Target Signature Research Articles

Approximation of the inverse scattering Steklov eigenvalues and the inverse spectral problem

In this paper, we consider the numerical approximation of the Steklov eigenvalue problem that arises in inverse acoustic scattering. The underlying scattering problem is for an inhomogeneous isotropic medium. These eigenvalues have been proposed to be used as a target signature since they can be recovered from the scattering data. A Galerkin method is studied where the basis functions are the Neumann eigenfunctions of the Laplacian. Error estimates for the eigenvalues and eigenfunctions are proven by appealing to Weyl’s law. We will test this method against separation of variables in order to validate the theoretical convergence. We also consider the inverse spectral problem of estimating/recovering the refractive index from the knowledge of the Steklov eigenvalues, since the eigenvalues are monotone with respect to a real-valued refractive index implying that they can be used for nondestructive testing. Some numerical examples are provided for the inverse spectral problem.

Complex Signal Kurtosis—Indicator of Ship Target Signature in SAR Images

Synthetic aperture radar (SAR) signatures of ship targets are often degraded by various types of distortions due to the distinctive imaging mechanism. The negative impacts could affect the characterization and identification of ships. Recently, complex signal kurtosis (CSK) was found to be a vital indicator of ship detection in SAR images. Since CSK can be used as an indicator of ship detection, we consider it can also be used to indicate ship target signature. One of the basic rationales is that the larger the CSK is, the easier it is to be detected and thus the more obvious its characteristics are as a ship. Defocusing and sidelobes are two common problems that affect the quality of ships. Their impacts on ship target signature are studied and compared from the perspective of CSK. Specifically, a ship target signature improvement methodology based on the maximum CSK criterion is proposed for both refocusing and sidelobe suppression. The role the CSK plays in these improvements and the underlying rationales are elaborated. In addition, a preliminary evaluation of the global imaging quality is also provided. Experimental results based on real data demonstrate that CSK can be used to indicate and improve ship target signature.

Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures

Notch1 is a crucial oncogenic driver in T-cell acute lymphoblastic leukemia (T-ALL), making it an attractive therapeutic target. However, the success of targeted therapy using γ-secretase inhibitors (GSIs), small molecules blocking Notch cleavage and subsequent activation, has been limited due to development of resistance, thus restricting its clinical efficacy. Here, we systematically compare GSI resistant and sensitive cell states by quantitative mass spectrometry-based phosphoproteomics, using complementary models of resistance, including T-ALL patient-derived xenografts (PDX) models. Our datasets reveal common mechanisms of GSI resistance, including a distinct kinase signature that involves protein kinase C delta. We demonstrate that the PKC inhibitor sotrastaurin enhances the anti-leukemic activity of GSI in PDX models and completely abrogates the development of acquired GSI resistance in vitro. Overall, we highlight the potential of proteomics to dissect alterations in cellular signaling and identify druggable pathways in cancer.

Anti-androgen therapy induces transcriptomic reprogramming in metastatic castration-resistant prostate cancer in a murine model

Despite recent development of next-generation androgen receptor (AR) antagonists, metastatic castration-resistant prostate cancer (CRPC) remains incurable and requires deeper understanding through studies in suitable animal models. Prostate-specific deletion of Pten and Smad4 in mice recapitulated the disease progression of human prostate adenocarcinoma, including metastasis to lymph nodes and lung. Moreover, Pten/Smad4 tumors fostered an immunosuppressive microenvironment dominated by myeloid-derived suppressor cells (MDSCs). However, the response of Pten/Smad4 tumors to androgen deprivation and anti-androgen therapies has not been described. Here, we report that the combination of surgical castration and enzalutamide treatment in Pten/Smad4 mice slowed down the tumor growth and prolonged the median survival of the mice for 8 weeks. Treatment-naïve and castration-resistant primary tumors exhibited comparable levels of immune infiltrations with the exception of reduced monocytic MDSCs in CRPC. RNA profiling of treatment-naïve and castration-resistant primary tumors revealed largely preserved transcriptome with modest expressional alterations of collagen-related and immune-related genes, among which CC chemokine receptor type 2 (Ccr2) downregulation and predicted negative activation in CRPC was consistent with reduced monocytic MDSC infiltration. Importantly, significant transcriptomic reprograming was observed in lung metastatic CRPC compared with primary CRPC and enriched for immune-related and coagulation-related pathways. At the individual gene level, we validated the expression changes of some of the most upregulated (Cd36, Bmp5, Bmp6, Etv5, Prex2, Ptprb, Egfl6, Itga8 and Cxcl12) and downregulated genes (Cxcl9 and Adamts5). Together, this study uncovers the inherent activity of Pten/Smad4 tumors to progress to CRPC and highlights potentially targetable transcriptomic signatures associated with CRPC metastasis.

