Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Fruit shape detection by level set

A novel approach for fruit shape detection in RGB space was proposed,which was based on fast level set and Chan-Vese model named as Modified Chan-Vese model(MCV) . This new algorithm is fast and suitable for fruit sorting because it does not need re-initializing. MCV has three advantages compared to the traditional methods. First,it provides a unified frame-work for detecting fruit shape boundary,and does not need any preprocessing even though the raw image is noisy or blurred. Second,if the fruit has different colors at the edges,it can detect perfect boundary. Third,it processed directly in color space without any transformations that may lose much information. The proposed method has been applied to fruit shape detection with promising result.

Malicious Node Detection Using Confidence Level Evaluation in a Grid-Based Wireless Sensor Network

In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.

Advanced topics on RF amplitude and phase detection for low-level RF systems

Low-level radio frequency(LLRF)systems stabilize the electromagnetic field in the RF cavities used for beam acceleration in particle accelerators.Reliable,accurate,and precise detection of RF amplitude and phase is particularly important to achieve high field stability for pulsed accelerators of free-electron lasers(FEL).The digital LLRF systems employ analog-to-digital converters to sample the frequency down-converted RF signal and use digital demodulation algorithms to calculate the RF amplitude and phase.Different sampling strategies and demodulation algorithms have been developed for these purposes and are introduced in this paper.This article focuses on advanced topics concerning RF detection,including accurate RF transient measurement,wideband RF detection,and RF detection with an asynchronous trigger,local oscillator,or clock.The analysis is based on the SwissFEL measurements,but the algorithms introduced are general for RF signal detection in particle accelerators.

Production and Detection of Ultracold Ground State 85Rb133Cs Molecules in the Lowest Vibrational Level by Short-Range Photoassociation

We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.

Environmental Sound Recognition Using Double-Level Energy Detection

The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.

Coal–rock interface detection on the basis of image texture features

Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.

Acoustic emission detection of slag performance in coal gasifiers

This work first investigated the detection of slags,slag pool liquid level,and slag accumulation height in laboratory scale based on acoustic emission(AE)detection,and further tried the feasibility of this method in an industrial scale coal gasifier.Results show that the energy and variance of acoustic signals can realize the accurate detection of large slag(criterion:E>1.5 E0,S>1.2 S0),and the average relative error is only 0.28%.The acoustic energy in the frequency range of 20–40 k Hz is defined as the characteristic energy,which can realize the accurate detection of slag accumulation height and slag pool liquid level,and the average relative error is only 3.94%.Furthermore,AE detection also realize accurate detection of large slag in an industrial scale gasifier and the acoustic signals at slag screen can be used to realize the early warning of the slag collapse(5 h earlier).

Comparative Study on Perimeter Intrusion Detection System of High-speed Railway

The perimeter intrusion detection system is critical to China’s railway safety.An efficient intrusion detection system can effectively avoid human casualties and property damage.This article makes a comprehensive comparison of popular detection systems in recent years.It first outlines the characteristics and classification of intrusion detection systems,and then introducestherelevantliteratureofcontactandnon-contactsystemsaccordingtodifferenttypes,andalsointroducesthe principles and architecture of the models they use in detail.Finally,the detection performance and suitable environment under different system models are analyzed by comparison.

Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.

A New Method of Small Moving Target Detection and Its Performance Analysis

This paper describes a new method of small moving target detection and analyzes the performance of this algorithm. The method is based on multi-level threshold decision-making and sliding trajectory confidence testing technology. The parameters of the algorithm are also given. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real-time performance.

Quantum Spin Liquid Phase in the Shastry–Sutherland Model Detected by an Improved Level Spectroscopic Method

We study the spin-1/2 two-dimensional Shastry–Sutherland spin model by exact diagonalization of clusters with periodic boundary conditions, developing an improved level spectroscopic technique using energy gaps between states with different quantum numbers. The crossing points of some of the relative(composite) gaps have much weaker finite-size drifts than the normally used gaps defined only with respect to the ground state, thus allowing precise determination of quantum critical points even with small clusters. Our results support the picture of a spin liquid phase intervening between the well-known plaquette-singlet and antiferromagnetic ground states, with phase boundaries in almost perfect agreement with a recent density matrix renormalization group study, where much larger cylindrical lattices were used [J. Yang et al., Phys. Rev. B 105, L060409(2022)]. The method of using composite low-energy gaps to reduce scaling corrections has potentially broad applications in numerical studies of quantum critical phenomena.

Defects Detection of TFT Lines of Flat Panel Displays Using an Evolutionary Optimized Recurrent Neural Network

This paper proposes an evolutionary optimized recurrent neural network for inspection of open/short defects on thin film transistor (TFT) lines of flat panel displays (FPD). The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor based non-contact sensor through scanning over TFT lines on the surface of mother glass of FPD. Irregular patterns on the waveform, sudden deep falls (open circuits) or sharp rises (short circuits), are classified and detected by employing the optimized recurrent neural network. The topology parameters of the recurrent neural network are optimized by a multiobjective evolutionary optimization process using a selected training data set. This method is an extension to our previous work, which utilized a feed-forward neural network, to address the drawbacks in it. Experimental results show that this method can detect defects on more realistic and noisy data than both of the previous method and the conventional threshold based method.

