Towards Self-supervised Learning for Multi-function Radar Behavior State Detection and Recognition

<div>The analysis of intercepted multi-function radar (MFR) signals has gained considerable attention in the field of cognitive electronic reconnaissance. With the rapid development of MFR, the switch between different work modes is becoming more flexible, increasing the agility of pulse parameters. Most of the existing approaches for recognizing MFR behaviors heavily depend on prior information, which can hardly be obtained in a non-cooperative way. This study develops a novel hierarchical contrastive self-supervise-based method for segmenting and clustering MFR pulse sequences. First, a convolutional neural network (CNN) with a limited receptive field is trained in a contrastive way to distinguish between pulse descriptor words (PDW) in the original order and the samples created by random permutations to detect the boundary between each radar word and perform segmentation. Afterward, the K-means++ algorithm with cosine distances is established to cluster the segmented PDWs according to the output vectors of the CNN’s last layer for radar words extraction. This segmenting and clustering process continues to go in the extracted radar word sequence, radar phase sequence, and so on, finishing the automatic extraction of MFR behavior states in the MFR hierarchical model. Simulation results show that without using any labeled data, the proposed method can effectively mine distinguishable patterns in the sequentially arriving PDWs and recognize the MFR behavior states under corrupted, overlapped pulse parameters.</div>

Towards Self-supervised Learning for Multi-function Radar Behavior State Detection and Recognition

<div>The analysis of intercepted multi-function radar (MFR) signals has gained considerable attention in the field of cognitive electronic reconnaissance. With the rapid development of MFR, the switch between different work modes is becoming more flexible, increasing the agility of pulse parameters. Most of the existing approaches for recognizing MFR behaviors heavily depend on prior information, which can hardly be obtained in a non-cooperative way. This study develops a novel hierarchical contrastive self-supervise-based method for segmenting and clustering MFR pulse sequences. First, a convolutional neural network (CNN) with a limited receptive field is trained in a contrastive way to distinguish between pulse descriptor words (PDW) in the original order and the samples created by random permutations to detect the boundary between each radar word and perform segmentation. Afterward, the K-means++ algorithm with cosine distances is established to cluster the segmented PDWs according to the output vectors of the CNN’s last layer for radar words extraction. This segmenting and clustering process continues to go in the extracted radar word sequence, radar phase sequence, and so on, finishing the automatic extraction of MFR behavior states in the MFR hierarchical model. Simulation results show that without using any labeled data, the proposed method can effectively mine distinguishable patterns in the sequentially arriving PDWs and recognize the MFR behavior states under corrupted, overlapped pulse parameters.</div>

Composite pulses for high fidelity population transfer in three-level systems

In this work, we propose a composite pulses scheme by modulating phases to achieve high fidelity population transfer in three-level systems. To circumvent the obstacle that not enough variables are exploited to eliminate the systematic errors in the transition probability, we put forward a cost function to find the optimal value. The cost function is independently constructed either in ensuring an accurate population of the target state, or in suppressing the population of the leakage state, or both of them. The results demonstrate that population transfer is implemented with high fidelity even when existing the deviations in the coupling coefficients. Furthermore, our composite pulses scheme can be extensible to arbitrarily long pulse sequences. As an example, we employ the composite pulses sequence for achieving the three-atom singlet state in an atom-cavity system with ultrahigh fidelity. The final singlet state shows robustness against deviations and is not seriously affected by waveform distortions. Also, the singlet state maintains a high fidelity under the decoherence environment.

Increasing of the Accuracy of Signalsʼ Time Parameters Measuring Using Double Pulse Trains

In modern diagnostics, much attention is paid to measuring of time parameters, as well as their change over time. The purpose of this work is to develop a method for measuring of time intervals which made it possible to increase the measurement accuracy by reducing errors associated with the instability of main parameters of the pulse signal.In the most of approaches used, the error associated with the instability of main parameters of signals under study is not enough taken into account. As an alternative, a spectral method is proposed in which the measurement of time intervals, as well as their changes, is performed based on the analysis of pulse sequences formed on the basis of characteristic points of the measured signal. For this a double pulse sequence was considered, an equation for the amplitudes of its spectral components was obtained, and in accordance with this it was determined that the delay time between double pulses is the most informative parameter.Using the Mathcad software, an analysis of the sensitivity regions was carried out for the change in the main parameters of the pulse sequence, namely the repetition rate, as the main destabilizing factor.As a result of the implementation of the developed technique, a structural diagram of the measuring system is proposed and an analysis of the measurement error associated with the instability of the main parameters of the pulse sequence is carried out. This error is estimated to be less than 0.01 %.The considered method makes it possible to increase the accuracy of measuring time intervals due to the almost complete elimination of the influence of the instability of the reference frequency and the amplitude of the generated pulses which is unattainable with modern hardware, including digital signal processing.

