Adaptive Thresholding Scheme Research Articles

An Efficient Saliency Detection Model Based on Wavelet Generalized Lifting

Saliency detection refers to the segmentation of all visually conspicuous objects from various backgrounds. The purpose is to produce an object-mask that overlaps the salient regions annotated by human vision. In this paper, we propose an efficient bottom-up saliency detection model based on wavelet generalized lifting. It requires no kernels with implicit assumptions and prior knowledge. Multiscale wavelet analysis is performed on broadly tuned color feature channels to include a wide range of spatial-frequency information. A nonlinear wavelet filter bank is designed to emphasize the wavelet coefficients, and then a saliency map is obtained through linear combination of the enhanced wavelet coefficients. This full-resolution saliency map uniformly highlights multiple salient objects of different sizes and shapes. An object-mask is constructed by the adaptive thresholding scheme on the saliency maps. Experimental results show that the proposed model outperforms the existing state-of-the-art competitors on two benchmark datasets.

Representation learning based adaptive multimode process monitoring

In this paper, a representation learning based adaptive monitoring method for multimode processes is proposed, in which mode identification and fault detection are integrated with an adaptive threshold strategy. Compared to conventional methods, the learned representations can integrate mode features with fault details here, which are formed by the composition of multiple non-linear transformations under a global modeling. To explore the expressive powers of AE net, a geometry interpretation based on the reformulated sigmoid function is presented. Moreover, take the significance of the dynamic characteristics into account, an adaptive thresholding scheme is proposed for the learned representations based on a modified exponentially weighted moving average (EWMA) control chart. Experiment results show that the proposed method not only improves the divisibility between multimode, but also exhibits superior performance of fault detection on an industrial benchmark of chemical process, Tennessee Eastman process (TEP).

New Verification Strategy for Finger-Vein Recognition System

This paper proposes a new finger-vein recognition system that uses a binary robust invariant elementary feature from accelerated segment test feature points and an adaptive thresholding strategy. Subsequently, the proposed a multi-image quality assessments (MQA) are applied to conduct a second stage verification. As oppose to other studies, the region of interest is directly identified using a range of normalized feature point area, which reduces the complexity of pre-processing. This recognition structure allows an efficient feature points matching using a robust feature and rigorous verification using the MQA process. As a result, this method not only reduces the system computation time, comparisons against former relevant studies demonstrate the superiority of the proposed method.

Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data

Universal thresholding methods have been developed to estimate the large sparse integrated volatility matrix of underlying assets based on high-frequency financial data. Since the integrated volatility matrix often has entries with a wide range of variability, universal thresholding estimators do not take the varying entries into consideration and may have unsatisfactory performances. This paper investigates adaptive thresholding estimation of the large integrated volatility matrix. We first construct an estimator for the asymptotic variance of the pre-averaging realized volatility estimator and then use the two estimators to develop an adaptive thresholding estimator of the large volatility matrix. It is shown that the adaptive thresholding estimator can achieve the optimal rate of convergence over the class of the sparse integrated volatility matrix when both the number of assets and sample size are allowed to go to infinity, while the universal thresholding estimator can achieve only the sub-optimal convergence rate. Also we discuss how to harness the adaptive thresholding scheme in the approximate factor model. The simulation study is conducted to check the finite sample performance of the adaptive thresholding estimators.

LTSD and GDMD features for Telephone Speech Endpoint Detection

AbstractThis paper proposes a new contour-based speech endpoint detector which combines the log-Group Delay Mean-Delta (log-GDMD) feature, an adaptive twothreshold scheme and an eight-state automaton. The adaptive thresholds scheme uses two pairs of thresholds - for the starting and for the ending points, respectively. Each pair of thresholds is calculated by using the contour characteristics in the corresponded region of the utterance. The experimental results have shown that the proposed detector demonstrates better performance compared to the Long-Term Spectral Divergence (LTSD) one in terms of endpoint accuracy. Additional fixed-text speaker verification tests with short phrases of telephone speech based on the Dynamic Time Warping (DTW) and left-to-right Hidden Markov Model (HMM) frameworks confirm the improvements of the verification rate due to the better endpoint accuracy.

