Tag Signal Research Articles

Simultaneous Detection of Clenbuterol and Higenamine in Urine Samples Using Interference-Free SERS Tags Combined with Magnetic Separation.

Sports doping remains a significant challenge in competitive sports. Given that urine analysis is the standard for detecting doping, developing rapid, sensitive, accurate, and high-throughput methods for stimulant detection in urine is crucial. Surface-enhanced Raman scattering (SERS) tag-based immunoassays have emerged as powerful analytical tools known for their high sensitivity and specificity, holding particular promise for stimulant detection in urine samples. However, both the Raman signals of typical SERS tags and sample matrices are within the Raman fingerprint region (<1800 cm-1), which could lead to spectrum overlap, potentially reducing detection accuracy and sensitivity. By recognizing this, we designed a competitive immunoassay that integrates two types of zero-background SERS tags and magnetic separation. These innovative SERS tags exhibit distinctive Raman peaks within the Raman-silent region (1800-2800 cm-1), effectively mitigating potential spectrum overlap with background sample signals. Moreover, magnetic separation not only enhances operational simplicity but also improves the system's anti-interference capability. Using clenbuterol (CL) and higenamine (HM) as model targets, the SERS-based competitive immunoassay demonstrated sensitive detection of individual CL or HM standards, with limits of detection (LODs) of 0.87 and 0.71 pg/mL, respectively. In multiplex mode, CL and HM can be simultaneously detected with LODs of 1.0 and 0.81 pg/mL, respectively. Furthermore, the recovery rates in urine samples ranged from 83 to 116% (relative standard deviation, RSD ≤ 6.4%) for CL and from 82 to 103% (RSD ≤ 5.1%) for HM, further confirming the reliability of the SERS-based immunoassay for practical applications.

DNA Walker-Driven Mass Nanotag Assembly System for Simultaneously Profiling Dual Markers of Oxidative Stress at Different Cellular Locations.

Simultaneous profiling of redox-regulated markers at different cellular sublocations is of great significance for unraveling the upstream and downstream molecular mechanisms of oxidative stress in living cells. Herein, by synchronizing dual target-triggered DNA machineries in one nanoentity, we engineered a DNA walker-driven mass nanotag (MNT) assembly system (w-MNT-AS) that can be sequentially activated by oxidative stress-associated mucin 1 (MUC1) and apurinic/apyrimidinic endonuclease 1 (APE1) from plasma membrane to cytoplasm and induce recycled assembly of MNTs for multiplex detection of the two markers by matrix-assisted laser desorption ionization mass spectrometry (MALDI MS). In the working cascade, the sensing process governs the separate activation of w-MNT-AS by MUC1 and APE1 in diverse locations, while the assembly process contributes to the parallel amplification of the ion signal of the characteristic mass tags. In this manner, the differences between MCF-7, HeLa, HepG2, and L02 cells in membrane MUC1 expression and cytoplasmic APE1 activation were fully characterized. Furthermore, the oxidative stress level and dynamics caused by exogenous H2O2, doxorubicin, and simvastatin were comprehensively demonstrated by tracking the fate of the two markers across different cellular locations. The proposed w-MNT-AS coupled MS method provides an effective route to probe multiple functional molecules that lie at different locations while participating in the same cellular event, facilitating the mechanistic studies on cellular response to oxidative stress and other disease-related cellular processes.

Deep learning-assisted monitoring of trastuzumab efficacy in HER2-Overexpressing breast cancer via SERS immunoassays of tumor-derived urinary exosomal biomarkers

Monitoring drug efficacy is significant in the current concept of companion diagnostics in metastatic breast cancer. Trastuzumab, a drug targeting human epidermal growth factor receptor 2 (HER2), is an effective treatment for metastatic breast cancer. However, some patients develop resistance to this therapy; therefore, monitoring its efficacy is essential. Here, we describe a deep learning-assisted monitoring of trastuzumab efficacy based on a surface-enhanced Raman spectroscopy (SERS) immunoassay against HER2-overexpressing mouse urinary exosomes. Individual Raman reporters bearing the desired SERS tag and exosome capture substrate were prepared for the SERS immunoassay; SERS tag signals were collected to prepare deep learning training data. Using this deep learning algorithm, various complicated mixtures of SERS tags were successfully quantified and classified. Exosomal antigen levels of five types of cell-derived exosomes were determined using SERS-deep learning analysis and compared with those obtained via quantitative reverse transcription polymerase chain reaction and western blot analysis. Finally, drug efficacy was monitored via SERS-deep learning analysis using urinary exosomes from trastuzumab-treated mice. Use of this monitoring system should allow proactive responses to any treatment-resistant issues.

