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The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front‐ends installed in them, together with a two‐stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back‐end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP‐2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof‐of‐concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L‐, S‐ and C‐bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than ±2.5⋅10−13 $\pm 2.5\cdot 1{0}^{-13}$ was measured. Within a single transceiver node, a fractional frequency lower than ±2⋅10−13 $\pm 2\cdot 1{0}^{-13}$ and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base t0 $\left({t}_{0}\right)$. The achieved synchronisation level increases the accuracy with which the chain of timed events at the transmitter and at the receiver side occur, intending to maximise the signal‐to‐noise ratio available at the receiver. The Universal software radio peripheral model X310 with two different daughterboard models as RF front‐end was used as SDR on either radar node. A network combining data and synchronisation purposes allowed the radar nodes under test to operate synchronously. The two‐stratum synchronisation system used glass fibres between 2 m and 5 km of length or coaxial cables with 2 m in length for network traffic, time and frequency dissemination purposes. The multiple RF front‐ends were stimulated by means of arbitrary waveform generators with calibrated traceability. Up‐chirp and sinusoid waveforms were used as stimuli for measuring the offset of either channel with regards of t0 ${t}_{0}$ to ultimately estimate the achievable coherency limits of the system under test. Both analogue and digital evaluation methods were considered. [ABSTRACT FROM AUTHOR]

Radar systems are evolving towards distributed receiver networks. As individual stations might be separated too far to install a cable link, novel methods are required to synchronise individual data records in the time domain. State‐of‐the‐art GNSS receivers disciplining a highly stable oscillator are able to output a timing signal with several nanosecond accuracy solely using non‐proprietary signals. However, they typically require a stable environment and become a major cost factor for receiver networks with a high number of nodes. A method is presented to passively synchronise data records via GNSS raw signals in a single record requiring only a GNSS antenna, an analogue‐to‐digital converter and computation hardware. The clock bias is estimated via the common view method with either full raw signal correlation or software‐based code correlation of individual signals from the GPS, Galileo and BeiDou constellation with sub‐nanosecond precision. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co‐expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine‐learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross‐validation, surpassing gene expression‐based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival. [ABSTRACT FROM AUTHOR]

Abstract Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co‐expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine‐learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross‐validation, surpassing gene expression‐based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Abstract This paper proposes a specific algebraic structure and demonstrates its nature as an extension field, enabling the construction of Golomb Costas (GC) arrays. It provides detailed instructions and examples for constructing GC arrays using this extension field, along with a corresponding flowchart. Additionally, the paper conducts a thorough analysis, incorporating calculations and comparisons, to evaluate the autocorrelation of a GC array derived from the extension field compared to that of a diagonal frequency hopping array. The analysis reveals the superior autocorrelation properties of GC arrays based on the extension field. Furthermore, the paper establishes a mathematical model for the signal coded by the frequency hopping array and subsequently simulates and compares the ambiguity function of the signal coded by a GC array with that of a signal coded by a diagonal frequency hopping array. This comparison underscores the thumbtack ambiguity function of frequency hopping signal coded by a GC array. Moreover, the paper thoroughly investigates the relationship between the correlation function of GC arrays and the roots of an algebraic equation in a finite field, and strictly proves the ideal autocorrelation properties of Golomb Costas arrays.

This paper proposes a specific algebraic structure and demonstrates its nature as an extension field, enabling the construction of Golomb Costas (GC) arrays. It provides detailed instructions and examples for constructing GC arrays using this extension field, along with a corresponding flowchart. Additionally, the paper conducts a thorough analysis, incorporating calculations and comparisons, to evaluate the autocorrelation of a GC array derived from the extension field compared to that of a diagonal frequency hopping array. The analysis reveals the superior autocorrelation properties of GC arrays based on the extension field. Furthermore, the paper establishes a mathematical model for the signal coded by the frequency hopping array and subsequently simulates and compares the ambiguity function of the signal coded by a GC array with that of a signal coded by a diagonal frequency hopping array. This comparison underscores the thumbtack ambiguity function of frequency hopping signal coded by a GC array. Moreover, the paper thoroughly investigates the relationship between the correlation function of GC arrays and the roots of an algebraic equation in a finite field, and strictly proves the ideal autocorrelation properties of Golomb Costas arrays. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing. [ABSTRACT FROM AUTHOR]

Abstract The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse‐Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real‐time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200 MHz bandwidth, and 268 ms integration time while processing a range of about 8.5 km. Additionally, the system can perform real‐time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off‐chip dynamic random access memory.

