Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

Improved Relative-transformation Principal Component Analysis Based on Mahalanobis Distance and Its Application for Fault Detection

主要部件分析(PCA ) 广泛地在过程工业被使用了，它能维持最大的差错察觉率。尽管许多问题在 PCA 被处理了，一些必要问题仍然保持未解决。这研究以下列方法为差错察觉性能改进 PCA。第一，一个相对转变计划基于 Mahalanobis 距离(MD ) 被介绍消除数据的尺寸的效果而不是无尺寸的标准化，并且改进精确性和差错察觉的即时性能。理论推导证明那相对转变能直接基于 MD 消除尺寸的效果并且在结果显示出的相对空间，分析和模拟给 PCA 的合理解释它的优势和有效性。第二，一个改进摆平的预言错误(SPE ) 统计数值被给改进标准化 PCA 的差错察觉表演，它能使标准化基于 PCA 的差错察觉方法成为对实际工业过程合适的更多。最后，二个改进方法被联合更有效地检测差错。建议方法被使用在热连续滚动过程检测 looper 系统的单个差错和多差错，模拟结果以易感知，精确性和差错察觉的即时性能为差错察觉性能表明这些改进的有效性。

A long-distance quantum key distribution scheme based on pre-detection of optical pulse with auxiliary state

We construct a circuit based on PBS and CNOT gates, which can be used to determine whether the input pulse is empty or not according to the detection result of the auxiliary state, while the input state will not be changed. The circuit can be treated as a pre-detection device. Equipping the pre-detection device in the front of the receiver of the quantum key distribution （QKD） can reduce the influence of the dark count of the detector, hence increasing the secure communication distance significantly. Simulation results show that the secure communication distance can reach 516 km and 479 km for QKD with perfect single photon source and decoy-state QKD with weak coherent photon source, respectively.

AN ALGORITHM OF DISTANCE CALCULATION BETWEEN A BOUNDING SPHERE AND A BOUNDING CSG PRIMITIVE

A simple method for calculating distance between a solid sphere and a constructive solid geometry (CSG) so lid primitive (including block, cone, cylinder, sphere, wedge and torus) is derived to support the collision detection algorithm. By decomposing the whole space into relative positions and geometric features of the sphere and the primitive considered, closed form distance formula are got. These calculations are very useful in the real time collision detection in which primitives are used as bounding volumes of complex objects.

Distance-based lateral flow biosensor for the quantitative detection of bacterial endotoxin

Bacterial endotoxin(a type of lipopolysaccharide,LPS)that acts as the strongest immune stimulant exhibits high toxicity to human health.The golden standard detection methods rely heavily on the use of a large amount of tachypleus amebocyte lysate(TAL)reagents,extracted from the unique blue blood of legally protected horseshoe crabs.Herein,a cost-effective distance-based lateral flow(D-LAF)sensor is demonstrated for the first time based on the coagulation cascade process of TAL induced by endotoxin,which causes the generation of gel-state TAL.The gelation process can increase the amount of trapped water molecules and shorten the lateral flow distance of the remaining free water on the pH paper.The water flow distance is directly correlated to the concentration of endotoxin.Noteworthy,the D-LAF sensor allows the detection of endotoxin with the reduced dosage of TAL reagents than the golden standard detection methods.The detection limit of endotoxin is calculated to be 0.0742 EU/mL.This method can be applied to the detection of endotoxin in real samples such as household water and clinical injection solution with excellent performance comparable to the commercial ELISA kit.

Density-based trajectory outlier detection algorithm

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

NEW HAUSDORFF DISTANCE-BASED VEHICLE LOCATION

Objective To realize accurate localization of moving vehicles from single monocular intens ity image. Methods The new modified Hausdorff distance(M2HD) was adopted, which used dominant points instead of edge maps as features for mea suring similarity between image and model projection. Modified simulated anneali ng (MSA) algorithm was used to find optimum localization parameters. Res ults M2HD reduces the computational complexity, and improves the matc hing precision. Furthermore, MSA can fast find global optimum instead of getting into partial one because of its high parallel and robust performance. C onclusion Experiments confirm that the combination of MSA and M 2H D can effectively localize the vehicles that are changed both in translation and rotation

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.

Near-duplicate document detection with improved similarity measurement

To quickly find documents with high similarity in existing documentation sets, fingerprint group merging retrieval algorithm is proposed to address both sides of the problem:a given similarity threshold could not be too low and fewer fingerprints could lead to low accuracy. It can be proved that the efficiency of similarity retrieval is improved by fingerprint group merging retrieval algorithm with lower similarity threshold. Experiments with the lower similarity threshold r=0.7 and high fingerprint bits k=400 demonstrate that the CPU time-consuming cost decreases from 1 921 s to 273 s. Theoretical analysis and experimental results verify the effectiveness of this method.

