Simultaneous Target Detection Research Articles

Dual-mode Janus Structural-Color films toward an integrated sensing device with synergistic optical and electrical outputs

Real-time sensing technologies have shown significant importance for environmental management, chemical monitoring, and industrial production. Current chemical sensors with high sensing performances toward gases or liquids have been realized individually; however, a simultaneous detection of targets in the both forms along with a low detection limit and a wide detection range remains a big challenge. Here, we present a novel switchable dual-mode Janus-structured sensor with integrated structural colors and electrical conductivity for chemical detection. The photonic layer formed by the self-assembly of cellulose photonic crystals shows highly selective responses to high-concentration targets via the color-changing channel, while the electrically conductive layer generated by the formation of carbon nanotube networks demonstrates target perceptions at low concentration and even ppm levels. Notably, the integrated sensor is further designed as a wearable sensing system for chemical monitoring in different application scenarios. These findings offer new thinking to design switchable multi-mode sensors for environmental and industrial surveillance.

Rdts-Slam: Real-Time Visual Slam for Dynamic Environments Using Simultaneous Target Detection and Semantic Segmentation

Rdts-Slam: Real-Time Visual Slam for Dynamic Environments Using Simultaneous Target Detection and Semantic Segmentation

Hand-Held Raman Spectrometer-Based Dual Detection of Creatinine and Cortisol in Human Sweat Using Silver Nanoflakes.

The conventional tissue biopsy method yields isolated snapshots of a narrow region. Therefore, it cannot facilitate comprehensive disease characterization and monitoring. Recently, the detection of tumor-derived components in body fluids─a practice known as liquid biopsy─has attracted increased attention from the biochemical research and clinical application viewpoints. In this vein, surface-enhanced Raman scattering (SERS) has been identified as one of the most powerful liquid-biopsy analysis techniques, owing to its high sensitivity and specificity. Moreover, it affords high-capacity spectral multiplexing for simultaneous target detection and a unique ability to obtain intrinsic biomolecule-fingerprint spectra. This paper presents the fabrication of silver nanosnowflakes (SNSFs) using the polyol method and their subsequent dropping onto a hydrophobic filter paper. The SERS substrate, which comprises the SNSFs and hydrophobic filter paper, facilitates the simultaneous detection of creatinine and cortisol in human sweat using a hand-held Raman spectrometer. The proposed SERS system affords Raman spectrometry to be performed on small sample volumes (2 μL) to identify the normal and at-risk creatinine and cortisol groups.

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems.

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

Single-Pulse Simultaneous Target Detection and Angle Estimation in a Multichannel Phased Array Radar

This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion (both one-step and two-step) where decision statistics leveraging the Maximum Likelihood Estimates (MLEs) of the parameters are compared with a detection threshold. If crossed, target presence is declared and the MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view, ML estimation involves a constrained fractional quadratic optimization problem whose optimal solution can be found via the Dinkelbach's algorithm or approximated through a fast-converging procedure based on a Coordinate Descent (CD) optimization. The performance analysis of the proposed architectures as well as the corresponding discussion is developed in terms of computational complexity, Constant False Alarm Rate (CFAR) behavior, detection performance, and angular estimation accuracy, also in comparison with some counterparts available in the open literature and theoretical benchmark limits.

Joint target detection and tracking with heavy-tailed noises

This paper investigates the problem of simultaneous target detection and tracking with heavy-tailed noises under Bernoulli filtering framework. By exploiting the Student-t distributions to formulate the outlier-corrupted process noise and measurement noise, a novel target tracking algorithm named the Student-t mixture Bernoulli filter is proposed. Under linear assumptions on target dynamic model, sensor measurement model and birth process, closed-form recursions that propagate the parameters of Bernoulli random finite set are derived. The validity of the proposed algorithm is verified by numerical simulations.

Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy.

Traditional tissue biopsy is limited in understanding heterogeneity and dynamic evolution of tumors. Instead, analyzing circulating cancer markers in various body fluids, commonly referred to as “liquid biopsy”, has recently attracted remarkable interest for their great potential to be applied in non-invasive early cancer screening, tumor progression monitoring and therapy response assessment. Among the various approaches developed for liquid biopsy analysis, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most powerful techniques based on its high sensitivity, specificity, tremendous spectral multiplexing capacity for simultaneous target detection, as well as its unique capability for obtaining intrinsic fingerprint spectra of biomolecules. In this review, we will first briefly explain the mechanism of SERS, and then introduce recently reported SERS-based techniques for detection of circulating cancer markers including circulating tumor cells, exosomes, circulating tumor DNAs, microRNAs and cancer-related proteins. Cancer diagnosis based on SERS analysis of bulk body fluids will also be included. In the end, we will summarize the “state of the art” technologies of SERS-based platforms and discuss the challenges of translating them into clinical settings.