Python-assisted detection and photothermal inactivation of Salmonella typhimurium and Staphylococcus aureus on a background-free SERS chip

In this study, a background-free surface-enhanced Raman scattering (SERS) chip with a sandwich configuration was fabricated to enable reliable detection and photothermal inactivation of multiple bacteria. The SERS chip consists of a graphene-coated, phenylboronic-modified plasmonic gold substrate (pAu/G/PBA), and two aptamer-functionalized core (gold)-shell (Prussian blue/Poly-L-lysine and 4-mercaptobenzonitrile/polydopamine) SERS tags (Au@PB@PLL@Apt and Au@MB@PDA@Apt). The detection signals rely on the characteristic and nonoverlapping Raman bands of the SERS tags within the Raman-silent region (1800-2800 cm−1), where no background signals from the sample matrix are observed, leading to improved detection sensitivity and accuracy. Considering the relatively large size of bacteria (e.g., micron level), a rapid Raman mapping technique was chosen over conventional point-scan methods to achieve more reliable quantitative analysis of bacteria. This technique involves collecting and analyzing intensity signals of SERS tags from all the scattering points with an average ensemble effect, which is facilitated by the use of Python. As a proof-of-concept, model bacterium of Salmonella typhimurium and Staphylococcus aureus were successfully detected using the SERS chip with a dynamic range of 10–107 CFU/mL. Additionally, the SERS chip demonstrated successful detection of these bacteria in whole blood samples. Moreover, the photothermal effect of pAu/G led to efficient bacteria elimination, achieving approximately 100% eradication. This study integrated a background-free SERS chip with a Python-assisted rapid Raman mapping technique, resulting in a reliable, rapid and accurate method for detecting and eliminating multiple bacteria, which may provide a promising alternative for multiple screening of bacteria in real samples.

Tag-Based PHY-Layer Authentication for RIS-Assisted Communication Systems

This paper proposes a tag-based approach for physical (PHY)-layer authentication in a reconfigurable intelligent surface (RIS) communication system. We first extract the intrinsic PHY-layer features of RIS communication systems in terms of channel gain and background noise, and then apply these PHY-layer features, a random signal as well as the private key of the transmitter to construct a robust cover tag signal against the impersonation attack. We adopt an asymmetric cryptography technique to encrypt tagged signals and to resist against unauthorized detection and tampering attacks during the transmission process. The receiver then applies the maximum a-posteriori (MAP) ratio test to conduct authentication based on the received tag signal, a reference tag signal transmitted in training phase and the knowledge of distributions of the channel gain, background noise and the random signal. We also provide security analysis to demonstrate how the proposed scheme can resist unauthorized detection, tampering attacks, etc. With the help of tools of the MAP ratio test, maximum likelihood estimation, we further analyze the distribution of the test statistics and derive analytical models for the false alarm and detection probabilities. Finally, extensive simulations are conducted to verify the theoretical results and to illustrate the performance of the proposed scheme.

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.

(Invited) Demonstration of ATPS Phase Separation, B-Field Concentration and Manipulation of Sars-Cov-2 Viral Particles

