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High-rate uncoded space-time labelling diversity with low-complexity detection

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第14期33卷 e4520-e4520页

作者： Patel, Sulaiman Saleem Quazi, Tahmid Xu, Hongjun KPMG Serv Pty Ltd Emerging Technol Digital Consulting Durban South Africa Univ KwaZulu Natal Sch Engn Durban South Africa

Uncoded space-time labelling diversity (USTLD) is a recent scheme that improved the error performance compared to conventional multiple-input, multiple-output systems. Thus far, USTLD has suffered from limited achievable data rates, as the original model uses only two transmit antennas. This motivates for the work in this paper, where the USTLD model is extended to allow for any desired number of transmit antennas. An analytical bound for the average bit error probability of this high-rate USTLD (HR-USTLD) system is derived. This expression is verified using the results of Monte Carlo simulations, which show a tight fit in the high signal-to-noise ratio region. The increased data rates associated with larger transmit antenna arrays in HR-USTLD systems come at the cost of increased detection complexity. Therefore, this paper studies the application of low-complexity detection algorithms based on the popular QR decomposition technique and proposes a new algorithm specifically designed for HR-USTLD systems. Analysis of this algorithm in terms of accuracy and computational complexity is also provided and benchmarked against maximum-likelihood detection (MLD). It is shown that the proposed algorithm achieves near-MLD accuracy, while reducing complexity by 79.75% and 92.53% for the respective 4 x 4 16QAM and 4 x 5 16PSK HR-USTLD systems investigated.

关键词： complexity analysis detection algorithms diversity techniques labelling diversity QR decomposition wireless communication systems

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Multinational License Plate Recognition Using Generalized Character Sequence detection

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IEEE ACCESS 2020年 8卷 35185-35199页

作者： Henry, Chris Ahn, Sung Yoon Lee, Sang-Woong Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam 13557 South Korea

Automatic license plate recognition (ALPR) is generally considered a solved problem in the computer vision community. However, most of the current works on ALPR are designed to work on license plates (LP) from specific countries and use country-specific information which limits their practical applicability. Such ALPR systems require changes in the algorithm to work on other countries LPs. Previous works on multinational LP recognition are tested on datasets from various countries that share the same LP layout. To address this issue, this study presents a deep ALPR system designed to be applicable to multinational LPs. The proposed approach consists of three main steps LP detection, unified character recognition, and multinational LP layout detection. The system is mainly based on the you only look once (YOLO) networks. Particularly, tiny YOLOv3 was used for the first step whereas the second step uses YOLOv3-SPP for a version of YOLOv3 that consists of the spatial pyramid pooling (SPP) block. The localized LP is fed into YOLOv3-SPP for character recognition. The character recognition network returns the bounding boxes of the predicted characters and does not provide information about the sequence of the LP number. A LP number with an incorrect sequence is considered wrong. Thus, to extract the correct sequence, we propose a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LPs. We collected our own Korean car plate (KarPlate) dataset and made it publicly available. The proposed system was evaluated on LP datasets from five countries which include South Korea, Taiwan, Greece, USA, and Croatia. In addition, a small dataset containing LPs from 17 countries was collected to evaluate the effectiveness of the multinational LP layout detection algorithm. The proposed ALPR system consumes about 42 ms per image on average for extracting LP number. Experimental results demonstrate the effectiveness of our ALPR system.

关键词： Licenses Layout Character recognition detection algorithms Automobiles Image segmentation Hidden Markov models License plate detection license plate recognition multinational license plate recognition

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A FFT-Like MIMO detection Algorithm

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IEEE ACCESS 2020年 8卷 188233-188242页

作者： Du, Peng Zhang, Yuan Ba, Teer Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper studies the multiple-input-multiple-output (MIMO) detection problem. Existing works model the MIMO detection as a tree or factor graph. This work adds a new member called the clique graph model to the MIMO detection graph model family so that MIMO detection can be transformed into FInding the maximum clique in the constructed graph. Then, a clique based MIMO detection algorithm is developed, which uses an architecture similar to the Fast Fourier Transform (FFT) and can be executed in parallel naturally. The execution time of the algorithm can be proportional to log2 Nt where Nt is the transmit antenna number. Simulation results are provided to evaluate the bit error rate (BER) performance of the proposed algorithm.

关键词： MIMO communication detection algorithms Complexity theory Transmitting antennas Bit error rate Approximation algorithms Image edge detection detection graph model MIMO parallel processing

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Ship detection in SAR Images via Local Contrast of Fisher Vectors

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第9期58卷 6467-6479页

作者： Wang, Xueqian Li, Gang Zhang, Xiao-Ping He, You Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada Naval Aeronaut Univ Inst Informat Fus Yantai 264001 Peoples R China

Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms.

