High-order Signals Research Articles

Gender difference on loudness perception and evaluation

Factors affecting gender differences in loudness perception and evaluation were examined from three perspectives: verbal criteria, brain perception (ratio scale evaluation), and cultural background. When the verbal criteria such as "soft" and "loud" was used for evaluation of perceived loudness, the gender differences were observed for Japanese, Chinese and Malaysian participants. Meanwhile, the gender difference was observed only for Chinese men and women when the ratio scale was used for evaluation of perceived loudness.However, this tendency was only observed for some stimuli, thus the experiment was conducted again with Chinese participants under more detailed evaluation conditions, and no clear gender difference was observed as in the case of Japanese and Malaysian participants. From these result, it was suggested that the gender difference in loudness perception might be caused by the process of more higher-order auditory signal processing that uses verbal expression.

Rapid emergence of latent knowledge in the sensory cortex drives learning.

Rapid learning confers significant advantages to animals in ecological environments. Despite the need for speed, animals appear to only slowly learn to associate rewarded actions with predictive cues1-4. This slow learning is thought to be supported by a gradual expansion of predictive cue representation in the sensory cortex2,5. However, evidence is growing that animals learn more rapidly than classical performance measures suggest6-8, challenging the prevailing model of sensory cortical plasticity. Here, we investigated the relationship between learning and sensory cortical representations. We trained mice on an auditory go/no-go task that dissociated the rapid acquisition of task contingencies (learning) from its slower expression (performance)7. Optogenetic silencing demonstrated that the auditory cortex (AC) drives both rapid learning and slower performance gains but becomes dispensable at expert. Rather than enhancement or expansion of cue representations9, two-photon calcium imaging of AC excitatory neurons throughout learning revealed two higher-order signals that were causal to learning and performance. First, a reward prediction (RP) signal emerged rapidly within tens of trials, was present after action-related errors only early in training, and faded at expert levels. Strikingly, silencing at the time of the RP signal impaired rapid learning, suggesting it serves an associative and teaching role. Second, a distinct cell ensemble encoded and controlled licking suppression that drove the slower performance improvements. These two ensembles were spatially clustered but uncoupled from underlying sensory representations, indicating a higher-order functional segregation within AC. Our results reveal that the sensory cortex manifests higher-order computations that separably drive rapid learning and slower performance improvements, reshaping our understanding of the fundamental role of the sensory cortex.

Adaptive simulation of digital signal transmission

High-order modulated signals have lower anti-interference ability than low-order modulated signals with the same modulation method and conditions. In situations with low signal-to-noise ratios, the quality of high-order signals can degrade significantly. An algorithm capable of intelligently switching the modulation order based on the current signal-to-noise ratio can effectively address this issue. This paper presents an adaptive signal transmission algorithm that intelligently selects different orders of Phase shift keying (PSK) modulation depending on varying signal noise ratio (SNR) conditions, while ensuring the user-specified upper limit of bit error rate (BER). This approach guarantees signal transmission quality. The algorithm is implemented in Python and involves simulating the relationship curve between SNR and BER for different PSK orders. This simulation is combined with theoretical transmission rate analysis, resulting in an adaptive algorithm that intelligently switches modulation methods under complex conditions to meet transmission requirements. The proposed algorithm dynamically adapts to diverse user requirements for signal-to-noise ratios in various environments. It achieves this by adjusting the modulation order, calculating theoretical transmission times based on the given signal frequency, and ultimately verifying the actual bit error rate through transmission and testing. Upon testing, this design successfully achieved its intended goals.

Molecularly imprinted nanoparticles reveal regulatory scaffolding features in Pyk2 tyrosine kinase.

Pyk2 is a multi-domain non-receptor tyrosine kinase that serves dual roles as a signaling enzyme and scaffold. Pyk2 activation involves a multi-stage cascade of conformational rearrangements and protein interactions initiated by autophosphorylation of a linker site. Linker phosphorylation recruits Src kinase, and Src-mediated phosphorylation of the Pyk2 activation loop confers full activation. The regulation and accessibility of the initial Pyk2 autophosphorylation site remains unclear. We employed peptide-binding molecularly imprinted nanoparticles (MINPs) to probe the regulatory conformations controlling Pyk2 activation. MINPs differentiating local structure and phosphorylation state revealed that the Pyk2 autophosphorylation site is protected in the autoinhibited state. Activity profiling of Pyk2 variants implicated FERM and linker residues responsible for constraining the autophosphorylation site. MINPs targeting each Src docking site disrupt the higher-order kinase interactions critical for activation complex maturation. Ultimately, MINPs targeting key regulatory motifs establish a useful toolkit for probing successive activational stages in the higher-order Pyk2 signaling complex.