Extended Target Estimation and Recognition Based on Multimodel Approach and Waveform Diversity for Cognitive Radar

This article proposes a cognitive radar (CR) framework to improve the performance of the estimation and recognition for range-extended targets. To overcome the angular uncertainty and exploit the temporal correlation of target signatures, we introduce the static multimodel approach and the exponential correlation model to describe the dynamics of target signatures. The estimate of target signatures and the probability distribution of target hypotheses are updated recursively in response to the latest echo, ruled by the Bayesian theory. A waveform optimization module is integrated into the framework, which designs the waveform for the next emission according to the knowledge extracted from the echo history. A design criterion based on mutual information (MI) is derived, which implies the average estimation capability for all target hypotheses and the mean between-hypotheses divergence. The proposed method is examined using both synthetic data and electromagnetic (EM) simulation data of realistic targets.

Target2DeNovoDrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrug Communicated by Ramaswamy H. Sarma

Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.

Megahertz repetition rate satellite laser ranging demonstration at Graz observatory.

Using ultra-high repetition rate lasers (≥100kHz) is one of the most promising strategies for the next generation of satellite laser ranging (SLR) systems. We present successful 1 MHz repetition rate SLR to targets up to inclined geosynchronous orbits at nighttime. Among those, a maximum return rate of up to 53% was achieved, equivalent to 265 k returns per second for the satellite Swarm-B. In addition, daytime megahertz (MHz) SLR was realized by utilizing a propagated MHz range gate to reduce the massive background noise. In the future, MHz SLR will greatly improve current technology with respect to data amount and data precision, shorter acquisition time, target signature detection, and attitude determination.

Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network

Hyperspectral remote sensing is opening new gateways for a multitude of applications with an added advantage of high spectral and spatial resolution. Target detection of urban objects has gained prominence during the past decade for maintaining a pace with increasing urbanization. This paper aims to identify roads and roofs as urban targets using a hybrid approach of the spectral and spatial aspect of hyperspectral data. The work highlights a brief taxonomy of morphological operators namely, Dilation, Erosion, Opening and Closing with fused spectral signatures of urban targets considered. Artificial neural network (ANN) has been used as a machine learning measure due to its high prediction capability and its effectiveness over conventional target detection approaches.

An investigation of the performances of polarimetric target decompositions using GB-SAR imaging

Ground-based synthetic aperture radar (GB-SAR) systems are mostly utilized to be practical practices in improved understanding of the complex mechanism of microwave backscattering. They also provide complementary information on evaluating the validity of the polarimetric analysis of air-borne or satellite-borne SAR applications. This study investigates some capabilities of polarimetric L-band GB-SAR imaging by testing its performance against a typical terrain and various kinds of manmade targets. Trihedral corner reflectors are also included in the analyses because of their importance in data calibration. Polarimetric backscattering signatures of different targets are analyzed in terms of qualitative assessment of amplitude images and identification and classification of scattering mechanisms through target decomposition techniques. The findings of these analyses and detailed discussions are presented. Specifically, the entropy/mean-alpha ((H/α ̅)) classification results are shown to be capable of clearly identifying the dominant scattering mechanisms occurring within the investigated scene.

