Degradation In Detection Performance Research Articles

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy.

Epilepsy detection based on electroencephalogram (EEG) signal is of great significance to diagnosis and treatment of epilepsy. The denoised EEG signal is adopted by most traditional epilepsy detection methods. But due to nonideal denoising ability, the loss of local information and residual noise will occur, resulting in detection performance degradation. To solve the problem, the paper proposed an epilepsy detection method in noisy environment. Although epileptic signals and nonepileptic signals have some discrimination, they need to overcome the interference of noise. Hence, the improved sample entropy and phase synchronization indexes of corresponding 2 intrinsic mode functions (IMFs) caused by variational mode decomposition (VMD) are proposed as features, which can reduce the impact of noise on detection performance. The experimental results show that the accuracy, sensitivity, and specificity are 91.78%, 91.27%, and 93.61%, respectively. It can be used as an auxiliary method for clinical treatment of epilepsy.

DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar

Multitarget detection is very challenging, especially when targets are densely distributed. In conventional constant false alarm rate (CFAR) detection algorithms, the detection threshold is determined based on a pre-estimated background level (BL). However, interfering targets inevitably lead to inaccurate BL estimation, resulting in degraded detection performance. In our previous work, a new solution was developed and verified to be effective; in that solution, compressed sensing (CS)-based detector performs target detection without relying on BL estimation, and a subsequent CFAR regulation processor achieves the desired false alarm rate based on the statistical information of the reduced samples obtained by removing the detected targets and adjacent guard cells from the original samples. However, for scenes with a high target density, the CS-based detector suffers from performance degradation while acquiring the correlation between linear measurements of the signal and the sensing matrix due to the high local signal sparsity. To address this shortcoming, this article proposes a deep neural network (DNN)-based detector that further improves the detection performance by converting the target detection into a problem of peak sequence classification of frequency intensity (FI) measurements from radar. A DNN detector trained on augmented simulated data shows excellent generalization ability for deployment in real scenes. In addition, to achieve better computational efficiency, a Taylor-series-based approximate maximum likelihood estimator with an explicit expression is applied in the CFAR regulation process for the first time. Both simulation and field tests are performed to verify the superiority of the proposed algorithm over conventional algorithms.

Double FCOS: A Two-Stage Model Utilizing FCOS for Vehicle Detection in Various Remote Sensing Scenes

Vehicle detection in various remote sensing scenes is a challenging task. Various remote sensing scenes are mixed up with images of multi-scene, multi-quality, multi-scale and multi-class. Vehicle detection models suffer from inadequate candidate boxes, weak positive proposal sampling and poor classification performance, resulting in a detection performance degradation when they are applied in various scenes. What is worse, there is no such a dataset covering various scenes which is for vehicle detection. This paper proposes a vehicle detection model called Double FCOS and a vehicle dataset called 4MVD for vehicle detection in various remote sensing scenes. Double FCOS is a two-stage detection model based on fully convolution one-stage object detection (FCOS). FCOS is exploited in the RPN stage to generate candidate boxes in various scenes. The two-stage positive and negative sample model (TPNSM) is carefully designed to enhance the positive proposal sampling effects, particularly the tiny or weak vehicles, which are ignored in FCOS. A two-step classification model (TSCM) has been designed in the RCNN stage with a proposal classification branch and point classification branch to enhance the classification performance between the various types of vehicles. 4MVD (multi-scene, multi-quality, multi-scale and multi-class vehicle dataset) is collected from various remote sensing scenes to evaluate the performance of Double FCOS. A mean average accuracy of 78.3 percentage for vehicle detection on 5 categories has been received by Double FCOS on 4MVD. Extensive experiments demonstrate that Double FCOS significantly improves the performance of vehicle detection in various remote sensing scenes.