Compression of Higher-Order Ambisonic Signals Using Directional Audio Coding

Compression of Higher-Order Ambisonic Signals Using Directional Audio Coding

Hybrid Constellation Shaping 64QAM Based on Hexagonal Lattice of Constellation Subset

Increasing demand for higher-speed and large-capacity data communications has driven the development of constellation shaping technology. This paper proposes a hybrid constellation shaping scheme for 64-quadrature amplitude modulation (64QAM) based on hexagonal lattice of a constellation subset. The proposed scheme aims to enhance the nonlinear tolerance of higher-order modulated signals and further improve the constellation shaping gain. The initial quantitative characterization of the constellation is firstly performed based on the hexagonal lattice structure. Then, the objective function of maximizing constellation figure of merits (CFM) is utilized to determine the position distribution of constellation points, resulting in the generation of the geometric shaping-64QAM (GS-64QAM) signal. Finally, according to concentric hexagonal layers, all constellation points are divided into multiple subsets where points within the same subset are assigned the same probability, and the hybrid shaping-64QAM (HS-64QAM) signal is generated. To validate the effectiveness of the proposed scheme, the experimental verification was demonstrated in a 120 Gbit/s multi-span coherent optical communication system. Experimental results indicate that, at the soft-decision forward error correction threshold, HS-64QAM achieves an optical signal-to-noise ratio (OSNR) gain of 1.9 dB and 4.1 dB over uniform GS-64QAM in back-to-back and 375 km transmission scenarios, respectively. Furthermore, HS-64QAM achieves an OSNR gain of 2.7 dB and 7.6 dB over uniform Square-64QAM in back-to-back and 375 km transmission scenarios, respectively.

Dirac signal processing of higher-order topological signals

Higher-order networks can sustain topological signals which are variables associated not only to the nodes, but also to the links, to the triangles and in general to the higher dimensional simplices of simplicial complexes. These topological signals can describe a large variety of real systems including currents in the ocean, synaptic currents between neurons and biological transportation networks. In real scenarios topological signal data might be noisy and an important task is to process these signals by improving their signal to noise ratio. So far topological signals are typically processed independently of each other. For instance, node signals are processed independently of link signals, and algorithms that can enforce a consistent processing of topological signals across different dimensions are largely lacking. Here we propose Dirac signal processing, an adaptive, unsupervised signal processing algorithm that learns to jointly filter topological signals supported on nodes, links and triangles of simplicial complexes in a consistent way. The proposed Dirac signal processing algorithm is formulated in terms of the discrete Dirac operator which can be interpreted as ‘square root’ of a higher-order Hodge Laplacian. We discuss in detail the properties of the Dirac operator including its spectrum and the chirality of its eigenvectors and we adopt this operator to formulate Dirac signal processing that can filter noisy signals defined on nodes, links and triangles of simplicial complexes. We test our algorithms on noisy synthetic data and noisy data of drifters in the ocean and find that the algorithm can learn to efficiently reconstruct the true signals outperforming algorithms based exclusively on the Hodge Laplacian.

MMKDGAT: Multi-modal Knowledge graph-aware Deep Graph Attention Network for remote sensing image recommendation

In the era of remote sensing (RS) big data, in order to alleviate the time cost of acquiring RS images, recommending RS images that meet users’ individual needs continues to be an urgent technology. However, the technology accomplished to date has two main problems: (1) they rely on the users’ queries and thus lack initiative and cannot tap the users’ potential interests and (2) they restrict the users’ preferences to temporal and/or spatial information while ignoring other attributes and are not compatible with visual information. In an effort to fully explore the features of RS images and thereby achieve accurate active recommendations, in this paper we propose a new Multi-modal Knowledge graph-aware Deep Graph Attention Network (MMKDGAT) which we built upon graph convolutional networks. Specifically, we first constructed a multi-modal knowledge graph (MMKG) for RS images to integrate their various attributes as well as visual information, and then we conduct deep relational attention-based information aggregation to enrich the node representations with multi-modal information and higher-order collaborative signals. Our extensive experiments on two simulated RS image recommendation datasets demonstrated that our MMKDGAT achieved noticeable improvement over several state-of-the-art methods in so far as active recommendation accuracy and cold-start recommendation.

Higher-Order Multidimensional and Pump-Probe Spectroscopies.