Multifrequency Study of Periodic Nulling and Subpulse Drifting in Pulsar J2048−1616

We have carried out a detailed study of single-pulse emission from the pulsar J2048−1616 (B2045−16), observed at 732, 1369, and 3100 MHz frequencies using the Parkes 64 m radio telescope. The pulsar possesses three well-resolved emission components, with the central component resembling core emission. The single pulses show the presence of two distinct periodic modulations using fluctuation spectral analysis. About 12% nulls are found to create alternating bunches of nulls and bursts in a quasiperiodic manner with longer periodicities of 83, 28, and 14 rotational periods for simultaneous observations at 732 and 3100 MHz. At 1369 MHz, the quasiperiodic nulling is detected, as well, to modulate across the entire profile both in the core and conal components simultaneously with a fluctuation rate of about 50 rotational periods, and the nulling fraction is estimated to be around 10%. Additionally, the quasiperiodic modulations are significantly dependent on time. In addition to nulling, the pulsar also presents subpulse drifting in the single-pulse sequences with shorter periodicity. The subpulse drifting is presented in the conal components and is absent in the central core emission. The leading component is modulated in longitude with a period of three pulses. The trailing component remains phase stationary within the pulse window but periodically modulates in amplitude with a period of three pulses. Finally, possible physical mechanisms are discussed.

All-optical noise spectroscopy of a solid-state spin

Noise spectroscopy elucidates the fundamental noise sources in spin systems, which is essential for developing spin qubits with long coherence times for quantum information processing, communication, and sensing. But noise spectroscopy typically relies on microwave coherent spin control to extract the noise spectrum, which becomes infeasible when there are high-frequency noise components stronger than the available microwave power. Here, we demonstrate an alternative all-optical approach to performing noise spectroscopy. Our approach utilises coherent Raman rotations of the spin state with controlled timing and phase to implement Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences. Analysing the spin dynamics under these sequences enables us to extract the noise spectrum of a dense ensemble of nuclear spins interacting with a single spin in a quantum dot, which has thus far only been modelled theoretically. By providing large spectral bandwidths of over 100 MHz, our Raman-based approach could serve as an important tool to study spin dynamics and decoherence mechanisms for a broad range of solid-state spin qubits.

Micromotion minimization using Ramsey interferometry

We minimize the stray electric field in a linear Paul trap quickly and accurately, by applying interferometry pulse sequences to a trapped ion optical qubit. The interferometry sequences are sensitive to the change of ion equilibrium position when the trap stiffness is changed, and we use this to determine the stray electric field. The simplest pulse sequence is a two-pulse Ramsey sequence, and longer sequences with multiple pulses offer a higher precision. The methods allow the stray field strength to be minimized beyond state-of-the-art levels. Using a sequence of nine pulses we reduce the 2D stray field strength to (10.5±0.8)mVm-1 in 11s measurement time. The pulse sequences are easy to implement and automate, and they are robust against laser detuning and pulse area errors. We use interferometry sequences with different lengths and precisions to measure the stray field with an uncertainty below the standard quantum limit. This marks a real-world case in which quantum metrology offers a significant enhancement. Also, we minimize micromotion in 2D using a single probe laser, by using an interferometry method together with the resolved sideband method; this is useful for experiments with restricted optical access. Furthermore, a technique presented in this work is related to quantum protocols for synchronizing clocks; we demonstrate these protocols here.

Increased heterogeneity and task-related reconfiguration of functional connectivity within a lexicosemantic network in autism

Autism spectrum disorder (ASD) is highly heterogeneous in etiology and clinical presentation. Findings on intrinsic functional connectivity (FC) or task-induced FC in ASD have been inconsistent including both over- and underconnectivity and diverse regional patterns. As FC patterns change across different cognitive demands, a novel and more comprehensive approach to network architecture in ASD is to examine the change in FC patterns between rest and task states, referred to as reconfiguration. This approach is suitable for investigating inefficient network connectivity that may underlie impaired behavioral functioning in clinical disorders. We used functional magnetic resonance imaging (fMRI) to examine FC reconfiguration during lexical processing, which is often affected in ASD, with additional focus on interindividual variability. Thirty adolescents with ASD and a matched group of 23 typically developing (TD) participants completed a lexicosemantic decision task during fMRI, using multiecho-multiband pulse sequences with advanced BOLD signal sensitivity and artifact removal. Regions of interest (ROIs) were selected based on task-related activation across both groups, and FC and reconfiguration were compared between groups. The ASD group showed increased interindividual variability and overall greater reconfiguration than the TD group. An ASD subgroup with typical performance accuracy (at the level of TD participants) showed reduced similarity and typicality of FC during the task. In this ASD subgroup, greater FC reconfiguration was associated with increased language skills. Findings suggest that intrinsic functional networks in ASD may be inefficiently organized for lexicosemantic decisions and may require greater reconfiguration during task processing, with high performance levels in some individuals being achieved through idiosyncratic mechanisms.