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse‐Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real‐time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200 MHz bandwidth, and 268 ms integration time while processing a range of about 8.5 km. Additionally, the system can perform real‐time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off‐chip dynamic random access memory. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) stands as a critical cybersecurity concern, representing a malicious tactic employed by hackers to disrupt online services, network resources, or host systems, rendering them inaccessible to legitimate users. DDoS attack detection is essential as it has a wide-ranging impact on the field of computer science. This is quantitative research to evaluate Multilayer Perceptron (MLP) classification algorithm with different optimization methods and different activation functions on UDP-based DDoS attack detection. The CIC-DDoS2019 DDoS evaluation dataset, known for its inclusion of modern DDoS attack types, was instrumental in this study by the Canadian Institute for Cyber Security. The CIC-DDoS2019 dataset encompasses eleven DDoS attack datasets, which are UDP, UDP-Lag, NTP, and TFTP datasets were utilized in this investigation. This study proposes a novel feature selection approach. It specifically targets datasets related to UDP-based DDoS attacks. The approach aims to identify groups of features that share the uncorrelated characteristic. It means None of the features within a subset have a significant relationship with each other as measured by three correlation methods: Pearson, Spearman, and Kendall. To further validate the proposed approach, the researchers conducted experiments on a specially crafted DDoS attack dataset. MLP classification algorithm along with ADAM optimization method and Tanh activation function produce the better results for UDP-based DDoS attack detection. This combination produces the better accuracy values of 99.97 for UDP Flood attack, 99.77 for UDP-Lag attack, 99.70 for NTP attack, 99.93 for TFTP attack and 99.76 for UDP customized DDoS attack. [ABSTRACT FROM AUTHOR]

Abstract Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart.

Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart. [ABSTRACT FROM AUTHOR]

Discriminating the role of input variables in a hydrological system or in a multivariate hydrological study is particularly useful. Nowadays, emerging tools, called feature importance measures, are increasingly being applied in hydrological applications. In this study, we propose a virtual experiment to fully understand the functionality and, most importantly, the usefulness of these measures. Thirteen importance measures related to four general classes of methods are quantitatively evaluated to reproduce a benchmark importance ranking. This benchmark ranking is designed using a linear combination of ten random variables. Synthetic time series with varying distribution, cross-correlation, autocorrelation and random noise are simulated to mimic hydrological scenarios. The obtained results clearly suggest that a subgroup of three feature importance measures (Shapley-based feature importance, derivative-based measure, and permutation feature importance) generally provide reliable rankings and outperform the remaining importance measures, making them preferable in hydrological applications. [ABSTRACT FROM AUTHOR]

Abstract To address the problem that the features extracted by CNN‐based facial expression recognition (FER) do not consider structural information, a region adaptive correlation deep network (RACN) is proposed. The network consists of two branches. In one branch, the features obtained by applying CNN to facial sub‐blocks are used as the input of the proposed second‐order region correlation network (SRCN), which obtains structural features by adaptively learning the correlation of facial regions. Furthermore, they are fused with the parallel branch‐extracted global features to obtain a comprehensive high‐semantic feature representation. Finally, weights are assigned to the two features through the channel attention mechanism for more accurate expression classification. Experimental results show that our method can effectively extract expression features in an end‐to‐end manner, improve the accuracy of FER, and achieve competitive performance without relying on any a priori knowledge. And the region‐adaptive correlation feature extraction branch RACN can be applied to other deep learning networks to extract discriminative structural‐adaptive features. To the best of our knowledge, our work is the first to enrich the feature representation for end‐to‐end static FER by adaptively obtaining more discriminative regional adaptive correlation feature vectors via the autocorrelation matrix combined with CNN compared to the existing literature.