Multifractal Correlation Analysis of Autoregressive Spectrum-Based Feature Learning for Target Detection Within Sea Clutter

Fractal theory has improved the target detection performance under sea clutter background. However, the traditional fractal methods in time domain or Fourier domain cannot accurately characterize the complex sea clutter properties, which leads to the degradation of the target detection performance under low signal-clutter-ratio (SCR) conditions. This article investigates the multifractal correlation property of sea clutter in the autoregressive (AR) spectrum domain for detection performance improvement. In this work, the traditional target detection problem is converted to a binary classification problem of sea clutter and targets. The refined fractal characteristics in various singularity scale interval and range bins are analyzed, and the AR singularity intensity correlation function width together with the accumulation area of AR multifractal correlation spectrum is extracted as intrinsic features. Then, a simple and efficient fully connected network is developed to realize the classification. Experimental results on real measured marine radar datasets demonstrate that the proposed method can increase the detection probability by about 15% than the state-of-art fractal-based methods under a low SCR condition.

Low-Complexity Large MIMO Detection via Layered Belief Propagation in Beam Domain

In large multi-user multi-input multi-output systems, the computational cost and circuit scale of base stations (BSs) are effectively reduced using two-stage signal processing consisting of a slow varying outer beamformer (OBF) based on long-term channel statistics and group-specific multi-user detection for instantaneous channel variations. However, the dimensionality reduction of the group-specific beam-domain channel based on the OBF causes significant performance degradation in the subsequent spatial-filtering detection. To compensate for this drawback, this paper introduces a novel layered belief propagation (BP) detector with a concatenated structure of beam- and antenna-domain BP layers for post-stage OBF processing. The proposed detector is designed for improving the convergence of iterative detection by suppressing intra- and inter-group interference in stages. The layered structure provides the advantages of both beam and antenna domains while maintaining low signal-processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate performance in both the uncoded and coded cases and the computational complexity.

Radar Target Detection With Multi-Task Learning in Heterogeneous Environment

From the perspective of feature extraction and classification, deep neural networks are widely used in radar target detection. However, in heterogeneous environment, the traditional deep neural networks are difficult to extract a robust feature, which leads to the degradation of network detection performance. In order to address this problem, a radar target detection method with multi-task learning in heterogeneous environment is proposed. Considering the influence of heterogeneous data distribution, the proposed method designs a contrastive learning module added to a multi-task autoencoder. It can learn a compact and distinguishable feature representation, which enhances the feature separability between the clutter and the target. Simultaneously, a classifier is introduced to realize a binary detection in the feature representation. Comprehensive experiments are carried out to show that the proposed method guarantees a good detection performance in heterogeneous environment and solves the issue of over-fitting to a certain extent. Compared with some classical detectors, the proposed method shows better performance.

Multiscale Semantic Guidance Network for Object Detection in VHR Remote Sensing Images

With the development of convolutional neural network (CNN), many CNN-based object detection methods have made a remarkable success in very high-resolution (VHR) remote sensing images (RSIs). However, the standard convolution has a fixed receptive field, which makes it deficient in dynamic feature capture; complex backgrounds may also lead to the degradation of detection performance. Accordingly, this letter proposes a novel multiscale semantic guidance network (MSGN) to tackle these problems, wherein, based on the deformable convolution, an improved feature extraction backbone is proposed to capture features dynamically. Moreover, features from different layers are used to ensure the ability for detecting multiscale objects. Furthermore, a multilevel semantic guidance filtering subnetwork is proposed based on the designed backward semantic guidance filtering (BSGF) module, to suppress the complex backgrounds. Experimental results show that the proposed MSGN has stronger robustness and a better accuracy for multiscale object detection, compared with other reference methods.

Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images

Few-shot object detection methods have made prodigious progress in recent years. However, these methods are designed for optical images at a single scale, which leads to significantly degraded detection performance due to object scale variation of remote sensing images. In this letter, we propose a few-shot object detection method for the problem of scale variation in remote sensing images. More specifically, our model contains two main components: a context-aware pixel aggregation (CPA) that allows the model to adapt to objects at different scales through different scale convolution and a context-aware feature aggregation (CFA) that enhances context awareness to obtain more semantic information through a graph convolution network (GCN). Experiments on the DIOR dataset demonstrate that our model can achieve a satisfying detection performance on remote sensing images, and our model performs significantly better than the state-of-the-art model.

