Target Identification Method Research Articles

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

Abstract 4115: KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with immune escape and immunotherapy response

Abstract Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To better capture the dynamic and compartmentalized nature of antitumor immune responses, we generated longitudinal “temporal atlases” of productive versus non-productive antitumor immune responses in murine tumor models. We utilized a 34-parameter full spectrum flow cytometry panel to comprehensively profile immune composition within tumors, draining and non-draining lymph nodes, and blood in and around key inflection points of tumor regression or progression. We leveraged two distinct preclinical models for this; the NPK-C1 ectopic prostate cancer model to map dynamics of spontaneous cancer immunoediting, and anti-PD-1 treated MC38 tumors to study response or non-response to immune checkpoint blockade (ICB). We utilized UMAP and FlowSOM algorithms for iterative dimensionality reduction and clustering, respectively, to reveal novel phenotypes associated with productive versus non-productive immunity across model systems, tissues, and time points. We discovered expression of KLRG1 within the intratumoral CD4 T cell compartment was highly associated with tumor progression and response to ICB. Specifically, both FoxP3+ Tregs and FoxP3- Tconv cells within tumors accumulated KLRG1 expression through disease progression, but this was not observed in CD4 T cell or other immune subsets residing in lymph nodes or circulating in blood. Among all intratumoral clusters, KLRG1+ Tconv were the only subset significantly correlated with tumor burden at each time point tested and across both models. KLRG1+ Tconv were significantly enriched in NPK-C1 tumors undergoing progression to escape versus those under immune-mediated equilibrium (p=0.0004) and were lost in animals undergoing curative responses to ICB (p=0.003). In the Treg compartment, unsupervised clustering revealed a KLRG1+Helios- tumor Treg subset that was positively correlated with transition from equilibrium to escape in the NPK-C1 model (p=0.005). Also indicating a potential functional significance, this phenotype was absent in tumors undergoing curative responses to ICB (p=0.0002). Systematic investigation of the functional characteristics, transcriptional programming, and translational significance of intratumoral KLRG1+ CD4 T cell subsets is ongoing. Together, these findings identify KLRG1+ CD4 T cell populations as subsets for further investigation in cancer and demonstrate the utility of longitudinal full spectrum flow cytometry profiling as an engine of dynamic biomarker and/or target discovery in immuno-oncology. Citation Format: Casey Ager, Matthew Chaimowitz, Shruti Bansal, Meri Rogava, Johannes Melms, Catherine Spina, Cory Abate-Shen, Charles G. Drake, Matthew Dallos, Benjamin Izar. KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with immune escape and immunotherapy response. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4115.

SB2301-mediated perturbation of membrane composition in lipid droplets induces lipophagy and lipid droplets ubiquitination

Lipid droplets (LDs) are involved in various biological events in cells along with their primary role as a storage center for neutral lipids. Excessive accumulation of LDs is highly correlated with various diseases, including metabolic diseases. Therefore, a basic understanding of the molecular mechanism of LD degradation would be beneficial in both academic and industrial research. Lipophagy, a selective autophagy mechanism/LD degradation process, has gained increased attention in the research community. Herein, we sought to elucidate a novel lipophagy mechanism by utilizing the LD-degrading small molecule, SB2301, which activates ubiquitin-mediated lipophagy. Using a label-free target identification method, we revealed that ethanolamine-phosphate cytidylyltransferase 2 (PCYT2) is a potential target protein of SB2301. We also demonstrated that although SB2301 does not modulate PCYT2 function, it induces the cellular translocation of PCYT2 to the LD surface and spatially increases the phosphatidylethanolamine (PE)/phosphatidylcholine (PC) ratio of the LD membrane, causing LD coalescence, leading to the activation of lipophagy process to maintain energy homeostasis.

Challenges and Perspectives in Target Identification and Mechanism Illustration for Chinese Medicine.

Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.

Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training

Objective. Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training. Approach. We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants’ statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions. Main results. The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2–1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs. Significance. Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.