Transient absorption and coherent two-dimensional spectroscopy are widely established methods for the investigation of ultrafast dynamics in quantum systems. Conventionally, they are interpreted in the framework of perturbation theory at the third order of interaction. Here, we discuss the potential of higher-(than-third-)order pump-probe and multidimensional spectroscopy to provide insight into excited multiparticle states and their dynamics. We focus on recent developments from our group. In particular, we demonstrate how phase cycling can be used in fluorescence-detected two-dimensional spectroscopy to isolate higher-order spectra that provide information about highly excited states such as the correlation of multiexciton states. We discuss coherently detected fifth-order 2D spectroscopy and its power to track exciton diffusion. Finally, we show how to extract higher-order signals even from ordinary pump-probe experiments, providing annihilation-free signals at high excitation densities and insight into multiexciton interactions.

To see further: Knowledge graph-aware deep graph convolutional network for recommender systems

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs is one of the most commonly used approaches for learning the representation of users and items in modern recommender systems. However, these models perform poorly in recommending high-order items due to the over-smoothing when the GCN deepens and the lack of connectivity between the cold-start items and the target user. To improve the ability to recommend higher-order items, we propose our Knowledge graph-aware Deep Graph Convolutional Network with Initial Residual Connection (KDGCN-IC) and Knowledge graph-aware Deep Graph Convolutional Network with Dense Connection (KDGCN-DC) methods. First, we introduce an item knowledge graph to rebuild the connectivity between nodes in the user-item graph, especially the users and the cold-start items. Then, we present our methods for improving the information flow by reusing features to alleviate the over-smoothing issue to deepen the GCN to capture the higher-order collaborative signal. More specifically, KDGCN-IC reuses the initial feature information, and KDGCN-DC reuses the feature information of each layer. Extensive experiments demonstrated that our models were able to achieve substantial improvement over state-of-the-art models in so far as their recommendation accuracy and higher-order recommendations.

Two-Stage LMMSE/DNN Receiver for High-Order Modulation

High-order modulation has been seen as a promising technology to increase the spectral efficiency of wireless communications. Unfortunately, higher-order signals are susceptible to power amplifier (PA) nonlinearities and multi-path effects of the channel, leading to nonlinear distortion and severe inter-symbol interference. To guarantee the reliable detection of high-order signals, a two-stage receiver consisting of linear minimize mean squared error (LMMSE) equalizer and deep neural network (DNN) demodulator, namely TSLD receiver, is proposed, where LMMSE equalizer and DNN are deployed to eliminate inter-symbol interference and handle the amplifier nonlinearity, respectively. To facilitate the implementation of our proposed receiver, the specific pilot structure is designed, where low-order modulations are used for LMMSE equalizer and high-order modulations are used for DNN training. Simulation results show that our proposed TSLD receiver for high-order demodulation could recover the transmitted symbols effectively, even under serious multi-path and nonlinerity scenarios.

Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty

Objectives. Automatic modulation recognition of unknown signals is an important task for various fields oftechnology such as radio control, radio monitoring, and identification of interference and sources of radio emission. The paper aims to develop a method for recognizing the types of signal modulation under conditions of parametric a priori uncertainty, including the uncertainty of carrier frequency- and initial signal phase values. An additional task consists in estimating the offset values of the carrier frequency or signal phase at the initial stage of the recognition process.Methods. A multi-task learning with artificial neural network and the theory of cumulants of random variables are used.Results. For signals with a carrier frequency and initial phase shift, cumulant approaches for QAM-8, APSK-16, QAM-64, and PSK-8 modulations are calculated. A multi-task learning with artificial neural network using cumulant features and a data standardization algorithm is presented. The results of the experiment show that using multi-task learning with an artificial neural network provides high accuracy of recognizing QAM-8 and APSK-16, QAM-64 and PSK-8 modulations with small mismatches of the carrier frequency or initial phase. The accuracy of determining the offset values from the carrier frequency or the initial phase for QAM-8, APSK-16, QAM-64, and PSK-8 modulation is high.Conclusions. The multi-task learning with neural network using high-order signal cumulants makes it possible not only to recognize modulation types with high accuracy under conditions of a priori uncertainty of signal parameters, but also to determine the offset values of carrier frequency or initial signal phase from expected values.