A technology, based on fluorescence sensing in a liquid sample and capable for an early detection of a pathogen at extremely low concentration, is very desirable though equally challenging to develop. In optical sensors, a typical fluorescent signal would be too weak to measure without special tools, unless the detected analyte, tagged with a fluorescent label, can be separated and concentrated.In this work a new combined technology[1] will be presented. An assembly of a monoclonal antibody (mAb) functionalized with a fluorescent tag with the S-proteins at the surface of inactivated coronavirus is achieved. In order to separate a bound fluorescent tag (signal) and free tag molecules (background), we simultaneously attach to the virus surface ferrite magnetic nanoparticles (mNP) functionalized with the mAb (without fluorescent tag). Since the surface of the SARS-CoV2 virus has a large number of binding sites, a "sandwich" assembly of mNP-mAb:virus:mAb-FL happens. At the same time, non-specific binding of the fluorescent tag to mNPs is excluded. As a result, the fluorescent tag bound to the analyte can be manipulated by magnetic force and moved with respect to (unused) free tag molecules.Fig.1 shows the fluorescence signal of the mNP-mAb:virus:mAb-FL sandwich assemblies, that were first concentrated by an aqueous two-phase system separation (ATPS)[2] – carefully designed mix of PEG/dextran solutions. Upon dividing the ATPS into PEG bulk phase and dextran bubbles, the viral assemblies (and free non-bound tag) were separated into the PEG phase. Applying vertical magnetic field as shown in the figure resulted in additional significant concentration of the assemblies against the wall of a dextran bubble. Thus, we achieved a clear separation of the virus (as detected by direct fluorescence signal) with respect to the free tag molecules (background) residing in the bulk solution.[1] Vyacheslav V. Rotkin, Ming Zheng, Tetyana Ignatova, Daniel Hayes, Suresh Kuchipudi; Methods and systems for early detection of viral diseases. https://patents.google.com/patent/US20210364517A1[2] Albertsson, P. A., Partition of Cell Particles and Macromolecules. first ed.; Wiley: New York, 1986. Acknowledgement: This work has been supported by NSF (CHE-2032582 and CHE-2032601). Figure 1

Towards Estimating Probability of Fish–Turbine Encounter: Using Drifters Equipped with Acoustic Tags to Verify the Efficacy of an Array of Acoustic Receivers

An area has been designated for demonstrating the utility of marine hydrokinetic turbines in Minas Passage, Bay of Fundy. Marine renewable energy may be useful for the transition from carbon-based energy sources, but there is concern for the safety of fish that might encounter turbines. Acoustic receivers that detect signals from acoustically tagged fish that pass through the tidal demonstration area and the detection efficiency of tag signals might be used to estimate the likelihood of fish encountering marine hydrokinetic turbines. The method requires that tagged fish passing through the development area will be reliably detected by a receiver array. The present research tests the reliability with which passing tags are detected by suspending tags beneath GPS-tracked drifters. Drifters carrying high residency Innovasea tags that transmitted every 2 s were usually detected by the receiver array even in fast currents during spring tides but pulse-position modulation tags were inadequate. Sometimes very few high residency tag signals were detected when fast tidal currents swept a drifter through the receiver array, so increasing the transmission interval degrades performance at the tidal energy development area. High residency tags suspended close to the sea surface were slightly less likely to be detected if they passed by during calm conditions. Previously measured detection efficiencies were found to slightly overestimate the chances of a high residency tag carried by a drifter being detected as it passed by a receiver. This works elucidates the effectiveness with which acoustically tagged fish are detected in fast, highly turbulent tidal currents and informs the application of detection efficiency measurements to calculate the probability that fish encounter a marine hydrokinetic turbine.

Designing Fluorescent Nuclear Permeable Peptidomimetics to Target Proliferating Cell Nuclear Antigen.

Human proliferating cell nuclear antigen (PCNA) is a critical mediator of DNA replication and repair, acting as a docking platform for replication proteins. Disrupting these interactions with a peptidomimetic agent presents as a promising avenue to limit proliferation of cancerous cells. Here, a p21-derived peptide was employed as a starting scaffold to design a modular peptidomimetic that interacts with PCNA and is cellular and nuclear permeable. Ultimately, a peptidomimetic was produced which met these criteria, consisting of a fluorescein tag and SV40 nuclear localization signal conjugated to the N-terminus of a p21 macrocycle derivative. Attachment of the fluorescein tag was found to directly affect cellular uptake of the peptidomimetic, with fluorescein being requisite for nuclear permeability. This work provides an important step forward in the development of PCNA targeting peptidomimetics for use as anti-cancer agents or as cancer diagnostics.