Design of an Intermittent Herbicide Spray System for Open-Field Cabbage and Plant Protection Effect Experiments

To address the problem of herbicide residues exceeding the safety standard due to continuous spraying of herbicides on open-field cabbage, we propose an intermittent weed spraying control method integrating cabbage position, cabbage canopy size, and spraying machine operation speed. It is based on an early-stage cabbage target identification method obtained in the early stage and the operation requirements in open-field cabbage. Built with a C37 controller, a stable pressure spray system and an intermittent weed spraying control system for open-field cabbage, an integrated system was designed. Experimental verification was carried out through measurement indexes such as spraying precision, herbicide saving rate, herbicide efficacy, and herbicide residue. Since the industry is faced with a status quo of a lack of relevant operational norms and national standards for the precise weed spraying operation mode, this paper provides a relatively perfect experiment and evaluation method for this mode. The experimental results on the accuracy of weed spraying at different speeds showed that the mean absolute error (MAE), root mean square error (RMSE), and average spray cabbage coverage rate (ASCCR) of intermittent weed spraying increased, but the average effective spray coverage rate (AESCR) decreased with increasing operation speed. When the working speed was 0.51 m/s, the MAE and RMSE of intermittent weed spraying were less than 2.87 cm and 3.40 cm, respectively, and the AESCR was 98.4%, which verified the feasibility of operating the intermittent weed spraying of cabbage. The results of a field experiment showed that the average weed-killing rate of intermittent weed spraying for open-field cabbage was 94.8%, and the herbicide-saving rate could reach 28.3% for a similar weeding effect to that of constant-rate application, which not only met the needs of intermittent weed spraying in open-field cabbage but also had great significance for improving the herbicide utilization rate. Compared with the constant-rate application method, the herbicide residue concentration detected using intermittent weed spraying for cabbage decreased by 66.6% on average, which has important research significance and application value for ensuring the normal growth of crops and the safety of agricultural products.

Bayesian-Based Classification Confidence Estimation for Enhancing SSVEP Detection

The brain-computer interface (BCI) enables paralyzed people to directly communicate with and operate peripheral equipment. The steady-state visual evoked potential (SSVEP)-based BCI system has been extensively investigated in recent years due to its fast communication rate and high signal-to-noise ratio. Many present SSVEP recognition methods determine the target class via finding the largest correlation coefficient. However, the classification performance usually degrades when the largest coefficient is not significantly different from the rest of the values. This study proposed a Bayesian-based classification confidence estimation method to enhance the target recognition performance of SSVEP-based BCI systems. In our method, the differences between the largest and the other values generated by a basic target identification method are used to define a feature vector during the training process. The Gaussian mixture model (GMM) is then employed to estimate the probability density functions of feature vectors for both correct and wrong classifications. Subsequently, the posterior probabilities of being an accurate and false classification are calculated via Bayesian inference in the test procedure. A classification confidence value (CCValue) is presented based on two posterior probabilities to estimate the classification confidence. Finally, the decision-making rule can determine whether the present classification result should be accepted or rejected. Extensive evaluation studies were performed on an open-access benchmark dataset and a self-collected dataset. The experimental results demonstrated the effectiveness and feasibility of the proposed method for improving the reliability of SSVEP-based BCI systems.

Anti-migration, anti-proliferative, and pro-apoptotic effects on cancer cell line HCT116 using sponge extracts from Lugait, Misamis Oriental, Philippines

