses.

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Zhang, Yongxing, Wenping Ge, Pengju Zhang, Mengyao Gao, and Gecheng Zhang. "A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration." Symmetry 12, no. 10 (October 1, 2020): 1624. http://dx.doi.org/10.3390/sym12101624.

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Polar coding and sparse code multiple access (SCMA) are key technologies for 5G mobile communication, the joint design of them has a great significance to improve the overall performance of the transmitter-receiver symmetric wireless communication system. In this paper, we firstly propose a pruning iterative joint detection and decoding algorithm (PI-JDD) based on the confidence stability of resource nodes. Branches to be updated are dynamically pruned to avoid redundant iterative, which is able to reduce 24~50% complexity while achieving the approximate error performance of traditional serial joint iterative detection and decoding algorithm S-JIDD. Then, to further reduce the bit error rate (BER) of the receiver, a cyclic redundancy check (CRC) termination mechanism is added at the end of each joint iteration to avoid the convergence error caused by decoding deviation. Simulation results show that the addition of an early stopping criterion can achieve a remarkable performance gain compared with the S-JIDD algorithm. More importantly, the combined algorithm of the two proposed schemes can reduce the computational complexity while achieving better error performance.

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Müller-Schauenburg, W., H. Luig, W. Eschner, U. Feine, and P. Reuland. "Vergleich der klinischen Wertigkeit eines iterativen Rekonstruktionsverfahrens mit der gefilterten Rückprojektion bei der SPECT der Leber." Nuklearmedizin 28, no. 04 (1989): 139–44. http://dx.doi.org/10.1055/s-0038-1629485.

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We have tested an iterative reconstruction procedure against the usual filtered back-projection in 14 patients with SPECT-examinations of various liver diseases. The aim of the examinations was to assess the presence of liver tumors in most cases. Further indications were Budd-Chiari syndromes and a liver malconfiguration in one case. Three of six haemangiomas and both liver metastases were better delineated with the iterative method, in one patient the haemangioma was visible only with this method. An irregular pattern after filtered back-projection led to misinterpretation as multiple metastases in another patient in whom there was no irregularity after iteration. Diagnostic improvement was not reached in the Budd-Chiari syndromes or in an atypical liver configuration, with a more homogeneous pattern after iteration however. The iterative reconstruction procedure was superior to the filtered back- projection method in the detection of small focal liver diseases.

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Ahadiat, Mohammad Reza, Paeiz Azmi, and Afrooz Haghbin. "The Mitigation of the Effect of Impulsive Noise in OFDM-PLC Systems." Journal of Research in Science, Engineering and Technology 3, no. 01 (September 13, 2019): 18–25. http://dx.doi.org/10.24200/jrset.vol3iss01pp18-25.

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This paper proposes an new iterative method to recover the signals corrupted by impulsive noise in MIMO-OFDM systems over In-home PLC. In this iterative technique, preliminary decisions are made to get the impulsive noise detection algorithm for finding the locations and amplitudes of the impulses, and then signal estimation block for approximation the signals for two-branch 2*2 MIMO-OFDM at the receiver. In each iteration, this signals approximation are used to improve the noise estimate. After impulsive noise detection, an comparison - decision algorithm is employed to compare two noises estimated, and algorithm. This method use an adaptive threshold and soft decision to estimating and canceling the impulsive noises. Then, by using ML detection, an approximation of signal is obtained. As this impulsive noise detection , comparison - decision and ML detection loop continues, we get better approximates of the signal. The algorithm is analyzed and verified by computer simulations. A comparison between the performance of the different systems is presented and discussed. The Simulation results confirm the robustness of performance of the proposed algorithm.

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Luo, Peng, Buhong Wang, Jiwei Tian, Chao Liu, and Yong Yang. "Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management." Sensors 24, no. 11 (June 2, 2024): 3584. http://dx.doi.org/10.3390/s24113584.

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Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

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Shen, Dong, Li Chen, and Hao Liang. "Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems." Applied Sciences 13, no. 23 (November 24, 2023): 12638. http://dx.doi.org/10.3390/app132312638.

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Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss–Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme’s BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U2), whereas the MMSE detection algorithm has a computational complexity of O(U3), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity.

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Tran, Cuong H., and Seong G. Kong. "An Iterative Learning Scheme with Binary Classifier for Improved Event Detection in Surveillance Video." Electronics 12, no. 15 (July 30, 2023): 3275. http://dx.doi.org/10.3390/electronics12153275.

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This paper presents an iterative training framework with a binary classifier to improve the learning capability of a deep learning model for detecting abnormal behaviors in surveillance video. When a deep learning model trained on data from one surveillance video is deployed to monitor another video stream, its abnormal behavior detection performance often decreases significantly. To ensure the desired performance in new environments, the deep learning model needs to be retrained with additional training data from the new video stream. Iterative training requires manual annotation of the additional training data during the fine-tuning process, which is a tedious and error-prone task. To address this issue, this paper proposes a binary classifier to automatically label false positive data without human intervention. The binary classifier is trained on bounding boxes extracted from the detection model to identify which boxes are true positives or false positives. The proposed learning framework incrementally enhances the performance of the deep learning model for detecting abnormal behaviors in a surveillance video stream through repeated iterative learning cycles. Experimental results demonstrate that the accuracy of the detection model increases from 0.35 (mAP = 0.74) to 0.91 (mAP = 0.99) in just a few iterations.

