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Full-Text Articles in Engineering
A Hybrid Unsupervised Clustering-Based Anomaly Detection Method, Guo Pu, Lijuan Wang, Jun Shen, Fang Dong
A Hybrid Unsupervised Clustering-Based Anomaly Detection Method, Guo Pu, Lijuan Wang, Jun Shen, Fang Dong
Faculty of Engineering and Information Sciences - Papers: Part B
In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective and intelligent solutions are necessary. Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks. In the current paper, we present an unsupervised anomaly detection method, which combines Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to detect attacks without any prior knowledge. The proposed approach is evaluated using the well-known NSL-KDD dataset. The experimental results demonstrate …
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Faculty of Engineering and Information Sciences - Papers: Part B
With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized …
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Faculty of Engineering and Information Sciences - Papers: Part B
The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially …
Air Void Detection Using Variational Mode Decomposition With Low Rank, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Shivakumar Karekal
Air Void Detection Using Variational Mode Decomposition With Low Rank, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Shivakumar Karekal
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents an air-void detection technique for air-coupled radar, which emits electromagnetic waves to interrogate an air-void inside a medium or between two media. The reflections from the air-medium interfaces are usually corrupted by air-coupling, antenna ringing, and internal reflections, rendering air-void detection very difficult or, in certain cases, impossible. The proposed method exploits the low-rank structure of the background clutter to suppress these nuisance signals. A variational mode decomposition model is developed to extract the backscattering at different air-medium interfaces as signal modes. Real experiments are conducted using a stepped frequency radar. The experimental results show that the …
Large Expert-Curated Database For Benchmarking Document Similarity Detection In Biomedical Literature Search, Peter Brown, Relish Consortium, Yaoqi Zhou
Large Expert-Curated Database For Benchmarking Document Similarity Detection In Biomedical Literature Search, Peter Brown, Relish Consortium, Yaoqi Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were …
A Numerical Approach To Design The Kretschmann Configuration Based Refractive Index Graphene-Mos2 Hybrid Layers With Tio2-Sio2 Nano For Formalin Detection, Md. Biplob Hossain, Tamanna Tasnim, Lway Abdulrazak, Md Masud Rana, Md Rabiul Islam
A Numerical Approach To Design The Kretschmann Configuration Based Refractive Index Graphene-Mos2 Hybrid Layers With Tio2-Sio2 Nano For Formalin Detection, Md. Biplob Hossain, Tamanna Tasnim, Lway Abdulrazak, Md Masud Rana, Md Rabiul Islam
Faculty of Engineering and Information Sciences - Papers: Part B
In this paper, a Kretschmann configuration based surface plasmon resonance (SPR) sensor is numerically designed using graphene-MoS2 hybrid structure TiO2-SiO2 nano particles for formalin detection. In this design, the observations of SPR angle versus minimum reflectance and SPR frequency (FSPR) versus maximum transmittance (Tmax) are considered. The chitosan is used as probe legend to perform reaction with the formalin (40% formaldehyde) which acts as target legend. In this paper, both graphene and MoS2 are used as biomolecular acknowledgment element (BAE) and TiO2 as well as SiO2 bilayers is used to improve the sensitivity of the sensor. The numerical results show …
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features' trends and the estimation of the remaining useful life (RUL) of the slew bearing. In …
Gvm Based Intuitive Simulation Web Application For Collision Detection, Binbin Yong, Jun Shen, Zebang Shen, Huaming Chen, Xin Wang, Qingguo Zhou
Gvm Based Intuitive Simulation Web Application For Collision Detection, Binbin Yong, Jun Shen, Zebang Shen, Huaming Chen, Xin Wang, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Computer simulation, which has been proved to be an effective approach to problem solving, is nowadays widely used in modern science. However, it requires a lot of computing resources, which are difficult for general users to acquire. In this paper, we design a Web based system to implement on-line simulation system for ordinary users. As a useful example, the simulation of one type of collision detection model is presented in this paper. Moreover, the software application of simulation is offered as a service on Web. Meanwhile, the incorporation of general vector machine (GVM, a type of neural network) to intelligently …
Parallel Gpu-Based Collision Detection Of Irregular Vessel Wall For Massive Particles, Binbin Yong, Jun Shen, Hongyu Sun, Huaming Chen, Qingguo Zhou
Parallel Gpu-Based Collision Detection Of Irregular Vessel Wall For Massive Particles, Binbin Yong, Jun Shen, Hongyu Sun, Huaming Chen, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we present a novel GPU-based limit space decomposition collision detection algorithm (LSDCD) for performing collision detection between a massive number of particles and irregular objects, which is used in the design of the ADS (Accelerator Driven Sub-Critical) system. Test results indicate that, the collisions between ten million particles and the vessel can be detected on a general personal computer in only 0.5 second per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 seconds. Experiment results show that our algorithm is promising for fast collision detection.