Multifunctional G-quadruplex-based fluorescence probe coupled with DNA-templated AgNCs for simultaneous detection of multiple DNAs and MicroRNAs

A rapid, label-free, and multifunctional fluorescent probe for simultaneous detection of multiple targets was fabricated based on G-quadruplexes and DNA-templated silver nanoclusters (AgNCs). In this work, a probe with two capitated recognized regions was coupled with a locked G-quadruplex at the 5′-terminus and dark AgNC at the 3′-terminus. Upon the addition of the virus subtype H5N1 gene or microRNA-141, only the sequence of the G-quadruplex was released and bound with Thioflavin T (ThT) for a specific fluorescent response. On the contrary, with the presence of the influenza virus subtype H1N1 gene or microRNA-21, the fluorescence intensity was enhanced because of two split AgNCs approaching closely to produce a nanocluster dimer. Subsequently, with multiple target addition, fluorescence signals were produced for both G-quadruplexes and AgNCs. Moreover, this single and duplex detection for virus DNAs and microRNAs, which provides a versatile platform for different targets, was sufficiently sensitive for the expected detection limit, and still possessed unique selectivity with similar oligonucleotides. The simultaneous detection of targets in biological fluids indicated that there is great opportunity for this strategy to be further applied in biomedical research and clinical diagnosis.

Synthetic Aperture Radar Processing Approach for Simultaneous Target Detection and Image Formation.

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation.

Simultaneous Imaging of Endogenous Survivin mRNA and On-Demand Drug Release in Live Cells by Using a Mesoporous Silica Nanoquencher.

The design of multifunctional drug delivery systems capable of simultaneous target detection, imaging, and therapeutics in live mammalian cells is critical for biomedical research. In this study, by using mesoporous silica nanoparticles (MSNs) chemically modified with a small-molecule dark quencher, followed by sequential drug encapsulation, MSN capping with a dye-labeled antisense oligonucleotide, and bioorthogonal surface modification with cell-penetrating poly(disulfide)s, the authors have successfully developed the first mesoporous silica nanoquencher (qMSN), characterized by high drug-loading and endocytosis-independent cell uptake, which is able to quantitatively image endogenous survivin mRNA and release the loaded drug in a manner that depends on the survivin expression level in tumor cells. The authors further show that this novel drug delivery system may be used to minimize potential cytotoxicity encountered by many existing small-molecule drugs in cancer therapy.

Dynamics of aerial target pursuit

During pursuit and predation, aerial species engage in multitasking behavior that involve simultaneous target detection, tracking, decision-making, approach and capture. The mobility of the pursuer and the target in a three dimensional environment during predation makes the capture task highly complex. Many researchers have studied and analyzed prey capture dynamics in different aerial species such as insects and bats. This article focuses on reviewing the capture strategies adopted by these species while relying on different sensory variables (vision and acoustics) for navigation. In conclusion, the neural basis of these capture strategies and some applications of these strategies in bio-inspired navigation and control of engineered systems are discussed.

Automated acoustic detection and classification of small aircraft

Automated acoustic detection and classification of low flying aircraft can be used for the prevention of illicit operations. In this presentation, we review past techniques and a new acoustic classification and detection technique. The sound detection algorithm is based on locking in narrow frequency components in the spectra of the recorded aircraft sound. The classification algorithm uses tonal components in the spectral and cepstral domains using discrete time window peak picking. A set of chosen classifiers include: fundamental (pitch) frequency extracted by the cepstral analysis, frequency of tonal components with the maximal amplitude, power spectral estimate in various frequency bands, and number of peaks in predefined frequency and frequency windows. Various small single engine aircraft, ultralights, and helicopters are acoustically detected and classified as they approach the sensor. The algorithm enables simultaneous target detection, reduces noise sensitivity, and minimizes classifier feature space, while maintaining good classification separation. The acoustic classification algorithm was incorporated into the Acoustic Aircraft Detection system developed by Stevens. This combination allowed automated Doppler correction of the harmonic lines, the optimal part of the aircraft signal selection, and establishing classification merits based on aircraft track. [This work was funded by DHS S&T.]

Distributed Active Sonar Detection in Dependent $K$-Distributed Clutter

Distributed active sonar systems exploit the simultaneous detection of targets on multiple receiver platforms to improve system performance. False alarm performance modeling for such systems typically assumes independence from sensor to sensor; however, false alarms from clutter (e.g., shipwrecks, mud volcanoes, or rock outcroppings) are expected to produce statistically dependent data. In this paper, a clutter model exhibiting intersensor dependence and having K-distributed marginal probability density functions (pdfs) is proposed and analyzed. Under the constraint of equal sensor-level performance, the m-of- n fusion processor is seen to be optimal for most cases of interest for all but the heaviest tailed clutter. The system-level probability of false alarm is derived for the m-of- n fusion processor and approximations are developed for the system-level probability of detection for common target models. In analyzing fusion performance, the and processor (m = n) is seen to perform best in heavy clutter when the target model has some measure of intersensor consistency while m/n ap 0.25 is seen to perform best for the highly variable Gaussian target model. The performance of AND, OR (m = 1) , and median fusion processors is seen to degrade as the clutter pdf tails increase, although the effect is confounded between clutter tail heaviness and intersensor dependence. The importance of accounting for intersensor dependence, however, is illustrated by an up to 10-dB overestimation of performance when heavy tailed clutter data are incorrectly assumed to be independent across sensors.