Nonparametric spatial analysis for mine line detection

In human presence detection for Internet of Things (IoT) applications, Wi-Fi channel state information (CSI)-based methods offer significant advantages in cost-effectiveness and privacy preservation. Existing approaches typically rely on feature engineering or deep learning techniques to process CSI data. However, these methods often struggle to accurately model the complex, probabilistic nature of CSI measurements, particularly in environments with nonhuman moving elements. This leads to suboptimal detection probabilities under low false alarm rate (FAR) conditions. Therefore, this article introduces Wi-CCFAR, a controllable FAR human presence detector utilizing Wi-Fi CSI data. First, we employ a normalizing flow network with spline transformations for data modeling, capturing the complexity of CSI measurement distribution. Second, to address the inherent limitation of the same input and output dimensions in normalizing flow networks, we introduce an efficient feature extractor, which reduces input dimensions and computational requirements while maintaining high detection probabilities by preserving essential information. Last, we implement an enhanced information bottleneck loss function, which balances data fitness and class divergence, to achieve favorable detection probabilities under low FAR conditions. Experimental results demonstrate that Wi-CCFAR outperforms conventional methods, achieving higher detection probabilities with lower false alarm probabilities, even in challenging scenarios with various interferences such as moving objects and multiple occupants.

The two primary measures of detection performance are probability of detection (PD) and false alarm rate (FAR). Since these are statistical measures, the large statistical uncertainties associated with measurements on small data sets impose a severe limitation on the interpretation and extrapolation of the test results. The main difficulty with the data collected from many of the landmine detection experiments is that the number of mine targets is small, and the clutter data is measured on small areas. As a result of analyzing data from several detection tests for both UXO and mine detection technologies, the Institute for Defense Analyses (IDA) has generated a number of suggestions to improve the results obtained from future demonstrations and tests. In the absence of a rigorous error analysis, we can estimate the uncertainty in a probability of detection measurement with a simple model. If detections can be described as a binomial process weighted by the true probability of detection, then uncertainty equals (root)N, where N is the number of mines detected, and uncertainty in PD (percent) equals (root)N/N X 100 for large N. A similar statistical uncertainty is encountered in the measurement of probability of false alarm. An opportunity for a false alarm can be defined by the amount of ground covered by the projected area of the target plus an allowable miss distance, say 1 m2. Thus, for each 1 m2 of ground that it passes over, the system has one opportunity to declare a false alarm. A test field that is 1 m by 20 m offers only 20 m2 of area and thus only 20 samples for probability of false alarm measurement. A site that is 4 m wide and 4 km long covers 16,000 m2. Thus, there will be a substantial increase in the clutter environment samples by the sensor and a concomitant improvement in false alarm measurements. Of course, false alarm density will be highly site dependent, so extrapolation of the result is still uncertain. Unfortunately, this limitation is insurmountable in single test and at a single site.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Development of an algorithm to discriminate between valid and false alarms in a loose-parts monitoring system

This study proposed a method to evaluate proper experimental conditions of eddy current tests under a low signal-to-noise ratio. This method was applied to quantify the detection reliability of the eddy current test to detect corrosion pits on the surface of austenitic stainless-steel weld cladding from the perspectives of probability of detection and probability of false alarm. The fluctuations of flaw signal and noise were described using truncated bivariate normal distributions of a two-dimensional vector composed of the real and imaginary components of a signal. The probabilities of detection and false alarms were formalized as cumulative distributions of the estimated distributions of the maximum flaw signal and the noise exceeding a decision threshold. The receiver operating characteristic curves were created by coupling the probabilities of detection and false alarms at various thresholds. The area under the curve was used to quantify the detection reliability, simultaneously considering the probabilities of detection and false alarms. The results obtained in this study support the applicability of this method in quantitatively evaluating the detection reliability of a non-destructive inspection under a low signal-to-noise ratio.

In this paper, an adaptive multi-channel energy detector is presented for spectrum sensing applications. This method exploits the Empirical Mode Decomposition (EMD) and Cell-Averaging Constant False Alarm Rate (CA-CFAR) in an effort to maximize the probability of detection for a given probability of false alarm. First, the oversampled baseband signal is compared to its corresponding EMD noise-only model. If the band is occupied, then an EMD-CA technique is used to estimate the noise power for a given false alarm probability. Then, from the estimated noise power, double thresholds based on two given false alarm rates are calculated to detect and localize the occupied channels of band of interest. The proposed approach is able to work blindly and it is independent of the noise power. Simulations for different sampling and false alarm rates are used to validate the performance of the proposed detector. The results revealed the robustness of the proposed technique to the noise uncertainty and the capability to sense and localize multiple channels simultaneously.

