Correct Detection Rate Research Articles

Multi-Objective Association Detection of Farmland Obstacles Based on Information Fusion of Millimeter Wave Radar and Camera

In order to remedy the defects of single sensor in robustness, accuracy, and redundancy of target detection, this paper proposed a method for detecting obstacles in farmland based on the information fusion of a millimeter wave (mmWave) radar and a camera. Combining the advantages of the mmWave radar in range and speed measurement and the camera in type identification and lateral localization, a decision-level fusion algorithm was designed for the mmWave radar and camera information, and the global nearest neighbor method was used for data association. Then, the effective target sequences of the mmWave radar and the camera with successful data association were weighted to output, and the output included more accurate target orientation, longitudinal speed, and category. For the unassociated sequences, they were tracked as new targets by using the extended Kalman filter algorithm and were processed and output during the effective life cycle. Lastly, an experimental platform based on a tractor was built to verify the effectiveness of the proposed association detection method. The obstacle detection test was conducted under the ROS environment after solving the external parameters of the mmWave radar and the internal and external parameters of the camera. The test results show that the correct detection rate of obstacles reaches 86.18%, which is higher than that of a single camera with 62.47%. Furthermore, through the contrast experiment of the sensor fusion algorithms, the detection accuracy of the decision level fusion algorithm was 95.19%, which was higher than 4.38% and 6.63% compared with feature level and data level fusion, respectively.

Identifying discontinuities of flood frequency curves

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.

Computer cyberspace security mechanism supported by cloud computing.

To improve the cybersecurity of Cloud Computing (CC) system. This paper proposes a Network Anomaly Detection (NAD) model based on the Fuzzy-C-Means (FCM) clustering algorithm. Secondly, the Cybersecurity Assessment Model (CAM) based on Grey Relational Grade (GRG) is creatively constructed. Finally, combined with Rivest Shamir Adleman (RSA) algorithm, this work proposes a CC network-oriented data encryption technology, selects different data sets for different models, and tests each model through design experiments. The results show that the average Correct Detection Rate (CDR) of the NAD model for different types of abnormal data is 93.33%. The average False Positive Rate (FPR) and the average Unreported Rate (UR) are 6.65% and 16.27%, respectively. Thus, the NAD model can ensure a high detection accuracy in the case of sufficient data. Meanwhile, the cybersecurity situation prediction by the CAM is in good agreement with the actual situation. The error between the average value of cybersecurity situation prediction and the actual value is only 0.82%, and the prediction accuracy is high. The RSA algorithm can control the average encryption time for very large text, about 12s. The decryption time is slightly longer but within a reasonable range. For different-size text, the encryption time is maintained within 0.5s. This work aims to provide important technical support for anomaly detection, overall security situation analysis, and data transmission security protection of CC systems to improve their cybersecurity.

An In-Vehicle Warning Information Provision Strategy for V2V-Based Proactive Traffic Safety Management

The availability of vehicle interaction data, which is obtained by an in-vehicle forward collision warning system, including spacing between the leading and the following vehicle and time-to-collision, provides a valuable opportunity to predict crash risks in real time. When this opportunity is combined with connected vehicle technologies including vehicle-to-vehicle wireless communications, it is expected that more effective crash prevention would be achievable by providing predictive warning information as a part of proactive traffic safety management (PTSM). The purpose of this study is to develop a more reliable in-vehicle warning information provision strategy based on the prediction of crash risks using vehicle interaction data. A crash risk prediction model based on a long short-term memory was able to predict the crash risk after 3 seconds with a mean absolute percentage error of 8% using the data for the past 5 seconds. The predicted crash risk data were applied to derive the optimal threshold for triggering in-vehicle warning information, which is the essence of the proposed warning provision strategy. This study defined three indicators to evaluate the reliability of warning information: correct detection rate (CDR), detection failure rate (DFR), and information provision rate (IPR). An exemplar analysis result showed that the optimal threshold to minimize IPR in a situation where CDR and DFR are 100% and 0%, respectively, was identified as 0.69. The proposed methodology that predicts crash risks in real time and provides V2V-based warning information in a more proactive manner is expected to mitigate the crash risk significantly.

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer.

Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.

Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices

In this article is presented a robust and real-time low-cost automated method for detecting and measuring the various distress types of flexible and stone road pavements. The distress detection, classification and measurement are based on the applications of deep learning approach and YOLOv3 algorithm. A dataset for road pavements damage detection with approximately 9,150 images and 15,585 bounding boxes of flexible and stone road pavements damage was first created and then used in the neural networks training phase. The values reached by the metrics used in the research to evaluate the object detection performance (Loss, Precision, Recall, RMSE) prove that the proposed model detects the pavement distresses with high accuracy and precision. The validation of the method was performed by an error analysis obtained by comparing for some case studies the pavement distresses detected with the suggested method and the real ones. The correct detection rate in the pavement distress detection ranges from 91.0 % to 97.3% depending on the pavement and distress types. The effectiveness of the proposed technique in detecting and measuring flexible and stone pavements distress sheds light on new opportunities for carrying out preliminary and exhaustive inspections of flexible and stone pavements using low-cost detection devices and artificial intelligence techniques.

Detecting and Classifying Multiple Class Events in a Power Distribution System Based on an Eigenvalue Fluctuation Model

Detecting and classifying the occurrence of multiple class events is important in enhancing the wide-area situational awareness ability of power distribution systems. Multiple class event classification based on voltage magnitude measurement has not been largely deployed due to the inadequate elucidation between the occurrence of events and the corresponding eigenvalue perturbations in the measurement data. Herein, a detection and classification method for multiple class events in power distribution systems is proposed based on an eigenvalue fluctuation model. We first elucidated the interrelation between event occurrence and the eigenvalue fluctuations in the system voltage magnitude measurement data. Based on the discovery that different classes of events lead to their respective data eigenvalue behavior, we proposed detection criteria C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SRL</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLP1</sub> , and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLP2</sub> to quantitatively assess these behaviors. Since the different value changes in C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SRL</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLP1</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLP2</sub> can indicate the occurrence of different classes of events, we trained and utilized a supervised classifier to achieve multiple event detection and classification. Simulation tests are performed in 7 different test feeders. Eight classes of events can be accurately detected and classified with only voltage magnitude measurement data. An 80% correct detection rate can be obtained with only a 20% measurement device penetration rate.

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

In order to realize the automation and intelligence of agricultural equipment mechanical state detection and improve the efficiency and accuracy of agricultural machinery equipment detection technology, this paper proposes an intelligent agricultural machinery equipment state detection method based on computer data mining. This method uses support vector machine to classify the equipment status and uses ant colony algorithm to solve the optimization problem. Its basic logic is to collect the status data of agricultural machinery equipment, extract the status detection characteristics of agricultural machinery equipment, and use computer data mining to establish an automatic state detection model of agricultural machinery equipment. The application results show that the correct rate of the state detection of agricultural machinery equipment is more than 95%, and the false detection rate of the state of agricultural machinery equipment is far lower than that of other current state detection methods of agricultural machinery equipment. Conclusion. This method can realize high-precision real-time detection of agricultural machinery equipment and has higher practical value.

Detection of spreading depolarization events and spatiotemporal analysis for advancing stroke therapy.

While the presence of spreading depolarization (SD) and associated spreading depression have been well studied and known to be associated with post-ischemic brain damage, the spatiotemporal spread of these events from the site of injury is not well understood. With the recent development of high-density micro-electrocorticographic (ECoG) electrode arrays, monitoring the spread of the depolarizing events and associated depression is possible. The goal of this work is to define the electrocorticographic features of SD and associated depression across the multichannel array and search for patterns in these features that emerge across both space and time. We present the spatial distribution of features found from chronic ECoG recordings acquired from awake behaving rats induced with a rodent model of stroke. SD events were detected with an unsupervised algorithm that searched for a stereotyped pattern in the first derivative of the ECoG. The algorithm yielded a 58% correct detection rate on average across four rats, and a 36% false positive rate. We defined key electrophysiological features and mapped them onto the physical brain regions using MATLAB, such as the peak-to-peak amplitude of each SD event, the width (or duration) of the SD event, direct current (DC) level, and average rate of decline in the signal baseline. We performed k-means clustering to the activity in this feature space which yielded three contiguous regions in physical space. The elbow optimization method was applied to a distortion metric and indicated that 3 clusters was optimal. These findings motivate us to conduct future studies that would verify whether these 3 clusters in electrode-space correspond to immunohistochemically defined regions of tissue health, namely, infarct, penumbra, and healthy tissue. Clinical Relevance- The extent and severity of damage that stroke ultimately causes is suspected to be related to the progression of spreading depolarization and associated depression. An understanding of how the features of these electrophysiological events progress across the brain and over time is an important step toward eventual development of closed-loop therapies which limit and minimize the long-term effects of stroke.

Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features

Bubble plumes, as main manifestations of seabed gas leakage, play an important role in the exploration of natural gas hydrate and other resources. Multibeam water column images have been widely used in detecting bubble plume targets in recent years because they can wholly record water column and seabed backscatter strengths. However, strong noises in multibeam water column images cause many issues in target detection, and traditional target detection methods are mainly used in optical images and are less efficient for noise-affected sonar images. To improve the detection accuracy of bubble plume targets in water column images, this study proposes a target detection method based on the bag of visual words (BOVW) features and support vector machine (SVM) classifier. First, the characteristics of bubble plume targets in water column images are analyzed, with the conclusion that the BOVW features can well express the gray scale, texture, and shape characteristics of bubble plumes. Second, the BOVW features are constructed following steps of point description extraction, description clustering, and feature encoding. Third, the quadratic SVM classifier is used for the recognition of target images. Finally, a procedure of bubble plume target detection in water column images is described. In the experiment using the measured data in the Strait of Georgia, the proposed method achieved 98.6% recognition accuracy of bubble plume targets in validation sets, and 91.7% correct detection rate of the targets in water column images. By comparison with other methods, the experimental results prove the validity and accuracy of the proposed method, and show potential applications of our method in the exploration and research on ocean resources.

Identification of the associations between genes and quantitative traits using entropy-based kernel density estimation

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC)

In civil engineering, image recognition technology in artificial intelligence is widely used in structural damage detection. Traditional crack monitoring based on concrete images uses image processing, which requires high image preprocessing techniques, and the results of detection are vulnerable to factors, such as lighting and noise. In this study, the full convolutional neural networks FCN-8s, FCN-16s, and FCN-32s are applied to monitoring of concrete apparent cracks and according to the image characteristics of concrete cracks and experimental results. The FCN-8s model was tested with a correct crack monitoring rate of 0.6721, while the new network model had a correct crack detection rate of 0.7585, a significant improvement in the correct crack detection rate.

Use of Electronic Auscultation in Full Personal Protective Equipment to Detect Ventilation Status in Selective Lung Ventilation: A Randomized Controlled Trial.

Chest auscultation is the first procedure performed to detect endotracheal tube malpositioning but conventional stethoscopes do not conform to the personal protective equipment (PPE) protocol during the COVID-19 pandemic. This double-blinded randomized controlled trial evaluated the feasibility of using ear-contactless electronic stethoscope to identify endobronchial blocker established selective lung ventilation, simulating endobronchial intubation during thoracic surgery with full PPE. Conventional and electronic auscultation was performed without and with full PPE, respectively, of 50 patients with selective lung ventilation. The rates of correct ventilation status detection were 86 and 88% in the conventional and electronic auscultation groups (p = 1.00). Electronic auscultation revealed a positive predictive value of 87% (95% CI 77 to 93%), and a negative predictive value of 91% (95% CI 58 to 99%), comparable to the results for conventional auscultation. For detection of the true unilateral lung ventilation, the F1 score and the phi were 0.904 and 0.654, respectively for conventional auscultation; were 0.919 and 0.706, respectively for electronic auscultation. Furthermore, the user experience questionnaire revealed that the majority of participant anesthesiologists (90.5%) rated the audio quality of electronic lung sounds as comparable or superior to that of conventional acoustic lung sounds. In conclusion, electronic auscultation assessments of ventilation status as examined during thoracic surgery in full PPE were comparable in accuracy to corresponding conventional auscultation assessments made without PPE. Users reported satisfactory experience with the electronic stethoscope.

