Search Results (10)

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods. Full article

Understanding the characteristics of underwater sound channels is essential for various remote sensing applications. Typically, the time-domain Green’s function or channel impulse response (CIR) is obtained using computationally intensive acoustic propagation models that rely on accurate environmental data, such as sound speed profiles and bathymetry. Ray-based blind deconvolution (RBD) offers a less computationally demanding alternative using plane-wave beamforming to estimate the Green’s function. However, the presence of noise can obscure low coherence ray arrivals, making accurate estimation challenging. This paper introduces a method using the waveguide invariant to improve the signal-to-noise ratio (SNR) of broadband Green’s functions for a moving source without prior knowledge of range. By utilizing RBD and the frequency shifts from the striation slope, we coherently combine individual Green’s functions at adjacent ranges, significantly improving the SNR. In this study, we demonstrated the proposed method via simulation and broadband noise data (200–900 Hz) collected from a moving ship in 100 m deep shallow water. Full article

Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to classify the patients’ murmur quality (i.e., harsh and blowing) from phonocardiogram (PCG) signals. The phonocardiogram recordings with murmurs used for this task are from the CirCor DigiScope Phonocardiogram dataset, which provides the murmur quality labels. The recordings were segmented, and a dataset of 1266 segments with average lengths of 4.1 s from 164 patients’ recordings was obtained. Each patient usually has multiple segments. A deep neural network model based on convolutional neural networks (CNNs) with channel attention and gated recurrent unit (GRU) networks was first used to extract features from the log-Mel spectrograms of segments. Then, the features of different segments from one patient were weighted by the proposed “Feature Attention” module based on the attention mechanism. The “Feature Attention” module contains a layer of global pooling and two fully connected layers. Through it, the different features can learn their weight, which can help the deep learning model distinguish the importance of different features of one patient. Finally, the detection results were produced. The cross-entropy loss function was used to train the model, and five-fold cross-validation was employed to evaluate the performance of the proposed methods. The accuracy of detecting the quality of patients’ murmurs is 73.6%. The F1-scores (precision and recall) for the murmurs of harsh and blowing are 76.8% (73.0%, 83.0%) and 67.8% (76.0%, 63.3%), respectively. The proposed methods have been thoroughly evaluated and have the potential to assist physicians with the diagnosis of cardiovascular diseases as well as explore the relationship between murmur quality and cardiovascular diseases in depth. Full article

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders. Full article

Visible light communication (VLC) is one of the key technologies for the sixth generation (6G) to support the connection and throughput of the Industrial Internet of Things (IIoT). Furthermore, VLC channel modeling is the foundation for designing efficient and robust VLC systems. In this paper, the ray-tracing simulation method is adopted to investigate the VLC channel in IIoT scenarios. The main contributions of this paper are divided into three aspects. Firstly, based on the simulated data, large-scale fading and multipath-related characteristics, including the channel impulse response (CIR), optical path loss (OPL), delay spread (DS), and angular spread (AS), are analyzed and modeled through the distance-dependent and statistical distribution models. The modeling results indicate that the channel characteristics under the single transmitter (TX) are proportional to the propagation distance. It is also found that the degree of time domain and spatial domain dispersion is higher than that in the typical rooms (conference room and corridor). Secondly, the density of surrounding objects and the effects of user heights on these channel characteristics are also investigated. Through the analysis, it can be observed that the denser objects can contribute to the smaller OPL and the larger RMS DS under the single TX case. Furthermore, due to the blocking effect of surrounding objects, the larger OPL and the smaller RMS DS can be observed at the receiver with a low height. Thirdly, due to the distance dependence of the channel characteristics and large time-domain dispersion, the link adaption method is further proposed to optimize the multipath interference problem. This method combines a luminary adaptive selection and delay adaption technique. Then, the performance of the link adaption method is verified from four aspects through simulation, including the signal-to-noise (SNR), the RMS DS, the CIRs, and the bit-error rate (BER) of a direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. The verification results indicate that our proposed method has a significant optimization for multipath interference. Full article

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection. Full article

To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The positioning accuracy was improved significantly. Full article

There is evidence that schizophrenia is characterized by activation of the immune-inflammatory response (IRS) and compensatory immune-regulatory systems (CIRS) and lowered neuroprotection. Studies performed on antipsychotic-naïve first episode psychosis (AN-FEP) and schizophrenia (FES) patients are important as they may disclose the pathogenesis of FES. However, the protein–protein interaction (PPI) network of FEP/FES is not established. The aim of the current study was to delineate a) the characteristics of the PPI network of AN-FEP and its transition to FES; and b) the biological functions, pathways, and molecular patterns, which are over-represented in FEP/FES. Toward this end, we used PPI network, enrichment, and annotation analyses. FEP and FEP/FES are strongly associated with a response to a bacterium, alterations in Toll-Like Receptor-4 and nuclear factor-κB signaling, and the Janus kinases/signal transducer and activator of the transcription proteins pathway. Specific molecular complexes of the peripheral immune response are associated with microglial activation, neuroinflammation, and gliogenesis. FEP/FES is accompanied by lowered protection against inflammation, in part attributable to dysfunctional miRNA maturation, deficits in neurotrophin and Wnt/catenin signaling, and adherens junction organization. Multiple interactions between reduced brain derived neurotrophic factor, E-cadherin, and β-catenin and disrupted schizophrenia-1 (DISC1) expression increase the vulnerability to the neurotoxic effects of immune molecules, including cytokines and complement factors. In summary: FEP and FES are systemic neuro-immune disorders that are probably triggered by a bacterial stimulus which induces neuro-immune toxicity cascades that are overexpressed in people with reduced anti-inflammatory and miRNA protections, cell–cell junction organization, and neurotrophin and Wnt/catenin signaling. Full article

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB. Full article

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m ${}^2$ . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system. Full article