Measuring Detection Efficiency of High-Residency Acoustic Signals for Estimating Probability of Fish–Turbine Encounter in a Fast-Flowing Tidal Passage

Semidiurnal tidal currents can exceed 5 ms−1 in Minas Passage, Bay of Fundy, where a tidal energy demonstration area has been designated to generate electricity using marine hydrokinetic turbines. The risk of harmful fish–turbine interaction cannot be dismissed for either migratory or local fish populations. Individuals belonging to several fish populations were acoustically tagged and monitored by using acoustic receivers moored within the Minas Passage. Detection efficiency ρ is required as the first step to estimate the probability of fish–turbine encounter. Moored Innovasea HR2 receivers and high-residency (HR) tags were used to obtain detection efficiency ρ as a function of range and current speed, for near-seafloor signal paths within the tidal energy development area. Strong tidal currents moved moorings, so HR tag signals and their reflections from the sea surface were used to measure ranges from tags to receivers. HR2 self-signals that reflected off the sea surface showed which moorings were displaced to lower and higher levels on the seafloor. Some of the range testing paths had anomalously low ρ, which might be attributed to variable bathymetry blocking the line-of-sight signal path. Clear and blocked signal paths accord with mooring levels. The application of ρ is demonstrated for the calculation of abundance, effective detection range, and detection-positive intervals. High-residency signals were better detected than pulse position modulation (PPM) signals. Providing that the presently obtained ρ applies to tagged fish that swim higher in the water column, there is a reasonable prospect that probability of fish–turbine encounter can be estimated by monitoring fish that carry HR tags.

Probability of Atlantic Salmon Post-Smolts Encountering a Tidal Turbine Installation in Minas Passage, Bay of Fundy

Tidal stream energy is a renewable energy resource that might be developed to offset carbon emissions. A tidal energy demonstration (TED) area has been designated in Minas Passage, Bay of Fundy, for testing and installing marine hydrokinetic (MHK) turbines. Regulations require quantification of the potential for MHK turbine installations to harm local populations of marine animals. Here, we use acoustic telemetry to quantify the probability that post-smolt inner Bay of Fundy salmon encounter a turbine installation at the TED area. Previous work has quantified the detection efficiency of Innovasea HR acoustic tags as a function of the current speed and range from a moored HR2 receiver and also demonstrated that drifters carrying HR tags will be effectively detected when the drifter track crosses the array of HR2 receivers in Minas Passage. Salmon smolts were tagged and released in Gaspereau and Stewiacke Rivers, Nova Scotia, in order that the HR2 receiver array could monitor seaward migration of the post-smolts through Minas Passage and particularly through the TED area. Presently, we formulate and apply a method by which tag signals detected by the HR2 array can be used to estimate the expected number of times that a post-smolt would encounter a single near-surface MHK turbine installation during its seaward migration.

Optimum Multiantenna Ambient Backscatter Receiver for Binary-Modulated Tag Signals

Ambient backscatter communication (AmBC) is becoming increasingly popular as a green Internet-of-things technology by enabling ultra-low-power data exchanges among simple tags. The bit-error-rate (BER) performance of the AmBC receivers is hampered by the low signal-to-interference-plus-noise ratio (SINR), which limits either the range or the data rate that can be supported by these systems. Among the alternatives, the solutions provided by a multiantenna receiver enable several practical methods to improve the SINR using array signal processing. In this paper, the optimum multiantenna AmBC receiver for any binary modulated tag signal is presented. Two receivers are derived from the maximum-a-posterior probability criterion for deterministic-unknown and for Gaussian distributed ambient signals. The closed-form cumulative distribution functions are derived for both of the receivers for performance evaluation purposes. It is discussed that these two receivers are equivalent under practical conditions, and the optimum receiver is a generalization of the multiantenna receivers available in the literature. Several implementation details are discussed, and numerical evaluation is provided to validate the development. Therefore, the presented receiver accommodates different tag modulations and achieves the best possible BER performance, which opens up new application possibilities by improving AmBC system flexibility.

Mass tag-encoded nanointerfaces for multiplexed mass spectrometric analysis and imaging of biomolecules.