Abstract Traditional visual tracking algorithms on RGB cannot effectively distinguish an object from background with similar colour feature, resulting in the migration problem in the tracking process. In order to solve this problem, a tracking algorithm with spectral information is proposed by using band selection, information fusion, and deep features. Firstly, the correlation analysis of 16 spectral band information is carried out, and genetic optimization method is introduced to obtain two bands with low correlation coefficient and abundant information. Moreover, under the framework of kernel correlation filtering tracking, guided filtering is adopted to fuse the information referring to the target region of the two bands. Besides, the feature maps are generated by histogram of gradient and pretrained visual geometry group network. Finally, target is detected via finding the maximum value of a strong response. The proposed genetic optimization based multifeature tracker is compared with multiple tracking methods including correlation filtering and deep learning. Experimental results with multiple groups of spectral videos and corresponding RGB videos demonstrate that the genetic optimization based multifeature tracker method achieves good results in subjective vision and objective evaluation.

Abstract Bladder cancer (BC) is a common cancer worldwide with a high prevalence. This study was conducted to elucidate the expression and clinical significance of Sorbin and SH3 domain‐containing protein 1 (SORBS1) in BC as well as to explore its molecular mechanism in BC tumourigenesis. RNA‐sequencing data, microarray, and Immunohistochemistry (IHC) were applied to elucidated the SORBS1 expression at multiple levels. After that, the relationship between tumour‐immune infiltration and SORBS1 was also explored. Finally, SORBS1‐related genes in BC were identified to perform functional enrichment analyses. The expression integration revealed that the comprehensive expression of SORBS1 at the mRNA level was −1.02 and that at the protein level was −3.73, based on 12 platforms, including 1221 BC and 187 non‐BC samples. SORBS1 was negatively correlated with tumour purity (correlation = −0.342, p

Abstract Due to their perfect periodic autocorrelation property, Zadoff–Chu sequences are often used as stimulus signals in the measurement of radio channel responses. In this letter, the cross‐correlation of a linear shift‐invariant system's response to a Zadoff–Chu sequence is derived for the case that a multiplicative perturbation such as phase noise is present at its output. The perturbation signal is shown to incur a spurious discrete Fourier transform, thus degrading the correlation's peak to sidelobe ratio. The implications for the performance limits of channel measurement systems are discussed.

To address the problem that the features extracted by CNN‐based facial expression recognition (FER) do not consider structural information, a region adaptive correlation deep network (RACN) is proposed. The network consists of two branches. In one branch, the features obtained by applying CNN to facial sub‐blocks are used as the input of the proposed second‐order region correlation network (SRCN), which obtains structural features by adaptively learning the correlation of facial regions. Furthermore, they are fused with the parallel branch‐extracted global features to obtain a comprehensive high‐semantic feature representation. Finally, weights are assigned to the two features through the channel attention mechanism for more accurate expression classification. Experimental results show that our method can effectively extract expression features in an end‐to‐end manner, improve the accuracy of FER, and achieve competitive performance without relying on any a priori knowledge. And the region‐adaptive correlation feature extraction branch RACN can be applied to other deep learning networks to extract discriminative structural‐adaptive features. To the best of our knowledge, our work is the first to enrich the feature representation for end‐to‐end static FER by adaptively obtaining more discriminative regional adaptive correlation feature vectors via the autocorrelation matrix combined with CNN compared to the existing literature. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is a common cancer worldwide with a high prevalence. This study was conducted to elucidate the expression and clinical significance of Sorbin and SH3 domain‐containing protein 1 (SORBS1) in BC as well as to explore its molecular mechanism in BC tumourigenesis. RNA‐sequencing data, microarray, and Immunohistochemistry (IHC) were applied to elucidated the SORBS1 expression at multiple levels. After that, the relationship between tumour‐immune infiltration and SORBS1 was also explored. Finally, SORBS1‐related genes in BC were identified to perform functional enrichment analyses. The expression integration revealed that the comprehensive expression of SORBS1 at the mRNA level was −1.02 and that at the protein level was −3.73, based on 12 platforms, including 1221 BC and 187 non‐BC samples. SORBS1 was negatively correlated with tumour purity (correlation = −0.342, p < 0.001) and positively correlated with macrophage (correlation = 0.358, p < 0.001). The results of enrichment analyses revealed that the most significant biological pathways of SORBS1‐related genes were epithelial‐mesenchymal transition. SORBS1 was significantly down‐regulated in BC and may play a role as tumour suppressor. This study provides new directions and biomarkers for future BC diagnosis. [ABSTRACT FROM AUTHOR]