The performance of a face recognition algorithm is typically characterised by correct identification rate under the closed-world assumption. To be of greatest practical use, the closed-world assumption must be relaxed and the classifier used both for detection and identification. It is put forward that for open-world applications, the false alarm rate of the classifier is at least as important as the identification rate. Under a repeated verification model, all face recognisers exhibit a rapid non-linear increase in false alarm rate with the false alarm rate of the one-to-one verification used. If the one-to-one false alarm rate is not strictly controlled, the overall classifier are unusable. A method is presented to predict the false alarm rate of a large gallery classifier using only a small data set. It is then shown that the false alarm error rate is always greater than the identification error rate. Therefore the false alarm rate is a more difficult criterion to minimise when designing a classifier.

The outcome of this research developed a new qualitative image processing approach to finding subpixel level anomalies in hyperspectral imagery. There were several steps involved in this process and there were 2 phases. First, anomalies were placed in the input hyperspectral image. In Phase 1, we chose a set of random chips or random pixels which were from the NxMxP hyperspectral image. Here the number of rows, columns, and bands of the hyperspectral image corresponds with N, M, and P respectively. A metric involving edge and turbulence is computed for each 3x3 neighborhood of pixels in the image. From this, a function is constructed where the domain (i.e. horizontal axis) is the band number and the range (i.e., vertical axis) is the corresponding “edge divided by turbulence” value after a first thresholding is applied. Throughout the paper, “edge divided by turbulence” is referred to as E/T. A second threshold is applied to this E/T function to determine the optimal bands to select from the hyperspectral image. In Phase 2, a transition into determining the probability of detection and probability of false alarm by adjusting the incongruence threshold H is performed. For this effort, a suite of Matlab tools was developed detailing information related to probability of detection (POD) and false alarm rate (FAR) for varying levels of contamination, incongruence, and band thresholding. A 224-band hyperspectral image was used as the evaluation image for this effort. The result of this effort was a generic workflow for determination of subpixel anomalies in any hyperspectral image.

A lane departure warning system (LDWS) should give as much warning time as possible, while triggering few, if any, false alarms. In this paper, a virtual lane boundary based LDWS (VLWM) is proposed to approach this goal. VLWM allows the driver to drift beyond the physical lane boundary by adding a virtual lane boundary. Accounting for the driving habit of the driver, lane geometry, and the local driver behavior changes, the virtual lane width is determined using a fuzzy-logic inference method. When the vehicle is predicted to exceed the virtual lane boundary, an alarm is triggered. Real world driving data are used to test the LDWSs. Compared with time-to-lane-crossing (TLC) based method, the VLWM has a much lower false alarm rate, while their warning time is almost similar. Meanwhile, VLWM has a much longer warning time than the roadside rumble strip (RRS), while the false alarm rate is almost as low as that of the RRS.

Many different demands can be made of intrusion detection systems. An important requirement is that it be effective i.e. that it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level.This paper aims to demonstrate that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate, P(Intrusion|Alarm), we have to achieve—a perhaps unattainably low—false alarm rate.A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.

A novel semantic Minimum Bayes ternary hypothesis detector based on the Rayleigh distribution is presented to decrease detection false alarm rate of synthetic aperture radar image. The method not only employs the strong scattering features of the target which is used in constant false alarm rate (CFAR) but employs the shadow features of the target which is used in the Power Ratio detector. Furthermore, target, shadow, and clutter regions are merged into the ternary image to estimate Rayleigh distribution parameters and weights. Meanwhile, the semantic relationship between the strong scattering features and the shadow features is established to partly reduce the false alarm targets. By the experiments on the Mini-SAR image of the 4-in resolution, it is shown that our method outperforms the semantic CFAR method and CFAR algorithm by a much lower false alarm rate.

This paper presents a novel analytical derivation of the false alarm probability (FAP) and detection probability (DP) for non-line-of-sight (NLOS) detection based on LTE signals. Position estimation in indoor or urban area with cellular wireless communication systems is getting more and more important. However, especially in these environments NLOS propagation causes positioning errors in the order of hundreds of meters. Thus, detection and mitigation of NLOS signals is a critical task. In this paper, we derive the FAP and DP on power-scaled detectors based on LTE multipath signals. The derivation considers frequency-selective fading channels taking sub-sample multipath interference, channel dynamics and initial frequency offsets into account. The results show a simple way to calculate the DP versus FAP. Simulation results verify the analytical probabilities for the WINNER II C2 channel model. Additionally, the simulations show the improved FAP of an antenna array at the receiver.