Detection rate of 18F-rhPSMA-7.3 PET in patients with suspected prostate cancer recurrence: Results from a phase 3, prospective, multicenter study (SPOTLIGHT)

9 Background: 18F-labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET) ligands offer a longer half-life and improved spatial resolution compared with 68Ga-PSMA. Here, we report the detection rate (DR) of 18F-rhPSMA-7.3, a novel high affinity theranostic PET ligand with potential for low bladder activity. Methods: SPOTLIGHT (NCT04186845) assessed the coprimary endpoints of patient level Correct Detection Rate (CDR - % of all patients scanned with ≥1 true positive lesion) and region level Positive Predictive Value (all PET positive regions combined) of 18F-rhPSMA-7.3 using histopathology and/or conventional imaging as a composite Standard of Truth (SoT). These metrics were also evaluated in a subset of patients with a histopathology-only SoT. Prespecified statistical thresholds of at least 36.5% and 62.5% were set for CDR and PPV, respectively. Overall DR stratified by PSA was also documented. Men with prostate cancer recurrence underwent PET 50-70 minutes after IV administration of 296 MBq 18F-rhPSMA-7.3. Image guided biopsies of PET lesions took place ≤60 days post-PET and any confirmatory imaging took place ≤90 days. Three blinded central readers evaluated the scans with majority read accepted as final interpretation. A separate Truth Panel established SoT for each patient. Results: The overall DR in the 389 men (median [range] PSA, 1.10 [0.03-134.6] ng/mL) who had an evaluable 18F-rhPSMA-7.3 scan is 83% by majority read (See Table). In the 366 men (median [range] PSA, 1.27 [0.03-134.6] ng/mL) with a composite SoT, the patient level CDR is 56.8% (95% CI, 51.6-62.0), meeting the prespecified threshold. The region level PPV is 59.7% (54.7-64.7), not meeting the prespecified threshold. In the subset of patients with a histopathology-only SoT, the patient level CDR and region level PPV are higher at 81.2% (69.9-89.6) and 71.6% (62.5-80.7), respectively. Both of these values would have met the prespecified thresholds. Conclusions: 18F-rhPSMA-7.3 has a clinically meaningful CDR that meets the prespecified threshold. While the region level PPV does not meet the prespecified threshold, the data in patients with a composite SoT are limited by the high proportion of prostate/bed scans (92%) that relied on conventional imaging, a suboptimal SoT. For the subset of patients with the more robust histopathology SoT for at least one region per patient, a high PPV is seen and the lower bound of the 95% CI would have exceeded the prespecified threshold. Together, these data support the clinical utility of 18F-rhPSMA-7.3 PET in men with recurrent prostate cancer across a wide PSA range. Clinical trial information: NCT04186845. [Table: see text]

The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers.

The subject of this work is the analysis of methods of detecting soiling of photovoltaic panels. Environmental and weather conditions affect the efficiency of renewable energy sources. Accumulation of soil, dust, and dirt on the surface of the solar panels reduces the power generated by the panels. This paper presents several variants of the algorithm that uses various statistical classifiers to classify photovoltaic panels in terms of soiling. The base material was high-resolution photos and videos of solar panels and sets dedicated to solar farms. The classifiers were tested and analyzed in their effectiveness in detecting soiling. Based on the study results, a group of optimal classifiers was defined, and the classifier selected that gives the best results for a given problem. The results obtained in this study proved experimentally that the proposed solution provides a high rate of correct detections. The proposed innovative method is cheap and straightforward to implement, and allows use in most photovoltaic installations.

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Quality control is a critical aspect of the modern electronic circuit industry. In addition to being a pre-requisite to proper functioning, circuit quality is closely related to safety, security, and economic issues. Quality control has been reached through system testing. Meanwhile, device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably. Hence, traditional test processes based on manual inspections have become outdated and inefficient. More recently, the concept of Advanced Manufacturing or Industry 4.0 has enabled the manufacturing of customized products, tailored to the changing customers’ demands. This scenario points out additional requirements for electronic system testing: it demands a high degree of flexibility in production processes, short design and manufacturing cycles, and cost control. Thus, there is a demand for circuit testing systems that present effectiveness and accessibility without placing numerous test points. This work is focused on automated test solutions based on machine learning, which are becoming popular with advances in computational tools. We present a new testing approach that uses autoencoders to detect firmware or hardware anomalies based on the electric current signature. We built a test set-up using an embedded system development board to evaluate the proposed approach. We implemented six firmware versions that can run independently on the test board – one of them is considered anomaly-free. In order to obtain a reference frame to our results, two other classification techniques (a computer vision algorithm and a random forest classification model) were employed to detect anomalies on the same development board. The outcomes of the experiments demonstrated that the proposed test method is highly effective. For several test scenarios, the correct detection rate was above 99%. Test results showed that autoencoder and random forest approaches are effective. However, random forests require all data classes to be trained. Training an autoencoder, on the other hand, only requires the reference (anomaly-free) class.

