Adaptive Threshold Detection Research Articles

Single-Channel Circular SAR Ground Moving Target Detection Based on LRSD and Adaptive Threshold Detector

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

Adaptive Double Threshold Detection Method for Range-Spread Targets

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets.

An Adaptive Two-Stage Load Event Detection Method for Nonintrusive Load Monitoring

Load event detection is fundamental to event-based nonintrusive load monitoring (NILM) solution. It is directly related to whether the transient and steady-state signatures of appliances can be accurately extracted. In the real world, the load composition is random and complicated, the power consumption patterns of appliances are diverse, and multiple events may occur simultaneously or near each other, all these make it difficult for any one single event detection method to achieve satisfactory robustness. In coping with this, an adaptive two-stage event detection method is proposed in this article. First, an adaptive detection threshold whose value can be adjusted adaptively according to the load fluctuations is adopted, thus improving the ability to detect events of different amplitudes. Then, considering the different event geometric features, an improved edge detection method and a window-based detection method combining moving average with moving t-test are proposed for step-like events and long-transient events, respectively, and they are executed consecutively to achieve the effective capture of the complete transient process of appliances. Furthermore, an event separation step is taken to locate and separate the near-simultaneous events which often appear in low-frequency data. Specific event detection performance evaluation metrics are also designed. Comparison test results on private and public datasets show that the proposed method achieves high detection accuracy for different events of various appliances and maintains strong robustness in different operation scenarios.

LSTM-Based Collaborative Source-Side DDoS Attack Detection

As denial of service attacks become more sophisticated, the source-side detection techniques are being studied to solve the limitations of target-side detection techniques such as delayed detection and difficulty in tracking attackers. Recently, some source-side detection techniques are being studied to use an adaptive attack detection threshold considering seasonal behavior of network traffic. However, because patterns of network traffic usage have become irregular with increased randomness and explosive traffic, the performance of the adaptive threshold technique has deteriorated. In addition, by limitations of the local view of a single site, distributed attacks from multiple sites may not be detected. In this paper, we propose a LSTM (Long Short Term Memory) based collaborative source-side DDoS (Distributed Denial of Service) attack detection framework which provides the attack detection result of a collaboration network in a global view. The proposed framework applies LSTM-based adaptive thresholds to each source-side network to mitigate performance degradation caused by irregular network traffic behavior. Also, in order to overcome the limitation of performance caused by the local view of single source-side network, the proposed framework constructs a collaborative network through multiple detection sites and aggregates feedback from each site, such as detection rates, local traffic patterns, and timestamp. The collaborative attack detection technique uses the aggregated feedback to determine whether the attack is finally detected and shares the finial detection results with multiple sites. Depending on this final detection result, the adaptive thresholds of each site are reset. Through extensive evaluation of actual network traffic data, the proposed collaborative source-side attack detection technique shows around 15% lower false positive rate than the single source-side attack detection technique while maintaining a high detection rate.

Bayesian track-before-detect algorithm for nonstationary sea clutter

Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.

An efficient fault diagnostic approach for active distribution networks considering adaptive detection thresholds

As support to fault location algorithms, this paper presents a new method for detecting and classifying faults in active distribution networks using the energy moving averages of the current signals measured at the feeder head. A proposal for updating the energy thresholds for fault detection is introduced, aiming to achieve higher sensitivity in fault detection, especially for scenarios with high penetration levels of distributed generation (DG). Since the power injected by DG can rapidly change, this proposal is essential for maintaining the fault detection method’s reliability. Also, the fault classification is based on an analytical approach, making it possible to be easily applied to different operating scenarios of a distribution system. Thus, its potential for real-world implementation is higher when compared to classifiers based on supervised learning algorithms. Performance tests on the IEEE 34-node test feeder showed that this method is reliable for detecting and classifying different fault types, with diverse generation-load scenarios and noisy signals.

