Probability Of Successful Detection Research Articles

Conditional generative adversarial networks for image-based sunlight analysis of residential blocks

Sunlight simulation is often used to analyze whether buildings receive sufficient sunlight in complex design cases. However, it is time-consuming to establish geometrical and physical models, and non-negligible computation time (from a few seconds to minutes) leads to high time cost of iterative design optimization. Therefore, this study builds surrogate models to instantaneously detect problematic buildings (those receiving insufficient sunlight) using images of residential blocks. Different conditional generative adversarial networks (CGANs), including Pix2Pix and conditional attention GAN (CAGAN), are employed to train surrogate models. The attention mechanisms and the multi-scale vision give CAGAN an edge in capturing the global structure of buildings and learning high-level knowledge related to the occlusion relationships between buildings. In addition, this study also proposes a stepwise strategy to construct composite surrogate models. The first sub-model provides more informative images to help the second sub-model learn the features of problematic buildings. This study focuses on evaluating the models based on the detection performance in addition to image structural similarity and pixel similarity, which have been used in most studies. The CAGAN models outperform the Pix2Pix models in the average detection accuracy (ADA) and the 100 % successful detection probability (SDP) on the same dataset. Moreover, the stepwise strategy increases the ADA and SDP to 89.88 % and 71.07 %, respectively. The proposed methods can be extended to other urban environmental performance diagnoses, such as thermal comfort or the wind environment.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

An Optimization Method for Collaborative Radar Antijamming Based on Multi-Agent Reinforcement Learning

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar’s and jammer’s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar’s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively.

Statistical analysis for explosives detection system test and evaluation

The verification of trace explosives detection systems is often constrained to small sample sets, so it is important to support the significance of the results with statistical analysis. As binary measurements, the trials are assessed using binomial statistics. A method is described based on the probability confidence interval and expressed in terms of the upper confidence interval bound that reports the probability of successful detection and its level of statistical confidence. These parameters provide useful measures of the system’s performance. The propriety of combining statistics for similar tests—for example in trace detection trials of an explosive on multiple surfaces—is examined by statistical tests. The use of normal statistics is commonly applied to binary testing, but the confidence intervals are known to behave poorly in many circumstances, including small sample numbers. The improvement of the normal approximation with increasing sample number is shown not to be substantial for the typical numbers used in this type of explosives detection system testing, and binary statistics are preferred. The methods and techniques described here for testing trace detection can be applied as well to performance testing of explosives detection systems in general.

An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.

Intelligent Reflecting Surface-Assisted Multi-Antenna Covert Communications: Joint Active and Passive Beamforming Optimization

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

Optimal Approach for Signal Detection in Steady-State Visual Evoked Potentials in Humans Using Single-Channel EEG and Stereoscopic Stimuli.

In EEG studies, one of the most common ways to detect a weak periodic signal in the steady-state visual evoked potential (SSVEP) is spectral evaluation, a process that detects peaks of power present at notable temporal frequencies. However, the presence of noise decreases the signal-to-noise ratio (SNR), which in turn lowers the probability of successful detection of these spectral peaks. In this paper, using a single EEG channel, we compare the detection performance of four different metrics to analyse the SSVEP: two metrics that use spectral power density, and two other metrics that use phase coherency. We employ these metrics find weak signals with a known temporal frequency hidden in the SSVEP, using both simulation and real data from a stereoscopic apparent depth movement perception task. We demonstrate that out of these metrics, the phase coherency analysis is the most sensitive way to find weak signals in the SSVEP, provided that the phase information of the stimulus eliciting the SSVEP is preserved.