Revealing multiple biomolecules in the physiopathological environment simultaneously is crucial in biological and biomedical research. Mass spectrometry (MS) features unique technical advantages in multiplexed and label-free analyses. However, owing to comparably low abundance and poor ionization efficiency of target biomolecules, direct MS profiling of these biological species in vitro or in situ remains a challenge. An emerging route to solve this issue is to devise mass tag (MT)-encoded nanointerfaces which specifically convert the abundance or activity of biomolecules into amplified ion signals of mass tags, offering an ideal strategy for synchronous MS assaying and mapping of multiple targets in biofluids, cells and tissues. This review provides a thorough and organized overview of recent advances in MT-encoded nanointerfaces elaborately tailored for several practical applications in multiplexed MS bioanalysis and biomedical research. First, we start with elucidation of the structural characteristics and working principle of MT-encoded nanointerfaces in specific labeling and sensing of multiple biological targets. In addition, we further discuss the application scenarios of MT-encoded nanointerfaces particularly in multiplexed biomarker assays, cell analysis, and tissue imaging. Finally, the current challenges are pointed out and future prospects of these nanointerfaces in MS analysis are forecast.

RFID Authentication System Based on User Biometric Information

Traditional authentication technologies usually perform identity authentication based on user information verification (e.g., entering the password) or biometric information (e.g., fingerprints). However, there are security risks when applying only these authentication methods. For example, if the password is compromised, it is unlikely to determine whether the user entering the password is legitimate. In this paper, we subdivide biometric information into physiological and behavioral information, and we propose a novel user authentication system, RF-Ubia, which utilizes the low-cost radio frequency identification (RFID) technology to capture unique biological or behavioral information rooted in the user and can be used in two schemes for user authentication. Consisting of an array of nine passive tags and a commercial RFID reader, RF-Ubia provides double assurance for security of identity authentication by combining user information and biometric characteristics. It first verifies the user’s password, and then identifies the biometric characteristics of the legitimate user. Due to the coupling effect among tags, any change in tag signal caused by the user’s touch will affect other tag signals at the same time. Since each user has different fingertip impedance, their touch will cause unique tag signal changes. Therefore, by combining biometric information, the tag array will uniquely identify users. The evaluation results show that RF-Ubia achieves excellent authentication performance with an average recognition rate of 93.8%.

Arrhythmia classification algorithm based on multi-head self-attention mechanism

Cardiovascular disease is a major illness that causes human death, especially in the elderly. Timely and accurate diagnosis of arrhythmia types is the key to early prevention and diagnosis of cardiovascular diseases. This paper proposed an arrhythmia classification algorithm based on multi-head self-attention mechanism (ACA-MA). First, an ECG signal preprocessing algorithm based on wavelet transform is put forward and implemented using db6 wavelet transform to focus on improving the data quality of ECG signals and reduce the noise of ECG signals. Second, a linear projection layer for acquiring semantic features of ECG signals is designed using the matching relationship between ECG tag and segmented ECG signals. Third, a position encoding-based spatiotemporal characterization method of ECG signal sequences is designed to integrate time series information into a matrix operation. Fourth, a multi-head self-attentive mechanism capable of capturing global contextual information is proposed to extract relationships and semantic features between ECG segments and achieve semantic association and information stitching of nonadjacent ECG signals. Finally, experimental results on the arrhythmia dataset MIT/BIH show that ACA-MA outperforms other state-of-the-art methods with an overall classification accuracy of 99.4%, a specific rate of 99.41%, and a sensitivity of 97.36%.

Wear-free gesture recognition based on residual features of RFID signals

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.

Graphene oxide-based three-dimensional Au nanofilm with high-density and controllable hotspots: A powerful film-type SERS tag for immunochromatographic analysis of multiple mycotoxins in complex samples

Co-contamination of mycotoxins in food and environment is a common phenomenon, which easily causes cumulative and synergistic damaging effects to human and animal health, poses great health and economy burdens to the world. A convenient technology for detection of multiple and low-concentration mycotoxins in real samples is highly desired but remains a challenge. Here, we proposed a multiplexed surface-enhanced Raman scattering (SERS)-immunochromatographic assay (ICA) that can sensitively and simultaneously detect three mycotoxins in unprocessed complex samples, using a graphene oxide-based three-dimensional (3D) Au nanofilm (called GO@Au-Au) as the film-type SERS tag. A precise sub-1 nm PEI layer was constructed into the GO@Au-Au nanostructure as built-in nanogap, which can accommodate Raman reporter molecules and create stable hotspots between the inner GO@Au film and outer assembled AuNP satellites. Grafting the 30 nm AuNPs onto the 2D GO@Au nanofilm greatly improved the SERS activity and colorimetric signal of film-type tags. Compared with traditional spherical SERS tags, the film-type GO@Au-Au can provide greater reactioninterface, excellent stability and dispersibility and multiple SERS hotspots over large area for ICA using. Given these advantages, the proposed SERS-ICA can achieve simultaneous quantitative detection of fumonisin B1, aflatoxin B1, and zearalenone, with low detection limit (0.529, 0.745, and 5.90 pg mL−1), short testing time (20 min), and high accuracy for real food/experimental samples. Our method shows great potential to fulfill practical requirements for detection of multiple mycotoxins.