A novel multi-scale adaptive sampling-based approach for energy saving in leak detection for WSN-based water pipelines

In this paper, we propose a novel monitoring strategy for a wireless sensor networks (WSNs)-based water pipeline network. Our strategy uses a multi-pronged approach to reduce energy consumption based on the use of two types of vibration sensors and pressure sensors, all having different energy levels, and a hierarchical adaptive sampling mechanism to determine the sampling frequency. The sampling rate of the sensors is adjusted according to the bandwidth of the vibration signal being monitored by using a wavelet-based adaptive thresholding scheme that calculates the new sampling frequency for the following cycle. In this multimodal sensing scheme, the duty-cycling approach is used for all sensors to reduce the sampling instances, such that the high-energy, high-precision (HE-HP) vibration sensors have low duty cycles, and the low-energy, low-precision (LE-LP) vibration sensors have high duty cycles. The low duty-cycling (HE-HP) vibration sensor adjusts the sampling frequency of the high duty-cycling (LE-LP) vibration sensor. The simulated test bed considered here consists of a water pipeline network which uses pressure and vibration sensors, with the latter having different energy consumptions and precision levels, at various locations in the network. This is all the more useful for energy conservation for extended monitoring. It is shown that by using the novel features of our proposed scheme, a significant reduction in energy consumption is achieved and the leak is effectively detected by the sensor node that is closest to it. Finally, both the total energy consumed by monitoring as well as the time to detect the leak by a WSN node are computed, and show the superiority of our proposed hierarchical adaptive sampling algorithm over a non-adaptive sampling approach.

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Detection of structural changes from an operational process is a major goal in machine condition monitoring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combining with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of-the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme

This paper presents main results of fault detection and diagnosis in a cement manufacturing plant using a new monitoring scheme. The scheme is based on multivariate statistical analysis and an adaptive threshold strategy. The process is statistically modeled using Principle Component Analysis (PCA). Instead of the conventional fixed control limits, adaptive thresholds are used to evaluate the common T2 and Q statistics as faults indicators. The adaptive thresholds are computed and updated using a modified Exponentially Weighted Moving Average (EWMA) chart. These techniques are merged together to construct a novel monitoring scheme whose effectiveness is demonstrated using involuntary real fault of a cement plant process and some simulated faulty cases.

Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

Local binary pattern (LBP) is sensitive to the noise and suffers from limited discriminative capability, and many LBP variants are reported in the recent literatures. Although a lot of significant progresses have been made, most LBP variants still have limitations of noise sensitivity, high dimensionality, and computational inefficiency. In view of this, we propose a new noise-robust local image descriptor named the diamond sampling structure-based local adaptive binary pattern (DLABP) in this letter, which aims at achieving both efficiency and simplicity at the same time. It mainly features three contributions: 1) an effective diamond sampling structure to decrease the feature dimensionality significantly by fixing the number of sampling neighbors to a constant of 8; 2) a simple and new “average method on the radial direction” to enhance the noise robustness; and 3) an effective adaptive quantization threshold strategy to restore the noise-corrupted nonuniform patterns back to possible uniform patterns. Extensive experiments are conducted on three benchmark texture databases of Outex, UIUC, and CUReT. Compared to state-of-the-art LBP-like methods, the proposed approach consistently demonstrates superior performances both in noise-free conditions and in the presence of high levels of noise, while it has a low complexity and a smaller feature dimension.

Modified Gradient Descent Bit-Flipping Decoding for Low-Density Parity-Check Codes

In this paper, we modify the gradient descent bit-flipping (GDBF) decoding of low-density parity-check codes based on the syndrome. Firstly, the syndrome weight is utilized to detect the decoding loop, which seriously effects the performance of GDBF decoding. Then the syndrome information is introduced to update the reliability of the flipped bit nodes. Since the modified GDBF, denoted as MGDBF, only uses the syndrome weight and syndrome information, there is small complexity increased. Simulation results indicate that the two modifications bring about significant improvement in error-rate performance. For single MGDBF decoding, its performance is not only better than that of GDBF, but also is better than that of noisy GDBF. For multi MGDBF decoding, it can obtain fast convergence rate and good performance by employing the appropriate adaptive threshold scheme.

An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

Algorithms for retinal vessel segmentation are powerful tools in automatic tracking systems for early detection of ophthalmological and cardiovascular diseases, and for biometric identification. In order to create more robust and reliable systems, the algorithms need to be accurately evaluated to certify their ability to emulate specific human expertise. The main contribution of this paper is an unsupervised method to detect blood vessels in fundus images using a coarse-to-fine approach. Our methodology combines Gaussian smoothing, a morphological top-hat operator, and vessel contrast enhancement for background homogenization and noise reduction. Here, statistics of spatial dependency and probability are used to coarsely approximate the vessel map with an adaptive local thresholding scheme. The coarse segmentation is then refined through curvature analysis and morphological reconstruction to reduce pixel mislabeling and better estimate the retinal vessel tree. The method was evaluated in terms of its sensitivity, specificity and balanced accuracy. Extensive experiments have been conducted on DRIVE and STARE public retinal images databases. Comparisons with state-of-the-art methods revealed that our method outperformed most recent methods in terms of sensitivity and balanced accuracy with an average of 0.7819 and 0.8702, respectively. Also, the proposed method outperformed state-of-the-art methods when evaluating only pathological images that is a more challenging task. The method achieved for this set of images an average of 0.7842 and 0.8662 for sensitivity and balanced accuracy, respectively. Visual inspection also revealed that the proposed approach effectively addressed main image distortions by reducing mislabeling of central vessel reflex regions and false-positive detection of pathological patterns. These improvements indicate the ability of the method to accurately approximate the vessel tree with reduced visual interference of pathological patterns and vessel-like structures. Therefore, our method has the potential for supporting expert systems in screening, diagnosis and treatment of ophthalmological diseases, and furthermore for personal recognition based on retinal profile matching.

