Radio Frequency Signal Research Articles

Development of an Automated Ultrasound Signal Indicator of Lung Interstitial Syndrome.

The number and distribution of lung ultrasound (LUS) imaging artifacts termed B-lines correlate with the presence of acute lung disease such as infection, acute respiratory distress syndrome (ARDS), and pulmonary edema. Detection and interpretation of B-lines require dedicated training and is machine and operator-dependent. The goal of this study was to identify radio frequency (RF) signal features associated with B-lines in a cohort of patients with cardiogenic pulmonary edema. A quantitative signal indicator could then be used in a single-element, non-imaging, wearable, automated lung ultrasound sensor (LUSS) for continuous hands-free monitoring of lung fluid. In this prospective study a 10-zone LUS exam was performed in 16 participants, including 12 patients admitted with acute cardiogenic pulmonary edema (mean age 60 ± 12 years) and 4 healthy controls (mean age 44 ± 21). Overall,160 individual LUS video clips were recorded. The LUS exams were performed with a phased array probe driven by an open-platform ultrasound system with simultaneous RF signal collection. RF data were analyzed offline for candidate B-line indicators based on signal amplitude, temporal variability, and frequency spectrum; blinded independent review of LUS images for the presence or absence of B-lines served as ground truth. Predictive performance of the signal indicators was determined with receiving operator characteristic (ROC) analysis with k-fold cross-validation. Two RF signal features-temporal variability of signal amplitude at large depths and at the pleural line-were strongly associated with B-line presence. The sensitivity and specificity of a combinatorial indicator were 93.2 and 58.5%, respectively, with cross-validated area under the ROC curve (AUC) of 0.91 (95% CI = 0.80-0.94). A combinatorial signal indicator for use with single-element non-imaging LUSS was developed to facilitate continuous monitoring of lung fluid in patients with respiratory illness.

Photonic distributed compressive sampling of multi-node wideband sparse radio frequency signals.

A photonic distributed compressive sampling (PDCS) approach for identifying the spectra of multi-node wideband sparse signals is proposed. The scheme utilizes wavelength division multiplexing (WDM) technology to transmit multi-node signals to a central station, where distributed compressive sampling (DCS) based on the random demodulator (RD) model is employed to simultaneously identify the signal spectrum. By exploiting signal correlations among nodes, DCS achieves a higher compression ratio of the sampling rate than single-node compressive sampling (CS). In a semi-physical simulation experiment, we demonstrate the feasibility of the approach by recovering the spectra of two wideband sparse signals from nodes located 20 km and 10 km away. The spectra of two signals with a mixed support-set sparsity of 2 and 4 are recovered with a compression ratio of 8 and 4, respectively. We further investigate the impact of common parts and the number of nodes on PDCS performance through numerical simulation. The proposed system takes advantage of the ultra-high bandwidth of photonic technology and the low loss of optical fiber transmission, making it suitable for long-distance, multi-node, and large-coverage electromagnetic spectrum identification.

Unsupervised Domain Adaptation for RF-Based Gesture Recognition

Human gesture recognition with Radio Frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this paper, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo-labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo-labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo-labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi dataset and millimeter wave (mmWave) radar dataset, respectively.

Ensemble Deep Learning for Sustainable Multimodal UAV Classification

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth in popularity and applications, UAVs have also been used for malicious purposes. In such instances, their timely detection and identification has garnished rising interest from government, industry and academia. While much work has been done for detecting UAVs, there still exist limitations related to the impact of extreme environmental conditions and big dataset requirements. This paper proposes a novel ensemble deep learning framework that has hybrid synthetic and deep features to detect unauthorized or malicious UAVs by using acoustic, image/video and wireless radio frequency (RF) signals for robust UAV detection and classification. We present the performance evaluation of the proposed approach using numerical results obtained from experiments using acoustic, image/video and wireless RF signals. The proposed approach outperforms the existing related approaches for detecting malicious UAVs.