Traditional visual tracking algorithms on RGB cannot effectively distinguish an object from background with similar colour feature, resulting in the migration problem in the tracking process. In order to solve this problem, a tracking algorithm with spectral information is proposed by using band selection, information fusion, and deep features. Firstly, the correlation analysis of 16 spectral band information is carried out, and genetic optimization method is introduced to obtain two bands with low correlation coefficient and abundant information. Moreover, under the framework of kernel correlation filtering tracking, guided filtering is adopted to fuse the information referring to the target region of the two bands. Besides, the feature maps are generated by histogram of gradient and pretrained visual geometry group network. Finally, target is detected via finding the maximum value of a strong response. The proposed genetic optimization based multifeature tracker is compared with multiple tracking methods including correlation filtering and deep learning. Experimental results with multiple groups of spectral videos and corresponding RGB videos demonstrate that the genetic optimization based multifeature tracker method achieves good results in subjective vision and objective evaluation. [ABSTRACT FROM AUTHOR]

Abstract The traditional K‐singular value decomposition (K‐SVD) algorithm has poor image‐denoising performance under strong noise. An image‐denoising algorithm is proposed based on improved K‐SVD and dictionary atom optimization. First, a correlation coefficient‐matching criterion is used to obtain a sparser representation of the image dictionary. The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary. Then, non‐local regularity is incorporated into the denoising model to further improve image‐denoising performance. Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.

Due to their perfect periodic autocorrelation property, Zadoff–Chu sequences are often used as stimulus signals in the measurement of radio channel responses. In this letter, the cross‐correlation of a linear shift‐invariant system's response to a Zadoff–Chu sequence is derived for the case that a multiplicative perturbation such as phase noise is present at its output. The perturbation signal is shown to incur a spurious discrete Fourier transform, thus degrading the correlation's peak to sidelobe ratio. The implications for the performance limits of channel measurement systems are discussed. [ABSTRACT FROM AUTHOR]

Abstract The traditional track correlation methods have problems such as limitation of the application scenarios, unstable performances and poor practicalities. To solve those problems, a new adaptive track correlation method for multiple scenarios was proposed in this paper using the theories and methods from machine learning. Through interpreting and translating the track correlation problem in the field of information fusion into the classification recognition problem in the field of machine learning, the new method was derived based on deep convolutional neural networks. The association performance and adaptation capabilities of the proposed method had been validated by simulation experiments. The results show that the proposed method is better than the traditional methods with respect to association performance and adaptation capabilities, and can solve the track association problems for multiple scenarios, for example sensors have systematic errors and targets are dense or in formation. Thus, it can be predicted that the proposed method would have a well‐applied foreground.

Abstract The BDS‐3 recently started broadcasting a new civil B1C signal to provide open services for global users, which brings benefits to GNSS‐based applications. The BDS‐3 B1C signal modulates a long secondary code on the primary code in the pilot component, and it is useful to acquire the secondary code so as to extend coherent integration time when acquiring weak BDS‐3 B1C signals. However, the long secondary code of the BDS‐3 B1C signal puts FFT‐based and multi‐hypothesis‐based secondary code acquisition methods in trouble from the high computational burden. Therefore, the authors propose a novel secondary code acquisition algorithm called the partial correlation method (PCM) for the BDS‐3 B1C signal. The PCM acquires the secondary code in three steps to reduce the complexity and acquisition time, and it supports up to 110ms coherent integration and can be applied for the case of C/N0≥25dB‐Hz, which satisfies most cases. Further, a matched‐filter‐based architecture of the PCM is presented. Additionally, the characteristic length vector to determine the secondary code chip position quickly is proposed, which is better than the existing characteristic length method. Finally, experimental results based on real BDS‐3 B1C signals data show that the proposed PCM is effective.

