Intentional Modulation Research Articles

Intentional maintenance of antiphase bimanual pattern at transition frequency: Is it associated with inhibition processes?

This study aimed at demonstrating the intentional modulation of bimanual coordination dynamics at transition frequency and determining whether it is associated with perceptual and/or motor inhibition capacities. Healthy adults (N = 29) performed in a random order: i) bimanual anti-phase (AP) movements at the maximal individual transition frequency, with the instruction to either let go, or intentionally maintain the initial movement pattern and oppose to the spontaneous transition to in-phase (IP) movements, and ii) The Motor and Perceptual Inhibition Test, giving separate scores for perceptual and motor inhibition. Results showed that in the intentional condition participants were able to delay (more movement cycles before the transition) and suppress (more trials without transition) the spontaneous transition from AP to IP. A statistically significant, though weak, correlation was found between motor performance and perceptual inhibition scores. We interpreted our findings as an indicator of the presence of an inhibitory mechanism underlying intentional dynamics that is partially associated to perceptual inhibition in healthy adults. This could have implications in populations with compromised inhibitory capacities, which might entail motor repercussions, and suggests the possibility of using bimanual coordination as means to stimulate both cognitive and motor capacities.

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Extensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalization ability, we analytically investigate how intentional modulation and unintentional modulation affect moments. A framework for extracting moment-based fingerprint features is proposed through fine-segmenting slices. Fingerprint features are extracted, followed by segmenting signals into a combination of sinewaves and calculating their moments. The proposed framework shows advantages in mechanism interpretability and generalizing ability. Simulations and experiments verified the correctness and effectiveness of the proposed framework.

Specific emitter identification based on frequency and amplitude of the signal kurtosis

Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.

Concentrate on Hardware Imperfection via Aligning Reconstructed States

Specific emitter identification (SEI) is to authenticate communication devices via analyzing unintentional modulation (UIM) from hardware imperfection. A great challenge existing in current methods is the interference of intentional modulation (IM) and channel noise. To address this issue, this letter firstly analyzes how IM, UIM, and additive noise (AN) affect reconstructed states, which describes the dynamic characteristics of communication devices. The findings indicate that IM and UIM bring a scaling transform, while AN corresponds to a translation transform. Based on theoretical analysis, a novel SEI method concentrating on UIM is proposed. An advanced PointNet architecture is designed with stacked spatial transformer networks (STN), namely PointNet-Alignment Point by Point (PointNet-APBP). PointNet-APBP aligns reconstructed states into a canonical space and extracts spatial interaction as fingerprint features. Simulated and real-world experiments validate that the proposed method can mitigate the effects of IM as well as AN, concentrate feature extractors on UIM, and in turn achieve higher accuracy and stronger robustness.

Intrinsic Low-Dimensional Nonlinear Manifold Structure of Radio Frequency Signals

Nonlinear dynamic features are crucial in specific emitter identification (SEI), which works based on the nonlinearity of the emitter. Nevertheless, its underlying principles and stability have not been systematically studied, so these methods are close to a black box, making the recognition performance of the system unstable in applications. In this study, we provide an in-depth analysis of its mechanism and theoretical model, extracting, for the first time, the intrinsic low-dimensional nonlinear manifold structure (IMS) of radio frequency signals based on manifold learning and analyzing its robustness and distinctiveness for SEI. Hardware type and intentional modulation are found to determine the overall shape of IMS, and the device-specific information for SEI is subtle and distributed locally on IMS. Notably, sampling-related random disturbances cause the distortion, zooming, or rotation of IMS, which lowers the identification accuracy by a range of 1.74%–52%, depending on the feature, device type, signal type, and other factors.

Unintentional modulation microstructure enlargement

Radio frequency fingerprinting (RFF) is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level, device-specific imperfections. The RFF-related information is mainly in the form of unintentional modulation (UIM), which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation (IM). It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF. This paper proposes a UIM microstructure enlargement (UMME) method based on feature-level adaptive signal decomposition (ASD), accompanied by autocorrelation and cross-correlation analysis. The common IM part is evaluated by analyzing a newly-defined benchmark feature. Three different indexes are used to quantify the similarity, distance, and dependency of the RFF features from different devices. Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode. The visual image qualitatively shows the magnification of feature differences; different indicators quantitatively describe the changes in features. Compared with the original RFF feature, recognition results based on the Gaussian mixture model (GMM) classifier further validate the effectiveness of the proposed algorithm.

