Robust Detection Scheme Research Articles

Robust Early Detection and Filtering Scheme to Locate Vampire Attack in Wireless Sensor Networks

Robust Early Detection and Filtering Scheme to Locate Vampire Attack in Wireless Sensor Networks

Robust optimal position detection scheme for relational database watermarking through HOLPSOFA algorithm

Nowadays, relational database watermarking is a challenging problem in many content distribution applications and internet-based application environments. The watermarking scheme for relational databases based on a hybrid algorithm, named as HOLPSOFA is proposed. In HOLPSOFA, the combination of Orthogonal Learning Particle Swarm Optimization and Firefly Algorithm is used. This new approach combines the advantages of Orthogonal Learning particle swarm optimization (OLPSO) and Firefly algorithm (FA), which can find the time-optimal solutions simultaneously. The overall relational database watermarking scheme consists of three stages, (1) Optimal location identification through HOLPSOFA algorithm, (2) Watermark embedding and (3) watermark extraction. The proposed HOLPSOFA algorithm is also compared with OLPSO and Firefly algorithm. The performance of the proposed watermarking method is analyzed through Mean Square Error (MSE) and normalized correlation (NC). Our extensive analysis illustrates that the proposed method is robust against various forms of database attacks, including insertion, deletion and alteration. Computer simulations show that the hybrid algorithm is very effective in diminishing different kind of attacks in terms of MSE and NC than existing algorithms.

R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human–computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit–receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

Traffic Anomaly Detection Based on Robust Principal Component Analysis Using Periodic Traffic Behavior

In this paper, we study robust Principal Component Analysis (PCA)-based anomaly detection techniques in network traffic, which can detect traffic anomalies by projecting measured traffic data onto a normal subspace and an anomalous subspace. In a PCA-based anomaly detection, outliers, anomalies with excessively large traffic volume, may contaminate the subspaces and degrade the performance of the detector. To solve this problem, robust PCA methods have been studied. In a robust PCA-based anomaly detection scheme, outliers can be removed from the measured traffic data before constructing the subspaces. Although the robust PCA methods are promising, they incure high computational cost to obtain the optimal location vector and scatter matrix for the subspace. We propose a novel anomaly detection scheme by extending the minimum covariance determinant (MCD) estimator, a robust PCA method. The proposed scheme utilizes the daily periodicity in traffic volume and attempts to detect anomalies for every period of measured traffic. In each period, before constructing the subspace, outliers are removed from the measured traffic data by using a location vector and a scatter matrix obtained in the preceding period. We validate the proposed scheme by applying it to measured traffic data in the Abiline network. Numerical results show that the proposed scheme provides robust anomaly detection with less computational cost.

Reflectance-based skin detection in the short wave infrared band and its application to video

Robust reflectance-based skin detection is a potentially powerful tool for security and search and rescue applications, especially when applied to video. However, to be useful it must be able to account for the variations of human skin, as well as other items in the environment that could cause false detections. This effort focused on identifying a robust skin detection scheme that is appropriate for video application. Skin reflectance was modeled to identify unique skin features and compare them to potential false positive materials. Based on these comparisons, specific wavelength bands were selected and different combinations of two and three optical filters were used for actively identifying skin, as well as identifying and removing potential false positive materials. One wavelength combination (1072/1250 nm) was applied to video using both single- and dual-camera configurations based on its still image performance, as well as its appropriateness for video application. There are several important factors regarding the extension of still image skin detection to video, including light available for detection (solar irradiance and reflectance intensity), overall intensity differences between different optical filters, optical component light loss, frame rate, time lag when switching between filters, image coregistration, and camera auto gain behavior.