Sponge samples coded KL-03P, KL-12P, KL-15P, KL-16P, and KL-18NP collected from Lugait, Misamis Oriental, Philippines were subjected to fractionation resulting in hexane (H), dichloromethane (D), and aqueous (A) fractions of each sponge. They were subsequently subjected to anti-migration, anti-proliferative, and pro-apoptotic activity testing using the Scratch Wound assay, the 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyl tetrazolium bromide (MTT) assay, and the Caspase Glo 3/7 assay, respectively. Fractions KL-03P (D), KL-12P (D), KL-16P H) and KL18NP (D) showed anti-proliferative and pro-apoptotic effects against HCT116 at 30-ppm concentration. Cell shrinkage and noticeable fragmentations indicative of apoptosis were apparent among these fractions based on MTT final snaps. But among these active fractions, the unprecedented abilities of KL-18NP (D) were very notable across assays conducted. This fraction showcased the inhibition of cell migration, which gave a signal for potentially reduced oncogenicity or metastatic capability of HCT116. The negative percentage of wound healing exhibited by this fraction is a manifestation of its cytotoxicity effect. Very remarkably, KL-18NP (D) has a similar degree of responses across the positive controls, respectively. Hence, there is a significant negative relationship between the negative control versus KL-18NP (D) in terms of its anti-migratory and anti-proliferative properties, p<0.05. Additionally, there is a significant positive relationship between the negative control and KL-18NP (D) when it comes to its pro-apoptotic capability, p<0.05. These results make KL-18NP (D) a bioactive hit as an anti-cancer agent source. Confirmatory techniques, structure determination, and target identification methods will further ascertain its mechanism of action as well as the identification of its cellular targets.

An analytical platform for the screening and identification of pyrrolizidine alkaloids in food matrices with high risk of contamination

An analytical platform for the detection of pyrrolizidine alkaloids (PAs) in honey, pollen, teas, herbal infusions, and dietary supplements is proposed; it includes a wide-scope suspect screening method, based on a diagnostic product ion filtering strategy for the characterization of PAs, and a target screening and identification method for the high-throughput detection of 118 PAs of a high-resolution mass spectral library. Salting-out assisted liquid–liquid extraction of aqueous extracts combined to ultra-high performance liquid chromatography–high-resolution tandem mass spectrometry was employed. The limit of identification (0.6–30 µg kg−1) of 28 standards were fit-for-purpose in PA-monitoring applications, with a false negative rate <1.3 % at 4 µg L–1. The wide-scope suspect screening method allowed the tentative identification of 88 compounds. The screening of 282 commercial samples revealed a broad contamination of the studied matrices, demonstrating the effectiveness of the platform in detecting and identifying both target and untarget PAs.

Multistage approach for automatic target detection and recognition in infrared imagery using deep learning

Automatic target recognition (ATR) is a challenging task for several computer vision applications. It requires efficient, accurate, and robust methods for target detection and target identification. Deep learning has shown great success in many computer vision applications involving color RGB images. However, the performance of these networks in ATR with infrared sensor data needs further investigation. In this paper, we propose a multistage automatic target detection and recognition (ATDR) system that performs both target detection and target classification on infrared (IR) imagery using deep learning. Our system processes large IR image frames where targets take <1 % of the total number of pixels. First, we train a state-of-the-art object detector you only look once (YOLO) to localize all potential targets in the input image frame. Then, we train a convolutional neural network (CNN) to identify these detections as targets or false alarms. In this second phase, we adapt and analyze the performance of three CNN architectures: a compact and fully connected CNN, VGG16 with batch normalization, and a wide residual neural network (WRN). We also explore the use of a loss function that optimizes directly the area under the receiver operating characteristic (ROC) curve (AUC), and adapt it to our ATR application. To enhance the robustness of the proposed ATR to perturbation and variations introduced during the detection stage, we train our CNN classifiers on automatically detected targets using YOLO, in addition to ground truth bounding boxes and apply selected data augmentation techniques. To simulate real testing environments, where the spatial location of the targets within the image frame is unknown, only YOLO-detected boxes are used during validation. We evaluate our ATDR on a real benchmark dataset that includes different vehicles captured at different resolutions. Our experiments have shown that YOLO can detect most of the targets at the expense of generating a high number of false alarms. We show that the VGG-16 network with batch normalization, which is the best performing model, can correctly identify the classes of the targets, as well as classify the majority of YOLO’s false detections into an additional nontarget class. We also show that the proposed training modification to optimize an AUC-based loss function for ATR proved to be advantageous mainly in identifying difficult targets.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Target Identification via Multi-View Multi-Task Joint Sparse Representation