Gerschgorin disk estimator method for source number estimation based on peak-to-average power ratio

Gerschgorin disk estimator (GDE) method is difficult to detect the number of signal sources effectively under low signal-to-noise ratio (SNR). In view of this situation, we propose GDE method based on peak-to-average power ratio (GDE-PAR) by combining the peak-to-average power ratio (PAR) with the Gerschgorin disk radius. We extract the eigenvectors from the received data and use them as the weights of the data. The PAR is used to modify the corresponding Gerschgorin disk radius, and the number of signal sources is determined by GDE method. This paper adopts Matlab simulation on the Gerschgorin disk radius before and after correction under different SNR conditions. In the case of double target of equal intensity and double target of unequal intensity, three target of equal intensity and three target unequal intensity using Monte Carlo method, we use Akaike information theory criterion (AIC), minimum description length (MDL) criterion, PAR method and GDE-PAR method to estimate the number of signal sources respectively. The results show that under the conditions of low SNR and multi-target of unequal intensity, the probability of successful detection of source by GDE-PAR method is higher than that of AIC, MDL criterion and PAR method.

The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

Several works show that the linear Angle of Arrival (AoA) methods such as Projection Matrix (PM) have low computational complexity compared to the subspace methods. Although the PM method is classified as a subspace method, it does not need decomposition of the measured matrix. This work investigates the effect of the sampled columns within the covariance matrix on the projection matrix construction. To the authors’ knowledge, this investigation has not been addressed in the literature. Unlike the subspace methods such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Minimum Norm, Propagator, etc., which have to use a specific number of columns, we demonstrate this aspect is not applicable in the PM method. To this end, the projection matrix is formed based on a various number of sampled columns to estimate the arrival angles. A theoretical analysis is accomplished to illustrate the relationship between the number of the sampled columns and the degrees of freedom (DOFs). The analysis shows that with the same aperture size, the DOFs can be increased by increasing only the number of sampled columns in the projection matrix calculation step. An intensive Monte Carlo simulation for different scenarios is presented to validate the theoretical claims. The estimation accuracy of the PM method, based on the proposed selected sampling methodology outperforms all the other techniques with less complexity compared to the Capon and MUSIC methods. The estimation accuracy is evaluated in terms of Root Mean Square Error (RMSE) and the Probability of Successful Detection (PSD). The results are presented and discussed.

Optimization of Spectrum Utilization in Cooperative Spectrum Sensing.

This paper presents an analytical framework for the probability of spectrum hole utilization (PSHU) of a cognitive radio system with soft cooperative spectrum sensing (CSS) under a practical consideration of fixed frame structure. In practical systems, the length of a time-frame is generally fixed, where the time-frame consists of sensing, reporting, and transmission durations. Thus, increasing sensing and reporting time duration in cooperative spectrum sensing improves the probability of successful detection of the primary user’s (PU) presence or the absence but reduces transmission time duration, which results in a lower PSHU. A large reporting duration is required when more secondary users (SUs) report their sensed information to the fusion center (FC) and/or multiple bits are used by each SU in soft cooperative spectrum sensing. Thus, reporting time in terms of the number of SUs and reporting bits also have a similar effect on PSHU. Based on this interesting trade-off between PSHU and the sensing and reporting time duration, this paper analyzes the impact of an increasing number of SUs and reporting bits on PSHU.

Design and Analysis of Wireless Sensor Networks for Animal Tracking in Large Monitoring Polar Regions Using Phase-Type Distributions and Single Sensor Model

Data gathering through wireless sensor networks (WSNs) has been used for the monitoring of endangered species. However, when it comes to animals that live in difficult access environments with large areas, where human access is extremely difficult, such as in polar regions, remote monitoring by traditional methods becomes complicated and even inefficient. This paper proposes the characterization of the animal random trajectories by means of the random walk model in order to select the appropriate detection range and number of nodes to guarantee a target detection probability. The animals are detected by static gatherer sensor nodes placed on land either by mobile sensor nodes attached to the animal or by land sensor nodes that detect them through movement, sound, or temperature among other methods. Due to their natural movement, the animal may be outside or inside the sensor nodes coverage radius. In order to reduce energy consumption, it is proposed that nodes be active and inactive, effectively increasing the system lifetime. As such, an inherent compromise between energy consumption and reporting efficiency is present in the design of the network. Building on this, careful network design is required in order to calculate the probability of successful detection and system lifetime. To this end, a mathematical model based on a Markov chain is proposed and developed. The model suitability is assessed via numerical simulations. The obtained results allow concluding that their trajectories can be modeled using specific phase type distributions. Finally, instead of considering that in polar regions, it is not feasible to have a conventional WSNs, where many nodes are placed together, single nodes are placed in strategic locations isolated among them, and communication between nodes is not possible. As such, it proposes a novel and simplified model, where a single sensor is used to analyze the performance of the complete multi-sensor network.

