Reduce Background Noise Research Articles

The Variation of b-Value of Earthquakes During COVID-19 Lockdowns: Case Studies from the Cascadia Subduction Zone and New Zealand

During the COVID-19 outbreak that took place in early 2020, restrictions on human activities were imposed by government-mandated lockdowns and stay-at-home orders. In this study, we analyse the impact of reduced anthropogenic activities on the detection of seismic events. We hypothesise and show that with reduced background noise levels due to the COVID-19 lockdowns, low-magnitude earthquakes are more easily detectable. We investigate the magnitudes of earthquakes recorded at the seismometers before and after COVID-19 lockdowns for two regions — Cascadia Subduction Zone and New Zealand. Gutenberg–Richter law, which gives a relationship between the number of earthquakes and their magnitudes (b-value), was applied in these two areas. Our results point to an increase in detection of smaller-magnitude earthquakes, as observed by an increased b-value during the COVID-19 period compared to those obtained in the pre-COVID time periods. Previous studies have shown that changes in b-value of an area over a sustained period of time affect the short-time probabilistic risk assessment. The variability of b-value also gives useful insights into the prevailing stress state of the region, crustal heterogeneity, pore pressure and tectonic setting of the area.

Comparing the Effect of Digital Noise Reduction Technique on Frequency Gain of Behind-the-Ear Digital Hearing Aids

Background and Aim: Background noise as a serious challenge mainly affects the speech perception in people with hearing loss. One of the methods used to control noise is digital noise reduction (DNR) technique. The present study aimed to investigate the effect of DNR program (activated and deactivated) on the frequency gain of basic and advanced behind-the-ear (BTE) Oticon hearing aids, using different DNR strategies to reduce background noise. Methods: Two behind-the-ear Oticon hearing aids (Opn1 S105 and GetP) were used in this study. The Affinity 2.0 test box was first used to measure their DNR (off/on) gains using the national acoustic laboratories-non linear2 (NAL-NL2) and desired sensation level multi-stage [input/output] (DSLm[I/O]) formulas at sound pressure levels of 45, 65 and 85 dB SPL at a frequency range of 250-8000 Hz for three hearing loss (HL) patterns using the international speech test signal and broad band noise. Results: There was a significant difference in DNR performance between the Opn1 S105 and GetP models for all three HL patterns at 45 and 65 dB SPL and most frequencies. Conclusion: The DNR performance of advanced and basic hearing aids is different for different HLs at 45 and 65 dB SPL and most frequencies. The performance of advanced hearing aids is significant using the DSLm[I/O] formula at most frequencies. Keywords: Digital noise reduction; international speech test signal; frequency response; broad band noise

HiCuT: An efficient and low input method to identify protein-directed chromatin interactions.

3D genome organization regulates gene expression, and disruption of these long-range (>20kB) DNA-protein interactions results in pathogenic phenotypes. Chromosome conformation methods in conjunction with chromatin immunoprecipitation were used to decipher protein-directed chromatin interactions. However, these methods required abundant starting material (>500,000 cells), sizable number of sequencing reads (>100 million reads), and elaborate data processing methods to reduce background noise, which limited their use in primary cells. Hi-C Coupled chromatin cleavage and Tagmentation (HiCuT) is a new transposase-assisted tagmentation method that generates high-resolution protein directed long-range chromatin interactions as efficiently as existing methods, HiChIP and ChIA-PET, despite using 100,000 cells (5-fold less) and 12 million sequencing reads (8-fold fewer). Moreover, HiCuT generates high resolution fragment libraries with low background signal that are easily interpreted with minimal computational processing. We used HiCuT in human primary skin cells to link previously identified single nucleotide polymorphisms (SNPs) in skin disease to candidate genes and to identify functionally relevant transcription factors in an unbiased manner. HiCuT broadens the capacity for genomic profiling in systems previously unmeasurable, including primary cells, human tissue samples, and rare cell populations, and may be a useful tool for all investigators studying human genetics and personalized epigenomics.

The Impact of Personal Protective Equipment on Speech Discrimination and Verbal Communication in the Operating Room and the Role of Audio Communication Devices.