Performance optimization and parameters estimation for MIMO-OFDM dual-functional communication-radar systems

Dual-function communication radar systems use common Radio Frequency (RF) signals are used for both communication and detection. For better compatibility with existing communication systems, we adopt Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals. First, we analyze the Cramer-Rao Lower Bound (CRLB) of parameter estimation. Then, the transmit powers over different subcarriers are optimized to achieve the best tradeoff between the transmission rate and the estimation performance. Finally, we propose a more accurate estimation method that uses Canonical Polyadic Decomposition (CPD) of the third-order tensor to obtain the parameter matrices. Due to the characteristic of the column structure of the parameter matrices, we only need to use DFT / IDFT to recover the parameters of multiple targets. The simulation results show that tensor-based estimation method can achieve a performance close to CRLB, and the estimation performance can be improved by optimizing the transmit powers.

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning

Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053). In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85±0.04 (mean±SD) for IDH1, 0.84±0.05 for TERTp, and 0.88±0.04 for 1p/19q. The AUC is 0.89±0.02 (95% CI, 0.84-0.94) for IDH1, 0.80±0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85±0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Performance analysis of the RoFSO system over a log-normal turbulent link

Abstract This study provides a comprehensive analysis of the design and performance evaluation of a radio over free space transmission link using Optisystem v.20 software. The research focuses on the integration of four transmission channels through the use of a multiplexer to achieve a transfer rate of 40 Gbps over a distance of 2 km, with each channel carrying 10 Gbps of data. This is achieved by modulating data on 20 GHz radio frequency signals, which are subsequently transmitted via laser waves at different frequencies. The performance of the proposed communication is assessed by evaluating its behavior in the presence of different levels of atmospheric turbulence, classified as light, medium, and strong. This analysis involves calculating based on metrics such as the Q-factor, bit error rate (BER), and log values (BER). The results showed that the Q factor decreased by (4.7) under the influence of a strong disturbance, while in contrast, it decreased by (0.2) under the influence of a weak disturbance. The results also indicated a minimum log BER (at −7.96) for the wavelength (193.3 THz) at 1.5 km. Besides, the best frequency for data transfer was (193.1 THz), followed by (193.4 THz).

Over 5 × 103-Fold Enhancement of Responsivity in Ga2O3-Based Solar Blind Photodetector via Acousto-Photoelectric Coupling.

The emergence of the wide-band-gap semiconductor Ga2O3 has propelled it to the forefront of solar blind detection activity owing to its key features. Although various architectures and designs of Ga2O3-based solar blind photodetectors have been proposed, their performance still falls short of commercial standards. In this study, we demonstrate a method to enhance the performance of a simple metal-semiconductor-metal-structured Ga2O3-based solar blind photodetector by exciting acoustic surface waves. Specifically, we demonstrate that under a bias voltage of 100 mV and a radio frequency signal of 20 dBm, the responsivity and detectivity can increase from 2.78 to 1.65 × 104 A/W and from 8.35 × 1014 to 2.66 × 1016 jones, respectively, rivaling a commercial photomultiplier tube. The over 5 × 103-fold enhancement in responsivity could be attributed to the acousto-photoelectric coupling mechanism. Furthermore, since surface acoustic waves can also serve as signal receivers, such photodetectors offer the prospect of dual-mode detection. Our findings reveal a promising pathway for achieving high-performance Ga2O3-based electronics and optoelectronics.

Energy Minimization in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle-Enabled Wireless Powered Mobile Edge Computing Systems with Rate-Splitting Multiple Access

In this study, a reconfigurable intelligent surface (RIS)-assisted wireless-powered mobile edge computing (WP-MEC) system is proposed, where a single-antenna unmanned aerial vehicle (UAV)-mounted cloudlet provides offloading opportunities to K user equipments (UEs) with a single antenna, and the K UEs can harvest the energy from the broadcast radio-frequency signals of the UAV. In addition, rate-splitting multiple access is used to provide offloading opportunities to multiple UEs for effective power control and high spectral efficiency. The aim of this paper is to minimize the total energy consumption by jointly optimizing the resource allocation in terms of time, power, computing frequency, and task load, along with the UAV trajectory and RIS phase-shift matrix. Since coupling issues between optimization variable designs are caused, however, an alternating optimization-based algorithm is developed. The performance of the proposed algorithm is verified via simulations and compared with the benchmark schemes of partial optimizations of resource allocation, path planning, and RIS phase design. The proposed algorithm exhibits high performance in WP-MEC systems with insufficient resources, e.g., achieving up to 40% energy reduction for a UAV with eight elements of RIS.