A Novel Cycle Slips Detection Model for the High Precision Positioning

Quality of the observations, which is significantly affected by cycle slips, is a key factor affecting the positioning results in the high precision positioning. Traditional observation data error detection algorithms always miss some special data error combinations, which is also called the “blind detection spots” problem. For solving the problem, a complementary symmetric geometry-free (CSGF) method is proposed, which makes the detection of cycle slips more comprehensive and accurate. However, when the observation sampling interval becomes larger, the channel state cannot be seen as a constant which reduces the successful detection probability of the cycle slips. Then, a CSGF second-order differential model is further proposed to deal with this problem. The experimental results show that the proposed model significantly improves the accuracy of cycle slip detection even with long sampling interval. The results also indicate that the accuracy and convergence speed of the positioning solution are significantly improved than other schemes.

Pilot Spoofing Attack by Multiple Eavesdroppers

In this paper, we investigate the design of a pilot spoofing attack (PSA) carried out by multiple single-antenna eavesdroppers (Eves) in a downlink time-division duplex system, where a multiple antenna base station (BS) transmits confidential information to a single-antenna legitimate user. During the uplink channel training phase, multiple Eves collaboratively impair the channel acquisition of the legitimate link, aimed at maximizing the wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data transmission phase. Two different scenarios are investigated: 1) the BS is unaware of the PSA and 2) the BS attempts to detect the presence of the PSA. For both scenarios, we formulate wiretapping SNR maximization problems. For the second scenario, we also investigate the probability of successful detection and constrain it to remain below a pre-designed threshold. The two resulting optimization problems can be unified into a more general non-convex optimization problem, and we propose an efficient algorithm based on the minorization–maximization (MM) method and the alternating direction method of multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to converge to a stationary point of the general problem. In addition, we propose a semi-definite relaxation (SDR) method as a benchmark to evaluate the efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM algorithm achieves near-optimal performance and is computationally more efficient than the SDR-based method.

Performance Analysis of Detecting Packet Arrival for Downclocking Wi-Fi

Energy-Minimizing idle Listening (E-MiLi) is a Wi-Fi power-saving amendment using a state-of-the-art downclocking technique that reduces power consumption by lowering sampling rate. It introduces a Sampling Rate Invariant Detection (SRID) algorithm to detect packet arrival at a low clock rate. Upon a successful detection, the node reverts to a full clock rate to receive the packet immediately. In this paper, we theoretically study the crucial impact of SRID attributes (e.g., downclocking factor, correlation threshold, energy ratio threshold and tolerance threshold) on the packet detection performance, namely, the false-alarm probability and the successful detection probability. Extensive Monte Carlo results show that our performance model is very accurate.

Reliability-Security Trade-Off Analysis of Cognitive Radio Networks with Jamming and Licensed Interference

Cognitive radio networks (CRNs) allow coexistence of unlicensed users (UUs) and licensed users (LUs) and hence, mutual interference between UUs and LUs is neither ignored nor considered as Gaussian-distributed quantity. Additionally, exploiting jamming signals to purposely interfere with signal reception of eavesdroppers is a feasible solution to improve security performance of CRNs. This paper analyzes reliability-security trade-off, which accounts for maximum transmit power constraint, interference power constraint, jamming signal, and Rayleigh fading, and considers interference from LUs as non-Gaussian-distributed quantity. Toward this end, exact closed-form expressions of successful detection probability and successful eavesdropping probability, from which reliability-security trade-off is straightforwardly visible, are first suggested and then validated by Monte-Carlo simulations. Various results demonstrate that interference from LUs considerably decreases both probabilities while jamming signal enlarges the difference between them, emphasizing its effectiveness in improving security performance.

