Frame Detection Based on Cyclic Autocorrelation and Constant False Alarm Rate in Burst Communication Systems

Frame detection is important in burst communication systems for its contribu- tions in frame synchronization. It locates the information bits in the received data stream at receivers. To realize frame detection in the presence of additive white Gaussian noise （AWGN） and frequency offset, a constant false alarm rate （CFAR） detector is proposed through exploitation of cyclic autocorrelation feature implied in the preamble. The frame detection can be achieved prior to bit timing recovery. The threshold setting is independent of the signal level and noise level by utilizing CFAR method. Mathematical expressions is derived in AWGN channel by considering the probability of false alarm and probability of detection, separately. Given the probability of false alarm, the mathematical relationship between the frame detection performance and EJNo of received signals is established. Ex- perimental results are also presented in accor- dance with analysis.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.

Solution of false alarm and slow response in flame detector

A new flame detector with one ultraviolet and two infrared detectors is designed. The ultraviolet detector is of rapid response(≤10 μs) while the two infrared detectors usually have a response time of more than 5 ms. The ultraviolet detector is applied to deal with the flame of large scales. When facing the flame of mid or small scales, the three detectors cooperate. Employing the high-order derivatives of the sample data of the infrared circuits to improve the sensitivity, the response speed is greatly improved. The data of the temperature sensor is used to adjust circuit parameters in real time, thus reducing the effect of temperature drift. The flame detectors are tested at different distances and the response time is as rapid as 0.65 ms. The test results show that the new flame detector has the characteristics of high speed and a low rate of false alarms.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Screening-Assisted Dynamic Multiple Testing with False Discovery Rate Control

In the era of big data,high-dimensional data always arrive in streams,making timely and accurate decision necessary.It has become particularly important to rapidly and sequentially identify individuals whose behavior deviates from the norm.Aiming at identifying as many irregular behavioral patterns as possible,the authors develop a large-scale dynamic testing system in the framework of false discovery rate(FDR)control.By fully exploiting the sequential feature of datastreams,the authors propose a screening-assisted procedure that filters streams and then only tests streams that pass the filter at each time point.A data-driven optimal screening threshold is derived,giving the new method an edge over existing methods.Under some mild conditions on the dependence structure of datastreams,the FDR is shown to be strongly controlled and the suggested approach for determining screening thresholds is asymptotically optimal.Simulation studies show that the proposed method is both accurate and powerful,and a real-data example is used for illustrative purpose.

Support Recovery of Gaussian Graphical Model with False Discovery Rate Control

This paper focuses on the support recovery of the Gaussian graphical model(GGM)with false discovery rate(FDR)control.The graceful symmetrized data aggregation(SDA)technique which involves sample splitting,data screening and information pooling is exploited via a node-based way.A matrix of test statistics with symmetry property is constructed and a data-driven threshold is chosen to control the FDR for the support recovery of GGM.The proposed method is shown to control the FDR asymptotically under some mild conditions.Extensive simulation studies and a real-data example demonstrate that it yields a better FDR control while offering reasonable power in most cases.

A New Two-Stage Tunable Space-Time Adaptive Detector

In order to improve the rejection capability of mismatched interferer signals,a new two-stage detector is proposed under homogeneous scenarios with unknown covariance matrix,which is obtained by cascading the adaptive matched filter(AMF)detector and the enhanced RAO(EnRAO)detector.The new detector has constant false alarm performance,and the closed-form expression of probability of false alarm and probability of detection is derived.The performance of the new detector is assessed,and analyzed in comparison with other detectors.The results show that,the proposed detector can provide enhanced rejection capability in the case of mismatch,but the performance of the detector is slightly lost under the condition of matching.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Detection optimization for resonance region radar with dense multi-carrier waveform

Unlike the existing resonance region radar systems （RRRS ） that transmit the orthogonal frequency division multiplexing （OFDM）multi-carrier waveform,the dense multi-carrier （DMC）radar waveform which has a narrower frequency interval than the traditional OFDM waveform is proposed.Therefore,in the same frequency bandwidth,the DMC waveform contains more sub-carriers and provides more frequency diversity.Additionally,to further improve detection performance,a novel optimal weight accumulation target detection （OWATD）method is proposed,where the echo electromagnetic waves at different frequencies are accumulated with the optimal weight coefficients.Then,with the signal-to-noise ratio （SNR）of echo waveform approaching infinity,the asymptotic detection performance is analyzed, and the condition that the OWATD method with the DMC outperforms the matched filter with the OFDM is presented.Simulation results show that the DMC outperforms the OFDM in the target detection performance,and the OWATD method can further improve the detection performance of the traditional methods with both the OFDM and DMC radar waveform.