Time-varying gravity field model of Sichuan-Yunnan region based on the equivalent mass source model

High-precision time-varying gravity field is an effective way to study the internal mass movement and understanding the spatio-temporal evolution process of the geodynamic system. Compared to the satellite gravity measurement, the repeated terrestrial gravity observation can provide a more high-order signal related to the shallow crust and subsurface. However, the suitable and unified method for gravity model estimation is a key problem for further applications. In this study, we introduce the spherical hexahedron element to simulate the field source mass and forward model the change of gravity field located at the Sichuan-Yunnan region (99–104°E, 23–29°N) in the four epochs from 2015 to 2017. Compared to the experimental results based on Slepian or spherical harmonics frequency domain method, this alternative approach is suitable for constructing the equivalent mass source model of regional-scale gravity data, by introducing the first-order smooth prior condition of gravity time-varying signal to suppress the high-frequency component of the signal. The results can provide a higher spatial resolution reference for regional gravity field modeling in the Sichuan-Yunnan region.

Galaxy cluster rotation revealed in the MACSIS simulations with the kinetic Sunyaev–Zeldovich effect

ABSTRACT The kinetic Sunyaev–Zeldovich (kSZ) effect has now become a clear target for ongoing and future studies of the cosmic microwave background (CMB) and cosmology. Aside from the bulk cluster motion, internal motions also lead to a kSZ signal. In this work, we study the rotational kSZ effect caused by coherent large-scale motions of the cluster medium using cluster hydrodynamic cosmological simulations. To utilize the rotational kSZ as a cosmological probe, simulations offer some of the most comprehensive data sets that can inform the modelling of this signal. In this work, we use the MACSIS data set to investigate the rotational kSZ effect in massive clusters specifically. Based on these models, we test stacking approaches and estimate the amplitude of the combined signal with varying mass, dynamical state, redshift, and map-alignment geometry. We find that the dark matter, galaxy and gas spins are generally misaligned, an effect that can cause a suboptimal estimation of the rotational kSZ effect when based on galaxy motions. Furthermore, we provide halo-spin–mass scaling relations that can be used to build a statistical model of the rotational kSZ. The rotational kSZ contribution, which is largest in massive unrelaxed clusters (≳100 $\mu$K), could be relevant to studies of higher order CMB temperature signals, such as the moving lens effect. The limited mass range of the MACSIS sample strongly motivates an extended investigation of the rotational kSZ effect in large-volume simulations to refine the modelling, particularly towards lower mass and higher redshift, and provide forecasts for upcoming cosmological CMB experiments (e.g. Simons Observatory, SKA-2) and X-ray observations (e.g. Athena/X-IFU).

Radio-over-fiber systems with delta-sigma-modulation for MMW-based indoor communication

In this paper, we propose and demonstrate a delta-sigma modulator (DSM) based radio-over-fiber (RoF) system for MMW indoor communication. In the experiment, 2-bit DSMs are used to quantize the high-order QAM orthogonal frequency division multiplexing (OFDM) signal into the 16QAM signal. At the receiver, the original high-order signal can be recovered by the low pass filter (LPF) and down-sampling operation. Based on the experimental results, up to 16,384 QAM signals can be transmitted over the DSM-based RoF system without noticeable performance degradation. When the received optical power is high enough, the corresponding error vector magnitude (EVM) and bit error rate (BER) are 0.53% and 7 × 10−3, respectively.

A Novel Online Diagnosis Method for Tubular Solid Oxide Fuel Cells Using Total Harmonic Distortion Analysis

Fast and accurate online prognostic technology is urgently needed for the long-term and stable operation of solid oxide fuel cell (SOFCs). Total harmonic distortion analysis (THDA) is a potential candidate that obtains the dynamic information of an electrochemical system by analyzing high-order signals and has been successfully utilized in PEMFC fault diagnosis. In this study, the THDA method is utilized in a tubular SOFC under fuel and air starvation conditions at different hydrogen and air flow rates. The total harmonic distortion spectra are obtained in the frequency range of 0.1~100 Hz. The effects of different excitation current amplitudes on the results accuracy are further investigated. As a consequence, the excitation amplitude range, frequencies with high sensitivity and appropriate threshold are chosen to diagnose different faults efficiently.

Radar Signal Processing Architecture for Early Detection of Automotive Obstacles

With the mass adoption of automotive vehicles, road accidents have become a common occurrence. One solution to this problem is to employ safety systems that can provide early warning for potential accidents. These systems alert drivers to brake or take active control of a vehicle in order to make braking safer and smoother, thereby protecting drivers and all other road traffic participants. Most such safety systems utilize millimeter-wave radar as primary sensors, and one of the main challenges is real-time data processing from multiple sensors integrated into a single passenger car. When an obstacle is too close to a vehicle, often there is insufficient time to run higher-order digital signal processing algorithms; hence, the decision to brake must be made based on low-level hardware processing only. For that purpose, a hardware generator for the early detection of automotive obstacles that does not impede the operation of higher-order signal processing algorithms is described. The proposed generator is captured in the Chisel hardware design language and a method for reducing the overall ranging latency is presented. The system constraints are calculated using an exemplary radar front-end and the proposed generator parameters. The obtained analytical results are experimentally confirmed with a prototype composed of a typical industrial radar front-end while the signal processing back-end instance of the described generator was implemented on an FPGA board. The measurements demonstrate that with the fast proximity alert, objects can be detected in less than a hundred microseconds, thus considerably reducing the system reaction delay and braking distance.