Recently, the monitoring efficiency and accuracy of visible and infrared video have been relatively low. In this paper, we propose an automatic target identification method using surveillance video, which provides an effective solution for the surveillance video data. Specifically, a target identification method via multi-view and multi-task sparse learning is proposed, where multi-view includes various types of visual features such as textures, edges, and invariant features. Each view of a candidate is regarded as a template, and the potential relationship between different tasks and different views is considered. These multiple views are integrated into the multi-task spare learning framework. The proposed MVMT method can be applied to solve the ship’s identification. Extensive experiments are conducted on public datasets, and custom sequence frames (i.e., six sequence frames from ship videos). The experimental results show that the proposed method is superior to other classical methods, qualitatively and quantitatively.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Interrupted sampling repeater jamming countermeasure technology based on random interpulse frequency coding LFM signal

Interrupted sampling repeater jamming (ISRJ) is an active coherent jamming with the characteristic of suppression and deception, which poses a great threat to modern radars. In this work, a false target identification and suppression method based on random interpulse frequency coding linear frequency modulation (RIFC-LFM) signal processing is proposed. First, we analyze the characteristics of the RIFC-LFM signal and ISRJ using the ambiguity function and cross ambiguity function, respectively. Next, taking advantage of the property that most of the false targets formed by ISRJ will be whitened after range-Doppler processing, a false target range-Doppler (RD) spectrum reconstruction method is proposed. Using the reconstructed false target RD spectrum, we can obtain the positions of the unwhitened false targets in the RD spectrum and distinguish the true and false targets. Finally, we design the suppression matrix based on the reconstructed false target RD spectrum to suppress the false targets. The simulation results demonstrate that the proposed approach can effectively suppress three different ISRJs without estimating the ISRJ parameters.

A High-Resolution and Low-Cost Entangled Photon Quantum Imaging Framework for Marine Turbulence Environment

Entangled photon quantum imaging technology has become very promising for marine imaging due to its high resolution, anti-turbulence, and anti-interference properties. The turbulence and optical attenuation caused by oceanic water would impose severe limitations on conventional imaging equipment. Quantum imaging based on entangled light sources could make it possible to overcome the constraints of low imaging resolution, large maritime environmental impact and low confidentiality in traditional underwater target imaging. Hence, the recent technological developments in terms of quantum imaging in free space motivate our investigation into high-resolution quantum imaging based on spatially correlated properties of entangled photon pairs in a marine environment. Moreover we propose and design an innovative underwater quantum imaging optical path for experimental verification. We conduct extensive experiments to assess the effectiveness of our proposed quantum imaging approach in marine environments. In order to solve the problem of the slow quantum imaging process, we propose an innovative compressive sensing reconstruction algorithm. Experiments show that the compressive sensing method could be utilized to reduce the imaging time. Finally, we propose an underwater quantum target identification method based on deep learning, further providing the possibility for underwater quantum target identification of entangled light sources.

Discovery of Leishmania Druggable Serine Proteases by Activity-Based Protein Profiling.

Leishmaniasis are a group of diseases caused by parasitic protozoa of the genus Leishmania. Current treatments are limited by difficult administration, high cost, poor efficacy, toxicity, and growing resistance. New agents, with new mechanisms of action, are urgently needed to treat the disease. Although extensively studied in other organisms, serine proteases (SPs) have not been widely explored as antileishmanial drug targets. Herein, we report for the first time an activity-based protein profiling (ABPP) strategy to investigate new therapeutic targets within the SPs of the Leishmania parasites. Active-site directed fluorophosphonate probes (rhodamine and biotin-conjugated) were used for the detection and identification of active Leishmania serine hydrolases (SHs). Significant differences were observed in the SHs expression levels throughout the Leishmania life cycle and between different Leishmania species. Using iTRAQ-labelling-based quantitative proteomic mass spectrometry, we identified two targetable SPs in Leishmania mexicana: carboxypeptidase LmxM.18.0450 and prolyl oligopeptidase LmxM.36.6750. Druggability was ascertained by selective inhibition using the commercial serine protease inhibitors chymostatin, lactacystin and ZPP, which represent templates for future anti-leishmanial drug discovery programs. Collectively, the use of ABPP method complements existing genetic methods for target identification and validation in Leishmania.

