Detection Of Multiple Targets Research Articles

YOLO-Based UAV Technology: A Review of the Research and Its Applications

In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

Active sonar detection with acoustic spiral waves: experiment and modeling

Active spiral-wave active localization is a technique for determining location of acoustic targets [after Dzikowicz et al., JASA 146, 4821–4830 (2019)], which relies on measuring the phase difference between a reference and spiral wavefronts. This research presents a monostatic model of spiral detection and tracking for multiple targets in sparse noise limited environments. Simulation and experimental results from an acoustic spiral wave beacon in a small underwater test tank will be presented. Results show good agreement to resolve targets in horizontal and vertical dimensions with a compact aperture. [Work supported by ONR 321MS.]

Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging.

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to 3.07 dB, a Signal-to-Mean Ratio of up to 20.78 dB and a Location Error of 1.58 mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.

Dual Emission Carbon Dots for Simultaneous Detections of Pb2+ and Fe3+ Ions in Water Via Distinct Sensing Mechanisms.

Herein, dual-emission carbon dots (DE-CDs) were synthesized using a one-pot hydrothermal method. DE-CDs exhibited two well-separated peaks at 433 and 513nm under ultraviolet excitation. The prepared DE-CDs offer selective detection of Fe3+ ions via inner filter effect (IFE) and Pb2+ ions via aggregation-induced enhancement (AIE). The obtained DE-CDs showed a good affinity for both Fe3+ and Pb2+ ions in the presence of various interfering ions. The limits of detection were 0.797 ppm and 4.739 ppm for Pb2+ and Fe3+, respectively. The finding reveals the huge potential of DE-CDs for the selective detection of multiple targets in one solution.

Droplet Encoding-Pairing Enabled Multiplexed Digital Loop-Mediated Isothermal Amplification for Simultaneous Quantitative Detection of Multiple Pathogens.

Despite the advantages of digital nucleic acid analysis (DNAA) in terms of sensitivity, precision, and resolution, current DNAA methods commonly suffer a limitation in multiplexing capacity. To address this issue, a droplet encoding-pairing enabled DNAA multiplexing strategy is developed, wherein unique tricolor combinations are deployed to index individual primer droplets. The template droplets and primer droplets are sequentially introduced into a microfluidic chip with a calabash-shaped microwell array and are pairwise trapped and merged in the microwells. Pre-merging and post-amplification image analysis with a machine learning algorithm is used to identify, enumerate, and address the droplets. By incorporating the amplification signals with droplet encoding information, simultaneous quantitative detection of multiple targets is achieved. This strategy allows for the establishment of flexible multiplexed DNAA by simply adjusting the primer droplet library. Its flexibility is demonstrated by establishing two multiplexed (8-plex) droplet digital loop-mediated isothermal amplification (mddLAMP) assays for individually detecting lower respiratory tract infection and urinary tract infection causative pathogens. Clinical sample analysis shows that the microbial detection outcomes of the mddLAMP assays are consistent with those of the conventional assay. This DNAA multiplexing strategy can achieve flexible high-order multiplexing on demand, making it a desirable tool for high-content pathogen detection.

Multifunctional Cd-CP for fluorescence sensing of Cr(VI), MnO4−, acetylacetone and ascorbic acid in aqueous solutions

The development of multifunctional fluorescent chemosensors for the detection of multiple targets remains challenging but of great importance. In this paper, one novel coordination polymer (CP), denoted as [Cd2(edda)(phen)2]∙H2O (compound 1, H4edda = 5,5′ (ethane-1,2-diylbis(oxy)) diisophthalic acid, phen = 1,10-phenanthroline) is successfully designed and prepared under hydrothermal conditions. Structural analysis indicates that compound 1 possesses a one-dimensional (1D) double chain structure, then self-assembles into a three-dimensional (3D) supramolecular framework via π…π interactions between phen molecules. Interestingly, compound 1 is found to be tolerant in wide range of acidic to alkaline aqueous solutions (pH = 2–13). Fluorescent spectral investigations reveal that compound 1 exhibits highly selective and sensitive fluorescence responses toward MnO4−, Cr(VI) ions, acetylacetone (acac) and ascorbic acid (AA) by fluorescence quenching in the aqueous phase. The detection limits are in the very low range, reaching μM level for the detection of MnO4−, Cr(VI) ions, nM for AA and ppm for acac detection. The distinguished multi-responsive performance suggests compound 1 to be a potential multifunctional probe. Furthermore, the possible quenching mechanisms have also been systematically investigated in this work.