Multi-pass search and reconnaissance missions provide unique opportunities for hyperspectral target detection systems to operate at drastically reduced false alarm rates. In the simplest example, confirmed false alarms generated by an anomaly detector on an initial pass can be archived for future reference. A more sophisticated approach captures false alarm signatures to update background clutter models that inform detection algorithms. Or if detections are confirmed as targets on one pass, then matched-filter maps based on their spectra can be compared over time to monitor changes. As a final example, when sub-pixel registration is feasible, multi-temporal spectral covariance relations can be estimated from the data and used to detect anomalous changes at low false alarm rates, using no target signature information. All but the simplest of such methods require that the spectral evolution of a terrestrial background -- its chromodynamics -- be modeled sufficiently that naturally occurring changes are not confused with unnatural ones. This paper describes several detection paradigms that rely on multi-pass missions. Optimal linear algorithms to predict scene and target evolution are discussed, as are more realistic methods with relaxed operational requirements. One of these, called Covariance Equalization, is shown to perform nearly as well as the minimum error solution based on the matrix Wiener filter, which requires subpixel registration accuracy.

When old/new recognition memory is tested with equal numbers of studied and nonstudied items and no rewards or instructions that favour one response over the other, there is no obvious reason for response bias. In line with this, Canadian undergraduates have shown, on average, a neutral response bias when we tested them on recognition of common English words. By contrast, most subjects we have tested on recognition of richly detailed images have shown a conservative bias: they more often erred by missing a studied image than by judging a nonstudied image as studied. Here, in an effort to better understand these materials-based bias effects (MBBEs), we examined changes in hit and false alarm (FA) rates (and in sensitivity and bias) from the first to fourth quartile of a recognition memory test in eight experiments in which undergraduates studied words and/or images of paintings. Response bias for images tended to increase across quartiles, whereas bias for words showed no consistent pattern across quartiles. This pattern could be described as an increase in the MBBE over the course of the test, but the underlying patterns for hits and FAs are not easily reconciled with this interpretation. Hit rates decreased over the course of the test for both materials types, with that decline tending to be steeper for images than words. For words, FA rates tended to increase across quartiles, whereas for paintings FA rates did not increase across quartiles. We discuss implications of these findings for theoretical accounts of the MBBE.