Integrated microwave photonic notch filter using a heterogeneously integrated Brillouin and active-silicon photonic circuit

Microwave photonics (MWP) has unlocked a new paradigm for Radio Frequency (RF) signal processing by harnessing the inherent broadband and tunable nature of photonic components. Despite numerous efforts made to implement integrated MWP filters, a key RF processing functionality, it remains a long-standing challenge to achieve a fully integrated photonic circuit that can merge the megahertz-level spectral resolution required for RF applications with key electro-optic components. Here, we overcome this challenge by introducing a compact 5 mm × 5 mm chip-scale MWP filter with active E-O components, demonstrating 37 MHz spectral resolution. We achieved this device by heterogeneously integrating chalcogenide waveguides, which provide Brillouin gain, in a complementary metal-oxide-semiconductor (CMOS) foundry-manufactured silicon photonic chip containing integrated modulators and photodetectors. This work paves the way towards a new generation of compact, high-resolution RF photonic filters with wideband frequency tunability demanded by future applications, such as air and spaceborne RF communication payloads.

Innovative Curved-Tip Reactor for Non-Thermal Plasma and Plasma-Treated Water Generation: Synergistic Impact Comparison with Sodium Hypochlorite in Dental Root Canal Disinfection.

Non-thermal plasmas (NTPs), known as cold atmospheric plasmas (CAPs), hold great potential for diverse medical applications, including dentistry. However, traditional linear and rigid dielectric barrier discharge reactors used for NTP generation encounter limitations in accessing oral cavities and root canals. To address this issue, we have developed an innovative NTP reactor featuring an angled end for improved accessibility. The central copper electrode, with a 0.59 mm diameter and adjustable length for desired angulation, is coated with zircon powder (ZrSiO4) to ensure stable NTP generation. This central electrode is housed within a stainless steel tube (3 mm internal diameter, 8 mm external diameter, and 100 mm length) with a 27° angle at one end, making it ergonomically suitable for oral applications. NTP generation involves polarizing the reactor electrodes with 13.56 MHz radio frequency signals, using helium gas as a working medium. We introduce plasma-treated water (PTW) as an adjunctive therapy to enhance biofilm eradication within root canals. A synergistic approach combining NTP and PTW is employed and compared to the gold standard (sodium hypochlorite, NaOCl), effectively neutralizing Enterococcus faecalis bacteria, even in scenarios involving biofilms. Moreover, applying NTP in both gaseous and liquid environments successfully achieves bacterial inactivation at varying treatment durations, demonstrating the device's suitability for medical use in treating root canal biofilms. The proposed NTP reactor, characterized by its innovative design, offers a practical and specific approach to plasma treatment in dental applications. It holds promise in combatting bacterial infections in root canals and oral cavities.

NuWa: off-state tolerant backscattering system with uncontrolled excitation traffics

Backscatter communication relies on passive reflections of the existing radio frequency (RF) signals, making it suitable for low-power and low-complexity communication in IoT applications. However, the performance of existing systems severely degrades in real-life environments, due to irregular “on” and “off” states of ambient signals like WiFi, which are not controllable. In this paper, we propose a joint coding and framing scheme for the backscattering physical layer to fight against the off-state in the excitation signal. We first design transmission schemes including both the Reed-Solomon (RS) codes and the frame structure, to correct the burst error caused by the off states. In order to implement the codes at the resource-constrained tag, we design a look-up table for the encoding process. We prototype our system NuWa that could efficiently backscatter with uncontrolled traffics generated randomly. We demonstrate that NuWa could achieve a 1 Mbps transmission throughput when the tag is over 1 m away from the receiver in high traffic load and 150 kbps in low traffic load. Finally, we evaluate the throughput with respect to the distance change between the tag and the receiver, and 950 kbps is achieved at a distance of 6,m.