Development of a Duplex LAMP Assay with Probe-Based Readout for Simultaneous Real-Time Detection of Schistosoma mansoni and Strongyloides spp. -A Laboratory Approach to Point-Of-Care

Loop-mediated isothermal amplification (LAMP) is the most popular technology for point-of-care testing applications due its rapid, sensitive and specific detection with simple instrumentation compared to PCR-based methods. Many systems for reading the results of LAMP amplifications exist, including real-time fluorescence detection using fluorophore-labelled probes attached to oligonucleotide sequences complementary to the target nucleic acid. This methodology allows the simultaneous detection of multiple targets (multiplexing) in one LAMP assay. A method for multiplexing LAMP is the amplification by release of quenching (DARQ) technique by using a 5'-quencher modified LAMP primer annealed to 3'-fluorophore-labelled acting as detection oligonucleotide. The main application of multiplex LAMP is the rapid and accurate diagnosis of infectious diseases, allowing differentiation of co-infecting pathogens in a single reaction. Schistosomiasis, caused among other species by Schistosoma mansoni and strongyloidiasis, caused by Strongyloides stercoralis, are the most common helminth-parasite infections worldwide with overlapping distribution areas and high possibility of coinfections in the human population. It would be of great interest to develop a duplex LAMP to detect both pathogens in the same reaction. In this study, we investigate the use of our two previously developed and well-stablished LAMP assays for S. mansoni and Strongyloides spp. DNA detection in a new duplex real-time eight-primer system based on a modified DARQ probe method that can be performed in a portable isothermal fluorimeter with minimal laboratory resources. We also applied a strategy to stabilize the duplexed DARQ-LAMP mixtures at room temperature for use as ready-to-use formats facilitating analysis in field settings as point-of-care diagnostics for schistosomiasis and strongyloidiasis.

Innovative Cognitive Approaches for Joint Radar Clutter Classification and Multiple Target Detection in Heterogeneous Environments

The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community. In this paper, we provide a solution to this problem by devising detection architectures capable of classifying the range bins according to their clutter properties and detecting possible multiple targets whose positions and number are unknown. Remarkably, the information provided by the proposed architectures makes the system aware of the surrounding environment and can be exploited to enhance the entire detection and estimation performance of the system. At the design stage, we assume three different signal models and apply the latent variable model in conjunction with estimation procedures based upon the expectation-maximization algorithm. In addition, for some models, the maximization step cannot be computed in closed-form (at least to the best of authors' knowledge) and, hence, suitable approximations are pursued, whereas, in other cases, the maximization is exact. The performance of the proposed architectures is assessed over synthetic data and shows that they can be effective in heterogeneous scenarios providing an initial snapshot of the radar operating scenario.

Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conven-tional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter dis-tribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the trimmed-mean CFAR (TM-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.

Target-mediated rolling circle transcription coupling with CRISPR/Cas12a-Cas13a for simultaneous detection of HPV16 and HPV18.

Simultaneous detection of multiple targets can provide important data support for clinical diagnosis and treatment. Here, we report a facile isothermal assay based on target-mediated rolling circle transcription coupling with CRISPR/Cas12a-Cas13a (TM-RCT/Cas12a-Cas13a). Through facile one-step amplification (TM-RCT), two target DNAs are converted to RNA amplified products. The simultaneous detection of HPV16 and HPV18 is then achieved by combining two CRISPR/Cas systems. This system shows excellent sensing performance and provides a universal method for simultaneous detection.

Biosensor model based on single hairpin structure for highly sensitive detection of multiple targets.

Nowadays, due to the genetic information carried by nucleic acids, they can serve as a biomarker for the early diagnosis of diseases, including tumors and cardiovascular disease, among others, making genetic testing a hotspot of biomedicine. Therefore, we have designed a universal fluorescence biosensor that can detect multiple DNA sequences with good performance. In our designed biosensor, λ exonuclease is used due to its ability to digest double-stranded DNA from the phosphorylated 5'- end and promote the targeted cycle. The exonuclease is introduced into a DNA hairpin containing a target recognition sequence. Hence, with the target, λ exonuclease-assisted targeted recycling can be activated. The hydrolyzed DNA hairpin triggers a strand displacement reaction between the hairpin probe (H1) and F-Q double DNA strand (F-Q), increasing the distance between the fluorescent chain (F) and quenching chain (Q); thus the fluorescence signal is emitted. It is exciting that the detection limit of the biosensor is 300 fM, which is relatively low, and there is an excellent linear relationship between fluorescence intensity and target concentration. Moreover, the biosensor we designed has universal applicability in the detection of other genes, and the range of RSD is 1.28-2.45%. Hence, it has good application prospects and practical value in the early detection of some diseases and the design of fluorescent biosensors.