Passive Shallow Water Automated Target Recognition using Deep Convolutional Bi directional Long Short Term Memory

The extremely challenging nature of passive acoustic surveillance makes it a key area of research in NavalNon-Co-operative Target Recognition especially in Anti-Submarine Warfare systems. In shallow waters, thecomplex acoustics due to the highly varying ambient background noise as well as the multi-modal propagation in the surface-bottom bounded channel makes surveillance even difficult. In this work, an ensemble of Convolutional Neural Networks and Bidirectional Long Short Term Memory stages employing soft attention is used to effectively capture the spectro-temporal dynamics of the target signature. In order to alleviate the overall computational cost associated with the optimal model search in the extensive hyperparameter space, a recursive model elimination scheme, making frugal use of the available resources, is also proposed. Experimental analysis on acoustic target records, collected from the shallows of Arabian Sea, has yielded encouraging results in terms of model accuracy, precision and recall.

Molecular Signatures and Their Clinical Utility in Pancreatic Neuroendocrine Tumors.

Pancreatic neuroendocrine tumors (PNETs) are classified based on their histologic differentiation and proliferative indices, which have been used extensively to determine prognosis. Advances in next-generation sequencing and other high-throughput techniques have allowed researchers to objectively explore tumor specimens and learn about the genetic alterations associated with malignant transformation in PNETs. As a result, targeted, pathway-specific therapies have been emerging for the treatment of unresectable and metastatic disease. As we continue to trial various pharmaceutical products, evidence from studies using multi-omics approaches indicates that clinical aggressiveness stratifies along other genotypic and phenotypic demarcations, as well. In this review, we explore the clinically relevant and potentially targetable molecular signatures of PNETs, their associated trials, and the overall differences in reported prognoses and responses to existing therapies.

Monostatic RCS of Rectangular Patch Antenna in C-Band

RCS is very significant to confirm data for target designation. The targets signature is different, because every target has own signature, and this specific signature is utilized to recognize the type of structure which will be tested. The monostatic radar utilizes one antenna on the body for reducing the scattering of signals for given polarization and receive and transmit in this type in the same location. Microstrip patch antenna is widely used in microwave systems, especially for space applications. In this paper, rectangular microstrip antenna in c-band frequency is designed and simulated as a target to calculate monostatic RCS area using CST software 2019. The strip antenna is simulated and modeled for different target angles (0, 45, 60and 90) degree. The monostatic radar cross section is simulated at 8GHz with incident angles from 0-180 degree. The best RCS of rectangular patch antenna is about -31m2 at the incident angle θ=60o.

Efficient Generation of Artificial Training DB for Ship Detection Using Satellite SAR Images

In this paper, we propose an effective scheme to generate artificial training database (DB) to mitigate the deficiency in the amount of training DB for ship detection using satellite synthetic aperture radar (SAR) images. In the proposed scheme, SAR signatures of ship targets are first obtained from two different types of image: 1) KOMPSAT-5 images with automatic identification system (AIS) information and 2) simulated SAR images which are acquired using electromagnetic numerical analysis (EMNA). Then, the ship signatures are combined with sea clutter models to generate realistic SAR patches. For overall ship detection tasks (i.e., training and testing), the oriented bounding box (OBB) concept was adopted to appropriately represent slender ship targets in SAR images. When compared with the rash collection of a large number of real SAR images, the proposed scheme is more efficient in terms of cost and time. Experimental results demonstrated that the proposed method can considerably aid in increasing the ship detection capacity of deep learning models. Thus, it is expected that the proposed scheme can be usefully exploited to solve the lack of the amount of training DB.

Novel insights into clear cell renal cell carcinoma prognosis by comprehensive characterization of aberrant alternative splicing signature: a study based on large-scale sequencing data