Abstract Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non‐parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state‐of‐the‐art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state‐of‐the‐art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state‐of‐the‐art methods.

Abstract DOA estimation based on sparse representation in a non‐uniform noise environment is proposed using a tail minimisation technique. The noise‐free covariance matrix is modelled as diagonal and off‐diagonal components relating to incoming signals. In order to combat coherent signals, the auto‐ and cross‐correlation entries of the incident signal are considered separately by using the least square (LS) criterion. Subsequently, from the perspective of difference co‐array a vectorised covariance matrix is obtained to emulate the received signal similar to the output of a single snapshot. The effect of non‐uniform noise is significantly mitigated via a removal operation. A tail minimisation scheme that is capable of deriving sharper spectra peaks is then applied to detect the DOAs. The proposed approach has the advantages of a larger virtual array aperture beneficial to DOA estimations of uncorrelated and/or coherent signals. The technique can suppress false spectral peaks to a certain extent and is also computationally effective. Numerical simulation verifies the effectiveness of the proposed method.

Abstract Material recognition plays an important role in the interaction between robots and the external environment. For example, household service robots need to replace humans in the home environment to complete housework, so they need to interact with daily necessities and obtain their material performance. Images provide rich visual information about objects; however, it is often difficult to apply when objects are not visually distinct. In addition, tactile signals can be used to capture multiple characteristics of objects, such as texture, roughness, softness, and friction, which provides another crucial way for perception. How to effectively integrate multi‐modal information is an urgent problem to be addressed. Therefore, a multi‐modal material recognition framework CFBRL‐KCCA for target recognition tasks is proposed in the paper. The preliminary features of each model are extracted by cascading broad learning, which is combined with the kernel canonical correlation learning, considering the differences among different models of heterogeneous data. Finally, the open dataset of household objects is evaluated. The results demonstrate that the proposed fusion algorithm provides an effective strategy for material recognition.

Abstract A weighted stochastic gradient algorithm is proposed for cost‐efficient tracking of unknown, correlated spatial signals from randomly distributed sensor observations in localized wireless sensor field. The algorithm is implemented in spatial modelling and spatial tracking phases. In spatial modelling phase, the algorithm finds the model parameters, and in spatial tracking phase, it updates these parameters. The spatial signal is modelled with its M iso‐contour lines at equally spaced levels {ℓ}k=1M and the sensors with sensor observations in Δ margin of these contour levels report to the fusion centre (FC) for spatial monitoring purpose. Based on progressive learning and in successive iterations, the algorithm improves its findings of the signal strength's range, and the spatial, temporal and spectral attributes of the signal. To reduce the cost, in each iteration, only a subset of wireless sensors transmits the observations to the FC, in response to its query. In this article, the percentage of the reporting sensors to the FC is defined as the algorithm's cost. With importance sampling perspective, the sample space is reduced to those sensors whose observations are within a Δ margin of atleast one of these M contour levels. The Δ margin is pruned or enhanced using the proposed weighted stochastic gradient algorithm, dynamically in order to reduce the spatial tracking cost. The evaluation results show that after spatial modelling, spatial tracking is drastically of low cost and its performance is better than that of the conventional stochastic gradient method. The modelling error, the cost and the convergence of the proposed algorithm are investigated extensively, in this article. Spatial correlation in signal distribution and the coordinates of the wireless sensors are the only initial assumptions in spatial monitoring of the unknown signal distribution. The main purpose of this algorithm is low‐cost identification of unknown correlated spatial signals from sensor observations, over time. An example for application of the proposed algorithm is environmental monitoring using wireless sensor observations.