A traffic anomaly detection scheme for non-directional denial of service attacks in software-defined optical network

As a promising centralized control architecture, software-defined network (SDN) has been widely used and developed in the field of optical access network. Though, its centralized control architecture has many advantages, it is also hindered by various security threats. Among all the threats, the attack, Denial-of-Service (DoS) is the most severe attack into the software defined optical network (SDON). In spite of, so many developments in tools and technology, there are few effective schemes to detect denial of service attacks in SDON. In our research work, we proposed a traffic anomaly detection scheme by analyzing and defining the specific security threat non-directional denial of service attack (ND-DoS) faced by the SDON. In this scheme, we first designed the function construction of the controller and the extension of the OpenFlow protocol, and then used the adaptive threshold detection algorithm based on time sliding window (TSW-ATD) and the repeated flow detection algorithm (RFD) to complete the first detection and re-detection detection of abnormal traffic, and finally designed a general formulaic measurement method. The proposed scheme is verified by simulation experiments. The experimental results show that compared with the existing related solutions, the forwarding success rate of this scheme is increased by about 29.4%, the data processing rate in the unit window is increased by about 39.3%, and the CPU occupancy rate is reduced by about 17.5%. Therefore, this scheme can effectively deal with DoS attacks in SDON with a higher detection rate and lower resource overhead.

A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows.

Automated Adaptive Wideband Acoustic Reflex Threshold Estimation in Normal-hearing Adults.

Acoustic stapedius reflex threshold (ART) tests are included in a standard clinical acoustic immittance test battery as an objective cross-check with behavioral results and to help identify site of lesion. In traditional clinical test batteries, middle-ear admittance of a 226 Hz probe is estimated using ear-canal measurements in the presence of a reflex-activating stimulus. In the wideband (WB) acoustic immittance ART test used in this study, the pure-tone probe is replaced by a WB probe stimulus and changes in absorbed power are estimated using ear-canal measurements in the presence of the activator. The ART is defined as the lowest level at which a criterion change in admittance (clinical) or absorbed power (WB) is observed in the presence of the activator. In the present study, ARTs were obtained in adults with normal hearing using the clinical, manual method and with a new WB automated adaptive threshold detection method. It was hypothesized that the WB test would result in lower ARTs than the clinical test because reflex-related changes in power absorbance could be observed across multiple frequency bands in the WB test compared with a single frequency in the traditional test. Data were collected in a prospective research design. ARTs were obtained in ipsilateral and contralateral conditions using 500, 1000, 2000 Hz, and broadband noise (BBN) activators on a clinical system and on an experimental WB system. The bandwidth of the BBN activator was 125 to 4000 Hz on the clinical system and 200 to 8000 Hz on the wideband system. ARTs were estimated at both tympanometric peak pressure (TPP) and ambient pressure on the WB system. Data were collected in both ears of 39 adults (21 males) of mean age 47.7 years (range 23-72 years). Differences in ARTs among the three threshold estimation methods (clinical, WB at TPP, WB at ambient) were examined using the general linear model repeated measures test in SPSS. Post-hoc pairwise comparisons were completed with Bonferroni correction for multiple comparisons. Statistical significance was defined as p < 0.05 for all analyses. ARTs obtained on the WB system at TPP and ambient pressure were significantly lower than obtained on the clinical system. ARTs obtained on the WB system at TPP were significantly higher than at ambient pressure in the 500 and 2000 Hz ipsilateral conditions. WB automated adaptive ARTs in normal-hearing adults were lower than for clinical methods when measured at TPP and ambient pressure. Lower presentation levels required to estimate ART in the WB test may be more tolerable to patients. Patients with ARTs that are not present at the maximum level of a traditional reflex test may have present ARTs with a WB ART test, which may reduce the need to refer for additional testing for possible retrocochlear involvement. Automation of the test may allow clinicians more time to attend to the other requisite tasks of a hearing evaluation and make the system useful for telehealth applications.

UWB Radio-Based Motion Detection System for Assisted Living.

Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.