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Zi-jun, Liu, Wang Rong-rong, and Wei Dong. "Detection Method of Protruding Defects of Parts Based on Improved Threshold Segmentation." Journal of Physics: Conference Series 2506, no. 1 (May 1, 2023): 012002. http://dx.doi.org/10.1088/1742-6596/2506/1/012002.

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Abstract Aiming at a series of problems that the traditional threshold segmentation method has low detection accuracy in detecting part bulge defects, a part bulge defect detection method based on improved threshold segmentation is proposed in this paper. Firstly, the parts to be tested are obtained, and then the image is filtered and denoised. Because the traditional median filter has some limitations, a fast weighted median filtering algorithm is proposed. Then the image is enhanced, and the method based on Laplace operator is used to enhance the image. Then Otsu threshold segmentation and iterative threshold segmentation are used respectively. It is found that there are still many non convex defects, which have a great impact on the detection accuracy. Therefore, an improved iterative threshold segmentation is proposed. The difference operation is carried out between the standard part and the part to be tested, and the above three methods are compared. The results indicate that the better iterative threshold segmentation method can effectively test the convex fault of the compent.

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GAO, Yan, Tai-hua WANG, Fan GUO, and Min YU. "DDoS detection with non-iterative Apriori algorithm." Journal of Computer Applications 31, no. 6 (April 5, 2012): 1521–24. http://dx.doi.org/10.3724/sp.j.1087.2011.01521.

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Di Gesú, Vito, and Bertrand Zavidovique. "Iterative symmetry detection: Shrinking vs. decimating patterns." Integrated Computer-Aided Engineering 12, no. 4 (September 1, 2005): 319–32. http://dx.doi.org/10.3233/ica-2005-12401.

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Yu Jingjing, 余景景, 田. 晶. Tian Jing, 王海玉 Wang Haiyu, and 李启越 Li Qiyue. "Bioluminescence Tomography Based on Iterative Support Detection." Acta Optica Sinica 37, no. 7 (2017): 0711004. http://dx.doi.org/10.3788/aos201737.0711004.

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Aliesawi, Salah A., Charalampos C. Tsimenidis, Bayan S. Sharif, and Martin Johnston. "Iterative Multiuser Detection for Underwater Acoustic Channels." IEEE Journal of Oceanic Engineering 36, no. 4 (October 2011): 728–44. http://dx.doi.org/10.1109/joe.2011.2164954.

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Liu, Xumin, Xiaojun Wang, and Zilong Duan. "Canny Edge Detection Based On Iterative Algorithm." International Journal of Security and Its Applications 8, no. 5 (September 30, 2014): 41–60. http://dx.doi.org/10.14257/ijsia.2014.8.5.05.

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Lee, Jihyeon, Hyeongyong Lim, and Dongweon Yoon. "Soft-Decision Detection for Iterative MIMO Systems." Journal of Korean Institute of Information Technology 15, no. 6 (June 30, 2017): 45–51. http://dx.doi.org/10.14801/jkiit.2017.15.6.45.

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Wang, Yilun, and Wotao Yin. "Sparse Signal Reconstruction via Iterative Support Detection." SIAM Journal on Imaging Sciences 3, no. 3 (January 2010): 462–91. http://dx.doi.org/10.1137/090772447.

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Liu, Lei, Yuhao Chi, Chau Yuen, Yong Liang Guan, and Ying Li. "Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection." IEEE Transactions on Signal Processing 67, no. 7 (April 1, 2019): 1758–73. http://dx.doi.org/10.1109/tsp.2019.2896242.

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Rui Zhang and J. M. Cioffi. "Iterative Spectrum Shaping with Opportunistic Multiuser Detection." IEEE Transactions on Communications 60, no. 6 (June 2012): 1680–91. http://dx.doi.org/10.1109/tcomm.2012.032012.090013a.

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Fan, Ya-Ru, Yilun Wang, and Ting-Zhu Huang. "Enhanced joint sparsity via iterative support detection." Information Sciences 415-416 (November 2017): 298–318. http://dx.doi.org/10.1016/j.ins.2017.06.034.

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Tehrani, Arash Saber, Alexandros G. Dimakis, and Michael J. Neely. "SigSag: Iterative Detection Through Soft Message-Passing." IEEE Journal of Selected Topics in Signal Processing 5, no. 8 (December 2011): 1512–23. http://dx.doi.org/10.1109/jstsp.2011.2169042.

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Wolfgang, A., J. Akhtman, S. Chen, and L. Hanzo. "Iterative MIMO Detection for Rank-Deficient Systems." IEEE Signal Processing Letters 13, no. 11 (November 2006): 699–702. http://dx.doi.org/10.1109/lsp.2006.879453.

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Cook, Gericke, Catherine Jarnevich, Melissa Warden, Marla Downing, John Withrow, and Ian Leinwand. "Iterative Models for Early Detection of Invasive Species across Spread Pathways." Forests 10, no. 2 (January 29, 2019): 108. http://dx.doi.org/10.3390/f10020108.

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Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

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Journal articles on the topic 'Iterative detection'

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