SPAM revisited for median filtering detection using higher‐order difference

AbstractIn this paper, we propose a robust median filtering detection scheme utilizing the conditional joint distribution of high‐order differences. The higher‐order differences provide additional information about the correlation between a pixel and its neighbors. The relationship between a pixel and its neighbors becomes more prominent in median‐filtered images as compared with non‐median filtered images. We evaluate the performance of our method and compare it with the state of the art median filtering detection techniques on multiple databases in various environments. We use a linear discriminant classifier based on Moore–Penrose pseudoinverse matrix for classifying the median and non‐median filtered images. Experimentally, we find that our method is more effective for all types of images including uncompressed and highly compressed JPEG low‐resolution images. We also evaluate the performance of our method on Gaussian‐filtered, averaging‐filtered, and median‐filtered images of the BOWS2 and UCID databases. Copyright © 2016 John Wiley & Sons, Ltd.

Detection of intermittent faults for linear stochastic systems subject to time‐varying parametric perturbations

In this study, the robust detection problem of intermittent faults (IFs) for linear stochastic systems subject to time-varying parametric perturbations is addressed. The authors consider the case that an IF appears and disappears non-deterministically, and lasts for random periods of active time with unknown magnitudes. A novel robust fault detection method is presented to detect all the appearing time and the disappearing time of an IF. Based on the output of an observer-type residual generator, a novel robust residual is constructed by utilising a sliding-time window. Two hypothesis tests are provided to detect all the appearing and the disappearing time, respectively. Moreover, the detectability of the IF by using the proposed robust detection scheme is defined in a probabilistic sense, and a sufficient detectability condition is presented within the given framework. Capacities of false alarm rates and missed detection rates are rigorously analysed. Finally, the application of the presented scheme is illustrated on a simplified radial flight control system and the results show that the IF can be effectively detected in the presence of perturbations and noises.

Robust Pilot Detection Techniques for Channel Estimation and Symbol Detection in OFDM Systems

In this letter, we propose new frequency-domain pilot-multiplexing techniques (FDPMTs) for the channel estimation and equalization in orthogonal frequency-division multiplexing (OFDM) systems. A robust and effective pilot insertion and detection scheme is devised thereby. These pilot positions are optimally selected to minimize the distortion of the transmitted time-domain signal caused by the subcarrier-removal at the corresponding pilot positions. Besides, three different blind pilot-detection techniques are designed at the receiver without any a priori knowledge of the pilot positions, and the distorted data symbols can thus be iteratively reconstructed. Rigorous theoretical analysis and Monte Carlo simulation results both demonstrate that our proposed new OFDM system using dynamical pilot positions is more robust than the conventional OFDM system using the fixed pilot positions over multipath fading channels.

Iterative Soft Interference Cancellation for Sparse BPSK Signals

Compressed Sensing based Multi-User Detection (CS-MUD) is a novel MUD approach applied in sporadic Machine Type Communication (MTC) to identify actively transmitting sensors nodes at the same time as the transmitted data. In this context, different reconstruction algorithms from CS as well as detection concepts well established in communications have been adapted, but either are not exploiting finite alphabets or are highly complex. In this paper, we focus on an iterative soft interference cancellation scheme that efficiently exploits the sparsity of the signal, the finite alphabet of the transmit data and the usually employed channel coding to obtain a robust high performance detection scheme.

Robust scream sound detection via sound event partitioning

This paper proposes a robust scream-sound detection scheme for acoustic surveillance applications. To enhance the discriminability between scream and non-scream sounds, a sound-event partitioning (SEP) method that facilitates the extraction of multiple acoustic vectors from a single sound event is developed. Regularized principal component analysis (PCA) and normalization are applied to the acoustic vectors, which are then classified by support vector machines (SVMs). Experimental results based on 1000 sound events show that the proposed scheme is effective even if there are severe mismatches between the training and testing conditions. The experimental results also show that the proposed scheme can reduce the equal error rate (EER) by up to 60 % when compared to a classical approach that uses mel-frequency cepstral coefficients (MFCC) as features. Extensive analyses on different processing stages of the proposed sound detection scheme also suggest that sound partitioning and feature normalization play important roles in boosting the detection performance.

