Signal Information Research Articles

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

The most widely used sensing technique for detecting energy is due to its ease of use and lack of need for signal information beforehand. Traditional methods often employ a two-threshold strategy in low signal-to-noise ratio scenarios, which can lead to temporary detection outcomes when the signal's energy is between the low and high threshold values. This subsequently results in inadequate detection accuracy, characterized by a reduced probability of detection, which ultimately results in an extended duration of the spectrum sensing. The paper suggests enhancing the efficiency of spectrum sensing through the utilization of a modified version of the Anderson-Darling test statistic, as opposed to other test statistics.To detect the presence of licensed users in each subchannel from the base station, the cognitive user implements the Anderson Darling test statistics as a sensing technique. The proposed work involves the mathematical derivation of the Anderson-Darling test statistics under a bandwidth of 6MHz.The simulation outcomes show that the sensing technique presented in this research study achieved a detection probability of 0.963 at a false alarm probability of 1%, using a small sample size of 20 at a signal-to-noise ratio of -20dB. This performance outperformed the other three detection algorithms.

A multimodal shared network with a cross-modal distribution constraint for continuous emotion recognition

Continuous emotion recognition has been a compelling topic in affective computing because it can interpret human emotions subtly and continuously. Existing studies have achieved advanced emotion recognition performance using multimodal knowledge. However, these studies generally ignore the circumstances where some particular modalities are missing in the inference phase and thus become sensitive to the absence of modalities. To resolve this issue, we propose a novel multimodal shared network with a cross-modal distribution constraint, i.e. the DS-Net, which aims to improve the robustness of the model to missing modalities. The training process of the proposed network generally includes two components: multimodal shared space modeling and a cross-modal distribution matching constraint. The former utilizes the local and temporal information of multimodal signals for multimodal shared space modeling, while the latter further enhances the multimodal shared space via a loose constraint method. Coupled with the latter, the former can effectively exploit the complementarity between videos and peripheral physiological signals (PPSs), thus enhancing the discriminative capability of the shared space. Based on the shared space, the DS-Net works during the inference phase with only one modality input and can leverage multimodal knowledge to improve emotion recognition accuracy. Comprehensive experiments were conducted on two public datasets. Results demonstrate that the proposed method is competitive or superior to the current state-of-the-art methods. Further experiments indicate that the proposed method can be extended to handle other modalities and to deal with partially missing modalities, demonstrating its potential in real-world applications.

An efficient polynomial-based verifiable computation scheme on multi-source outsourced data

With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner’s method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data.

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

The Effects of the Digital Transformation on Firm’s Trade Credit: Evidence from China

ABSTRACT This study investigates the impact of digital transformation (DT) on trade credit (TC) within Chinese A-share listed manufacturing companies from 2008 to 2020. Utilizing information asymmetry theory and signal transmission theory, the study explores the relationship between DT and TC. Our findings reveal that DT has a notably positive effect on TC, with this impact being more pronounced in privately-owned enterprises, small and medium-sized entities, and particularly those outside the high-tech industry. Additionally, DT is found to foster TC expansion through enhanced cooperation within supply chains and improved relationships with suppliers and customers. These insights offer a comprehensive understanding of how DT influences corporate TC financing behavior and have significant implications for DT practices and policy decisions.

Distributed event-triggered estimation for dynamic average consensus: A perturbation-injected privacy-preservation scheme

Previous studies on the distributed estimation problem for dynamic average consensus of multi-agent networks are usually based on the assumption that each agent continuously and honestly shares information with its neighbors. To relax this assumption, this paper focuses on the distributed event-triggered private estimation problem. By injecting random perturbations into the original reference signal, an adaptive robust distributed privacy-preserving event-triggered estimation algorithm is proposed. With the proposed algorithm, the convergence of the estimation error is guaranteed under intermittent communication, while protecting the private reference signal information from disclosure. To determine the timing for communication, an adaptive distributed dynamic triggering mechanism with a dynamically updated internal triggering variable is designed. In addition, a dynamically updated adaptive gain instead of a static gain is employed in the estimation algorithm and triggering mechanism to eliminate the dependence on some global information. Finally, numerical simulation results are presented to illustrate the validity of the theoretical results.

Investigating a defect width identification based on half-peak width for LDC-based eddy current testing

ABSTRACT The eddy current testing (ECT) technology based on the Inductance-to-Digital Converter (LDC) has the advantages of low power consumption and simple signal conditioning circuitry, which contributes to the miniaturisation of the instrument and low power consumption design. Existing defect identification methods primarily focus on the conventional Analog-to-Digital Converter (ADC)-based ECT. These methods rely on complex analytical models to analyze the phase and amplitude information of voltage signal. However, these approaches do not apply to the LDC-based ECT. The paper proposes a method to identify defects employing the R p -Inductance 2D plane, and use the half-peak width of the inductance signal to evaluate the defect width for symmetric slots. Using finite element simulation and experimental verification, the half-peak widths of the inductive signal wave crests at the defects are the same for the four slots with 1 mm width and different depths on the aluminum specimen; for four wire-cut slots with 4 mm depth and different widths, the half-peak widths are positively correlated with the defect widths. These experimental results demonstrate that the relationship between crack width, wave crest, and half-peak width can serve as the foundation for width identification and classification. This also offers a fresh perspective for further quantifying crack widths.

Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70°C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

The Effects of Dividend Policy On the Price of Shares of Microfinance Companies In Developing Countries

The strategy of dividend policy implementation in the context of business value growth is the subject of debates in theoretical and empirical research. Dividends and retained earnings are two alternatives that investors have when deciding how to distribute their income. According to the bird-in-the-hand theory, a company’s value is maximized by paying large dividends, because investors consider the payouts less risky than capital gains, regular high dividend payments serve as an information signal to market participations about the current and future financial stability, which attracts new investors and allows attracting more external resources. Instead, according to Miller and Modigliani’s dividend irrelevancy theory, corporate value is influenced not by the division of income into dividends and retained earnings, but by investment policy and income from assets. There is a lack of research in the literature that examines the relationship between dividend payouts and stock prices in developing countries (especially in Nepal) and there is no empirical evidence that profitability can moderate this relationship. The article examines the influence of dividend yield, retention ratio and dividend payout ratio on share price both in general and directly, and indirectly-through return on assets and return on equity. The information base is data for the 2017-2021 financial years on 61 Nepalese microfinance institutions (10 of which were the direct object of the study) registered on the Nepal Stock Exchange (NEPSE), obtained from the official websites of the relevant institutions, ShareSansar and NEPSE statistics. SPSS software (PROCESS v4.3) was used for data analysis. Descriptive statistics, the correlation and regression analysis were used for the analysis. The article analyzes three types of effects: total, direct and indirect. The overall effect of dividend yield on share price is negative, the retention ratio is positive and significant, and the dividend payout ratio is negative and significant. The direct effect of the dividend yield on the stock price is negative, the retention ratio and the dividend payout ratio are positive and insignificant. The indirect effect of dividend yield and retention ratio on share price through return on assets is negative and insignificant, and through return on equity it is positive and significant. The indirect effect of the dividend payout ratio through the return on assets is positive and significant. However, the return on shareholders’ capital has a negative and insignificant effect on the share price.

Exploiting channel state information of WiFi signal for human activity detection: an experimental study

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.

Modeling of memory effects in nanofluidic diodes

Nanofluidic diodes and ionic solutions find application in electrochemical circuits for information processing, controlled release, and signal conversion in hybrid devices. Here, we describe a physical model that accounts for the memory effects observed in conical nanopores in terms of the driving signal and ionic solution characteristics. The concepts invoked describe the device operation on the basis of the electrical interaction between the pore surface charges and the nanoconfined ionic solution. The physical insights provided can explain the experimental dependence of the nanofluidic tunability on the amplitude and frequency of the driving signal, the ionic concentration, and the solution pH. The model should also be useful for the design of electrochemical circuits based on ionic conduction in asymmetric memristors.

TSS-ConvNet for electrical impedance tomography image reconstruction

Objective. The objective of this study was to propose a novel data-driven method for solving ill-posed inverse problems, particularly in certain conditions such as time-difference electrical impedance tomography for detecting the location and size of bubbles inside a pipe. Approach. We introduced a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, whereas the spectral and truncated spectral paths provide the network with a global receptive field. This unique architecture helps eliminate the ill-posedness and nonlinearity inherent in the inverse problem. The three paths were designed to be interconnected, allowing for an exchange of information on different receptive fields with varied learning abilities. Our network has a bottleneck architecture that enables it to recover signal information from noisy redundant measurements. We named our proposed model truncated spatial-spectral convolutional neural network (TSS-ConvNet). Main results. Our model demonstrated superior accuracy with relatively high resolution on both simulation and experimental data. This indicates that our approach offers significant potential for addressing ill-posed inverse problems in complex conditions effectively and accurately. Significance. The TSS-ConvNet overcomes the receptive field limitation found in most existing models that only utilize local information in Euclidean space. We trained the network on a large dataset covering various configurations with random parameters to ensure generalization over the training samples.

On the Wave‐Normal Distribution of Lightning‐Generated Whistlers and Their Propagation Modes

AbstractObservations from Van Allen Probes are analyzed to obtain the statistical wave normal distribution of lightning‐generated whistlers (LGWs). An automatic algorithm is developed to identify burst mode waveform data with LGW signals and analyze the wave polarization information for these LGW signals. The spatial distribution of the LGW occurrence and the probabilities of different propagation types demonstrate that most LGWs can be observed in the low L‐shell region (L < 3), where the dominant propagation type is oblique outward. Parallel propagation dominates in the high L‐shell region, but the LGW occurrence is very small. Additionally, a small group of oblique but inwardly propagating LGWs are observed at low altitudes (<0.2RE). A ray tracing simulation is performed not only to confirm predominant oblique and outward wave vectors in the vast region of the plasmasphere but also to verify the existence of these inward propagating LGW signals at low altitudes near the topside ionosphere.

