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Interactive reflection simulation via physical shading model and fast environment mapping

ICIC Express Letters, Part B: Applications 2016年第2期7卷 351-356页

作者： Xu, Jie Fu, Jinhua School of Software Zhengzhou University of Light Industry China School of Computer and Communication Engineering Zhengzhou University of Light Industry No. 5 Dongfeng Road Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China

In order to enhance realistic reflection effects on some objects and realize faster rendering to meet interactive requirements, we present a novel method to produce interactive reflection simulation using physical shading model and fast environment mapping. The physical shading model could efficiently express ambient lighting, specular lighting and diffuse lighting. Also the model is based on the microfacet distribution function, which can describe physics-based characteristics. Fast environment mapping is realized by Spherical Harmonics (SH) transformation, and the rendering equation can be rewritten as a simple dot product of SH coefficients, which fits for GPU (Graphics Processing Unit) computation. The results show that we can realize faster rendering and acquire realistic reflection effects such as glossy and mapping scene on the surface. © 2016 ICIC International.

关键词： Lighting

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Accelerating Vehicle Detection in Low-Altitude Airborne Urban Video

Accelerating Vehicle Detection in Low-Altitude Airborne Urba...

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International Conference on Image and Graphics (ICIG)

作者： Xianbin Cao Renjun Lin Pingkun Yan Xuelong Li Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology Hefei Anhui China Center of OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy and Sciences Xi'an Shaanxi China

The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed.

关键词： Vehicles Feature extraction Vehicle detection Road transportation Cameras Atmospheric modeling Classification algorithms

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DETECTING THE RUN TIME ATTACKS IN THE CLOUD WITH AN EVIDENCE COLLECTION BASED APPROACH

DETECTING THE RUN TIME ATTACKS IN THE CLOUD WITH AN EVIDENCE...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Jie lin Chuanyi Liu Zhichun Ning Binxing Fang School of Computer Science Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education School of Software Engineering Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications

As the impacts of security threats are amplified and spread quickly in the cloud,especially with the attribute of large-scale *** of researches focus on monitoring the network ***,these methods don't check how the attack happened and what damage the attack caused for the virtual *** paper presents a logging method to analyze the potential attacks out of VM and a prototype was *** experimental results show that this method can detect the real world attacks effectively and the performance overhead is acceptable.

关键词： Virtual machine Cloud Security Control

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KLT Feature Based Vehicle Detection and Tracking in Airborne Videos

KLT Feature Based Vehicle Detection and Tracking in Airborne...

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International Conference on Image and Graphics (ICIG)

作者： Xianbin Cao Jinhe Lan Pingkun Yan Xuelong Li Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology Hefei Anhui China Center of OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy and Sciences Xi'an Shaanxi China

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are difficult to develop because of factors like UAV motion, scene complexity and so on. In this paper, we propose a new framework of multi-motion layer analysis to detect and track moving vehicles in airborne platform. Moving vehicles are firstly detected by registration and temporal differencing to establish motion layers. After motion layers are constructed, they are maintained over time for tracking vehicles. All vehicles are tracked by maintaining their corresponding motion layers. Our experimental results showed that compared with other previous algorithms, our method can achieve better results in terms of detection and tracking performance.

关键词： Vehicles Tracking Videos Feature extraction Vehicle detection Maintenance engineering Motion segmentation

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Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders

Robust and Unsupervised KPI Anomaly Detection Based on Highl...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shili Yan Bing Tang Qing Yang Yijia He Xiaoyuan Zhang Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China Beijing Jiaotong University Beijing China

To ensure the stability and reliability of service quality, large Internet companies need to closely monitor various KPIs (key Performance Indicators, such as network throughput, CPU usages) and trigger timely troubleshooting or mitigation when any anomaly occurs. However, the diversity and complexity of anomalies bring great challenges to this work, especially when there is no manual label and low delay is required. In this paper, we propose HS-VAE (Highly Sensitive Variational Auto-Encoders), an unsupervised, robust algorithm based on CVAE (Conditional Variational Auto-Encoders) with high sensitivity to anomalies for KPIs, which contains mainly 3 parts: a simple but important data filter before training, improved conditional VAE with two dropout layers and adjusted anomaly detection method based on reconstruction probability. Our experiments using real-world data show that, HS-VAE's best F1-score ranges from 0.91 to 0.98. In addition, HS- VAE is excellent in sensitivity to anomaly and works well even with low latency requirements.

关键词： Training Sensitivity key performance indicator Training data Manuals Throughput Reliability

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A Hybrid Genetic Algorithm for Vehicle Routing Problems with Dynamic Requests

A Hybrid Genetic Algorithm for Vehicle Routing Problems with...

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IEEE Symposium Series on Computational Intelligence

作者： Ruikang Yi Wenjian Luo Chenyang Bu Xin Lin Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China

In this paper, a hybrid Genetic Algorithm (GA) is proposed to solve the Vehicle Routing Problems with Dynamic Requests (VRPDR). The proposed hybrid GA primarily involves the following parts. First, a greedy split is introduced, which is fit for the VRPDR. Second, the adjacent-exchange-based local search is proposed. Third, a novel mutation operator, named the insert mutation, is proposed. The experimental results show that the proposed hybrid GA is effective.

关键词： Dynamic Vehicle Routing Genetic Algorithm Split Algorithm Local Search

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Scheduling Aircraft Landing Based on Clonal Selection Algorithm and Receding Horizon Control

Scheduling Aircraft Landing Based on Clonal Selection Algori...

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International Conference on Intelligent Transportation

作者： Xiaolan Jia Xianbin Cao Yuanping Guo Hong Qiao Jun Zhang Province Key Laboratory of Software Computing and Communication University of Science and Technology Hefei Anhui China University of Manchester Institute of Science and Technology UK Institute of Automation Chinese Academy and Sciences Beijing China School of Electronic and Information Engineering Beihang University Beijing China

The task of aircraft landing scheduling (ALS) is to give a landing sequence and landing times for a given set of aircrafts where many constraints must be satisfied. ALS is an NP-hard problem with large-scale and multi-constraint characteristics, thus it is hard to find optimal solution efficiently. In this paper, a hybrid algorithm of Clonal Selection Algorithm (CSA) and Receding Horizon Control (RHC) was proposed for ALS problem. In details, constrained CSA based on infeasibility degree (IFD) schedules aircrafts in current receding horizon, and then RHC repeats that optimization procedure using excellent gene segment spread (EGSS) until all aircrafts have landed. Comparative experiments show that the CSA-RHC hybrid algorithm is able to obtain an optimal landing sequence and landing times rapidly and effectively.

关键词： Aircraft Aerospace control Scheduling algorithm Air traffic control Processor scheduling Computational intelligence Sequences Intelligent transportation systems Linear programming Constraint optimization

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Smoothness in noise reduction of chaotic time series

Smoothness in noise reduction of chaotic time series

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2011 International Conference on Computers, communications, Control and Automation

作者： Chenxi Shao Jiangong Ji Jingyuan Shao Lipeng Xiao Department of Computer Science and Technology University of Science and Technology of China Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Information center Jianghuai Automobile Co.Ltd.

Noise is an omnipresent phenomenon. It obscures the real behavior of dynamical system. Lots of methods are proposed to remove the noise contaminating time series. However, almost all the methods consider the noise reduction in the phase space and often sharp points are kept. Different with these methods, this paper proposes a method directly on the time series itself, considering the gauss noise feature and the smoothness of the real data, uses curve-fitting way to eliminate the sharp points. The numeral results verify the effectiveness of our method.

关键词： smoothness,noise reduction,curve-fitting,Local-Geometric-Projection

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