Improved DPTE technique for impulsive noise mitigation over power-line communication channels

Signal blanking is a simple and efficient method commonly used to reduce the impact of impulsive noise (IN) over power-lines. There are two main ways to implement this method, namely, (a) the unmodified scheme and (b) the dynamic peak-based threshold estimation (DPTE) technique. Concerning the first, in order to optimally blank IN the noise characteristics must be made available at the receiver otherwise the system performance will degrade dramatically. Whereas in the DPTE case, only estimates of the signal peaks are required to achieve best performance. In this paper, however, we propose to enhance the capability of the conventional DPTE technique by preprocessing the signal at the transmitter side. To evaluate system performance, we consider the probability of blanking error (Pb), probability of missed blanking (Pm) and probability of successful detection (Ps). In light of this, closed-form analytical expressions for the three probabilities are derived which are then validated with simulations. The results reveal that the proposed DPTE technique can significantly minimize both Pb and Pm and maximize Ps. It is also shown that the proposed system is able to attain up to 3.5dB and 1dB SNR enhancement relative to the unmodified and the conventional DPTE techniques, respectively, as well as improving the symbol error rate performance.

Electromechanical Impedance - Based Structural Health Monitoring Instrumentation System Applied to Aircraft Structures and Employing a Multiplexed Sensor Array

The electromechanical impedance method has been seen as a promising tool for structural health monitoring regarding different types of structures and purposes. Most importantly, this method can be used in real-time applications. Frequently, massive, high-cost, single-channel impedance analyzers are used to process the time domain data, aiming at obtaining the complex, frequency-dependent electromechanical impedance functions and therefore infer about the presence, position and extent of an existing damage. However, for large structures, it is desirable to deploy an array of piezoelectric transducers over the area to be monitored and interrogate these transducers successively, in order to increase the probability of successful detection of damage at an early phase. The literature describes many miniaturized systems that can monitor large structures, however, presenting serious restrictions on data acquisition capabilities. This paper presents a hardware that is not limited to any data acquisition restriction, exhibiting an innovative way to measure the electromechanical impedance of multiplexed bonded transducers. Each logical block of the proposed architecture is presented in detail. The proposed system not only avoids costly fast Fourier transform analyzers/ algorithms, but also evades high-speed data acquisition hardware. A prototype using inexpensive integrated circuits and a digital signal processor was built and tested for two different types of structures: an aluminum beam and an aircraft aluminum panel. Simulated damages were introduced to each structure, and the detection performance of the prototype was tested. The actual prototype uses a universal serial bus connection to communicate with a personal computer.

The discrete tuberculosis transmission model with treatment of latently infected individuals

A discrete tuberculosis model with direct progression and treatment of latently infected individuals is presented. The model does not consider the drug-resistant TB, and it assumes that latently infected individuals develop the active disease only because of being endogenous reactive, and a small fraction of infected individuals is assumed to develop the active disease soon after infection. The global stability of a disease-free equilibrium, the persistence of system, and the local stability of endemic equilibrium are discussed. The basic reproductive numbers with different control measures are determined and analyzed, and we give the critical value of probability of successful detection and treatment of infectious individuals. If a treatment only of infectious individuals cannot control TB transmission, the treatment of latent TB individuals should be carried out, and we give the critical value of the probability of treatment of infectious individuals. Numerical simulations are done to demonstrate the complex dynamics of the model.

Restoring a silhouette of the hand in the problem of recognizing gestures by adaptive morphological filtering of a binary image

Algorithms are presented for the adaptive processing of binary images of silhouettes of the human hand obtained by means of color-brightness filters. These algorithms are based on the use of a combination of elementary morphological operations that take into account the direction of the fingers. Algorithms are presented for removing noise on binary images that are adapted to the result of the operation of a color filter, and a method is presented for filling internal contours of a silhouette of the hand in order to remove grouped marking errors. The experimental results show that the proposed image-processing method increases the probability of successful detection, tracking of the hand, and recognition of gestures.

Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.