Recent work has highlighted communication difficulties when wearing personal protective equipment (PPE) in the clinical setting, but currently, there are little objective data on its effects. We assessed the impact of PPE on verbal communication in a simulated operating room and evaluated use of an audio communication device. Frontline health professionals across specialties including surgery, anesthetics, and nursing undertook speech discrimination testing with and without standardized levels of PPE in a simulated operating room environment. Background noise (30- and 70-a-weighted decibel multitalker babble) at 2 distances (2 and 4 m) were selected representative of operating room environments. Bamford-Kowal-Bench (BKB) scoring (192 sentences per participant) was performed. A Digital Multichannel Transceiver System (DMTS) was evaluated. We assessed the effects of PPE use, distance, and use of the DMTS with pairwise comparisons, using a Bonferroni correction, and assessed participant experience via Likert scales. Thirty-one healthcare professionals were tested. Without PPE in 70-a-weighted decibel "babble," median BKB sentence scores were 90% and 76% at 2 and 4 m (adjusted P < 0.0005). The median BKB sentence scores dropped to 8% and 4% at 2 and 4 m in PPE (adjusted P < 0.0005). Improved speech discrimination was achieved with DMTS use to 70% and 76% at 2 and 4 m. Personal protective equipment led to a statistically significant reduction in BKB scores across all conditions compared with baseline. Overall participant confidence in PPE clinical communication was low. Addition of PPE dramatically impairs speech discrimination and communication in high levels of background noise characteristic of clinical environments, which can be significantly improved using DMTS. Measures should be taken by teams through both through reduction of background noise and consideration of assistive technologies maximizing patient safety. This may be further rehearsed in a simulation environment.

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

PurposeTo evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).Materials and methodsTwo patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.ResultsThe background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).ConclusionsDLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

Effects of atom probe analysis parameters on composition measurement of precipitates in an Al-Mg-Si-Cu alloy

Defining analysis parameter space for reliable composition measurement of materials is of significance for atom probe tomography applications. This research carefully explores the influence of specimen temperature and ultraviolet laser energy on measured compositions of precipitates and the matrix in an Al-Mg-Si-Cu alloy with atom probe tomography using voltage pulsing and laser pulsing. Low specimen temperature and high laser energy are beneficial to reduce background noise and improve mass resolution. Under both voltage pulsing and laser pulsing, the detected compositions and Mg/Si ratios of precipitates are highly sensitive to specimen temperature in the range of 20 - 80 K. In contrast, at a fixed temperature of 20 K, laser energy variation from 40 to 80 pJ provides consistent measurements in composition and Mg/Si ratio of precipitates. Related field-induced preferential evaporation and surface migration of certain elements have been discussed.

Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features

Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision.

Coupling immuno-magnetic capture with LC-MS/MS(MRM) as a sensitive, reliable, and specific assay for SARS-CoV-2 identification from clinical samples.

Recently, numerous diagnostic approaches from different disciplines have been developed for SARS-CoV-2 diagnosis to monitor and control the COVID-19 pandemic. These include MS-based assays, which provide analytical information on viral proteins. However, their sensitivity is limited, estimated to be 5 × 104 PFU/ml in clinical samples. Here, we present a reliable, specific, and rapid method for the identification of SARS-CoV-2 from nasopharyngeal (NP) specimens, which combines virus capture followed by LC–MS/MS(MRM) analysis of unique peptide markers. The capture of SARS-CoV-2 from the challenging matrix, prior to its tryptic digestion, was accomplished by magnetic beads coated with polyclonal IgG-α-SARS-CoV-2 antibodies, enabling sample concentration while significantly reducing background noise interrupting with LC–MS analysis. A sensitive and specific LC–MS/MS(MRM) analysis method was developed for the identification of selected tryptic peptide markers. The combined assay, which resulted in S/N ratio enhancement, achieved an improved sensitivity of more than 10-fold compared with previously described MS methods. The assay was validated in 29 naive NP specimens, 19 samples were spiked with SARS-CoV-2 and 10 were used as negative controls. Finally, the assay was successfully applied to clinical NP samples (n = 26) pre-determined as either positive or negative by RT-qPCR. This work describes for the first time a combined approach for immuno-magnetic viral isolation coupled with MS analysis. This method is highly reliable, specific, and sensitive; thus, it may potentially serve as a complementary assay to RT-qPCR, the gold standard test. This methodology can be applied to other viruses as well.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00216-021-03831-5.