A survey on encoding and decoding technology of non-invasive brain-computer interface

A survey on encoding and decoding technology of non-invasive brain-computer interface

Class-associative multiple target recognition for highly compressed color images in a joint transform correlator

We propose a practical near real-time compression-based class-associative multiple target detection technique for color images. The size of the color images is reduced to its utmost ratio to speed up the processing time and to increase the storage capacity of the recognition system. A fringe-adjusted joint transform correlation technique is employed to successfully detect the compression-based multiple targets in colored images. In addition, to eliminate the false alarms and zero-order terms due to multiple desired and undesired objects in an input scene, we have used the shifted phase-encoded and the reference phase-encoded techniques. The performance of detecting the class-associative multiple targets for large compression ratios (up to 94%) and under strong noisy conditions (Gaussian and salt and pepper noises) is assessed through many computer simulations experiments. Furthermore, detecting small targets or even a small part of a target is presented for images occluded up to 90%.

ACCURATE DETECTION OF MULTIPLE TARGETS BY UNIFORM RECTANGULAR ARRAY RADAR WITH THRESHOLD SOFT UPDATE AND AREA RESCANNING

Background. If the intensity of moving targets within a surveyed area is low, an optimal number of uniform rectangular array (URA) radar sensors is in either the minimally-sized URA (or close to it) or maximally-sized URA (or close to it), where the URA size is regulated by (symmetrically) turning off vertical and horizontal sensors. However, this does not guarantee detection of any target because sometimes the threshold detection, by which the main parameters of a pair of two targets are estimated, fails even by using the soft threshold approach when the threshold is gradually decreased while the detection fails.
 Objective. In order to improve detection of multiple ground-surface targets by a URA radar, the goal is to decrease a number of detection fails, when targets are just missed. For this, the approach of threshold soft update and a set of quasioptimal URA sizes included and URAs are to be used by rescanning the area if the detection fails.
 Methods. To achieve the goal, the functioning of the URA radar is simulated for a set of randomly generated targets, where roughly a half of the set is to be of single targets, and the other half is to be of pairs of targets. The simulation is configured and carried out by using MATLAB® R2021b Phased Array System ToolboxTM functions based on a model of the monostatic radar.
 Results. Neither the soft threshold approach, nor the rescanning increase the detection accuracy. However, when either the soft threshold or rescanning is applied, or they both are applied, the number of detections is increased. The increment can be evaluated in about 2.7 %, but the expected high-accurate detection performance slightly drops. This is caused by that the soft thresholding and rescanning attempt at retrieving at least some information about the target instead of the detection fail.
 Conclusions. Using the threshold soft update approach along with a more frequent rescanning decreases a number of detection fails. Besides, the soft thresholding and rescanning allow slightly decreasing the number of URA sensors sufficient to maintain the same detection accuracy by increasing the averaged number of single-target and two-target detections at least by 2.5 %. The increment in a number of detected targets on average is equivalent to increasing the probability of detection.

Adaptive multiple targets detection for FDA-MIMO radar with Gaussian clutter

This paper presents an adaptive multiple targets detection framework for frequency diverse array multiple-input multiple-output radar embedded in Gaussian noise with an unknown covariance matrix. To this end, we define the one-range-cell multiple targets model as a summation expression and then design four detectors, i.e., the generalized likelihood ratio test, adaptive matched filter, Rao, and Wald tests for the above newly built problem. Closed-form expressions for the probability of false alarm and the probability of detection are provided to assess the aforementioned detectors. The numerical examples show that the proposed architectures can ensure better detection performance than the considered competitors. Finally, notice that each proposed detector has a specific behavior in terms of robustness to low volumes of data. Thus, the choice of a specific solution is dictated by the operating requirements of the radar system.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Recent Advances in the Immunoassays Based on Nanozymes.