Review on Robust Estimator-Correlator for Spectrum Sensing in Cognitive Radio

In communication, Cognitive radio (CR) plays important role for increasing the spectral efficiency in communication bands. Reason behind increasing efficiency of the spectrum usage is due to increasing demand for higher data rates, better Qos etc. Spectrum sensing is one of the most challenging issues in research area as the cognitive radio is concern. In this paper, we present various traditional techniques in order to detect presence of primary user but there are some limitations. Therefore, we introduces robust detection scheme, with respect to the uncertainty in the estimation of true covariance matrix. This paper provide idea behind the MIMO concept in cognitive radio where multiple nodes can be placed both on primary user and secondary user and results evaluate the better detection of primary user. This method implemented by using the Eigen value theory. In this paper, we are trying to improve the primary user detection in comparison with the traditional technologies. KeywordsCognitive radio (CR), Spectrum sensing, multiple-input multiple-output (MIMO), RECD __________________________________________________*****_________________________________________________

A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude

The growing uses of camera-based barcode readers have recently gained a lot of attention. This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps. Blur detection is achieved by approximating the functional relationship between these features using a feed forward neural network. Simulation results show that the proposed scheme gives robust blur detection scheme.

A non-conservative [formula omitted] solution for early and robust fault diagnosis in aircraft control surface servo-loops

The presented work is undertaken within the FP7-ADDSAFE (Advanced Fault Diagnosis for Sustainable Flight Guidance and Control) project, a European collaborative project that aims to propose new fault diagnosis techniques for AIRBUS aircraft that could significantly advance the aircraft performance, e.g. by optimizing the aircraft structural design (weight saving) or decreasing its environmental footprint (e.g. less fuel consumption and noise). The paper discusses the design of a model-based fault detection scheme for robust and early detection of faults in aircraft control surfaces servo-loop. The proposed strategy consists of two fault detectors: The first fault detector is based on a H−/H∞ residual generator that maximizes sensitivity to any kind of control surface servo-loop faults whilst simultaneously minimizes the influence of unknown inputs. The second fault detector consists of a pure H∞ residual generator that is sensitive to a restricted set of faults and robust to unknown inputs. By such a structured strategy, it is shown that it is possible to discriminate between different fault types occurring in the control surfaces servo-loop. Monte-Carlo campaigns from a highly representative simulator provided by AIRBUS as well as experimental results obtained on AIRBUS test facilities demonstrate the fault detection performance, robustness and viability of the proposed technique.

Nondestructive Detection of an Optical Photon

All optical detectors to date annihilate photons upon detection, thus excluding repeated measurements. Here, we demonstrate a robust photon detection scheme that does not rely on absorption. Instead, an incoming photon is reflected from an optical resonator containing a single atom prepared in a superposition of two states. The reflection toggles the superposition phase, which is then measured to trace the photon. Characterizing the device with faint laser pulses, a single-photon detection efficiency of 74% and a survival probability of 66% are achieved. The efficiency can be further increased by observing the photon repeatedly. The large single-photon nonlinearity of the experiment should enable the development of photonic quantum gates and the preparation of exotic quantum states of light.

Detection and Statistics of Wind Power Ramps

Ramps events are a significant source of uncertainty in wind power generation. Developing statistical models from historical data for wind power ramps is important for designing intelligent distribution and market mechanisms for a future electric grid. This requires robust detection schemes for identifying wind ramps in data. In this paper, we propose an optimal detection technique for identifying wind ramps for large time series. The technique relies on defining a family of scoring functions associated with any rule for defining ramps on an interval of the time series. A dynamic programming recursion is then used to find all such ramp events. Identified wind ramps are used to propose a new stochastic framework to characterize wind ramps. Extensive statistical analysis is performed based on this framework, characterizing ramping duration and rates as well as other key features needed for evaluating the impact of wind ramps in the operation of the power system. In particular, evaluation of new ancillary services and wind ramp forecasting can benefit from the proposed approach.