MIMO Radio Frequency Identification: A Brief Survey.

In this paper, we briefly look at the latest state-ot-the-art in the domain of multi-input multi-output (MIMO) radio frequency identification (RFID) systems while detailing the work done in the domain of anti-collision, range enhancements, bit error rate (BER) improvements and security. Various passive ultra-high frequency (UHF) RFID implementations are considered that employ multiple antennas at the reader and single or multiple antennas at each tag. We look at several recent works those explored MIMO for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the BER as well as range extension. With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems under-perform in such settings causing low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision schemes. We provide a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved.

Diagnosing predated tags in telemetry survival studies of migratory fishes in river systems

BackgroundAcoustic telemetry is a powerful tool for studying fish behavior and survival that relies on the assumption that tag detection reflects the presence of live study subjects. This assumption is violated when tag signals continue to be recorded after consumption by predators. When such tag predation is possible, it is necessary for researchers to diagnose and remove these non-representative detections. Past studies have employed a variety of data-filtering techniques to address the issue, ranging from rule-based algorithms that rely on expert judgements of behavior and movement capabilities of study subjects and their predators to automated pattern-recognition techniques using multivariate analyses. We compare four approaches for flagging suspicious tracks or detection events: two rule-based expert-opinion approaches of differing complexity and two unsupervised pattern-recognition approaches with and without data from deliberately tagged predators. We compare alternative approaches by applying these four filters to a case study of survival estimation of acoustic-tagged juvenile Chinook salmon (Oncorhynchus tshawytscha) in the San Joaquin River, California, United States.ResultsFiltering approaches differed in the number and composition of tags suspected of being consumed by predators; the largest differences occurred between the two broad categories, rule-based versus pattern recognition. All methods required some investigator judgement and all flagged a small subset (5%) of suspicious tags that had exceptionally long residence times and evidence of upstream transitions; 27% of tags showed evidence of predation based on at least one filter. The complex rule-based filter deemed the most tags suspicious (21%) and the simpler pattern-recognition method the fewest (10%). Reach-specific survival estimates from the four filters were mostly within 2% of the unfiltered estimates, but differences up to 11% were observed.ConclusionsSensitivity of survival results to tag predation and predator filtering depends on the study setting, spatiotemporal scale of inference, and habitat use of predators. Choice of filtering technique depends on the data available and knowledge of the study system. We recommend that survival studies include clear documentation of filtering methods and report on robustness of results to the filtering approach selected.

An RFID multi-Tag anti-collision algorithm based on Successive Interference Cancellation with power adaptive regulation

Radio Frequency Identification (RFID) is a key technology supporting the Internet of Things (IoT). The simultaneous identification of multiple Tag signals by a Reader will generate the Tag collision problem, which will significantly reduce the identification efficiency of the system and prolong the identification time in the scenario of large-scale Tags. In order to solve this problem, this paper researches RFID Tag collision prevention algorithm, and establishes a multi-Tag collision prevention algorithm model by analyzing the Aloha Tag collision prevention algorithm, which can accurately portray the slot efficiency and time efficiency of the system when recognizing multiple Tags; then establishes a power adaptive regulation RFID multi-Tag identification algorithm using Successive Interference Cancellation (SIC) technology. After simulation and verification results show that the constrained Reader can resolve the signal to noise ratio threshold of 6.9db, the slot efficiency and time efficiency of the SIC-based RFID Tag anti-collision algorithm is improved by 179% and 154% compared to the ISOC algorithm; the designed RFID power adaptive regulation algorithm is improved by 259% and 203% compared to the ISOC algorithm in terms of slot efficiency and time efficiency 203%.