Research on Adaptive Threshold of Received Signal in Communication System

When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.

Application of Polarimetric-SAR Decompositions on RADARSAT-2 Fine Quad-Pol Images to Enhance the Performances of Ships Detection Algorithms

Remote sensing of vessels is an important tool for ship safety and security at sea. In this work, we are interested in improving ships detection using polarimetric Synthetic Aperture Radar (SAR). To develop the appropriate method, different processing techniques are applied on Pol-SAR images such as fusion and polarimetric decompositions and we use adaptive threshold detectors to assess the performances of the processing techniques. The data exploited in this work were acquired on a port area of the city of Vancouver by using RADARSAT-2 satellite. In this paper it is shown first that when exploiting single polarization, the HH channel provides the highest score of detection probability (PD) of 87.2% for a false alarm probability (PFA) of 0.05%, and this while using the cell averaging constant false alarm rate (CA-CFAR) detector. The result is obtained comparatively with other polarizations (HV, VV) and detection algorithms. Second, the fusion of polarimetric channels achieves its best performances with the CA-CFAR detector, comparatively with the two parameters (2P)-CFAR and generalized likelihood ratio test (GLRT). Third, we find that among the conventional polarimetric techniques, the singular value decomposition (SVD) combined with CA-CFAR detector gives the best results and achieves a detection probability of 91% for a false alarm of 0.05%. This result was obtained by comparing the performances of other combinations of decompositions (Pauli, Freeman, Yamaguchi), fusion and ships detection algorithms. In this paper, we obtain with the proposed approach an increase of 3.8% in detection probability for false alarm probability of 0.05%.

Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment

ABSTRACT We propose a new adaptive threshold detection and enrichment design in which the biomarker threshold is adaptively estimated and updated by optimizing a trade-off between the size of the biomarker positive population and the magnitude of the treatment effect in that population. Enrichment is based on an enrollment criterion that accounts for the uncertainty in estimation of the threshold. Early termination for futility is allowed based on predictive success probability. Valid testing and estimation techniques for the treatment effect overall and inpatient subgroups are studied. Simulations and an example demonstrate advantages of the proposed design over existing designs.

Performance evaluation and enhancement of the modified OOK based IM/DD techniques for hybrid fiber/FSO communication over WDM-PON systems

In this paper, we have studied the performance evaluation and enhancement of a wavelength-division-multiplexing (WDM) for the passive optical network (PON) in the hybrid fiber/free-space optical (HFFSO) communication system using the modified on–off keying (OOK) based intensity modulation and direct detection (IM/DD) techniques. This work describes the design and analysis of the 8-channels WDM based on the PON-HFFSO transmission system at 2.5 Gbps under different atmospheric turbulence (AT) conditions. Inter-channel crosstalk (ICC), fluctuation, amplified spontaneous emission noise, AT, and impairments are investigated in the paper. A hybrid fiber/WDM-FSO over the PON structure using IM/DD and OOK significantly improves reliability link and accessibility as well as data rate, also improves the system capacity; hence the proposed model will reduce power consumption while maintaining system efficiency. Both impairments and ICC are investigated here and the results are presented in the form of bit-error-rate (BER), transmission distances, the required optical power, and electrical signal-to-noise ratios (SNRs). The system uses the modified OOK based on IM/DD to implement adaptive and electrical SNRs detection thresholds. Numerical results show that the SNR gap between the electrical-SNR-optimized detection system and adaptive detection system is 2.2 dB and 4.2 dB at a BER of 10−5 without a state offset ξ = 0 and ξ = 0.21, respectively. The proposed model enhances performance in terms of BER and electrical SNRs. An improvement in the power penalty of 2.0–4.0 dB at a BER of 10−12 can be potentially achieved using the modified OOK technique when impaired by the weak turbulence.