Multiplane Video Stabilization

AbstractThis paper presents a novel video stabilization approach by leveraging the multiple planes structure of video scene to stabilize inter‐frame motion. As opposed to previous stabilization procedure operating in a single plane, our approach primarily deals with multiplane videos and builds their multiple planes structure for performing stabilization in respective planes. Hence, a robust plane detection scheme is devised to detect multiple planes by classifying feature trajectories according to reprojection errors generated by plane induced homographies. Then, an improved planar stabilization technique is applied by conforming to the compensated homography in each plane. Finally, multiple stabilized planes are coherently fused by content‐preserving image warps to obtain the output stabilized frames. Our approach does not need any stereo reconstruction, yet is able to produce commendable results due to awareness of multiple planes structure in the stabilization. Experimental results demonstrate the effectiveness and efficiency of our approach to robust stabilization on multiplane videos.

A constrained‐optimization methodology for the detection phase in contact mechanics simulations

SUMMARYThe detection phase in computational contact mechanics can be subdivided into a global search and a local detection. When potential contact is detected by the former, a rigorous local detection determines which surface elements come or may come in contact in the current increment. We first introduce a rigorous definition of the closest point for non‐differentiable lower‐dimensional manifolds. We then simplify the detection by formulating an optimization problem subject to inequality constraints. The formulation is then solved using different techniques from the field of mathematical optimization, for both linear and quadratic finite element meshes. The resulting general and robust detection scheme is tested on a set of problems and compared with other techniques commonly used in computational geometry. Copyright © 2013 John Wiley & Sons, Ltd.

An Accurate Collision Detection Method for Cable Simulation

In this paper, we present a fast and robust collision detection (CD) and resolution scheme for deformable cable using a new method based on the shortest distance of cable segment axis. We employ a bounding sphere hierarchy (BVH) by exploiting the topology of cable for reducing the collision detection query space. After searching the collision through the bounding sphere hierarchy, the collision detection algorithm will find the two segments which are close enough to require an exact collision check. Furthermore, the exact collision state is decided by our proposed method. Penalty force method is applied to the collision resolution. The comparative experiments show that the proposed method performs more accurate than existing algorithms for deformable cable simulation without substantial computational cost.

Non-Antipodal Signaling Based Robust Detection for Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks

In this letter, we present novel detection schemes for non-antipodal signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio (CR) networks, which are robust against the uncertainty in channel estimates. We consider a scenario in which the secondary users (SU) cooperate by reporting the sensed data to the fusion center for soft combining towards primary user (PU) detection. We formulate this problem employing the optimal linear discriminant and model the uncertainties in the channel state information (CSI) as ellipsoidal uncertainty sets. It is then demonstrated that this problem of PU detection with uncertainty in the channel estimates for cooperative spectrum sensing in a CR system can be formulated as second order cone program (SOCP). Further, we extend this paradigm to the associated relaxed robust detector (RRD) and multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids in low signal-to-noise power (SNR) and deep fade channel conditions. We present a closed form solution for the proposed robust detector for the above MIMO cooperative spectrum sensing scenario. Simulation results demonstrate a significant improvement in the detection performance of the proposed uncertainty aware robust detection schemes in comparison to the conventional uncertainty agnostic matched filter detector for cooperative MIMO PU detection.

Actuator fault detection in aircraft systems: Oscillatory failure case study

This paper is devoted to the detection of Oscillatory Failure Case (OFC) in the Electrical Flight Control System (EFCS). Such failures lead to a strong interaction between loads and aeroelasticity and must be quickly detected and passivated. This paper proposes a hybrid monitoring scheme for robust and early detection of such unauthorized oscillatory events. The proposed technique has been developed within ADDSAFE project (a collaborative project supported by the European Seventh Framework Program: Advanced Fault Diagnosis for Sustainable Flight Guidance and Control). A robust finite-time differentiator is used to estimate derivatives in a noisy environment. Fault reconstruction is next achieved by solving on-line a nonlinear equation using a gradient descent method. Finally, fault detection and confirmation stage is based on the decision making rules currently used for in-service Airbus A380 airplane. Robustness and performance of the proposed fault detection scheme are tested using a high fidelity benchmark and intensive Monte Carlo simulations for several flight scenarios as specified within ADDSAFE project.