Radar Emitter Identification Based on Spectral Resemblance Coefficient

The traditional radar emitter identification method based on the five parameters of the pulse is difficult to achieve the desired effect in today’s abnormally dense electromagnetic environment. Modern radars often impose intentional modulation in the signal to improve the anti-jamming and low interception performance. Therefore, a radar emitter identification method based on the spectrum resemblance coefficient is proposed in this paper, which reflects the approximation degree between the signal spectrum and various basis functions by calculating the resemblance coefficients between them. Simulation results show that the method proposed in this paper has strong anti-interference capability and can effectively distinguish different types of intra-pulse modulated radar signals.

Analysis of Guidance Laws With Nonmonotonic Line-of-Sight Rate Convergence

This study presents analyses of guidance laws that involve non-monotonic convergence in heading error from a new perspective based on an advanced stability concept. Pure proportional navigation with range-varying navigation gain is considered, and the gain condition to guarantee asymptotic convergence to the collision course is investigated while allowing the heading error to exhibit patterns that involve intermediate diversion. The extended stability criterion considered in this study allows local increase of the Lyapunov function in some finite intervals, which is less conservative than the standard Lyapunov stability theorem. The existing guidance laws involving intentional modulation of the heading error as well as the design of the navigation gain are discussed with respect to the new stability criterion.

Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition.

Ibrutinib (Imbruvica; PCI-32765) is an orally administered inhibitor of Bruton's tyrosine kinase that has transformed the treatment of B-cell malignancies. However, ibrutinib has very low oral bioavailability that contributes to significant variability in systemic exposure between patients, and this has the potential to affect both efficacy and toxicity. We hypothesized that the oral bioavailability of ibrutinib is limited by CYP3A isoform-mediated metabolism, and that this pathway can be inhibited to improve the pharmacokinetic properties of ibrutinib. Pharmacokinetic studies were performed in wild-type mice and mice genetically engineered to lack all CYP3A isoforms [CYP3A(-/-)] that received ibrutinib alone or in combination with CYP3A inhibitors cobicistat or ketoconazole. Computational modeling was performed to derive doses of ibrutinib that, when given after a CYP3A inhibitor, results in therapeutically-relevant drug levels. Deficiency of CYP3A in mice was associated with a ~10-fold increase in the area under the curve of ibrutinib. This result could be phenocopied by administration of cobicistat before ibrutinib in wild-type mice, but cobicistat did not influence levels of ibrutinib in CYP3A(-/-) mice. Population pharmacokinetic and prospectively validated physiologically-based pharmacokinetic models established preclinical and clinical doses of ibrutinib that could be given safely in combination with cobicistat without negatively affecting anti-leukemic properties. These findings signify a dominant role for CYP3A-mediated metabolism in the elimination of ibrutinib, and suggest a role for pharmacological inhibitors of this pathway to intentionally modulate the plasma levels and improve the therapeutic use of this clinically important agent.

Artificial Reflex Arc: An Environment-Adaptive Neuromorphic Camouflage Device

Cephalopods can swiftly merge into the surroundings by changing their skin color even without the involvement of brains. Inspired by this ability, we designed a tunable, environment- adaptive camouflagic artificial reflex arc that can independently activate electrochromism in response to optical stimuli. The receptor of this arc consists in a light-stimulated synaptic transistor (LSST) that also performs synaptic functions, e.g., excitatory post-synaptic current (EPSC) generation, paired-pulse facilitation (PPF), high-pass filtering, and modulation. Amplified EPSC causes color transform of an electrochromic device (ECD) upon external optical stimuli. We emulate the phenomenon that an octopus changes color according to the background light with or without intentional modulation to demonstrate the camouflage effect of our device.

Changes in intersegmental stability during gait in patients with spastic cerebral palsy.