关键词： Marine vehicles Radar polarimetry Detectors Clutter Synthetic aperture radar detection algorithms Feature extraction Fisher vectors (FVs) local contrast ship detection superpixels synthetic aperture radar (SAR)

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Atrial Fibrillation detection During Sepsis: Study on MIMIC III ICU Data

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第11期24卷 3124-3135页

作者： Bashar, Syed Khairul Hossain, Md Billal Ding, Eric Walkey, Allan J. McManus, David D. Chon, Ki H. Univ Connecticut Biomed Engn Dept Storrs CT 06269 USA Univ Massachusetts Med Sch Div Cardiol Worcester MA 01655 USA Boston Univ Sch Med Dept Med Boston MA 02118 USA

Sepsis is defined by life-threatening organ dysfunction during infection and is one of the leading causes of critical illness. During sepsis, there is high risk that new-onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. As a result, computer aided automated and reliable detection of new-onset AF during sepsis is crucial, especially for the critically ill patients in the intensive care unit (ICU). In this paper, a novel automated and robust two-step algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals is presented. First, several statistical parameters including root mean square of successive differences, Shannon entropy, and sample entropy were calculated from the heart rate for the screening of possible AF segments. Next, Poincare plot-based features along with P-wave characteristics were used to reduce false positive detection of AF, caused by the premature atrial and ventricular beats. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 198 subjects was used in this study. During the training and validation phases, both the simple thresholding as well as machine learning classifiers achieved very high segment-wise AF classification performance. Finally, we tested the performance of our proposed algorithm using two independent test data sets and compared the performance with two state-of-the-art methods. The algorithm achieved an overall 100% sensitivity, 98% specificity, 98.99% accuracy, 98% positive predictive value, and 100% negative predictive value on the subject-wise AF detection, thus showing the efficacy of our proposed algorithm in critically ill sepsis patients. The annotations of the data have been made publicly available for other investigators.

关键词： Electrocardiography Feature extraction MIMICs Heart rate variability Entropy Databases detection algorithms Sepsis ICU Atrial fibrillation P-wave Premature beat Poincaré plot Annotations

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detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

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IEEE ACCESS 2020年 8卷 97643-97657页

作者： Fan, Xiangyu Bai, Peng Liang, Xiaolong Zhang, Jiaqiang Liu, Bin Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Peoples R China Shaanxi Prov Lab Meta Synth Elect & Informat Syst Xian 710051 Peoples R China Air Force Engn Univ Fdn Dept Xian 710051 Peoples R China

Given that signal is weakened to a certain extent in the process of noise suppression using mainstream method, and that new noise is introduced by signal processing system, causing the decrease of detection performance, to improve the performance of detection to BPSK signal under the condition of strong noise and no prior information, the detection algorithm of BPSK signal of bistable stochastic resonance model based on scale change is proposed in this study. Using classical bistable stochastic resonance (BSR) system, only low-amplitude and low-frequency periodic signal can be processed. Scale change is first made to BSR in this study, verifying that BSR can be applied to high-frequency BPSK signal under high sampling frequency condition, and nonlinear threshold detection system is designed following Neyman-Pearson criterion to deduce and quantitatively show error rate of detector. Besides, complete flow for signal detection was built by taking it as feedback quantity to adjust the system parameters adaptively. Scale change feasibility and applicability of algorithm proposed in this study were verified through simulation experiment, which lays the theoretical basis for the detection of weak BPSK signal under low signal-to-noise ratio (SNR).

关键词： Binary phase shift keying Signal to noise ratio Signal detection Stochastic resonance Bit error rate detection algorithms Fans Strong noise detection to BPSK signal bistable stochastic resonance scale change Neyman-Pearson criterion

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Gamma-Ray Source detection Under Occlusions and Position Errors in Cluttered Urban Scenes

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2020年第6期67卷 1185-1194页

作者： Miller, Kyle Good, Jack H. Fawaz, Ian Howarth, Dan Dubrawski, Artur Carnegie Mellon Univ Auton Lab Pittsburgh PA 15213 USA

Spatial aggregation using Bayesian aggregation (BA) is effective in combining multiple measurements to detect weak sources of gamma radiation in mobile source detection applications. To perform spatial aggregation of evidence, the position of the sensor must be estimated over time, in synchronization with gamma-ray measurements. Prevalent low-cost position estimation approaches often suffer from inaccuracies on a scale which can affect aggregation performance. Due to the presence of large buildings, positioning technology modalities such as GPS can show higher levels of error in urban environments. Additionally, urban environments can have highly varying structures, be crowded and dynamic, causing time-varying occlusions in the line-of-sight from the sensor to the source. Both occlusions and errors in sensor position estimation can degrade the source detection performance. We use asymptotic analysis to characterize the magnitude of this degradation. Natural maximum-likelihood and marginalization-based extensions to a BA framework are then used to improve robustness of aggregation in the face of these issues. The proposed approach shows substantially improved robustness to positioning errors and dynamic occlusions when compared to the baseline BA.

