Recognition method for the bionic camouflage click communication trains modulated by time delay difference.

Bionic camouflage covert underwater acoustic communication has recently attracted great attention. However, we have not found relevant methods or literature to recognize these bionic camouflage communication signals (BCCSs) in the area of anti-reconnaissance. Focused on recognizing the BCCSs, this article proposes a recognition method based on the statistics of inter-click intervals to recognize the camouflaged click communication train (CCCT), which is modulated by time delay difference (TDD). We first analyze the characteristics of TDD distributions of CCCT and real click train (RCT). According to the coding principle, the TDDs of CCCTs present a ladder-like distribution with a fixed time step, and the TDDs are equal to the integral multiple of the fixed time step. On the contrary, the TDDs of RCTs are approximately random distribution within a certain time range. Therefore, based on the different TDD distributions, this article classifies CCCTs and RCTs by utilizing the statistical property of TDD distributions. To measure the TDDs of diverse cetacean clicks accurately, a new click location scheme based on the dynamic window energy ratio is proposed. Next, based on the statistics of TDD distribution, the influences of the TDDs that are caused by multipath interferences are eliminated by iteration. Simulations demonstrate the accuracy of the recognition method under different conditions.

An automated piecewise synthesis method for cetacean tonal sounds based on time-frequency spectrogram.

Bionic signal waveform design plays an important role in biological research, as well as bionic underwater acoustic detection and communication. Most conventional methods cannot construct high-similarity bionic waveforms to match complex cetacean sounds or easily modify the time-frequency structure of the synthesized bionic signals. In our previous work, we proposed a synthesis and modification method for cetacean tonal sounds, but it requires a lot of manpower to construct each bionic signal segment to match the tonal sound contour. To solve these problems, an automated piecewise synthesis method is proposed. First, based on the time-frequency spectrogram of each tonal sound, the fundamental contour and each harmonic contour of the tonal sound is automatically recognized and extracted. Then, based on the extracted contours, four sub power frequency modulation bionic signal models are combined to match cetacean sound contours. Finally, combining the envelopes of the fundamental frequency and each harmonic, the synthesized bionic signal is obtained. Experimental results show that the Pearson correlation coefficient (PCC) between all true cetacean sounds and their corresponding bionic signals are higher than 0.95, demonstrating that the proposed method can automatically imitate all kinds of simple and complex cetacean tonal sounds with high similarity.

Modeling and Experimental Testing of an Unmanned Surface Vehicle with Rudderless Double Thrusters.

Motion control of unmanned surface vehicles (USVs) is a crucial issue in sailing performance and navigation costs. The actuators of USVs currently available are mostly a combination of thrusters and rudders. The modeling for USVs with rudderless double thrusters is rarely studied. In this paper, the three degrees of freedom (DOFs) dynamic model and propeller thrust model of this kind of USV were derived and combined. The unknown parameters of the propeller thrust model were reduced from six to two. In the three-DOF model, the propulsion of the USV was completely provided by the resultant force generated by double thrusters and the rotational moment was related to the differential thrust. It combined the propeller thrust model to represent the thrust in more detail. We performed a series of tests for a 1.5 m long, 50 kg USV, in order to obtain the model parameters through system identification. Then, the accuracy of the modeling and identification results was verified by experimental testing. Finally, based on the established model and the proportional derivative+line of sight (PD+LOS) control algorithm, the path-following control of the USV was achieved through simulations and experiments. All these demonstrated the validity and practical value of the established model.

Self-Contained High-SNR Underwater Acoustic Signal Acquisition Node and Synchronization Sampling Method for Multiple Distributed Nodes.

Aiming at the application demand in underwater noise monitoring, observation of marine animal, antisubmarine and underwater target localization, a high-SNR underwater acoustic signal acquisition (UASA) node that combines a self-contained acquisition system and floating platform is designed to improve the acquisition performance of a single UASA node, and a high-accuracy synchronization sampling method among multiple distributed UASA nodes based on master-slave dual phase-locked loops (MSDPLL) is proposed to improve the synchronization sampling accuracy. According to the equivalent model of hydrophone and application requirements, low noise signal conditioning circuit and large-capacity data storage modules are designed. Based on the long-term monitoring requirements for underwater acoustic signal and distributed positioning requirements for underwater targets, the structure of a single UASA node is designed and MSDPLL is developed for high-accuracy synchronization sampling among multiple UASA nodes. Related experimental results verified the performance of the UASA node and the synchronization sampling method.

Bio-Inspired Covert Active Sonar Strategy.