An Accurate Technique for Discrimination between Transient and Permanent Faults in Transmission Networks

This paper presents an assessment study for adaptive single-pole automatic reclosure (ASPAR) schemes to select correctly the best scheme at responsible indices. Suggested solutions for the drawbacks that were noticed with the previous methods are investigated. In addition, this paper presents a proposed technique for ASPAR on transmission lines using wavelet packet transform (WPT). The ASPAR aims to discriminate between the faults' natures and to detect the instant of arc extinguished. The proposed technique uses an adaptive threshold level, and therefore, no adjustment is needed for various transmission systems. The proposed technique is examined at different fault scenarios that involve different fault locations, inception angles, and representation to secondary arc characteristics with various arc models. In addition, the behavior of ASPAR is well studied under power swing situation. The effects of shunt reactor compensators on the proposed technique are checked. The proposed scheme is tested for different network configurations, single and double circuits, to realize the robustness and the capability of the proposed scheme. The ATP/EMTP package is developed to emulate different fault signals to validate the proposed scheme. The proposed adaptive threshold scheme discriminates correctly between permanent and transient faults, thereby enhancing power system stability for compensated and uncompensated networks.

Efficient compressed sensing based object detection system for video surveillance application in WMSN

Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy and network lifetime. Furthermore, the proposed approach is compared with the conventional background subtraction approach. The simulation results show that the proposed approach yields better detection accuracy with 90% reduction in samples compared to conventional approach.

A new adaptive PCA based thresholding scheme for fault detection in complex systems

For large scale and complex processes, data-driven analysis methods are receiving increasing attention for fault detection and diagnosis to improve process operation by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. Common methods based on multivariate statistical analysis are widely used and particularly principal component analysis (PCA), fault detection indices used along with PCA including the Hotelling T² statistic and the sum of squared prediction error (SPE) known as the Q statistic can be used to identify faults. This paper develops a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic, which is effective in detecting small changes and abrupt shifts in the process operation. The aim is to enhance the performance of PCA methods for process monitoring, while maintaining a low false alarm rate with good sensitivity of anomalies. The performance of the developed scheme is compared to a conventional fixed thresholding technique by evaluating the detection performance across various types of faults that occurred in the Tennessee Eastman Process, The results demonstrate the promising capabilities of our proposed scheme.

A highly efficient and reliable heart rate monitoring system using smartphone cameras

In the field of healthcare, a fundamental research topic includes the development of a vital signs monitoring system that allows people to monitor their health state so that they can control the same. Given that the heart is at the center of the human system, it is important to constantly monitor the heart beat rate. Hence, recently, heart rate monitoring systems or methods have started to use smartphones (and their built-in cameras) as computing and sensing platforms. These are ubiquitous devices. However, as most smartphones still have low computational power and as camera features differ across smartphones, smartphone-based heart rate computations can suffer from time-consuming processes. Hence, the results can be inaccurate or inconsistent. In this paper, we propose a highly efficient and reliable method to measure the heart rate from the smartphone camera images of fingertips. The method is composed of region of interest-based signal extraction, signal noise/bias reduction using an adaptive threshold scheme, and cycle miss/duplication handling using an iterative outlier elimination scheme. Existing methods require the real-time operation of high-speed processors and work properly on only specific smartphones. In contrast, the proposed method works consistently in real time with high accuracy on smartphones of any level. This is a very important factor because health-care facilities must be universally accessible, including to individuals who cannot afford buying excessively expensive high-performance smartphones.