Gpu Based Simulations Of Collision Detection Of Irregular Vessel Walls, Binbin Yong, Jun Shen, Hongyu Sun, Zijian Xu, Jingfeng Liu, Qingguo Zhou
Gpu Based Simulations Of Collision Detection Of Irregular Vessel Walls, Binbin Yong, Jun Shen, Hongyu Sun, Zijian Xu, Jingfeng Liu, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Planogram Compliance Checking Based On Detection Of Recurring Patterns, Song Liu, Wanqing Li, Stephen J. Davis, Christian H. Ritz, Hongda Tian
Planogram Compliance Checking Based On Detection Of Recurring Patterns, Song Liu, Wanqing Li, Stephen J. Davis, Christian H. Ritz, Hongda Tian
Faculty of Engineering and Information Sciences - Papers: Part A
In this article, the authors propose a novel method for automatic planogram compliance checking in retail chains that doesn't require product template images for training. Product layout is extracted from an input image by means of unsupervised recurring pattern detection and matched via graph matching, with the expected product layout specified by a planogram to measure the level of compliance. A divide-and-conquer strategy is employed to improve the speed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region, respectively, and then merged together to estimate the product layout.
A Sensor Fault Detection Strategy For Air Handling Units Using Cluster Analysis, Rui Yan, Zhenjun Ma, Georgios Kokogiannakis, Yang Zhao
A Sensor Fault Detection Strategy For Air Handling Units Using Cluster Analysis, Rui Yan, Zhenjun Ma, Georgios Kokogiannakis, Yang Zhao
Faculty of Engineering and Information Sciences - Papers: Part A
Sensors are an essential component in the control systems of air handling units (AHUs). A biased sensor reading could result in inappropriate control and thereby increased energy consumption or unsatisfied indoor thermal comfort. This paper presents an unsupervised learning based strategy using cluster analysis for AHU sensor fault detection. The historical data recorded from sensors is first pre-processed to reduce the dimensions using principal component analysis (PCA). The clustering algorithmOrdering Points to Identify the Clustering Structure (OPTICS) is then employed to identify the spatial separated data groups (i.e. clusters),which possibly indicate the occurrence of sensor faults. The data points in …
Detecting Visual Spoofing Using Classical Cryptanalysis Methods In Plagiarism Detection Systems, Yang-Wai Chow, Willy Susilo, Ilung Pranata, Ari Moesriami Barmawi
Detecting Visual Spoofing Using Classical Cryptanalysis Methods In Plagiarism Detection Systems, Yang-Wai Chow, Willy Susilo, Ilung Pranata, Ari Moesriami Barmawi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Violent Scene Detection Using A Super Descriptor Tensor Decomposition, Muhammad Rizwan Khokher, Abdesselam Bouzerdoum, Son Lam Phung
Violent Scene Detection Using A Super Descriptor Tensor Decomposition, Muhammad Rizwan Khokher, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Engineering and Information Sciences - Papers: Part A
This article presents a new method for violent scene detection using super descriptor tensor decomposition. Multi-modal local features comprising auditory and visual features are extracted from Mel-frequency cepstral coefficients (including first and second order derivatives) and refined dense trajectories. There is usually a large number of dense trajectories extracted from a video sequence; some of these trajectories are unnecessary and can affect the accuracy. We propose to refine the dense trajectories by selecting only discriminative trajectories in the region of interest. Visual descriptors consisting of oriented gradient and motion boundary histograms are computed along the refined dense trajectories. In traditional …
Unsupervised Region Of Intrest Detection Using Fast And Surf, Abass Olaode, Golshah Naghdy, Catherine Todd
Unsupervised Region Of Intrest Detection Using Fast And Surf, Abass Olaode, Golshah Naghdy, Catherine Todd
Faculty of Engineering and Information Sciences - Papers: Part A
The determination of Region-of-Interest has been re cognised as an important means by which unimportant image content can be identified and exc luded during image compression or image modelling, however existing Region-of-Interest dete ction methods are computationally expensive thus are mostly unsuitable for managing l arge number of images and the compression of images especially for real-time video applicatio ns. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detec tion.