A Fast and Robust Open-Switch Fault Diagnosis Method for Variable-Speed PMSM System

Traditional open-switch fault diagnosis methods suffer from poor rapidity or robustness. To solve this issue, a new differential current observer-based fault diagnosis method is proposed in this article. With the designed differential observer, fault symptoms (residuals) can be generated and adopted for fault diagnosis easily. Considering that the residuals are sensitive to motor operating condition in the conventional model-based method due to model error, an adaptive fault detection threshold is designed. As a result, the false detection and missed detection caused by the change of working condition can be avoided, and stronger robustness against speed, load, and parameter variations can be achieved with superior rapidity compared with existing methods. Finally, the rapidity and robustness of the proposed fault diagnosis method are verified sufficiently through experimental results.

Adaptive multi-threshold object selection in remote sensing images

Introduction: Detection, selection and analysis of objects of interest in digital images is a major problem for remote sensing and technical vision systems. The known methods of threshold detection and selection of objects avoid using the processing results, therefore not providing a low probability of false alarms, and not keeping the shape of the selected objects well enough. There are only few results from the studies about quantifying the quality of such algorithms on either model or real images. Purpose: Studying the effectiveness of algorithms for detecting, selecting, and localizing objects of interest using their geometric characteristics, when the object properties and background are a priori uncertain, and the shape of the selected objects is kept unchanged. Results: We have obtained and studied the characteristics of algorithms for detecting and selecting objects of interest on test models of monochrome images. These software-implemented algorithms use multi-threshold processing, providing a set of binary slices. This makes it possible to perform morphological processing of objects on each slice in order to analyze their geometric characteristics and then select them according to geometric criteria, taking into account the percolation effect which causes changes in the area, and fragmentation of the objects. As a result of analyzing these changes, an adaptive detection threshold is set for each of the selected objects. The selection allows you to significantly reduce the number of false positives during the detection and to use lower thresholds, increasing the correct detection probability. We present the detection characteristics and the results of test model processing, as well as the results of object selection on a real television and radar image, confirming the effectiveness of the considered algorithms. Practical relevance: The proposed algorithms can more effectively select objects on images of various nature obtained in remote sensing, material research or medical diagnostics systems. Their microprocessor implementation is much simpler than the implementation of universal trainable neural network algorithms.

Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions. Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte. Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts. Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.

GPS/BDS triple-frequency cycle slip detection and repair algorithm based on adaptive detection threshold and FNN-derived ionospheric delay compensation

GPS/BDS triple-frequency cycle slip detection and repair algorithm based on adaptive detection threshold and FNN-derived ionospheric delay compensation

DETECTION OF OBJECTS IN IMAGES USING AREA SELECTION

The aim of the work is to study the effectiveness of algorithms for detecting objects of interest in images obtained by remote surveillance systems. The main properties of objects in relation to the background are their compactness and isolation. The algorithms in question use multithresholding, which yields a set of binary slices. It allows to carry out morphological processing of objects on each slice for the purpose of the analysis of their geometrical characteristics and the subsequent selection. As a result, an adaptive object detection threshold can be set. The use of area selection can significantly reduce the number of false alarms during detection and use lower‑level thresholds, which increases the probability of correct detection of objects of interest. Results of processing of test models of images, and also results of detection of objects on the real image showing efficiency of the considered algorithm are presented.

Detection method for the dynamic signal of sky screen-based velocity measurement system using Bayesian Generalized Likelihood Ratio Tests

This paper presents a method using Bayesian Generalized Likelihood Ratio Tests (BGLRT) to detect the dynamic signal of sky screen-based velocity measurement System under poor signal-to-noise ratio (SNR). The characteristics of the dynamic signal are systematically analyzed, and then a Bayesian classification model is formulated based on BGLRT. According to Neyman-Pearson criterion, we construct an adaptive threshold detector, thereby detecting the dynamic signal, effectively, under this SNR (−1 to 5 dB). Through semi-physical simulations and live ammunition experiments, the results demonstrate the proposed method can increase the effective detection height of this velocity measurement system by approximately 20 % compared with the previous methods.