Photonic generation of a tunable multi-band linearly frequency-modulated waveform

A photonic-assisted tunable multi-band linearly frequency-modulated (LFM) waveform generator based on optical heterodyne detection is proposed and experimentally demonstrated. The multi-band LFM waveform is generated from a photodetector (PD), where a broadband LFM optical pulse and a multi-wavelength optical signal with the same magnitude at different wavelengths are heterodyne-detected. The LFM optical pulse is generated from a three-electrode distributed Bragg reflector laser diode (DBR-LD). The multi-wavelength optical signal is produced through a Mach-Zehnder modulator (MZM), in which a light wave from an optical fiber laser (OFL) is nonlinearly intensity-modulated by a radio frequency (RF) signal. By tuning the wavelength of the LFM optical pulse lower than that of the multi-wavelength optical signal, a multi-band LFM waveform is generated at the PD. The center frequency of the generated LFM waveform can be adjusted by manipulating the wavelength of the OFL. The versatility in the bandwidth and temporal duration of the LFM waveform is attained through the precise tuning of the LFM optical pulse. The tuning of the center frequency difference between frequency bands is performed by altering the frequency of the RF signal. Experimental results have corroborated the realization of a tunable multi-band LFM waveform generator. The bandwidth and temporal duration of each band and the center frequency difference between frequency bands have tuning ranges from 0.7 to 6.7 GHz, 1 to 50 μs, and 2 to 6.7 GHz, respectively. The demonstration shows new avenues to implement high-performance multi-band LFM waveform generators for applications in multi-functional modern radar systems.

The Design of a 1-6 GHz Broadband Double-Balanced Mixer.

This brief proposes a 1-6 GHz broadband double-balanced mixer. On the basis of the standard Marchand balun mixer, two techniques to enhance the performance of the mixer are proposed. Firstly, by loading capacitors, the amplitude and phase imbalance of the balun at high frequencies are improved, thereby expanding the relative bandwidth of the mixer by 0.1. Secondly, by cascading a first-order LC filter at the intermediate-frequency (IF) port, the leakage of radio frequency (RF) and local oscillator (LO) signals at the IF port is reduced by approximately 8 dB and 7 dB, respectively. This brief analyzes the various parameters that affect balun indicators and designs a broadband balun with a double-layer spiral line structure. As a result of these technologies, a 1-6 GHz double-balanced mixer is realized. The chip area is 1.13 mm × 0.95 mm, and the isolation between the IF port and the RF port is 34 dB and yields 13.5 dBm input power at 1 dB compression point (IP1 dB). The chip is fabricated via the GaAs pHEMT process.

A review of RFID Tag based on TFT

Radiofrequency identification is a non-contact wireless identification technology that uses wireless radio frequency signals for specific target identification or data transmission and has broad application prospects in the period of the Internet of Things. A thin film transistor is a kind of field-effect transistor which is widely applied in the field of flat panel display, but it can also be applied to RFID technology. This paper first introduces the development history of RFID technology and explains the working principle, then briefly describes the structure and characteristics of TFT. After that, the article summarizes the research progress of RFID tags based on different kinds of TFTs at home and abroad by literature review. It is found that RFID tags based on metal oxide thin film transistors have the most advantages, such as better electrical properties, lower cost, good flexibility, high transparency, etc., which meet the need of people in certain application scenarios. Finally, the application prospects of this tag are presented.

Comment on Redmayne, M.; Maisch, D.R. ICNIRP Guidelines' Exposure Assessment Method for 5G Millimetre Wave Radiation May Trigger Adverse Effects. Int. J. Environ. Res. Public Health 2023, 20, 5267.

This article discusses the contention in the commented-upon paper that Brillouin precursors generated by 5G New Radio (5G NR) and other cellular systems are a possible cause of tissue damage at deeper layers of tissue than the power penetration depth of the carrier frequency. The original theory for Brillouin precursors from pulsed radiofrequency signals (RF-EMF) and speculation about their possible health effects dates back to the 1990's and was based on studies of the propagation of very short (nanosecond) ultrawide-bandwidth RF pulses through water. This assumption is not correct for cellular telephone signals due to their narrow bandwidth. The commented-on paper provides no alternative rationale as to why Brillouin effects should cause tissue damage from RF-EMF radiation from cellular and other communications systems. Other inaccuracies in this paper concerning thermal responses of tissue to RF-EMF are also noted.