Pulsed-laser testing is used to accurately determine single-event latchup (SEL) thresholds for static random-access memories (SRAMs) with different body-tie-to-drain spacings on a mixed-signal application-specific integrated circuit (ASIC), a study that could not be completed using only broad-beam heavy-ion testing. Two distinct approaches were employed with the first approach, an empirical correlation method, exploiting the complementary nature of broad-beam heavy-ion and laser testing. Various single- and dual-port SRAMs exhibited SEL thresholds ranging from 1 MeV-cm2/mg to over 100 MeV-cm2/mg, with a dual-port device possessing the smallest body-tie-to-drain spacing being latchup immune at the highest available test conditions (>300 MeV-cm2/mg). These results have been used to inform a follow-on ASIC design resulting in a significant improvement in chip-level SEL sensitivity. The second approach, a calculational method, uses pulsed-laser measurements and charge-deposition modeling to predict linear energy transfer (LET) thresholds without heavy-ion data. This calculational approach agrees with the empirical approach and therefore shows promise as a predictive tool for SEL threshold estimation, but further validation is required. Overall, these approaches can be effective tools for evaluating SEL susceptibilities and mitigation strategies. [ABSTRACT FROM AUTHOR]

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

Previous deep learning methods usually required large‐scale annotated data, which is computationally exhaustive and unrealistic in certain scenarios. Therefore, few‐shot learning, where only a few annotated training images are available for training, has attracted increasing attention these days, showing huge potential in practical applications, such as portable equipment or security inspection, and so on. However, current few‐shot learning methods usually neglect the valuable semantic correlations between samples, thereby failing in extracting discriminating relations to achieve accurate predictive results. In this work, extending on a recent state‐of‐the‐art few‐shot learning method, transductive relation‐propagation network (TRPN), which considers the correlations between training samples, a constrained relation‐propagation network is proposed to further regularise the distilled correlations and thus achieve favourable few‐shot classification performance. The proposed framework contains three main components, namely preprocess module, relational propagation module, and relation constraint module. First, sample features are extracted and a relation graph node is constructed by treating the relation of each support–query pair as a graph node in the preprocess module. After that, in the relation propagation module (RPM), the valuable information of support–query pairs is modelled and propagated to directly generate the relational representations for further prediction. Then, a relation constraint module is introduced to regularise the relational representations and make it consistent with the ground‐truth relations as much as possible. With the guidance of the effective RPM and relation constraint module, the relational representations of the support–query pairs are distinguishable and thus can achieve accurate predictive results. Comprehensive experiments conducted on widely used benchmarks validate the effectiveness of our method compared to state‐of‐the‐art few‐shot classification approaches. [ABSTRACT FROM AUTHOR]

The propose algorithm finds the optimal reduced size of latent fingerprint. The algorithm accelerates the correlation methods of fingerprint registration. The Algorithm is based on decomposition and reduction of fingerprint to one dimension form by using the adoptive method of empirical modes. We choose the most appropriate internal mode to determine the minimum distance between the extremes of empirical modes. We can estimate how many times the fingerprint in the first step of the comparison can be reduced so as not to lose the accuracy of registration. This algorithm shows best results as compared to conventional fingerprint matching techniques that strongly depends on local features for registration. The algorithm was tested on latent fingerprints using FVC2002, FVC2004 and FVC2006 databases.

By scanning the charge-deposition profile produced by a pulsed laser throughout a device, the spatially dependent charge-collection efficiency (CCE) can be determined. This is demonstrated by extracting the depth-dependent CCE in two photodiodes. The resulting collection efficiency curves are found to be consistent with their device structures and the expected charge-collection mechanisms. By applying the efficiency curves to the charge-deposition curves for both heavy ions and pulsed X-rays, the calculated collected charge values show very good agreement with experimental results. This suggests that the profiles extracted using the laser can help predict charge-collection data from other excitation sources and could improve modeling efforts by determining sensitive volumes. [ABSTRACT FROM AUTHOR]