Experimental demonstration of 201.6-Gbit/s coherent probabilistic shaping QAM transmission with quantum noise stream cipher over a 1200-km standard single mode fiber.

A probabilistic shaping (PS) quadrature amplitude modulation (QAM) based on Y-00 quantum noise stream cipher (QNSC) has been proposed. We experimentally demonstrated this scheme with data rate of 201.6Gbit/s over a 1200-km standard single mode fiber (SSMF) under a 20% SD-FEC threshold. Accounting for the 20% FEC and 6.25% pilot overhead, the achieved net data rate is ∼160Gbit/s. In the proposed scheme, a mathematical cipher (Y-00 protocol) is utilized to convert the original low-order modulation PS-16 (22 × 22) QAM into ultra-dense high-order modulation PS-65536 (28 × 28) QAM. Then, the physical randomness of quantum (shot) noise at photodetection and amplified spontaneous emission (ASE) noise from optical amplifiers are employed to mask the encrypted ultra-dense high-order signal for further improving the security. We further analyze the security performance by two metrics known in the reported QNSC systems, namely the number of masked signals (NMS) of noise and the detection failure probability (DFP). Experimental results show it is difficult or even impossible to extract transmission signals from quantum or ASE noise for an eavesdropper (Eve). We believe that the proposed PS-QAM/QNSC secure transmission scheme has the potential to be compatible with existing high-speed long-distance optical fiber communication systems.

Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation

Textual reviews contain rich semantic information that is useful for making better recommendation, as such semantic information may indicate more fine-grained preferences of users. Recent efforts make considerable improvement on recommendation by integrating textual reviews in rating-based recommendations. However, there still exist major challenges on integrating textual reviews for recommendation. On the one hand, most existing works focus on learning a single representation from reviews but ignoring complex relations between users (or items) and reviews, which may fail to capture user preferences and item attributes together. On the other hand, these works independently learn latent representations from ratings and reviews while omitting correlations between rating-based features and review-based features, which may harm recommendation performance. In this article, we capture the aspect-aware relations by constructing heterogeneous graphs from reviews. Furthermore, we propose a new recommendation model, namely AHOR, to jointly distill rating-based features and review-based features, which are derived from ratings and reviews, respectively. To explore the multi-hop connectivity information between users, items, and aspects, a novel graph neural network is introduced to learn aspect-aware high-order representations. Experiments based on public datasets show that our approach outperforms state-of-the-art methods. We also provide detailed analysis on the high-order signals and the aspect importance to show the interpretability of our proposed model.

Performance improvement of non-orthogonal multiple access with a 3D constellation and a 2D IFFT modulator.

In this paper, we propose a performance improvement of non-orthogonal multiple access (NOMA) with a three-dimensional (3D) constellation and a two-dimensional Inverse Fast Fourier Transform IFFT modulator (2D-IFFT) for the passive optical network (PON). Two kinds of 3D constellation mapping are designed for the generation of a three-dimensional NOMA (3D-NOMA) signal. Higher-order 3D modulation signals can be obtained by superimposing signals of different power levels by pair mapping. Successive interference cancellation (SIC) algorithm is implemented at the receiver to remove interference from different users. Compared with the traditional two-dimensional NOMA (2D-NOMA), the proposed 3D-NOMA can increase the minimum Euclidean distance (MED) of constellation points by 15.48%, which enhances the bit error rate (BER) performance of the NOMA. The peak-to-average power ratio (PAPR) of NOMA can be reduced by 2 dB. A 12.17 Gb/s 3D-NOMA transmission over 25 km single-mode fiber (SMF) is experimentally demonstrated. The results show that at the bit error rate (BER) of 3.8 × 10-3, the sensitivity gain of the high-power signals of the two proposed 3D-NOMA schemes is 0.7 dB and 1 dB compared with that of 2D-NOMA under the condition of the same rate. Low-power level signal also has 0.3 dB and 1 dB performance improvement. Compared with 3D orthogonal frequency-division multiplexing (3D-OFDM), the proposed 3D-NOMA scheme could potentially expand the number of users without obvious performance degradation. Due to its good performance, 3D-NOMA is a potential method for future optical access systems.