Dysfunction in peripheral and neural structure with spastic cerebral palsy (CP) causes impaired performance and stability of various behaviors. Recent progress of quantification methods for the stability properties, which is based on the uncontrolled manifold hypothesis, has been applied to various neurological disorders. A prior study revealed that the ability for purposeful regulation of stability properties is weakened with CP during finger and hand actions. Successive regulation of stability properties is crucial for human locomotion; therefore, it is imperative to quantify the changes in the intersegmental coordination as to the stable performance in CP individuals during gait. We hypothesized that (1) Spastic CP group will show smaller step length and gait velocity with larger variability, and (2) Spastic CP group will show no changes in average stability indices for both the COM and head position stabilization, while the smaller difference between stable and unstable posture during the gait cycle. Whole-body kinematic data during walking were collected from CP and control subjects. Step length, velocity, and coefficient of variation (CV) were calculated as spatiotemporal parameters. We quantified the intersegmental stability index in time-series during gait for the stabilization of the whole-body COM and head position. The CP subjects showed smaller step length and velocity with larger CV than the controls. However, the CP group showed a significantly less difference in the stability indices between the single- and double-limb support phases as compared to the controls for both the COM and head position stabilization. Present study is the first to document the quantification of changing intersegmental stability in the spastic CP during locomotion. The dysfunction of intentional modulation of stability properties in CP individuals may be a more common problem, which is not limited to a specific body effector.

Imitation Learning of Hierarchical Driving Model: From Continuous Intention to Continuous Trajectory

One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this letter, we propose a hierarchical driving model with explicit models of continuous intention and continuous dynamics, which decouples the complexity in the observation-to-action reasoning in the human driving data. Specifically, the continuous intention module takes perception to generate a potential map encoded with obstacles and intentions. Then, the potential map is regarded as a condition, together with the current dynamics, to generate a continuous trajectory as output by a continuous function approximator network, whose derivatives can be used for supervision without additional parameters. Finally, our method is validated by both datasets and stimulation, demonstrating that our method has higher prediction accuracy of displacement and velocity and generates smoother trajectories. Our method is also deployed on the real vehicle with loop latency, validating its effectiveness. To the best of our knowledge, this is the first work to produce the driving trajectory using a continuous function approximator network. Our code is available at https://github.com/ZJU-Robotics-Lab/CICT.

Adaptive Membrane Fluidity Modulation: A Feedback Regulated Homeostatic System and Target for Pharmacological Intervention.

Alterations of plasma membrane fluidity are characteristic of many diseases but the intentional modulation of membrane fluidity with drugs has been less studied. We examined the therapeutic potential of the membrane fluidizer diethyl azelate (DEA) and related azelates. The effects of azelates on plasma membrane fluidity and cell signaling were examined in primary human and murine cells and in vivo. Endpoints were queried using single target and multiplexed immunoassays. Unique membrane-fluidizing properties and biomarker signatures suggest that azelates are not prodrugs. DEA decreased cytokine signaling from pattern recognition receptors in human dendritic cells, disabled membrane attack of cholera toxin in vitro, and prevented immunosuppression by anthrax lethal toxin in vitro and in vivo. In the murine sepsis model, DEA increased survival and reduced organ damage. Azelates represent a new class of drugs, membrane active immunomodulators, which target an innate homeostatic mechanism, adaptive membrane fluidity modulation.

Removal of malachite green dye from aqueous solution by adsorbents derived from polyurethane plastic waste

Upcycling polyurethane plastic waste (PUPW) into activated carbon (AC) for the removal of malachite green dye (MG) in wastewater via physical adsorption could provide abundant low-cost adsorbent and facilitate treatment of plastic waste and wastewater simultaneously. For this reason, four types of PUPW-ACs were prepared by various preparation procedures. The properties of PUPW-ACs were characterized using BET, FT-IR, XPS, Raman spectroscopy, and XRD; the adsorption performances of PUPW-ACs were also evaluated. It was found that the properties of PUPW-ACs varied in terms of porosity, microstructures, and surface chemistry. Intentional modulation of PUPW-AC would be possible via preparation procedure adjustment. Two-step preparation procedure (1. carbonization; 2. activation) was recommended to acquire PUPW-AC (i.e., PUPW-AC-C-A) with a high specific surface area of 1034 m2 g−1 and the maximum MG adsorption capacity of 1428 mg g-1. Electrostatic attraction, oxygen-containing groups (especially carboxyl functional groups), and π–π interaction were believed to contribute to the adsorption driving force for the outstanding adsorption performance of MG on PUPW-AC-C-A. The PUPW-AC prepared from PUPW could serve as a promising adsorbent used in MG dye removal from aqueous systems.