Target Identification-Based Analysis of Mechanism of Betulinic Acid-Induced Cells Apoptosis of Cervical Cancer SiHa

Cervical cancer is the fourth most common female malignancy with high morbidity and mortality, which urgently needs novel anti-cancer drugs. Accumulating investigations have focused on the antitumor activity of betulinic acid (BA), which is a natural compound with low toxicity and high efficiency. Although the effect of BA on SiHa cells is obvious, the specific mechanism is seldom studied. Target identification is an important part of research on the internal mechanism of action. In this current study, an integrated method based on literature collection, target prediction, enrichment analysis, network analysis, and western blotting experiments was performed to identify the potential key targets of BA-induced apoptosis. Then, combined with the identified potential key targets, the specific mechanism of BA-induced cervical cancer SiHa cells apoptosis was elucidated. Our present study demonstrated that BA significantly reduces the viability of cervical cancer SiHa cells in a dose- and time-dependent manner. In addition, 8 potential key targets (AKT1, CASP8, LMNA, TNF, BCL2, CASP3, PARP1, and XIAP) were obtained through our integrated target identification method. Meanwhile, western blotting showed that within a certain concentration range, the expression of cleaved-caspase 3, cleaved-PARP, and cytochrome c increased with the BA concentration, while XIAP was almost unchanged. Therefore, the effect of BA on cervical cancer is noticeable. BA-induced SiHa cells apoptosis is a multi-molecule coordinated process. In this process, BA is not only a participant in either the extrinsic or intrinsic pathways, but also a regulator of apoptosis effector molecules of the CASP3/PARP1 axis.

Unmodified methodologies in target discovery for small molecule drugs: A rising star

Target discovery, involving target identification and validation, is the prerequisite for drug discovery and screening. Novel methodologies and technologies for the precise discovery and confirmation of drug targets are powerful tools in understanding the disease, looking for a drug and elucidating the mechanism of drug treatment. Among the common target identification and confirmation methods, the modified method is time-consuming and laborious, which may reduce or change the activity of natural products. The unmodified methods developed in recent years without chemical modification have gradually become an important means of studying drug targets. A wide range of unmodified approaches have been reported, introducing and analyzing the recent emerging methodologies and technologies. This review highlights the advantages and limitations of these methods for the application of drug target discovery and presents an overview of their contributions to the target discovery of small molecule drugs. The application and future development trends of methodologies in target discovery are also prospected to provide a reference for drug target research.

A method of air target recognition based on combined-rules

Abstract In order to improve the authenticity, reliability and accuracy of air target identification in complex situation, a processing method based on combined-rules is proposed to further enhance the automation degree of air target identification among various air target identification methods. Rule combination and conflict resolutionare used to reduce the workload of human intervention and manual judgment and make target recognition more rapid and accurate.

표적 탐지 및 식별 성능 향상을 위한 레이더 측정치 분석

Using artificial intelligence, radar measurements can improve the performance of target detection and identification methods. Testing data was generated from existing radar data, high-resolution range profile, and inverse synthetic aperture radar images. Radar cross section was chosen as the main parameter because it has been widely studied and its parameters are well known. To ensure the quality of the training dataset, we used simulation tools to determine which parameters had the greatest effect on the generated data. Missile models with different shapes were applied during simulations to confirm the possibility of detection and identification when suitable parameters were applied.