ABSTRACT Clear cell renal cell carcinoma (ccRCC) is the most common type with poor prognosis in kidney tumor. Growing evidence has indicated that aberrant alternative splicing (AS) events are efficacious signatures for tumor prognosis prediction and therapeutic targets. However, the detailed roles of AS events in ccRCC are largely unknown. In our study, level 3 RNA-seq data was acquired from The Cancer Genome Atlas dataset and corresponding AS profiles were detected with the assistance of SpliceSeq software. A total of 2100 aberrant survival-associated AS events were identified via differential expression and univariate cox regression analysis. The final prognostic panel formed by 17 specific events was developed by stepwise least absolute shrinkage and selection operator (LASSO) penalty, with the area under curve (AUC) values of receiver operator characteristic (ROC) curves keeping above 0.7 spanning 1 year to 5 years. And the results from functional enrichment analyses are unanimous that autophagy could be a potential mechanism of splicing regulation in ccRCC. Furthermore, splicing regulatory network was constructed via Spearman correlation between splicing factors and AS events. Finally, unsupervised clustering analysis revealed three clusters with distinct survival patterns, and associated with specific clinicopathological phenotypes. In overall, we developed a robust and individualized predictive model based on large-scale sequencing data. The identified AS events and splicing network may be valuable in deciphering the crucial posttranscriptional mechanisms on tumorigenesis of ccRCC.

Performance of SAR Polarimetric Calibration Using Hybrid Corner Reflectors: Numerical Simulations and Experimental Measurements

In this article, we devise a polarimetric calibration method utilizing only three types of calibration targets, i.e., trihedral, dihedral, and a 22.5°-rotated dihedral corner reflectors and requiring no assumptions on the scene statistics. The influence of the roll angle in a 22.5°-rotated dihedral corner reflector is assessed by a full-wave numerical simulation. The effectiveness and accuracy of the method are verified with a ground-based synthetic aperture radar system and an airborne L-band Pi-SAR data. We demonstrate that an amplitude error as low as 0.5 dB and a phase error within 3° can be achieved, showing an excellent agreement between the theoretical and calibrated polarimetric signatures for the reference targets. We further exploit the calibration on the Pauli decomposition, Yamaguchi four-component decomposition, and the estimation of the polarimetric parameters derived from target scattering vector method decomposition to show the superior performance of the devised calibration method.

A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars

With the recent advancements in radar systems, radar sensors offer a promising and effective perception of the surrounding. This includes target detection, classification and tracking. Compared to the state-of-the-art, where the state vector of classical tracker considers only localization parameters, this paper proposes an integrated Bayesian framework by augmenting state vector with feature embedding as appearance parameter together with localization parameter. In context of automotive vulnerable road users (VRUs) such as pedestrian and cyclist, the classical tracker poses multiple challenges to preserve the identity of the tracked target during partial or complete occlusion, due to low inter-class (pedestrian-cyclist) variations and strong similarity between intra-class (pedestrian-pedestrian). Subsequently, feature embedding corresponding to target's micro-Doppler signature are learned using novel Bayesian based deep metric learning approaches. The tracker's performance is optimized due to a better separability of the targets. At the same time, the classifiers' performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of the classifier's embedding vector. In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar.

Existence and stability of electromagnetic Stekloff eigenvalues with a trace class modification

<p style='text-indent:20px;'>A recent area of interest is the development and study of eigenvalue problems arising in scattering theory that may provide potential target signatures for use in nondestructive testing of materials. We consider a generalization of the electromagnetic Stekloff eigenvalue problem that depends upon a smoothing parameter, for which we establish two main results that were previously unavailable for this type of eigenvalue problem. First, we use the theory of trace class operators to prove that infinitely many eigenvalues exist for a sufficiently high degree of smoothing, even for an absorbing medium. Second, we leverage regularity results for Maxwell's equations in order to establish stability results for the eigenvalues with respect to the material coefficients, and we show that this generalized class of Stekloff eigenvalues converges to the standard class as the smoothing parameter approaches zero.

Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks.

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.

Implementation of the CMS-SUS-19-006 analysis in the MadAnalysis 5 framework (supersymmetry with large hadronic activity and missing transverse energy; 137 fb−1)

We present the MadAnalysis 5 implementation and validation of the analysis Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum (CMS-SUS-19-006). The search targets signatures with at least two jets and large missing transverse momentum in the all-hadronic final state. The analyzed luminosity is 137 fb[Formula: see text], corresponding to the Run 2 proton-proton data set recorded by the CMS detector at 13 TeV. This implementation has been validated in a variety of simplified models, by comparing derived cut flow tables and histograms with information provided by the CMS collaboration, using event samples that we simulated for the purpose of this re-implementation study. The validation is found to reproduce the signal acceptance in most cases.