Local Disorder-Induced Elevation of Intrinsic Anomalous Hall Conductance in an Electron-Doped Magnetic Weyl Semimetal.

Topological materials are expected to show distinct transport signatures owing to their unique band-inversion characteristic and band-crossing points. However, the intentional modulation of such topological responses through experimentally feasible means has yet to be explored in depth. Here, an unusual elevation of the anomalous Hall effect (AHE) is obtained in electron (Ni)-doped magnetic Weyl semimetals Co_{3-x}Ni_{x}Sn_{2}S_{2}, showing peak values in the anomalous Hall-conductivity, Hall-angle, and Hall-factor at a relatively low doping level of x=0.11. The separation of intrinsic and extrinsic contributions using the TYJ scaling model indicates that such a significant enhancement is dominated by the intrinsic mechanism of the electronic Berry curvature. Theoretical calculations reveal that compared with the Fermi-level shifting from electron filling, a usually overlooked effect of doping, that is, local disorder, imposes a striking effect on broadening of the bands and narrowing of the inverted gap, thus resulting in an elevation of the integrated Berry curvature. Our results not only realize an enhancement of the AHE in a magnetic Weyl semimetal, but also provide a practical design principle for modulating the bands and transport properties in topological materials by exploiting the local disorder effect from doping.

Identification and parameter estimation algorithm of radar signal subtle features

With the rapid development of electronic technology and its widespread application in modern warfare, the modulation methods of individual radar signal sources are more flexible, the parameters are diverse, and the modern battlefield electromagnetic signal environment is complex, so that radar signal recognition based on traditional conventional parameters Technology cannot meet real needs. Among the many radar signal modulation recognition algorithms that have appeared in recent years, neural network theory and intra-pulse analysis algorithms are widely used, but they each have their own shortcomings, and a single method cannot meet the needs of increasingly complex modulation signal recognition and parameter estimation needs. . Based on the above background, the research content of this paper is to identify the subtle characteristics of radar signals and their parameter estimation algorithms. In this paper, two types of typical modulation signals in intentional modulation of radar signals, namely phase-coded (PSK) signals and frequency-modulated (FM) signals, are proposed. A SVEFD algorithm based on classification from coarse to fine. According to the characteristics of the 3 dB bandwidth of the frequency spectrum of the PSK signal and the FM signal, the coarse classification between the classes is performed first. Finally, through experimental simulations, the results show that the correct recognition probability of the SVEFD algorithm at a lower signal-to-noise ratio is much higher than the m algorithm and also higher than the SVD algorithm. When the signal-to-noise ratio is greater than 1 dB, the average recognition rate reaches 94%, which proves that The effectiveness of the proposed SVEFD algorithm. When the signal-to-noise ratio is .1 dB, in addition to maintaining the correct recognition rate of more than 90% for the LFM and COSTAS signals, the other six types of signals have low correct recognition rates, so the SVEFD algorithm has advantages in comparison.

Discriminative adversarial networks for specific emitter identification

The crucial issue in specific emitter identification (SEI) is the extraction of fingerprint features which can represent the differences among individual emitters of the same type. Considering that these emitters have the same intentional modulation on pulse, the fingerprint features originated from the unintentional modulation on pulse are extremely imperceptible and less detectable. However, existing feature extractions, either traditional handcrafted ones or deep learning based ones, have failed to ensure that their extracted features are rich in the unintentional modulation information (UMI) and not interfered by the intentional modulation information (IMI). To adequately take advantage of deep learning to address SEI, this Letter proposes a novel neural networks, named discriminative adversarial networks (DAN). By demarcating a clear boundary between IMI and UMI, DAN isolates IMI and thus reduces the burden of UMI mining during its feature extraction process. Experimental results demonstrate that DAN outperforms most methods in the literature.