Postoperative Examination of Laryngeal Malignant Tumor Based on Narrowband Imaging Resolution Enhancement Technology.

The application of endoscopic imaging in the biopsy of malignant laryngeal lesions is one of the current research hotspots in the medical field. Based on the narrowband imaging resolution enhancement technology, a model for postoperative examination of laryngeal malignant tumor was constructed in this paper. The article calculated the biopsy detection rate of malignant lesions and the correct biopsy detection rate of the two groups and made a statistical comparison. In the NBI mode group, the mucosal morphology and superficial mucosal microvascular morphology of the same lesion under two different modes of white light and NBI were compared, which solved the problem of data processing of cases. During the case comparison process, patients who needed biopsies to be sent for pathology were selected for inclusion in the study and were randomly divided into two groups. The coincidence rate of EUS combined with NBI diagnosis was significantly higher than that of ordinary white light gastroscopy (47.92%), and the difference was statistically significant (P=0.000 < 0.05). The experimental results compared the accuracy of the normal white light mode and the NBI mode to diagnose the nature of the lesions: according to the Kudo classification criteria, 23 cases of tumor lesions were to be diagnosed in the normal white light mode, with an accuracy rate of 69.70%, and the NBI mode was to be used to diagnose tumors. There were 81 cases of sexual lesions, with an accuracy rate of 93.94%. The NBI mode was more accurate in diagnosing the nature of the lesions under the Kudo classification standard (P < 0.05). In 64 cases, the accuracy rate was 63.63%. Under the NBI mode, 29 cases of tumor lesions were proposed to be diagnosed, and the accuracy rate was 87.88% to promote the application of NBI endoscopy in the biopsy of malignant laryngeal lesions.

Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as &#x201C;clean&#x201D; or &#x201C;normal&#x201D;, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as &#x201C;spurious&#x201D; or &#x201C;abnormal&#x201D; in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity between <i>unknown</i> testing samples and the <i>given</i> training samples (including normal samples), we first propose a so-called <i>deviation-based algorithm</i>. Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-called <i>AoA-based algorithm</i>. Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected.

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

The paper considers mobile location tracking and trajectory data reduction techniques for applications pertaining to mobile contact tracing and proximity detection in wireless cellular networks. Unscented Kalman filtering with non-line-of-sight bias mitigation is first applied for robust mobile trajectory estimation. An approach for modeling and analysis of pair-wise proximity and multi-mobile clustering scenarios is then introduced within a hypothesis testing framework, and a thorough performance evaluation is presented to assess the achievable detection and false alarm probabilities based on factors pertaining to proximity distance and timespan, ranging accuracy and bias statistics. For scenarios of practical interest, results show that correct proximity detection rates in excess of 70-to-80% range can be achieved while maintaining very low false alarm rates. Data reduction using the discrete Haar transform is subsequently applied for efficient storage of trajectory data. An analysis of the tradeoffs between reduction level and proximity detection reliability is presented to demonstrate the viability of the proposed approach with its low complexity and good performance at moderate reduction levels. Additional comparative analysis is presented to assess the impact of specific distance measures and wavelet types, and it is found that the Chebyshev distance offers improvements in detection accuracy compared to Euclidean and Manhattan measures, while wavelet change, when retaining short support, didn’t have a significant impact.

Cotton Fabric Defect Detection Based on K-SVD Dictionary Learning

ABSTRACT To improve the detection for different fabric types and defect kinds, an approach based on K-Singular Value Decomposition (K-SVD) dictionary learning method is designed to detect fabric defects, which remains important and challenging in the field of textile engineering. The proposed method has two main parts, namely the training and detection. The 32 defect-free fabric samples were used to train the dictionary by K-SVD. The detection process mainly consists of image segmentation, reconstruction by the trained dictionary, and detection. To improve the detection speed, the patch size was applied for detecting the defects, and the learned dictionaries were trained offline. For selecting general patch size, three patch sizes of 16 16 26 26, and 36 36 were used in the experiment. A comparative study found that the 26 26 has a comprehensive performance. The size and location of the defects were denoted by the black rectangle. Experimental results on 55 fabric samples demonstrated that the proposed method can efficiently detect different kinds of fabric defects and fabric types based on false detection rate and the correct detection rate.