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind direction can improve wind energy utilisation and extend the life of the wind turbine yaw system effectively. The 'measurement–sharing–correlation–prediction–verification' (MSCPV) wind parameter sensing technology based on the turbine network provides a new perspective for wind direction prediction, in which the screening of correlated wind turbines is a key step. In this study, a calculation method of wind direction spatial correlation is proposed, and correlated turbines are selected according to the correlation strength. The study first analyses the limitations of traditional wind direction correlation based on the principle of MSCPV and proposes the concept of correlated turbine screening based on yawing correlation. Then, the modelling and calculation of yawing correlation are analysed in detail. The prediction based on correlated wind turbines selected by different methods is evaluated. Case study results show that compared to wind direction correlation and wind turbines' distance, the yawing correlation has a better effect on the screening of the spatially correlated turbines. The prediction accuracy based on different correlated turbines is positively correlated with the degree of yawing correlation, indicating the proposed screening method is effective. [ABSTRACT FROM AUTHOR]

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity‐transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi‐sequential Monte Carlo simulation is presented. This study proposes a novel reliability‐oriented multi‐objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large‐scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fourier transform infrared (FTIR) spectrometry is commonly used for the identification of reference substances (RSs) in solid, liquid, or gaseous mixtures. An expert is generally required to perform the analysis, which is a bottleneck in emergency situations. This study proposes a support vector machine (SVM)‐based algorithm, the peak correlation classifier (PCC), designed to rapidly detect the presence of a specific threat or reference substance in a sample. While SVM has been used in various spectrographic contexts, it has rarely been used on FTIR spectra. The proposed algorithm discovers correlation similarities between the FTIR spectrum of the RS and the test sample and then uses SVM to determine whether or not the RS is present in the sample. The study also shows how the additive nature of FTIR spectra can be used to create 'synthetic' substances that significantly improve the detection capability and decision confidence of the SVM classifier. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on improving the spatio–temporal representation ability of three‐dimensional (3D) convolutional neural networks (CNNs) in the video domain. They observe two unfavourable issues: (i) the convolutional filters only dedicate to learning local representation along input channels. Also they treat channel‐wise features equally, without emphasising the important features; (ii) traditional global average pooling layer only captures first‐order statistics, ignoring finer detail features useful for classification. To mitigate these problems, they proposed two modules to boost 3D CNNs' performance, which are temporal‐channel correlation (TCC) and bilinear pooling module. The TCC module can capture the information of inter‐channel correlations over the temporal domain. Moreover, the TCC module generates channel‐wise dependencies, which can adaptively re‐weight the channel‐wise features. Therefore, the network can focus on learning important features. With regards to the bilinear pooling module, it can capture more complex second‐order statistics in deep features and generate a second‐order classification vector. We can get more accurate classification results by combining the first‐order and second‐order classification vector. Extensive experiments show that adding our proposed modules to I3D network could consistently improve the performance and outperform the state‐of‐the‐art methods. The code and models are available at https://github.com/caijh33/I3D_TCC_Bilinear. [ABSTRACT FROM AUTHOR]

High‐resolution algorithms to estimate the depth of near‐surface interface using ground‐penetrating radar (GPR) can provide a measurement of thin to thick top layer using synthetic data for surface‐coupled antennas. In practice, the air‐coupled antenna configuration in the target depth estimation needs to determine the air‐gap distance between the antenna module and the surface. In this study, the authors present an improved approach of estimating the antenna height for measuring thin to thick top coal layer using the high‐resolution algorithms. In GPR, backscattered echoes are generally correlated in nature. In order to apply high‐resolution algorithms, the authors use two improved spatial smoothing techniques for fighting against the correlation between echoes. Here, in the existence of air–coal–shale–clay and air–coal–shale–coal interface, step frequency continuous wave GPR is used to measure the thickness of the coal layer. Simulation results on the antenna height estimation using synthetic data models, full‐wave model and plane wave model are shown to assess the estimation algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