Pulse oximetry provides a simple, continuous, noninvasive assessment of arterial blood oxygen saturation. Introduced by Nicolai [1] in 1932, oximetry did not gain widespread acceptance until the 1970s, when it was first reported that arterial hemoglobin oxygen saturation could be measured by analysis of pulsatile blood flow [2]. The pulse oximeter operates by measuring the attenuation of red (660 nm) and infrared light (940 nm) as it passes through a pulsatile tissue bed. Pulsatile blood flow is essential to allow differentiation of arterial from other absorbers of light such as venous and capillary blood components, as well as overlying tissue and skin [2]. Conceptually, the value of pulse oximetry is in identification and correction of hypoxemic events, which, if untreated, could result in serious complications such as brain injury, cardiac arrest, or death [3]. In a review of anesthetic-related, closed malpractice claims, Tinker et al. [4] stated that 31.5% of cases with adverse outcomes could have been prevented through additional monitoring. Eichhorn [5] in a review of 1,001,000 ASA physical status I and II patients, found 11 major intraoperative accidents that could have been attributed solely to anesthesia; seven were deemed preventable. Unrecognized hypoventilation was the most common intraoperative event associated with severe patient injury. Other extensive clinical series document that pulse oximetry increases the identification of hypoxemic events [6,7]. Although pulse oximetry is now well established, the technology has not been greatly improved since 1988 [8], and a number of limitations persist. Kestin et al. [9] reported that 75% of all auditory alarms during routine anesthesia did not originate from changes in physiological variables and only 3% represented patient risk. Freund et al. [10] reported 11,046 cases, with a 1.12% failure rate, defined as the inability to obtain any pulse oximetry reading for a cumulative period of 30 minutes or more after all mechanical problems had been eliminated. A higher rate of failure was present in elderly patients, those with ASA physical status III, IV, and V, and during long or emergency operations. An algorithm developed by Timcenko and Reich [11] predicted a more frequent rate of pulse oximetry failure depending on patient age, ASA physical status, body temperature (<35 degrees C or >37 degrees C), and type of operation (cardiopulmonary bypass, vascular, and orthopedic operations), while Moller et al. [6], by analysis of data from 20,802 patients, identified a total failure rate of 2.5%, increasing to 7.2% in ASA physical status IV patients. A comprehensive review by Severinghaus and Kelleher [8] determined that failure of pulse oximetry could often be explained in terms of low signal-to-noise ratio due to either inadequate signal (e.g., low perfusion, vasoconstrictor use, improper probe placement) or excessive noise (e.g., motion, ambient light, electrocautery, venous pressure waves). Thus, technological evolution recently has been focused on improving function by optimizing the signal-to-noise ratio [12-21] and decreasing the frequency of false alarms [22-24]. Pan and James [23] found that incorporation of a 60-second delay could reduce the false alarm frequency by 88%. However, during a true hypoxemic event, such a delay would reduce the time available for intervention. Egbert et al. [24] developed a neural network pattern classifier for detecting artifact in pulse oximetric signals. Using factor analysis to differentiate pulse oximetry waveforms, they correctly classified 91.4% of the corrupted waveforms and 72.7% of the clean waveforms used to train the network, suggesting that further improvements in signal analysis could reduce artifact and improve performance. Artifact due to motion also can be reduced by coupling an electrocardiograph to the oximeter signal [25]. In this issue, Dumas et al. [12] report the use of another technological advance, Masimo signal extraction technology (SET Trademark), to differentiate the "true" signal from artifact during low perfusion and motion. Recent studies have demonstrated that this technique may have a significantly lower failure rate and a lower false positive alarm rate [12,18,19,21] than conventional oximetric monitoring. Dumas et al. evaluated a prototype Masimo SET Trademark pulse oximeter in 50 ASA physical status I-IV patients in the postanesthesia care unit and found a lower failure rate and a lower frequency and duration of false alarms when compared with conventional pulse oximetry. A similar prototype also alarmed less frequently than a conventional oximeter during inflation of a blood pressure cuff [17]. At present, Masimo SET Trademark awaits Food and Drug Administration approval, although the manufacturers anticipate that the technique will be available for general marketing in late 1996. As anesthesiologists, should we anticipate that this technological advance will improve pulse oximetry sufficiently to advance clinical practice? The answer may vary, depending upon the circumstances in which oximetry is used. In daily practice, the two most annoying limitations of current technology are signal failure in patients with poor peripheral perfusion and signal artifact in patients who have distal extremity movement. Masimo SET Trademark does little to improve the first problem; however, movement artifact complicates pulse oximetry in the postanesthesia care unit and intensive care unit and during emergence from general anesthesia. Anecdotally, the frequency of false alarms is sufficiently high that many personnel are desensitized; they regard a desaturation on pulse oximetry as artifactual until proven otherwise. Perhaps many of us have witnessed delays in treatment because "we didn't trust the monitor!" The Masimo SET Trademark should increase confidence in the validity of alarms, as it increased the incidence of true positive alarms from 12% to 41%. Perhaps our practice will be a bit improved. How much additional cost is justified? Perhaps a bit. However, any technological improvement in pulse oximetry can represent only a small incremental improvement on an enormous advance--and the enormous advance has been shown to make no apparent difference in clinical outcome [6,7].

Reliance on Automated Target Recognition (ATR) technology is essential to the future success of Intelligence, Surveillance, and Reconnaissance (ISR) missions. Although benefits may be realized through ATR processing of a single data source, fusion of information across multiple images and multiple sensors promises significant performance gains. A major challenge, as ATR fusion technologies mature, is the establishment of sound methods for evaluating ATR performance in the context of data fusion. The Deputy Under Secretary of Defense for Science and Technology (DUSD/S&T), as part of their ongoing ATR Program, has sponsored an effort to develop and demonstrate methods for evaluating ATR algorithms that utilize multiple data source, i.e., fusion-based ATR. This paper presents results from this program, focusing on the target detection and cueing aspect of the problem. The first step in assessing target detection performance is to relate the ground truth to the ATR decisions. Once the ATR decisions have been mapped to ground truth, the second step in the evaluation is to characterize ATR performance. A common approach is to vary the confidence threshold of the ATR and compute the Probability of Detection (PD) and the False Alarm Rate (FAR) associated with each threshold. Varying the threshold, therefore, produces an empirical performance curve relating detection performance to false alarms. Various statistical methods have been developed, largely in the medical imaging literature, to model this curve so that statistical inferences are possible. One approach, based on signal detection theory, generalizes the Receiver Operator Characteristic (ROC) curve. Under this approach, the Free Response Operating Characteristic (FROC) curve models performance for search problems. The FROC model is appropriate when multiple detections are possible and the number of false alarms is unconstrained. The parameterization of the FROC model provides a natural method for characterizing both the operational environment and the ability of the ATR algorithm to detect targets. One parameter of the FROC model indicates the complexity of the clutter by characterizing the propensity for false alarms. The second parameter quantifies the separability between clutter and targets. Thus, the FROC model provides a framework for modeling and predicting ATR performance in multiple environments. This paper presents the FROC model for single sensor data and generalizes the model to handle the fusion case.