关键词： Photonics Gamma-rays Bayes methods Attenuation Urban areas Robustness detection algorithms Bayesian aggregation (BA) source detection spatial aggregation

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A Novel Spike detection Algorithm Based on Multi-Channel of BECT EEG Signals

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3592-3596页

作者： Wang, Zimeng Wu, Duanpo Dong, Fang Cao, Jiuwen Jiang, Tiejia Liu, Junbiao Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Univ City Coll Coll Informat & Elect Engn Hangzhou 310015 Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Key Lab IOT & Informat Fus Technol Zhejiang Hangzhou 310018 Peoples R China Zhejiang Univ Sch Med Childrens Hosp Hangzhou 310052 Peoples R China Hangzhou Neuro Sci & Technol Co Ltd Algorithm Dept Hangzhou 310052 Peoples R China

Benign childhood epilepsy with centro-temporal spikes (BECT) is one of the most common epilepsy syndromes in childhood which is typically characterized by localized discharges in the central and temporal regions. Traditionally, the recognition of spikes requires visual assessment of long-term EEG recordings which is time consuming and subjective because it depends on the knowledge and experience of the doctor. Therefore, a novel multi-step spike detection algorithm based on average reference (AV) channel and bipolar (BP) channel BECT EEG is proposed, including candidate spike detection algorithm, false positive spike (FPS) elimination, spike feature extraction and random forest (RF) classification. The proposed method is evaluated using 7 routine EEG recordings. This brief shows that the sensitivity (Sen), specificity (Spe), selectivity (Sel) and accuracy (AC) obtained by the proposed method are 97.4%, 96.5%, 96.6% and 96.9%, respectively. Experimental results show that the proposed method is capable of detecting BECT spikes efficiently.

关键词： Electroencephalography Feature extraction Radio frequency detection algorithms Epilepsy Discharges (electric) Training BECT spikes AV channel BP channel candidate spike detection FPS elimination

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Discriminative Reconstruction for Hyperspectral Anomaly detection With Spectral Learning

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第10期58卷 7406-7417页

作者： Lei, Jie Fang, Shuo Xie, Weiying Li, Yunsong Chang, Chein-I Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Dalian Maritime Univ Informat & Technol Coll Ctr Hyperspectral Imaging Remote Sensing Dalian 116026 Peoples R China Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu 64002 Yunlin Taiwan Univ Maryland Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA Providence Univ Dept Comp Sci & Informat Management Taichung 02912 Taiwan

Recently, autoencoder (AE)-based anomaly detection has drawn considerable interest in hyperspectral image (HSI) analysis. In this article, we propose a novel discriminative reconstruction method for hyperspectral anomaly detection images with spectral learning (SLDR). The proposed algorithm has the following innovations. First, we use the spectral error map (SEM) to detect anomalies because the SEM can preferably reflect the spectral similarity of each pixel between the input and the reconstruction. Second, the loss function of the proposed SLDR model additionally introduces the spectral angle distance (SAD), which constrains the model to generate a reconstruction having greater spectral similarity to the input. Third, a constraint is imposed on the encoder, forcing it to generate latent variables that obey a unit Gaussian distribution, which helps the decoder to reconstruct a better background with respect to the input. Compared with the ReedXiaoli (RX), collaborative representation detection (CRD), attribute and edge-preserving filtering-based anomaly detection (AED) and adversarial autoencoder-based anomaly detection (AAE), through two real HSI data sets, the detection performance of the proposed SLDR method is found to be competitive.

关键词： Anomaly detection Hyperspectral imaging Image reconstruction Decoding Detectors detection algorithms Anomaly detection hyperspectral image (HSI) reconstruction spectral learning

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Understanding Shilling Attacks and Their detection Traits: A Comprehensive Survey

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IEEE ACCESS 2020年 8卷 171703-171715页

作者： Sundar, Agnideven Palanisamy Li, Feng Zou, Xukai Gao, Tianchong Russomanno, Evan D. Indiana Univ Purdue Univ Dept Comp & Informat Sci Indianapolis IN 46202 USA Indiana Univ Purdue Univ Dept Comp Informat & Graph Technol Indianapolis IN 46202 USA Southeast Univ Sch Cyber Sci & Engn Nanjing 210000 Peoples R China Ball State Univ Dept Informat Syst & Operat Management Muncie IN 47306 USA

The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems;unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.

关键词： Collaboration Recommender systems detection algorithms Robustness Licenses Biological system modeling Information science Collaborative filtering detection traits and algorithms profile injection attacks robust algorithms shilling attacks

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