A differential amplitude variation based pilot relaying scheme for microgrid integrated distribution system

Abstract The inclusion of inverter-interfaced distributed generating sources (IIDGS) in the active distribution network offers operational and protection constraints. Dynamic fault behavior of these DGS results in changes like the radial distribution systems. Thus, conventional protection schemes fail to discriminate the in-zone and out-zone faults. Hence, this manuscript describes a new protection scheme based on current variation at the relay location used to discriminate the internal and external faults. This technique requires phasor information of the current signals of both ends and adjacent lines using micro phasor measurement units (µPMUs). Using the current phasor information two components named internal and external are computed at the relay location which is called differential amplitude variation index (DAVI), used to identify the internal and external faults. A modified two-feeder radial distribution network and modified IEEE-13 bus test systems are modeled using EMTDC/PSCAD to validate the proposed strategy. Numerous disturbance cases have been simulated and the feasibility and applicability attributes of the suggested method are verified from the results.

Design of a Short Reference Differential Chaotic Shift Keying Communication System Based on Reference Signal Scrambling Transformation

A short reference differential chaotic shift keying communication system based on reference signal scrambling transformation (RSST-SR-DCSK) is proposed in this paper. The system introduces information-bit-driven scramblers at the transmission end to effectively eliminate the correlation between the reference signal and information signal. At the receiving end, a descrambler is used to restore the correlation, thereby enhancing information transmission security. Additionally, the system employs a short reference transmission mechanism to further increase the information transmission rate. The transmitter and receiver structures of the system are designed through signal structure analysis. Theoretical analyses and simulations conducted in an additive white Gaussian noise (AWGN) channel demonstrate promising performance, including a low bit error rate (BER) and a favorable data-energy-to-bit-energy ratio (DBR). Simulation verification is performed to examine the impact of parameters on the bit error rate performance. A comparative analysis with traditional DCSK and SR-DCSK systems highlights the superior performance of the RSST-SR-DCSK system.

A de-noising method of magnetotelluric signals based on the generalized S-transform

The generalized S-transform has excellent time-frequency resolution capability. It can effectively demonstrate the non-stationary characteristics of signals and the dynamic changes in energy. This provides the conditions for denoising in the time-frequency domain. The magnetotelluric (MT) survey method is often affected by various artificial noises, and these noises are very prominent in certain frequency bands. To address this problem, a method based on the generalized S transform is proposed to de-noise MT data. The generalized S transform is used to improve the time-frequency localization of the different signal components, meanwhile the electromagnetic noise can be conveniently suppressed in the time-frequency domain. Simulated signals and measured MT signals contaminated by common sinusoidal and triangular wave interference are processed. The results were compared with those of the Wavelet threshold method. Results show that the presented method is more conducive to the identification and suppression of electromagnetic noise and shows better de-noising results than the Wavelet threshold method. After de-noising, the useful information of the MT signal affected by strong interference is highlighted, which provides favorable conditions for power spectrum and impedance estimation in the future. Further, the response parameter curve calculated from the de-noised data becomes stable. The proposed method provides a new alternative for de-noising of MT signals, especially those with low signal-to-noise ratios.

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal.

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies.

What Does That Mean? Complementizers and Epistemic Authority.

A core goal of research in language is to understand the factors that guide choice of linguistic form where more than one option is syntactically well-formed. We discuss one case of optionality that has generated longstanding discussion: the choice of either using or dropping the English complementizer that in sentences like I think (that) the cat followed the dog. Existing psycholinguistic analyses tie that-usage to production pressures associated with sentence planning (Ferreira & Dell, 2000), avoidance of ambiguity (Hawkins, 2004), and relative information density (Jaeger, 2010). Building on observations from cross-linguistic fieldwork, we present a novel proposal in which English that can serve to mark a speaker's "epistemic authority" over the information packaged within the embedded clause; that is, it indicates that the speaker has more knowledge of the embedded proposition compared with their addressee and thus has a perspective that they believe their addressee doesn't share. Testing this proposal with a forced-choice task and a series of corpus surveys, we find that English that is keyed to the use of embedded speaker (first-person) subject pronouns and occurs in sentences containing newsworthy information. Our account of that-optionality takes into account why that is associated with both (i) a dense information signal and (ii) semantic-pragmatic content, as well as extending to cases of non-optionality in subject/sentence-initial clauses (e.g., *(That) the cat is following the dog, I already know) and fragment answers (e.g., What do you already know? *(That) the cat is following the dog), where that is required.