Particle detection enhancement by combining coherent Fourier scatterometry with synthetic optical holography

By combining coherent Fourier scatterometry (CFS) with synthetic optical holography (SOH) we show that the sensitivity of detection of isolated nanoparticles on surfaces can be substantially increased. This improvement is a result of the boost in the signal at the detector due to the added reference beam, and the reduction of background noise caused by the electronics. We demonstrate an improvement of sensitivity of about 4 dB for the case of detection of a 60 nm polystyrene latex (PSL) particle on a silicon wafer at the wavelength of 633 nm (∼ λ/10).

The unique physical shading pattern of Rayleigh scattering for the generally improved detection of scattering particles.

The Rayleigh scattering method is a rising analytical technique because of its high sensitivity and simple operability in the detection of scattered particles. Herein, an efficient and facile physical shading method (covering the cuvette) based on the Rayleigh scattering pattern was developed, which exhibited overall promotion of sensitivity, linearity, detection limit, precision and accuracy because of its greatly reduced background noise upon measurement even if complicated chemical reagents and instruments were not involved. Protein, as a common scattering particle, is a direct or indirect expression of an organism's state of life. Thus, considering the simple and cost-effective features of the shading treatment, a combination strategy based on the probe was adapted to further enhance the performance of protein detection (the limit of detection (3σ) was reduced by at least 20 times, using Eriochrome Black T as a probe), and the possible reasons for the improvement were deduced theoretically. Furthermore, the technology was successfully applied to detect protein in human urine, giving consistent results. In general, this improvement strategy can be handily integrated into most detection systems based on Rayleigh scattering, and its realization delineates a blueprint for the rapid development of sensors.

Heterojunction bilayers serving as a charge transporting interlayer reduce the dark current and enhance photomultiplication in organic shortwave infrared photodetectors.

Previous approaches to induce photomultiplication in organic diodes have increased the photosignal but lacked control over reducing background noise. This work presents a new interlayer design based on a heterojunction bilayer that concurrently enables photomultiplication and suppresses the dark current in organic shortwave infrared detectors to improve the overall detectivity. The heterojunction bilayer consists of a hole-transporting material copper thiocyanate and an electron-transporting material tin oxide, and this combination offers the ability to block charge injection in the dark. Under illumination, the bilayer promotes trap-assisted photomultiplication by lowering the tunneling barrier and amplifying the photocurrent through the injection of multiple carriers per absorbed photon. Upon incorporating the heterojunction interlayer in photodiodes and upconversion imagers, the devices achieve an external quantum efficiency up to 560% and a detectivity of 3.5 × 109 Jones. The upconversion efficiency of the imager doubles with a 1.7 fold improvement in contrast compared to the imager without the heterojunction interlayer. The new interlayer design is generalizable to work with different organic semiconductors, making it attractive and easy to integrate with emerging organic infrared systems.

Context-Based Oriented Object Detector for Small Objects in Remote Sensing Imagery

Object detection in remote sensing imagery is a challenging task in the field of computer vision and has high research value. To improve the classification accuracy and positioning accuracy of object detection, we propose a new multi-scale oriented object detector suitable for small objects. Firstly, the feature fusion network based on information balance (IBFF) is proposed to reduce the reuse of different layers’ features from the backbone network and reduce the interference of redundant information based on the premise that the output features have sufficient information, and retain enough shallow detail information. Secondly, to efficiently utilize deep and shallow features, enhance important features, and reduce background noise interference, different attention-based context feature fusion modules (DACFF) are designed according to the characteristics of different feature fusion stages. Finally, an improved strategy of oriented bounding box regression is proposed to obtain the oriented bounding box with a simpler and more effective strategy. The proposed method was evaluated on two public remote sensing datasets, DOTA and HRSC2016, and their mAP values are 80.96% and 95.01%, respectively, which verified the effectiveness of the proposed algorithm.

Quantifying Endothelial Transcytosis with Total Internal Reflection Fluorescence Microscopy (TIRF).