As a rapid and simple method for the detection of multiple targets, immunoassay has attracted extensive attention due to the merits of high specificity and sensitivity. Notably, enzyme-linked immunosorbent assay (ELISA) is a widely used immunoassay, which can provide high detection sensitivity since the enzyme labels can promote the generation of catalytically amplified readouts. However, the natural enzyme labels usually suffer from low stability, high cost, and difficult storage. Inspired by the advantages of superior and tunable catalytic activities, easy preparation, low cost, and high stability, nanozymes have arisen to replace the natural enzymes in immunoassay; they also possess equivalent sensitivity and selectivity, as well as robustness. Up to now, various kinds of nanozymes, including mimic peroxidase, oxidase, and phosphatase, have been incorporated to construct immunosensors. Herein, the development of immunoassays based on nanozymes with various types of detection signals are highlighted and discussed in detail. Furthermore, the challenges and perspectives of the design of novel nanozymes for widespread applications are discussed.

Compressive detection of multiple targets in passive bistatic radar

AbstractThe problem of compressive detection of multiple targets in a passive bistatic radar system consisting of one illuminator of opportunity and one receiver is considered. Specifically, a measurement matrix is designed to remove the direct‐path and multi‐path interference and obtain the compressive observations simultaneously. Considering that the degree of sparsity, that is, the number of targets, is unknown, an orthogonal matching pursuit (OMP)‐based detector, referred to as the ‖rk‖∞‐based OMP detector, is proposed by analysing the distribution characteristics of the infinite norm of the residual. It is shown that the test statistic of the proposed detector leads to an order statistic, and then a closed‐form expression of probability of false alarm and detection of the proposed detector is provided. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.

Extended Drones Tracking From ISAR Images With Doppler Effect and Orientation Based Robust Sub-Random Matrices Algorithm

The simultaneous imaging and tracking of drones and small UAVs is one of the most challenging problems in inverse synthetic aperture radar (ISAR) signal processing and has received significant attention recently. Wide-angle ISAR imaging may offer better resolution and richer geometrical features. Aiming at this issue, we propose a new algorithm for multiple extended drone targets (EDTs) detection and tracking from wide-angle ISAR images. Since a conventional tracking algorithm using a random matrices model (RMM) is not accurate enough to reconstruct a drone's extension by one ellipse, an appropriate technique needs to be developed for ISAR imaging and tracking of EDTs. This paper models the EDT's extension as multiple ellipses with a time-varying orientation angle and solves the resulting interference problem involving data association between the nonlinear measurements and sub-ellipses. Recently, the multi-Bernoulli (MB) filter for extended targets has been proposed with the RMM approach for linear ellipsoidal target tracking by additional state variables. However, the implementation of the MB-RMM filter for nonlinear non-Gaussian models and the Doppler effect has not been presented, especially for ISAR images. We consider linearizing the nonlinear observations to solve the nonlinearity due to the Doppler effect, target motion, and wide-angle ISAR images. We presented a new robust Sub-RMM-MB-TBD filter to obtain the estimated orientation of EDT's shape after using the range-only motion compensation that uses both range and Doppler information. Moreover, the authors applied the k-space algorithm to generate ISAR image. Simulation results demonstrate this remarkable performance.

Ultramultiplex NaLnF4 Nanosatellites Combined with ICP-MS for Exosomal Multi-miRNA Analysis and Cancer Classification.

Quantification of exosomal multi-miRNA can reveal the initiation, progression, and metastasis of tumors, which is conducive to the noninvasive early diagnosis of cancer. However, low-sensitivity and single-plex detection characteristics of traditional methods seriously hinder the accuracy and specificity of exosomal miRNAs in cancer diagnosis. Herein, we design an ultramultiplexing strategy that enables simultaneous and sensitive detection of multiple exosomal miRNAs by nanosatellites (magnetic beads (MBs) @ NaLnF4) and catalytic hairpin assembly (CHA) amplification in combination with inductively coupled plasma-mass spectrometry (ICP-MS) to diagnose cancer accurately. The competitive binding of target exosomal miRNAs with the recognition sequences on nanosatellites triggers the drop of NaLnF4 from MBs, followed by a CHA reaction that releases more NaLnF4 labels for ICP-MS detection. This method is used to detect ten types of miRNAs simultaneously with a detection limit of 0.01 fM, which is one order of magnitude lower than the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. Linear discriminant analysis as a machine learning algorithm is subsequently applied to analyze the signals of exosomal multi-miRNA, and the discrimination accuracy of ten cell exosomes reaches 98.6%. In a clinical cohort of 42 patients, including five cancer types and healthy controls, exosomal multi-miRNA analysis achieves accurate cancer diagnosis and classification with 100% accuracy. Our results show that the combination of nanosatellites, CHA, and ICP-MS provides a universal biosensing platform for simultaneous and ultrasensitive detection of multiple targets.