Investigation of Dynamic Multivariate Chemical Process Monitoring

Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.

New CFAR target detector for SAR images based on kernel density estimation and mean square error distance

A new constant false alarm rate （CFAR） target detector for synthetic aperture radar （SAR） images is developed. For each pixel under test, both the local probability density function （PDF） of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error （MSE） distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm （PFA）, an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.

Adaptive coherence estimator based on the Krylov subspace technique for airborne radar

A novel adaptive detector for airborne radar space-time adaptive detection （STAD） in partially homogeneous environments is proposed. The novel detector combines the numerically stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator （ACE）. It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher proba- bility of detection （PD）, especially when the number of the training data is low. Moreover, it is shown to be practically constant false alarm rate （CFAR）.

Research on data diagnosis method of acoustic array sensor device based on spectrogram

Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method.However,due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference,the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge(PRPD)map might occasionally presents incorrect results,thus affecting the power equipment operation and maintenance strategy.The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean(FFT-CLEAN)sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm,improves the spatial resolution of the acoustic array sensor device,and proposes an acoustic array sensor device based on the acoustic spectrogram.The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal,and reduce the false alarm rate of the acoustic array sensor device.The proposed method is tested by selecting power cables as the object,and its effectiveness is proved by laboratory verification and field verification.

Distribution-based CFAR detectors in SAR images

As traditional two-parameter constant false alarm rate （CFAR） target detec-tion algorithms in SAR images ignore target distribution, their performances are not the best or near best. As the resolution of SAR images increases, small targets present more pixels in SAR images. So the target distribution is of much significance. Distribution-based CFAR detection algorithm is presented. We unite the pixels around the test cell, and estimate the distribution of test cell by them. Generalized Likelihood Ratio Test （GLRT） is used to deduce the detectors. The performance of the distribution-based CFAR （DBCFAR） detectors is analyzed theoretically. False alarms of DBCFAR detection are fewer than those of CFAR at the same detection rate. Finally experiments are done and the results show the performance of DBCFAR is out of conventional CFAR. False alarms of DBCFAR detection are concentrated while those of CFAR detection are dispersive.

PERFORMANCE COMPARISON OF A LINEARLY COMBINED ORDERED-STATISTIC DETECTORS UNDER POSTDETECTION INTEGRATION AND NONHOMOGENEOUS SITUATIONS

Several Constant False Alarm Rate （CFAR） architectures, where radar systems often employ them to automatically adapt the detection threshold to the local background noise or clutter power in an attempt to maintain an approximately constant rate of false alarm, have been recently proposed to estimate the unknown noise power level. Since the Ordered-Statistics （OS） based algorithm has some advantages over the Cell-Averaging （CA） technique, we are concerned here with this type of CFAR detectors. The Linearly Combined Ordered-Statistic （LCOS） processor, which sets threshold by processing a weighted ordered range samples within finite moving window, may actually perform somewhat better than the conventional OS detector. Our objective in this paper is to analyze the LCOS processor along with the conventional OS scheme for the case where the radar receiver incorporates a postdetection integrator amongst its contents and where the operating environments contain a number of secondary interfering targets along with the primary target of concern and the two target types fluctuate in accordance with the Swerling Ⅱ fluctuation model and to compare their performances under various operating conditions.

Novel clutter map CFAR algorithm with amplitude limiter

The traditional clutter map constant false alarm rate (CM-CFAR) detector is affected by interference and self-masking[1] which will cause the low probability of detection. To solve these problems, a novel algorithm named clutter map CFAR with amplitude limiter (ALCM-CFAR) is proposed, in which the amplitude of the input signal is limited by a filter. The simulation results prove the effectiveness of ALCM-CFAR algorithm.

A Genome Wide Association Study for Longevity in Cattle

Longevity is regarded as the most important functional trait in cattle breeding with high economic value yet low heritability. In order to identify genomic regions associated with longevity, a genome wise association study was performed using data from 4887 Fleckvieh bulls and 33,556 SNPs after quality control. Single SNP regression was used for identification of important SNPs including eigenvectors as a means of correction for population structure. SNPs selected with a false discovery rate threshold of 0.05 and with local false discovery rate identified genomic regions associated with longevity which were subsequently cross checked with the National Center for Biotechnology Information (NCBI) database. This, to identify interesting genes in cattle and their homologue forms in other species. The most notable genes were SYT10 located on chromosome 5, ADAMTS3 on chromosome 6, NTRK2 on chromosome 8 and SNTG1 on chromosome 14 of the cattle genome. Several of the genes found have previously been associated with cattle fertility. Poor fertility is an important culling reason and thereby affects longevity in cattle. Several signals were located in regions sparse with described genes, which suggest that there might be several other non-identified genetic pathways for this important trait.