Apical-to-basal transcytosis by endothelial cells can be visualized and quantified using total internal reflection fluorescence (TIRF) microscopy of the basal membrane. Past techniques to study transcytosis including electron microscopy and transwells have several limitations such as confounding from paracellular leakage, low transfection efficiency, and the largely descriptive nature of electron microscopy. After the addition of a fluorescent ligand to the apical endothelial surface, using TIRF to measure exocytosis at the basal membrane bypasses these issues by studying transcytosis across a single cell of a confluent endothelial monolayer in real time. A major benefit of TIRF is that only a small volume of the cell is illuminated, thus greatly reducing background noise from the overlying cytosol in the images. This protocol outlines the steps to image and quantify exocytosis of apically applied fluorophore-tagged low-density lipoprotein (LDL) using TIRF microscopy and MATLAB. A similar approach can be used to study endothelial transcytosis of other ligands such as albumin or high-density lipoprotein.

Implementation of Modified Wiener Filtering in Frequency Domain in Speech Enhancement

The most common complaint about Digital Hearing Aids is feedback noise. Many attempts have been undertaken in recent years to successfully reduce feedback noise. A wiener filter, which calculates the wiener gain using before and after filtering SNR, is one technique to reduce background noise. Modified Noise Reduction Method (MNRM), a new way for reducing feedback noise Reduction, is presented in this work. In the Modified Noise Reduction Strategy, the advantages of a wiener filter are merged with a decision-directed approach and a twin-stage noise suppression technique The Modified Noise Reduction method can reduce the noise more successfully, according to comprehensive MATLAB programming, investigation, and findings analysis. After being modelled in MATLAB for seven distinct noise types, the SNR of the two architectures is compared.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction. Modern vehicles are nowadays increasingly supporting voice commands, which are one of the pillars of autonomous and SMART vehicles. Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle background noise. This article presents the new concept of a hybrid system, which is implemented as a virtual instrument. The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction. The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks, which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles. On average, the unfiltered voice commands were successfully recognized in 29.34% of all scenarios, while the LMS reached up to 71.81%, and LMS-ICA hybrid improved the performance further to 73.03%.

DeepResGRU: Residual gated recurrent neural network-augmented Kalman filtering for speech enhancement and recognition

With the recent research developments, deep learning models are powerful alternatives for speech enhancement and recognition in many real-world applications. Although state-of-the-art models achieve phenomenal results in terms of the background noise reduction, but the challenge is to design robust models for improving the quality, intelligibility, and word error rate. We propose a novel residual connection-based Bidirectional Gated Recurrent Unit (BiGRU) augmented Kalman filtering model for speech enhancement and recognition. In the proposed model, clean speech and noise signals are modeled as autoregressive process and the parameters are composed of linear prediction coefficients (LPCs) and driving noise variances. Recurrent neural networks are trained to estimate the line spectrum frequencies (LSFs) whereas an optimization problem is solved to attain noise variances such that to minimize the divergence between the modeled and predicted autoregressive spectrums of the noise contaminated speech. Augmented Kalman filtering with the estimated parameters are applied to the noisy speech for background noise reduction such that to improve the speech quality, intelligibility, and word error rates. Bidirectional GRUs network is implemented which predicts parameters both in the future and past contexts of the input sequence and outperform in terms of modeling the long-term dependencies. A compensated phase spectra is used to recover the enhanced speech signals. The Kaldi toolkit is employed to train the automatic speech recognition (ASR) system in order to measure the word error rates (WERs). By using the LibriSpeech dataset, the proposed model improved the quality, intelligibility, and word error rates by 35.52%, 18.79%, and 19.13%, respectively under various noisy environments.

A novel ResNet101 model based on dense dilated convolution for image classification

Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments.HighlightsThe multi-scale feature extraction module is introduced to extract multi-scale features of images, it can extract deep context information of the lesion region and surrounding tissues to enhance the feature extraction ability of the network.The depth feature extraction module is used to focus on the local features of the lesion region from both channel and space, weaken the influence of irrelevant information, and strengthen the recognition ability of the lesion region.The feature extraction module is enhanced by the parallel structure of dense dilated convolution, and the deeper feature information is obtained without losing the image feature information to improve the classification accuracy.

Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results

To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR). Whole-body DECTA with a reduced iodine dose (200 mg iodine/kg) was performed in 22 patients, and DECTA data at 1.25-mm section thickness with 50% overlap were reconstructed at 40 keV using 40% adaptive statistical iterative reconstruction with Veo (hybrid-IR group), and DLIR at medium and high levels (DLIR-M and DLIR-H groups). The CT attenuation values of the thoracic and abdominal aortas and iliac artery and background noise were measured. Arterial depiction and image quality on axial, multiplanar reformatted (MPR), and volume-rendered (VR) images were assessed by two readers. Quantitative and qualitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups. The vascular CT attenuation values were almost comparable between the three groups (p=0.013-0.97), but the background noise was significantly lower in the DLIR-H group than in the hybrid-IR and DLIR-M groups (p<0.001). The arterial depictions on axial and MPR images and in almost all arteries on VR images were comparable (p=0.14-1). The image quality of axial, MPR, and VR images was significantly better in the DLIR-H group (p<0.001-0.015). DLIR significantly reduced background noise and improved image quality in DECTA at 40 keV compared with hybrid-IR, while maintaining the arterial depiction in almost all arteries.

Impact of image averaging on vessel detection using optical coherence tomography angiography in eyes with macular oedema and in healthy eyes.

To assess the repeatability of multiple automatic vessel density (VD) measurements and the effect of image averaging on vessel detection by optical coherence tomography angiography (OCTA). An observational study was conducted in a series of healthy volunteers and patients with macular oedema. Five sequential OCTA images were acquired for each eye using the OptoVue HD device. The effect of the averaging of the 5 acquisitions on vessel detection was analysed quantitatively using a pixel-by-pixel automated analysis. In addition, two independent retina experts qualitatively assessed the change in vessel detection in averaged images segmented in 9 boxes and compared to the first non-averaged image. The automatic VD measurement in OCTA images showed a good repeatability with an overall mean intra-class correlation coefficient (ICC) of 0.924. The mean ICC was higher in healthy eyes compared to eyes with macular oedema (0.877 versus 0.960; p < 0.001) and in the superficial vascular plexus versus the deep vascular complex (0.967 versus 0.888; p = 0.001). The quantitative analysis of the effect of the averaging showed that averaged images had a mean gain of 790.4 pixels/box, located around or completing interruptions in the vessel walls, and a mean loss of 727.2 pixels/box. The qualitative analysis of the averaged images showed that 99.6% of boxes in the averaged images had a gain in vessel detection (i.e., vessels detected in the averaged image but not in the non-averaged image). The loss of pixels was due to a reduction in background noise and motion artifacts in all cases and no case of loss of vessel detection was observed. The automatic VD measurement using the OptoVue HD device showed a good repeatability in 5 acquisitions in a row setting. Averaging images increased vessel detection, and in about a third of boxes, decreased the background noise, both in healthy eyes and, in a greater proportion, in eyes with macular oedema.

Design and Implementation of Harmonic RFID Based on Conventional UHF System

Abstract There has been growing interest in the use of passive harmonic RFIDs for diverse range of applications. Conventional RFIDs are prone to self-jamming and multipath interference, and these challenges can be mitigated using the harmonic RFID design. Recently several harmonic RFID designs have been demonstrated. However, there are many designs related, packaging and intellectual property challenges associated with new tag designs. It has been well known that conventional RFIDs produce harmonic content, which is typically suppressed to reduce background noise. Previous experiments have demonstrated that the harmonics generated by conventional RFIDs can be utilized to enhance their performance. In this paper, an RFID chip is characterized for the generation of harmonic frequencies. This is carried out by designing a high frequency board that contains calibration structures along with structures to characterize the RFID chip using a one port network. An equivalent model is then developed, which in turn is used to design a dual band antenna that works at the fundamental and harmonic frequencies. In addition, the conventional RFID interrogator is modified to accommodate the measurement of harmonics generated by the RFIDs. A complete harmonic tag system is designed and implemented, and an example application of harmonic RFID is demonstrated. Here, the harmonic RFID tag is used in an industrial setting where there is large clutter (large reflections from metal structures).