Novel Experimental Setup For Time-Of-Flight Mass Spectrometry Ion Detection In Collisions Of Anionic Species With Neutral Gas-Phase Molecular Targets, J C. Oller, L Ellis-Gibbings, F Ferreira Da Silva, Paulo Limao-Vieira, Gustavo Garcia
Novel Experimental Setup For Time-Of-Flight Mass Spectrometry Ion Detection In Collisions Of Anionic Species With Neutral Gas-Phase Molecular Targets, J C. Oller, L Ellis-Gibbings, F Ferreira Da Silva, Paulo Limao-Vieira, Gustavo Garcia
Faculty of Engineering and Information Sciences - Papers: Part A
We report a novel experimental setup for studying collision induced products resulting from the interaction of anionic beams with a neutral gas-phase molecular target. The precursor projectile was admitted into vacuum through a commercial pulsed valve, with the anionic beam produced in a hollow cathode discharge-induced plasma, and guided to the interaction region by a set of deflecting plates where it was made to interact with the target beam. Depending on the collision energy regime, negative and positive species can be formed in the collision region and ions were time-of-flight (TOF) mass-analysed. Here, we present data on O-2 precursor projectile, …
An Efficient Background Modeling Approach Based On Vehicle Detection, Jia-Yan Wang, Limei Song, Jiangtao Xi, Qinghua Guo
An Efficient Background Modeling Approach Based On Vehicle Detection, Jia-Yan Wang, Limei Song, Jiangtao Xi, Qinghua Guo
Faculty of Engineering and Information Sciences - Papers: Part A
The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the …
Smoke Detection In Video: An Image Separation Approach, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
Smoke Detection In Video: An Image Separation Approach, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part A
Existing video-based smoke detection methods often rely on the visual features extracted directly from the original frames. In the case of light smoke, the background is still visible and it deteriorates the quality of the features. This paper presents an approach to separating the smoke component from the background such that visual features can be extracted from the smoke component for reliable smoke detection. Specifically, an image is assumed to be a linear blending of a smoke component and a background image. Given a video frame and its background, the estimation of the blending parameter and the actual smoke component …
An Approach For Assessing The Effectiveness Of Multiple-Feature-Based Svm Method For Islanding Detection Of Distributed Generation, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
An Approach For Assessing The Effectiveness Of Multiple-Feature-Based Svm Method For Islanding Detection Of Distributed Generation, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
Islanding detection is a critical protection issue, as conventional protection schemes such as vector surge (VS) and rate of change of frequency relays do not guarantee islanding detection for all network conditions. Integration of multiple distributed generation (DG) units of different sizes and technologies into distribution grids makes this issue even more critical. This paper presents a comprehensive analysis of the effectiveness of a new method for islanding detection in DG networks. The proposed method, which is based on multiple features and support vector machine (SVM) classification, has the potential to overcome the limitations of conventional protection schemes. The multifeature-based …
A Multifeature-Based Approach For Islanding Detection Of Dg In The Subcritical Region Of Vector Surge Relays, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
A Multifeature-Based Approach For Islanding Detection Of Dg In The Subcritical Region Of Vector Surge Relays, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
Anti-islanding protection is an important requirement which has to be considered prior to the integration of distributed generation into electricity grids. Conventional vector surge (VS) relays are usually used to detect islanding; however, there is a nondetection zone (NDZ) wherein islanding incidents are undetectable by VS relays. This paper proposes a multifeature-based technique for islanding detection in the subcritical region, defined as a subregion of the NDZ. In the proposed method, features are extracted from five network variables. The extracted features are then used as inputs to a support vector machine to classify the event as islanding or nonislanding. A …