Wald- and Rao-Based Detection for Maritime Radar Targets in Sea Clutter With Lognormal Texture

Non-Gaussian sea clutter causes conventional detectors designed in Gaussian clutter to suffer detection performance degradation, and some nuisance parameters cause the uniformly most powerful test to be unavailable. To improve the detection performance of maritime radar targets, we investigate the design of adaptive detectors in correlated non-Gaussian sea clutter via using suboptimal tests. The non-Gaussian sea clutter is modelled as a product of lognormal-distributed texture and complex Gaussian speckle. Two adaptive radar target detectors are developed by using the suboptimal two-step Wald and Rao tests. Specifically, a non-adaptive detector is derived by the Wald or Rao test when the clutter texture and speckle covariance matrix are assumed to be known in the first step; then the clutter parameters known in the first step are estimated, and the true parameters of the detector obtained in the first step are replaced with the estimated values. Theoretical proof and experimental verification indicate that the two proposed detectors have the constant false alarm property with regard to the clutter speckle covariance matrix and the clutter average power. Numerical results on simulated and measured radar data show that the proposed Rao-based detector outperforms its competitors, and has the stronger robustness to the signal mismatch compared to the proposed Wald-based detector.

ScaleReactor: A graceful performance isolation agent with interference detection and investigation for container‐based scale‐out workloads

SummaryStriking a balance between improved cluster utilization and guaranteed application QoS is a long‐standing research problem in multi‐tenants shared cluster. The typical solution is to detect performance degradation and investigate the root cause to conduct performance isolation. Existing efforts rely on lots of prior knowledge of applications and the assumption of interference‐free workload placement is possible. Performance interference is usually mitigated through application‐level approaches such as centralized rescheduling, which is usually an hindsight and a waste of resources. In this article, we present ScaleReactor, a graceful runtime agent on a per node basis that decouples the performance isolation from centralized resource management, and migrates the performance interference of scale‐out workloads in container‐based cluster using a lightweight black‐box approach. ScaleReactor analyzes the degree of contention for multi‐dimensional resources among co‐located workloads to detect the performance degradation without additional prior information, and uses correlation analysis to locate the cause of contention, while isolating resources in a graceful manner to reduce system overhead and the performance degradation of intrusive workloads. Experiments have demonstrated that ScaleReactor effectively reduces the job completion time of scale‐out applications in shared clusters, with the maximum value up to 36% and low system overhead against the existing isolation mechanism.

Chemometric Approach Based on Accuracy Profile and Data Chronological Distribution as a Tool to Detect Performance Degradation and Improve the Analytical Quality Control for Aflatoxins' Analysis in Almonds Using UPLC-MS/MS.

One of the main objectives of routine laboratories is the development of simple and reliable methods as well as meeting fit-for-purpose criteria for regulatory surveillance. In this study, the accuracy profiles and the evaluation of the distribution of results in the case of aflatoxins in almonds have been performed using ultraperformance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS). The method consists of designing the experiment and using certified reference material (CRM) to evaluate the bias, to calculate the combined uncertainty, and to construct the control charts. Good sensitivity (limit of quantifications (LOQs) 0.34–0.5 μg/kg) and recovery (between 82 and 107%) were achieved. The proposed method was successfully tested with a proficiency test in almond powder with acceptable z scores (−2 ≤ z ≤ 2). The results provided direct evidence for the proper functioning and stability of the whole analytical protocol, allowing acceptable combined uncertainty.

AutoDiagn: An Automated Real-Time Diagnosis Framework for Big Data Systems

Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems’ performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. In this article, we propose, develop and validate AutoDiagn. This generic and flexible framework provides holistic monitoring of a big data system while detecting performance degradation and enabling root-cause analysis. We present an implementation and evaluation of AutoDiagn that interacts with a Hadoop cluster deployed on a public cloud and tested with real-world benchmark applications. Experimental results show that AutoDiagn can offer a high accuracy root-cause analysis framework, at the same time as offering a small resource footprint, high throughput, and low latency.