Human emotion recognition is studied by means of unimodal channels over the last decade. However, efforts continue to answer tempting questions about how variant modalities can complement each other. This study proposes a multimodal approach using three‐dimensional (3D) convolutional neural networks (CNNs) to model human emotion through a modality‐referenced system while investigating the solution to such questions. The proposed modality‐referenced system selects the input data based on one of the modalities regarded as reference or master. The other modality which is referred to as a slave simply adjusts or attunes itself with the master in the temporal domain. In this context, the authors developed three multimodal emotion recognition system, namely, video‐referenced system, audio‐referenced system, and the audio–visual‐referenced system to explore the congruence impact of audio and video modalities on each other. Two pipelines of 3D CNN architectures are employed where k‐means clustering is used in the master pipeline and the slave pipeline adapts itself in a temporal sense. The outputs of the two pipelines are fused to improve recognition performance. In addition, canonical correlation analysis and t‐distributed stochastic neighbour embedding is used validating the experiments. Results show that temporal alignment of the data between two modalities improves the recognition performance significantly. [ABSTRACT FROM AUTHOR]

In the tracking‐by‐detection approach of online multiple object tracking (MOT), a major challenge is how to associate object detections on the new video frame with previously tracked objects. Two important aspects that directly influence the performance of MOT are quality of detection and accuracy in data association. The authors propose an efficient and unified MOT framework for improved object detection, followed by enhanced object tracking. The object detection and tracking are considered as two independent functions in the tracking‐by‐detection paradigm. In this study, object detection accuracy has been increased by employing a faster region‐based convolutional neural network (Faster R‐CNN) modified with the feedback region proposals from the tracker. Target association is performed by the correlation filter‐based Siamese CNN model, which finds the similarity score between the input image patches. The Siamese CNN is trained using a supervised hard sample mining strategy. An optical flow‐based motion model is employed to predict the next probable location of the targets from the tracker and these region proposals are fed back to the classifier module of Faster R‐CNN. The authors' extensive analysis of publicly available MOT benchmark datasets and comparison with the state‐of‐the‐art tracking methods demonstrate competitive tracking performance of the proposed MOT framework. [ABSTRACT FROM AUTHOR]

The discriminative correlation filter (DCF) method is widely used in target tracking due to its real‐time performance. However, the computational efficiency of DCF results in boundary effect, which reduces the tracking accuracy in fast motion scene. Besides, background noise is always required to be carefully handled for they will cause trouble in scenes such as background clutter, occlusion, deformation etc. To address the two issues, this study proposes masked discriminative correlation filter, which uses mask to process DCF filter as well as target samples so as to suppress boundary effect and background noise. Experimental results on benchmark datasets show that the proposed tracker performs better than a series of benchmark trackers, and is superior to them in almost various scenes. [ABSTRACT FROM AUTHOR]

This article discusses the mechanisms enabling spatial resolution in fault location methods based on full transient signals, as opposed to those only using their early-time portion. This idea is found in recent travelling-wave methods (TWM) and those based on electromagnetic time reversal (EMTR). Their spatial resolution is discussed in terms of the sensitivity of a system resonances to change in the fault position and their coherence bandwidth. It is proven that using the entire transient signal it is possible to bypass the Fourier transform uncertainty principle, which limits the spatial resolution of time-domain reflectometry and standard early-time TWM. Super-resolved fault location is shown to be possible only for resonating systems, enabling high spatial resolution without relying on wide-band data. A detailed theoretical analysis for laterals and numerical results for networks and a three-phase line show that significant differences can be observed for the spatial resolution associated to each resonance, most often resulting in a loss of spatial resolution. The interaction between separate resonant structures, such as laterals in networks and coupled conductors in three-phase lines are shown to be the main cause of resolution loss. [ABSTRACT FROM AUTHOR]

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign–malignant patterns in digital mammograms. This study proposes a new fully automated computer‐aided diagnosis (CAD) system for breast cancer diagnosis with high‐accuracy and low‐computational requirements. The expectation–maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi‐resolution and multi‐orientation features derived from a new feature extraction technique based on wavelet‐based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity‐based learning algorithm, called Q, for benign–malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real‐time systems. [ABSTRACT FROM AUTHOR]