Fast Edge Detection Based on the Combination of Fuzzy Subsets

A fast edge detection method basing on the combination of fuzzy subsets is developed, in which the detection of an edge as a classification problem will be considered, partitioning the image into two portions: the edge portion and the non-edge portion. The latter one, as the main constituent of an image, consists of the object and its background. Removing the non-edge portion from an image, the remainder is nothing but the edge of this image. As far as the fuzziness of the edge of an image is concerned, some fuzzy operations can be made. In this paper, the gray level histogram is partitioned into several sub-regions, and some operations are performed with the associated fuzzy subsets corresponding to those sub-edges in the sub-regions on the gray-level-square-difference histogram, and the edge of this image is finally obtained. Practical examples in this paper illustrate that, the described method is simple and effective to achieve an ideal edge image.

A NOVEL FRAMEWORK FOR SOCCER GOAL DETECTION BASED ON SEMANTIC RULE

Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.

复杂场景下无水尺水位的影像水位反演智能检测方法

实现精细化水务管控和洪涝灾害预警,需要实时、准确感知水位突变事件。现有技术不能满足夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下的水位识别需求。为此,本文提出一种融合改进YOLOv5与卡尔曼滤波原理的无水尺水位智能检测技术:①引入YOLOv5对水位线(水岸分界线)进行检测,并利用线性拟合方法获得实际水位线;②针对水位线在延伸方向无限大而在其法向无限小特点,提出强化中尺度特征的多层级特征融合方法改进原YOLOv5算法;③利用卡尔曼滤波引入水位历史信息作为先验知识,提高本技术对复杂恶劣环境的泛化性能;④将图像中事先标定的固定的标志物加入到深度学习网络中训练,根据标志位真实尺寸解算实际水位高程,实现无水尺检测方案。相关试验和实践表明,改进的YOLOv5更加轻量化;本文所述水位智能检测技术斜率准确性为97.3%,较原算法提高了2.4%;截距准确性为99.3%,较原算法提高了0.5%;在夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下可以自动、准确识别出水位高程,误差小于0.1 m。

改进水平集协同FLICM的汽车零部件表面缺陷识别

针对汽车机械零部件中检测效率低、误检率高等瓶颈问题,提出了一种结合图像处理技术的方法。基于汽车副车架表面结构复杂以及缺陷异形等特征,首先通过小波邻域收缩去噪和多尺度增强算法对零件图像进行预处理,再通过水平集函数初步定位缺陷区域,最后结合空间模糊聚类进一步精确定位缺陷区域,达到对零件表面缺陷识别的目的。对工件表面划伤、压痕、气孔三种缺陷进行检测。实验结果表明:该算法相对于其它两种算法的检测率分别93.3%、86%和90%,具有较高的检测成功率。为后续缺陷零部件分类奠定良好的基础且可以满足工业检测要求。

激光雷达和相机的决策级融合目标检测方法

【目的】激光雷达与相机这两类传感器检测数据格式不统一、分辨率不同,且数据级和特征级的融合计算复杂度高,故提出一种决策级的目标融合检测方法。【方法】对激光雷达与相机的安装位置进行联合标定,实现这两类传感器检测结果的坐标系转换;利用匈牙利算法将激光雷达点云检测目标框和相机图像检测目标框进行匹配,设定目标框重合面积阈值,检测获得目标物的位置、类型等。【结果】实车测试结果表明,根据检测目标检测框长宽比选取不同交并比阈值的方法使得车辆和行人的目标识别准确率分别提升了3.3%和5.3%。利用公开数据集KITTI对所提融合方法进行验证,结果表明,在3种不同难度等级场景下,所提融合方法的检测精度分别达到了75.42%、69.71%、63.71%,与现有常用的融合方法相比,检测精度均有所提升。【结论】这两类传感器的检测目标框重合面积阈值对决策级融合检测结果影响较大,根据检测目标检测框长宽比选取不同阈值可有效提升车辆和行人的目标识别准确率。决策级融合方法能准确匹配雷达和相机的检测目标,有效提升目标检测精度。

透明容器液面机器视觉检测方法

机器视觉检测容器内液体体积的关键在于液面图像识别。根据目标成像特征,研究了基于液面特征和基于参考特征的透明容器液面机器视觉检测方法。通过图像实例与成像机理分析发现,利用液面灰度特征和边缘特征的液面检测方法受液面形态和成像质量的影响较大,而利用外部参照物折射成像出现分离带的液面检测方法在工业相机平视液面时亦容易导致检测失败,且自上而下的搜索方式存在检测误差大的问题。为了提高检测稳定性,提出了一种基于外部参考成像特征的液面检测新方法。该方法基于透明容器影响参照物成像的规律作为检测原理,通过检测参照物的行间梯度,利用液面处梯度最大的特征,自下而上搜索和识别液面。新检测方法不仅增强了液面识别的鲁棒性,还提高了液面检测的准确性。

基于计算机视觉的常用玻璃量器自动检定装置

玻璃量器作为常规配套设备,其需求量日益增长,常用玻璃量器的检定任务越来越重,传统的人工检定已不能满足日益增长的业务需求。该文提出了一种图像法和反馈控制技术相结合的容量示值检定自动化技术,即一种基于图像处理的常用玻璃量器检定自动化装置,主要解决加液自动化功能、容量示值读数自动化功能。开发集数据采集、处理、原始记录生成、报告打印等多功能于一体的软件系统。