An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels

In massive multiple-input multiple-output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a message passing detection (MPD) algorithm with a convolutional neural network (CNN) (denoted as iterative MPD-CNN structure) under correlated noise channels, which is helpful to solve the issue of detection performance degradation in non-ideal AWGN channels. Firstly, the MPD algorithm based on the channel hardening phenomenon is used to initially estimate the transmitted signal, and then the CNN is concatenated to remove the estimation error for obtaining more accurate channel noise, which provides a beneficial noise distribution for the MPD algorithm. Finally, the theoretical analysis and simulation results show that the proposed iterative MPD-CNN structure can improve the detection performance in conditions of correlated noise channels and fewer antennas. Compared with the traditional MPD algorithm, its detection performance is more superior.

Exoplanet detection in starshade images

A starshade suppresses starlight by a factor of 1011 in the image plane of a telescope, which is crucial for directly imaging Earth-like exoplanets. The state-of-the-art in high-contrast post-processing and signal detection methods was developed specifically for images taken with an internal coronagraph system and focused on the removal of quasi-static speckles. These methods are less useful for starshade images where such speckles are not present. We are dedicated to investigating signal processing methods tailored to work efficiently on starshade images. We describe a signal detection method, the generalized likelihood ratio test (GLRT), for starshade missions and look into three important problems. First, even with the light suppression provided by the starshade, rocky exoplanets are still difficult to detect in reflected light due to their absolute faintness. GLRT can successfully flag these dim planets. Moreover, GLRT provides estimates of the planets’ positions and intensities and the theoretical false alarm rate of the detection. Second, small starshade shape errors, such as a truncated petal tip, can cause artifacts that are hard to distinguish from real planet signals; the detection method can help distinguish planet signals from such artifacts. The third direct imaging problem is that exozodiacal dust degrades detection performance. We develop an iterative GLRT to mitigate the effect of dust on the image. In addition, we provide guidance on how to choose the number of photon counting images to combine into one co-added image before doing detection, which will help utilize the observation time efficiently. All the methods are demonstrated on realistic simulated images.

Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives

This paper addresses control performance monitoring (CPM) and degradation recovering in automatic control systems. It begins with a re-visit of CPM techniques and a summary of the major limitations of the existing CPM methods. They are (i) deficit in assessing control performance degradation caused by different types of disturbances and environment uncertainties, (ii) incapability for predicting performance degradation, and (iii) deficiency of efficient performance degradation recovering methods. In order to meet increasing demands of next generation automatic control systems for higher system performance, novel CPM methods have been developed in recent years, including performance assessment of control systems with deterministic disturbances and uncertainties, prediction of control performance degradation, and recovery of control performance degradation. Some of these methods and algorithms are introduced in the second part of this paper. The basis of these methods is a so-called residual centred model of feedback control systems, which allows a unified handling of control, monitoring and diagnosis in feedback control systems corrupted by disturbances and uncertainties. The focuses of these methods are on (i) introduction of the loop performance degradation index for the assessment and prediction of performance degradation in automatic control systems, (ii) predictive detection and estimation of loop performance degradation, and (iii) a data-driven performance degradation recovering scheme. The paper is concluded by a short summary of three future perspective topics, (i) prediction of economic system performance monitoring and estimation, (ii) reinforcement learning aided system performance recovery, and (iii) CPM digital twin.

Anomalous Subgraph Detection in Given Expected Degree Networks With Deep Learning

Anomalous subgraph detection within networks is an important issue in many emerging applications. Existing algorithms, such as graph structure methods and spectral feature methods, usually focus on the special stochastic model (such as the Erdős-Renyi random graph) or may not efficiently extract the anomalous behaviors of the networks, which result in detection performance degradation. To mitigate the limitations, in this paper, we first present an anomalous subgraph detection framework associated with deep neural networks (DNN) for detecting anomalous behaviors within the networks. Furthermore, based on the developed framework, we propose a residual matrix-based convolutional neural network (RM-CNN) algorithm with respect to the given expected degree models, which are more general networks than the Erdős-Renyi random graphs. In particular, the trained RM-CNN can efficiently capture the anomalous changes of the network and then achieve the detection performance improvement. Simulation experiments display that the proposed RM-CNN algorithm is superior to the compared algorithms in both detection performance and detection speed.