Tunable spectrally-flat multi-wavelength cylindrical vector beam fiber laser by using a re-circulating frequency shifter loop with two external injections

We experimentally demonstrate a tunable spectrally -flat multi-wavelength cylindrical vector beam (CVB) fiber laser based on a two-mode long-period fiber grating (TM-LPFG) and a re-circulating frequency shifter loop with two external injections. The TM-LPFG is utilized as a mode converter in the re-circulating frequency shifter loop to excite the LP11 mode in the two-mode fiber. By adjusting the wavelength interval of the two injected seed signals, the power of the injected seed signals, and the pump power can produce a flat intensity envelope between the two injected wavelengths. Multi-wavelength CVBs of 12, 18, or 30 channels with a peak-to-peak power variation lower than 2 dB between the two injected wavelengths are obtained with a wavelength spacing of approximately 0.08 nm. The wavelength spacing can be adjusted by changing the frequency of the radio frequency signal. Multi-wavelength CVBs of 14 channels with a peak-to-peak power variation lower than 2 dB between the two injected wavelengths are obtained with a wavelength spacing of approximately 0.2 nm. The purity of the generated multi-wavelength CVBs is higher than 98%. Due to the absence of interference-type filter elements, the multi-wavelength CVB fiber laser exhibits excellent stability. The maximum wavelength deviation of each channel is less than 6.5 pm, and the maximum intensity fluctuation is less than 0.7 dB over a period of one hour.

38 Application of Real-Time Location Systems for Automated Behavioral Monitoring in Sheep

Abstract Evaluating how, when, and where an animal moves in their environment can provide insights into their health and welfare states. Spatial tracking systems for monitoring sheep are less well developed than accelerometer-based systems, but have the potential to add important behavioral information to Precision Livestock Farming (PLF) applications. Global Positioning Systems (GPS) can be used to track pasture-based animals, but are not suitable to establish indoor locations. Real-Time Location Systems (RTLS) are a recent development that use radio-frequency signals emitted by tags to track the indoor location of an animal online and continuously. Raw RTLS data in the form of x, y coordinates can be used to calculate multiple spatial metrics, including velocity, acceleration, distance moved, the proportion of time an animal spends in different functional areas, social network attributes, and range size. RTLS can also be integrated with accelerometers to potentially improve predictive accuracy of behavioral algorithms. RTLS have been primarily tested and validated for indoor tracking of dairy cattle and have been used to assess behavioral time budgets and to detect space-use changes related to estrus or health status. For on-farm applications, these data must be transformed into threshold-based algorithms to generate notifications on changes in the health or welfare status of an animal. Algorithm development needs to account for confounding factors such as breed, sex, age, parity, and stage of lactation, as well as environmental variability that can influence the performance of decision-making tools under real-life conditions. In addition, independent validation studies of commercial systems are lacking, and many calculate positioning error with stationary objects rather than moving animals, potentially inflating accuracy estimates. In conclusion, there is growing empirical support for the use of RTLS as a PLF technology for on-farm animal health and welfare assessment. However, research is needed to validate these systems in sheep and identify spatial metrics that are accurate and robust predictors of health and welfare states for commercial applications.

Design of a Low-Cost, Self-Adaptive and MRI-Compatible Cardiac Gating System.

Cardiac gating, synchronizing medical scans with cardiac activity, is widely used to make quantitative measurements of physiological events and to obtain high-quality scans free of pulsatile artefacts. This can provide important information for disease diagnosis, targeted control of medical microrobots, etc. The current work proposes a low-cost, self-adaptive, MRI-compatible cardiac gating system. The system and its processing algorithm, based on the monitoring and analysis of blood pressure waveforms, are proposed. The system is tested in an in vitro experiment and two living pigs using four-dimensional (4D) flow magnetic resonance imaging (MRI) and two-dimensional phase-contrast (2D-PC) sequences. in vitro and in vivo experiments reveal that the proposed system can provide stable cardiac synchronicity, has good MRI compatibility, and can cope with the fringe magnetic field of the MRI scanner, radiofrequency signals during image acquisition, and heart rate changes. High-resolution 4D flow imaging is successfully acquired both in vivo and in vitro. The difference between the 2D and 4D measurements is ≤ 21%. The incidence of false triggers is 0% in all tests, which is unattainable for other known cardiac gating methods. The system has good MRI compatibility and can provide a stable and accurate trigger signal based on pressure waveform. It opens the door to applications where the previous gating methods were difficult to implement or not applicable.

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.