Detection Of Crack Growth In Rail Steel Using Acoustic Emission, Andrii Kostryzhev, C L. Davis, C Roberts
Detection Of Crack Growth In Rail Steel Using Acoustic Emission, Andrii Kostryzhev, C L. Davis, C Roberts
Faculty of Engineering and Information Sciences - Papers: Part A
Increased traffic speeds and axle loads on modern railways enhance rail track degradation. To eliminate track failure due to rail defects, a condition monitoring system requires methods for the early detection of defects which grow in service. Acoustic emission (AE) monitoring is the only non-destructive technique which might be applied online to study the defect growth under traffic loading. However, a high level of traffic noise and a limited signal from crack growth, especially at low crack growth rates, significantly complicate the AE signal analysis. In the present work, the AE monitoring of rail steel fatigue was carried out in …
Combined Adaptive Lattice Reduction-Aided Detection And Antenna Shuffling For Dsttd-Ofdm Systems, Ngoc Phuc Le, Le Chung Tran, Farzad Safaei
Combined Adaptive Lattice Reduction-Aided Detection And Antenna Shuffling For Dsttd-Ofdm Systems, Ngoc Phuc Le, Le Chung Tran, Farzad Safaei
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we consider lattice reduction (LR) aided linear detection in a DSTTD-OFDM (double space-time transmit diversity- orthogonal frequency division multiplexing) system with antenna shuffling. We first derive an antenna shuffling criterion for the LR-aided DSTTD-OFDM system. Next, we propose a combined reduced-feedback and adaptive LR algorithm by exploiting the correlation between OFDM subcarriers in the frequency domain. The LR-aided DSTTD OFDM system with this algorithm requires low computational effort for the LR operation and small feedback information. Simulation results show that a significant improvement could be achieved in the proposed system compared to previous (non-LR-aided) systems under spatially …
Inter-Occlusion Reasoning For Human Detection Based On Variational Mean Field, Duc Thanh Nguyen, Wanqing Li, Philip O. Ogunbona
Inter-Occlusion Reasoning For Human Detection Based On Variational Mean Field, Duc Thanh Nguyen, Wanqing Li, Philip O. Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part A
Detecting multiple humans in crowded scenes is challenging because the humans are often partially or even totally occluded by each other. In this paper, we propose a novel algorithm for partial inter-occlusion reasoning in human detection based on variational mean field theory. The proposed algorithm can be integrated with various part-based human detectors using different types of features, object representations, and classifiers. The algorithm takes as the input an initial set of possible human objects (hypotheses) detected using a part-based human detector. Each hypothesis is decomposed into a number of parts and the occlusion status of each part is inferred …
Temporal Sentiment Detection For User Generated Video Product Reviews, M S. Barakat, C H. Ritz, D A. Stirling
Temporal Sentiment Detection For User Generated Video Product Reviews, M S. Barakat, C H. Ritz, D A. Stirling
Faculty of Engineering and Information Sciences - Papers: Part A
User generated video product reviews in social media is gaining popularity every day due to its creditability and the broad evaluation context provided by it. Extracting sentiment automatically from such videos will help the consumers making decisions and producers improving their products. This paper investigates the feasibility of sentiment detection temporally from those videos by analyzing the transcription generated by a speech recognition system which was not investigated before. Another two main contribution for this paper is introducing a solution to the problem of fixed threshold estimation for the Naive Bayesian classifier output probabilities and irrelative text filtering for improving …
Pedestrian Lane Detection For Assistive Navigation Of Blind People, M Le, Son Lam Phung, Abdesselam Bouzerdoum
Pedestrian Lane Detection For Assistive Navigation Of Blind People, M Le, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction is performed by using colour and intensity information. A probabilistic framework employing multiple geometric cues is then used to verify the extracted ROI. The experimental results have shown that the proposed method is robust under challenging illumination conditions and obtains superior performance …