An Online Fault Diagnosis System for Induction Motors via Instantaneous Power Analysis

ABSTRACTThis article aims to provide a new noninvasive method for the online diagnosis of bearing-localized faults under various loading conditions of the induction motors via instantaneous power analysis. The instantaneous noise variations and sensor offsets are considered to be one of the common factors that yield erroneous fault tracking in an online condition monitoring and fault diagnosis system. An adaptive threshold scheme has been designed to tackle the sensor offsets and instantaneous noise variations for reliable decision making on the existence of fault signatures in an arbitrary environment conditions. The performance of the designed threshold scheme has been evaluated on a motor with various bearing defects operating under various loading conditions. Detailed theoretical and experimental evaluations of several bearing-localized faults are presented. The results indicate the viability and effectiveness of the proposed method.

Murmur‐adaptive compression technique for phonocardiogram signals

As mobile and wearable health technologies have evolved, constant monitoring of bio-signals from patients has become important for accurate diagnosis using mobile and remote health services. As the need for constant monitoring of bio-signals increases, the amount of bio-signal data also increases, therefore an efficient compression method for specific bio-signals should be developed. In contrast to other types of signals, bio-signals contain important diagnostic information that should not be removed during compression. An effective compression algorithm for phonocardiogram (PCG) signals that contain important diagnostic information (murmur) is proposed. In this algorithm, the murmur is first estimated, then an adaptive thresholding scheme is applied in the wavelet domain to the normal portions and the murmur portions of PCGs depending on the murmur estimates during compression. Although other conventional compression methods result in substantial loss of murmur information, the proposed method is able to keep most of the murmur information in compressed PCGs.

Visible Light Communication Using Receivers of Camera Image Sensor and Solar Cell

We propose an electronic label and sensor system using visible light communication (VLC). The downlink signal is transmitted by a white-light light-emitting diode (LED) lamp that can provide lighting, VLC, and energy harvesting for mobile devices. The downlink is received by a solar cell. The uplink can be captured by a surveillance camera image sensor. However, using the camera image sensor as a VLC receiver (Rx) is challenging since the data rate is limited by the frame rate and due to uneven light exposure. The rolling shutter effect of the image sensor can be used to increase the data rate. In this paper, we propose and demonstrate how to demodulate the obtained rolling shutter pattern using a second-order polynomial (SOP) extinction ratio (ER) enhancement scheme, together with iterative and modified quick adaptive thresholding schemes. The ER enhancement scheme can significantly reduce the large ER fluctuation. Experimental results show that by using the proposed SOP ER enhancement scheme, the bit error rate (BER) improvement can be up to two orders of magnitude. We also believe that the proposed electronic label and sensor system may be applicable to Internet-of-Things sensing networks for connecting a number of mobile devices.

Reliable pose estimation of underwater dock using single camera: a scene invariant approach

It is well known that docking of Autonomous Underwater Vehicle (AUV) provides scope to perform long duration deep-sea exploration. A large amount of literature is available on vision-based docking which exploit mechanical design, colored markers to estimate the pose of a docking station. In this work, we propose a method to estimate the relative pose of a circular-shaped docking station (arranged with LED lights on periphery) up to five degrees of freedom (5-DOF, neglecting roll effect). Generally, extraction of light markers from underwater images is based on fixed/adaptive choice of threshold, followed by mass moment-based computation of individual markers as well as center of the dock. Novelty of our work is the proposed highly effective scene invariant histogram-based adaptive thresholding scheme (HATS) which reliably extracts positions of light sources seen in active marker images. As the perspective projection of a circle features a family of ellipses, we then fit an appropriate ellipse for the markers and subsequently use the ellipse parameters to estimate the pose of a circular docking station with the help of a well-known method in Safaee-Rad et al. (IEEE Trans Robot Autom 8(5):624---640, 1992). We analyze the effectiveness of HATS as well as proposed approach through simulations and experimentation. We also compare performance of targeted curvature-based pose estimation with a non-iterative efficient perspective-n-point (EPnP) method. The paper ends with a few interesting remarks on vantages with ellipse fitting for markers and utility of proposed method in case of non-detection of all the light markers.

Orthogonal component analysis: A fast dimensionality reduction algorithm

Most existing dimensionality reduction algorithms have two disadvantages: their computational cost is high and they cannot estimate the intrinsic dimension of the original dataset by themselves. To deal with these problems, in this paper we propose a fast linear dimensionality reduction method named Orthogonal Component Analysis (OCA). While avoiding solving eigenproblem and matrix inverse problem, OCA successfully achieves high-speed orthogonal component extraction. By proposing an adaptive threshold scheme, OCA is able to estimate the dimension of the feature space automatically. Meanwhile, the algorithm is guaranteed to be numerical stable. In the experiments, OCA is compared with several typical dimensionality reduction algorithms. The experimental results demonstrate that as a universal algorithm, OCA is efficient and effective.