Intentionally remembering or forgetting own- and other-race faces: Evidence from directed forgetting.

People are better at remembering faces of their own relative to another ethnic group. This so-called own-race bias (ORB) has been explained in terms of differential perceptual expertise for own- and other-race faces or, alternatively, as resulting from socio-cognitive factors. To test predictions derived from the latter account, we examined item-method directed forgetting (DF), a paradigm sensitive to an intentional modulation of memory, for faces belonging to different ethnic and social groups. In a series of five experiments, participants during learning received cues following each face to either remember or forget the item, but at test were required to recognize all items irrespective of instruction. In Experiments 1 and 5, Caucasian participants showed DF for own-race faces only while, in Experiment 2, East Asian participants with considerable expertise for Caucasian faces demonstrated DF for own- and other-race faces. Experiments 3 and 4 found clear DF for social in- and outgroup faces. These results suggest that a modulation of face memory by motivational processes is limited to faces with which we have acquired perceptual expertise. Thus, motivation alone is not sufficient to modulate memory for other-race faces and cannot fully explain the ORB.

Intentional Modulation of Portal Venous Pressure by Splenectomy Saves the Patient with Liver Failure and Portal Hypertension After Major Hepatectomy: Is Delayed Splenectomy an Acceptable Therapeutic Option for Secondary Portal Hypertension?

Patient: Female, 56Final Diagnosis: Secondary portal hypertensionSymptoms: Intractable ascitesMedication: —Clinical Procedure: SplenectomySpecialty: Gastroenterology and HepatologyObjective:Unusual clinical courseBackground:Major or aggressively-extended hepatectomy (MAEH) may cause secondary portal hypertension (PH), and postoperative liver failure (POLF) and is often fatal. Challenges to prevent secondary PH and subsequent POLF, such as shunt creation and splenic arterial ligation, have been reported. However, these procedures have been performed simultaneously only during the initial MAEH.Case Report:A 58-year-old female with chronic hepatitis C developed a solitary hepatic cellular carcinoma with portal tumor thrombosis. Blood examination and imaging revealed a decreased platelet count and splenomegaly. Her liver viability was preserved, and collaterals did not develop, and her tumor thrombosis forced us to perform a right hepatectomy from an oncological standpoint. The estimated volume of her liver remnant was 51.8%. A large volume of ascites and pleural effusion were observed on post-operative day (POD) 3, and ascetic infection occurred on POD 14. Hepatic encephalopathy was observed on POD 16. According to the post-operative development of collaterals due to secondary PH, submucosal bleeding in the stomach occurred on POD 37. Though it is unclear whether delayed portal venous pressure (PVP) modulation after MAEH is effective, a therapeutic strategy for recovery from POLF may involve PVP modulation to resolve intractable PH. We performed a splenectomy on POD 41 to reduce PVP. The initial PVP value was 32 mm Hg, and splenectomy decreased PVP to 23 mm Hg. Thereafter, she had a complete recovery from POLF.Conclusions:Our thought-provoking case is the first successfully-treated case of secondary PH and POLF after MAEH, achieved by delayed splenectomy for PVP modulation.

Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry

Specific emitter identification has attracted much attention of researchers in recent decades. This technology distinguishes different emitters by exploiting the unintentional modulation (UIM) contained in the signal. Heretofore, evaluation of UIM for pulse compression signals had not been properly solved, since previous evaluating results generally rely heavily on intentional modulation, and cannot solely and sufficiently describe the characteristics of UIM. In this study, the authors propose a novel feature extraction method which can naturally suppress intentional modulation, and meanwhile characterise the UIM. The proposed method utilises the facts that spectrum symmetry is an intrinsic and widespread property of modulated signals; and this symmetry will break when the UIM arises. By properly evaluating the spectrum asymmetry, the frequency-domain asymmetry distribution feature is derived. Experiments on real-world data demonstrate that the proposed feature outperforms conventional features in dealing with pulse compression signals. This study offers an effective way to extract UIM features from modulated signals, thus having broad applications to modern radar emitters.