A Soft-In Soft-Out Detection Approach Using Partial Gaussian Approximation, Qinghua Guo, Licai Fang, Defeng (David) Huang, Sven Nordholm
A Soft-In Soft-Out Detection Approach Using Partial Gaussian Approximation, Qinghua Guo, Licai Fang, Defeng (David) Huang, Sven Nordholm
Faculty of Engineering and Information Sciences - Papers: Part A
This paper concerns the implementation of the softin soft-out detector in an iterative detection system. A detection approach is proposed based on the properties of Gaussian functions. In this approach, for the computation of the APP (a posteriori probability) of a concerned symbol, the other symbols are distinguished based on their contributions to the APP of the concerned symbol, and the symbols with less contributions are treated as Gaussian variables to reduce the computational complexity. The exact APP detector and the well-known LMMSE (linear minimum mean square error) detector are two special cases of the proposed detector. Simulation results show …
Towards Formalizing A Reputation System For Cheating Detection In Peer-To-Peer-Based Massively Multiplayer Online Games, Willy Susilo, Yang-Wai Chow, Rungrat Wiangsripanawan
Towards Formalizing A Reputation System For Cheating Detection In Peer-To-Peer-Based Massively Multiplayer Online Games, Willy Susilo, Yang-Wai Chow, Rungrat Wiangsripanawan
Faculty of Engineering and Information Sciences - Papers: Part A
The rapidly growing popularity of Massively Multiplayer Online Games (MMOGs) has given rise to an increase in the number of players world wide. MMOGs enable many players interact together through a shared sense of presence created by the game. The Peer-to-Peer (P2P) network topology overcomes communication bottleneck problems associated with centralized client/server sys- tems. Thus, P2P-based MMOGs are seen as the way of the future, and many dierent P2P-based MMOG architectures have been proposed to date. However, many architectures are proposed in an ad hoc manner and enhancing the security of such systems is an elusive research problem. In this …
Pedestrian Lane Detection For The Visually Impaired, Manh Cuong Le, Son Lam Phung, Abdesselam Bouzerdoum
Pedestrian Lane Detection For The Visually Impaired, Manh Cuong Le, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
Traveling safely in outdoor environments is one of the most challenging activities for vision-disabled people. To improve the mobility of these people, an assistive navigation system is necessary. This paper is a step towards developing this system. We propose a robust method to detect the pedestrian marked lane at traffic junctions. The proposed method involves three stages: patches of interest (POI) extraction, lane marker detection and lane detection. The POI extraction is first performed to detect image patches located on the lane marker boundaries using template matching. Potential lane markers are then formed by using shape and intensity information. Finally, …
A State-Based Knowledge Representation Approach For Information Logical Inconsistency Detection In Warning Systems, Jun Ma, Guangquan Zhang, Jie Lu
A State-Based Knowledge Representation Approach For Information Logical Inconsistency Detection In Warning Systems, Jun Ma, Guangquan Zhang, Jie Lu
Faculty of Engineering and Information Sciences - Papers: Part A
Detecting logical inconsistency in collected information is a vital function when deploying a knowledge-based warning system to monitor a specific application domain for the reason that logical inconsistency is often hidden from seemingly consistent information and may lead to unexpected results. Existing logical inconsistency detection methods usually focus on information stored in a knowledge base by using a well-defined general purpose knowledge representation approach, and therefore cannot fulfill the demands of a domain-specific situation. This paper first proposes a state-based knowledge representation approach, in which domain-specific knowledge is expressed by combinations of the relevant objects' states. Based on this approach, …
A Low-Complexity Iterative Channel Estimation And Detection Technique For Doubly Selective Channels, Qinghua Guo, Li Ping, Defeng (David) Huang
A Low-Complexity Iterative Channel Estimation And Detection Technique For Doubly Selective Channels, Qinghua Guo, Li Ping, Defeng (David) Huang
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message …