A Hardware Trojan Detection Method Based on the Electromagnetic Leakage

Hardware Trojan(HT) refers to a special module intentionally implanted into a chip or an electronic system. The module can be exploited by the attacker to achieve destructive functions. Unfortunately the HT is difficult to detecte due to its minimal resource occupation. In order to achieve an accurate detection with high efficiency, a HT detection method based on the electromagnetic leakage of the chip is proposed in this paper. At first, the dimensionality reduction and the feature extraction of the electromagnetic leakage signals in each group(template chip, Trojan-free chip and target chip) were realized by principal component analysis(PCA). Then, the Mahalanobis distances between the template group and the other groups were calculated. Finally, the differences between the Mahalanobis distances and the threshold were compared to determine whether the HT had been implanted into the target chip. In addition, the concept of the HT Detection Quality(HTDQ) was proposed to analyze and compare the performance of different detection methods. Our experiment results indicate that the accuracy of this detection method is 91.93%, and the time consumption is 0.042s in average, which shows a high HTDQ compared with three other methods.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.

Performance of the geometric approach to fault detection and isolation in SISO,MISO,SIMO and MIMO systems

In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.

Study on the long-distance target apperception techniques for underwater vehicles

The limited physical size for autonomous underwater vehicles （AUV） or unmanned underwater vehicles （UUV） makes it difficult to acquire enough space gain for localizing long-distance targets. A new technique about long-distance target apperception with passive synthetic aperture array for underwater vehicles is presented. First, a synthetic aperture-processing algorithm based on the FFT transform in the beam space （BSSAP） is introduced. Then, the study on the flank array passive long-distance apperception techniques in the frequency scope of 11-18 kHz is implemented from the view of improving array gains, detection probability and augmenting detected range under a certain sea environment. The results show that the BSSAP algorithm can extend the aperture effectively and improve detection probability. Because of the augment of the transmission loss, the detected range has the trend of decline with the increase of frequency under the same target source level. The synthesized array could improve the space gain by nearly 7 dB and SNR is increased by about 5 dB. The detected range is enhanced to nearly 2 km under the condition of 108-118 dB of the target source level for AUV system in measurement interval of nearly 1 s.

Code Clone Detection Method Based on the Combination of Tree-Based and Token-Based Methods

This article proposes the high-speed and high-accuracy code clone detection method based on the combination of tree-based and token-based methods. Existence of duplicated program codes, called code clone, is one of the main factors that reduces the quality and maintainability of software. If one code fragment contains faults (bugs) and they are copied and modified to other locations, it is necessary to correct all of them. But it is not easy to find all code clones in large and complex software. Much research efforts have been done for code clone detection. There are mainly two methods for code clone detection. One is token-based and the other is tree-based method. Token-based method is fast and requires less resources. However it cannot detect all kinds of code clones. Tree-based method can detect all kinds of code clones, but it is slow and requires much computing resources. In this paper combination of these two methods was proposed to improve the efficiency and accuracy of detecting code clones. Firstly some candidates of code clones will be extracted by token-based method that is fast and lightweight. Then selected candidates will be checked more precisely by using tree-based method that can find all kinds of code clones. The prototype system was developed. This system accepts source code and tokenizes it in the first step. Then token-based method is applied to this token sequence to find candidates of code clones. After extracting several candidates, selected source codes will be converted into abstract syntax tree (AST) for applying tree-based method. Some sample source codes were used to evaluate the proposed method. This evaluation proved the improvement of efficiency and precision of code clones detecting.

Target detection and recognition in SAR imagery based on KFDA

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

Blackout Prevention by Power Swing Detection and Out-of-Step Protection

Power swing evoked by sudden changes like faults or switching operations will become more and more important for protective relaying, due to the growing load flow in electrical power networks. Unwanted trips of the distance protection function must be avoided to prevent cascading effects and blackouts in the network. Selective out-of-step-tripping is required to stop unstable power swing and to prevent damage to affected generators. Therefore a reliable method for detection of power swing is presented, which requires no settings for operation. Power swing can be detected from 0.1 Hz up to 10 Hz swing frequency, also during open pole condition and during asymmetrical operation. A blocking logic prevents unselective trips by the distance protection. However, faults that occur during a power swing must be detected and cleared with a high degree of selectivity and dependability. For unstable power swing a flexible out of step tripping function will be proposed. The coordination of power swing detection, distance protection and out of step protection provides a reliable system protection.

DISTINIT:Data poISoning atTacks dectectIon usiNg optIized jaCcard disTance

Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.

A QoE Assessment System in Distance Education

It is a challenging task to improve the real-time property and objectivity of the effect assessment for the distance education. This paper presents a QoE (Quality of Experience) assessment system based on the attention of online user. The system captures the video frames from two cameras periodically and synchronously, using the adaptive image binarization based on the linear average threshold for the pretreatment, then processing with edge detection and filtering in the cross-directions at the same time. System gets the position of computer screen and user eyeball. Analyzing the detection results comprehensively obtains the attention of online user by some judging conditions, and finally acquires the quality of user experience. Experimental results demonstrate the feasibility and efficiency.

Research on the Distance of the Active Sonar under the Effects of the Thermocline

The existence of thermocline changes the acoustic structure and effects the direction of the stared rays. This paper analyzes the working processs of the active sorer, and the mathematical models. The detection probaility of the active sonar under thenmoline is studied. First, the detection distance without thermocline is estimat- ed, then the effect of thermocline＇s depth and sound velocity changes on detecting submarine probability are discussed, and based on this, the effects of the sea condition on searching submarine probability is discussed, lastly the distance of active sonar is calculatod under thermocline. The results indicate that tufter thennocline, the distance of the active sonar becomes obvious short, and with the sea condition becoming rough, the effect is more dear.