A Quasi-Coherent Detection Framework for Mobile Multi-Agent Networks

Coherent processing in cooperative multi-agent networks can achieve significant performance gain via accurate alignment of observed signals. However, the inaccessibility of phase information in mobile scenes prevents the acquisition of a high degree of coherence for target detection. This paper presents a quasi-coherent mobile detection framework for cooperative multi-agent networks, which exploits the phase information inherent in the carrier wave of direct-path signals. Specifically, we derive the quasi-coherent detector with the aid of phase calibration by incorporating the maximum likelihood estimation of phase-related parameters obtained from direct-path signals. Next, we characterize the detection performance degradation induced by the mismatch of initial phase, transmitter position, orientation and clock. We further demonstrate the near-optimal detection capability of the proposed detector compared with generic coherent detectors and validate the effectiveness of phase information exploitation via numerical simulations.

Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble Self-Adaption Method

A deep ensemble convolutional neural network (CNN) model to inspect printed circuit board (PCB) board dual in-line package (DIP) soldering defects with Hybrid-YOLOv2 (YOLOv2 as a foreground detector and ResNet-101 as a classifier) and Faster RCNN with ResNet-101 and Feature Pyramid Network (FPN) (FRRF) achieved a detection rate of 97.45% and a false alarm rate (FAR) of 20%–30% in the previous study [34] . However, applying the method to other production lines, environmental variations, such as lighting, orientations of the sample feeds, and mechanical deviations, led to the degradation in detection performance. This article proposes an effective self-adaption method that collects “exception data” like the samples with which the Artificial Intelligent (AI) model made mistakes from the automated optical inspection inference edge to the training server, retraining with exceptions on the server and deploying back to the edge. The proposed defect detection system has been verified with real tests that achieved a detection rate of 99.99% with an FAR 20%–30% and less than 15 s of inspection time on a resolution $7296 \times 6000$ PCB image. The proposed system has proven capable of shortening inspection and repair time for online operators, where a 33% efficiency boost from the three production lines of the collaborated factory has been reported [6] . The contribution of the proposed retraining mechanism is threefold: 1) because the retraining process directly learns from the exceptions, the model can quickly adapt to the characteristic of each production line, leading to a fast and reliable mass deployment; 2) the proposed retraining mechanism is a necessary self-service for conventional users as it incrementally improves the detection performance without professional guidance or fine-tuning; and 3) the semiautomatic exception data collection method helps to reduce the time-consuming manual labeling during the retraining process.

Optimal energy efficiency resource allocation strategy for cognitive clustering network under PUEA attack

5G has pushed the use of radio spectrum to a new level, and cognitive clustering network can effectively improve the utilization of radio spectrum, which is a feasible way to solve the growing demand for wireless communications. However, cognitive clustering network is vulnerable to PUEA attack, which will lead to the degradation of system detection performance, thereby reducing the energy efficiency. Aiming at these problems, this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack. A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance. We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem, which is solved by Lagrangian function and Karush-Kuhn-Tucker condition. Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed. Simulation results indicate that proposed method is effective when subjected to PUEA attacks, and the impact of different parameters on energy efficiency is analyzed.

Enhancing the Orthogonality of Polyphase Signal With Multi-Pulse Joint Processing

In this paper, the problem that the polyphase signal may not satisfy the orthogonality requirement in the condition of limited bandwidth is investigated. The use of waveforms that lack orthogonality leads to possible harmful effects in multiple-input and multiple-output (MIMO) radar and distributed coherent aperture radar (DCAR). For the purpose of enhancing the orthogonality of the signal, a multi-pulse joint transmission strategy of polyphase signal and the corresponding echo signal processing algorithm based on sliding window are proposed. We further analyze the relationship between the degradation of detection performance and the step size of the sliding window. In addition, the Doppler tolerance of the signal is also derived. Finally, the enhancement of the orthogonality by using the multi-pulse joint processing strategy and the relationship between Doppler tolerance and coherent time are verified via numerical simulations.