The covertness of the active sonar is a very important issue and the sonar signal waveform design problem was studied to improve covertness of the system. Many marine mammals produce call pulses for communication and echolocation, and existing interception systems normally classify these biological signals as ocean noise and filter them out. Based on this, a bio-inspired covert active sonar strategy was proposed. The true, rather than man-made sperm whale, call pulses were used to serve as sonar waveforms so as to ensure the camouflage ability of sonar waveforms. A range and velocity measurement combination (RVMC) was designed by using two true sperm whale call pulses which had excellent range resolution (RR) and large Doppler tolerance (DT). The range and velocity estimation methods were developed based on the RVMC. In the sonar receiver, the correlation technology was used to confirm the start and end time of sonar signals and their echoes, and then based on the developed range and velocity estimation method, the range and velocity of the underwater target were obtained. Then, the RVMC was embedded into the true sperm whale call-train to improve the camouflage ability of the sonar signal-train. Finally, experiment results were provided to verify the performance of the proposed method.

A novel discrete zeroing neural network for online solving time-varying nonlinear optimization problems.

To reduce transportation time, a discrete zeroing neural network (DZNN) method is proposed to solve the shortest path planning problem with a single starting point and a single target point. The shortest path planning problem is reformulated as an optimization problem, and a discrete nonlinear function related to the energy function is established so that the lowest-energy state corresponds to the optimal path solution. Theoretical analyzes demonstrate that the discrete ZNN model (DZNNM) exhibits zero stability, effectiveness, and real-time performance in handling time-varying nonlinear optimization problems (TVNOPs). Simulations with various parameters confirm the efficiency and real-time performance of the developed DZNNM for TVNOPs, indicating its suitability and superiority for solving the shortest path planning problem in real time.

UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission.

While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous 3D positioning as well as wireless IMU data transmission through UWB pulses. The UI-MoCap comprises mobile UWB tags and hardware-synchronized UWB base stations. Each UWB tag, a compact circular PCB with a 3.4cm diameter, houses a nine-axis IMU unit and a UWB transceiver for data transmission. The base stations are equipped with a UWB transceiver and an Ethernet controller, ensuring efficient reception and management of messages from multiple tags. Experiments were conducted to evaluate the system's validity and reliability of 3D positioning and IMU data transmission. The results demonstrate that UI-MoCap achieves centimeter-level 3D positioning accuracy and maintains consistent positioning performance over time. Moreover, UI-MoCap exhibits high update rates and a minimal packet loss rate for IMU data transmission, significantly outperforming Wi-Fi-based transmission techniques. Future work will explore the fusion of UWB and IMU technologies to further enhance positioning performance, with a focus on human movement analysis and rehabilitation applications.

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.

Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunction, and electrode sheet detachment. These non-ideal factors severely compromise the accuracy of continuous motion recognition and the reliability of clinical applications. To tackle this challenge, this paper develops a multi-task parallel learning framework for continuous motion estimation with incomplete sEMG signals. Concretely, a residual network is incorporated into a recurrent neural network to integrate the information flow of hidden states and reconstruct random and consecutive missing sEMG signals. The attention mechanism is applied for redistributing the distribution of weights. A jointly optimized loss function is devised to enable training the model for simultaneously dealing with signal anomalies/absences and multi-joint continuous motion estimation. The proposed model is implemented for estimating hip, knee, and ankle joint angles of physically competent individuals and patients during diverse exercises. Experimental results indicate that the estimation root-mean-square errors with 60% missing sEMG signals steadily converges to below 5 degrees. Even with multi-channel electrode sheet shedding, our model still demonstrates cutting-edge estimation performance, errors only marginally increase 1 degree.

An advanced bionic knee joint mechanism with neural network controller.

In this article, a tensegrity-based knee mechanism is studied for developing a high-efficiency rehabilitation knee exoskeleton. Moreover, the kinematics and dynamics models of the knee mechanism are explored for bringing about further improvement in controller design. In addition, to estimate the performance of the bionic knee joint, based on the limit function of knee patella, the limit position functionality of the bionic knee joint is developed for enhancing the bionic property. Furthermore, to eliminate the noise item and other disturbances that are constantly generated in the rehabilitation process, a noise-tolerant zeroing neural network (NTZNN) algorithm is utilized to establish the controller. This indicates that the controller shows an anti-noise performance; hence, it is quite unique from other bionic knee mechanism controllers. Eventually, the anti-noise performance and the calculation of the precision of the NTZNN controller are verified through several simulation and contrast results.

Elevated red blood cell distribution width levels predict depression after intracerebral hemorrhage: A 3-month follow-up study.

Objectives: Inflammation vitally impacts the progression of depression resulting from intracerebral hemorrhage (ICH), while red blood cell distribution width (RDW) marks inflammatory-related diseases. The present study aimed at evaluating how RDW affects depression after ICH. Methods: From prospective analyses of patients admitted to our department between January 2017 and September 2022, ICH patients with complete medical records were evaluated. The 17-item Hamilton Depression (HAMD-17) scale was used for measuring the depressive symptoms at 3 months after ICH. Diagnosis of post-ICH depression was conducted for patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) criteria. Results: A total of 438 patients were enrolled in the study, out of which 93 (21.23%) patients had PSD at the 3-month follow-up. Accordingly, patients with depression had higher RDW levels (13.70 [IQR: 13.56-13.89] vs.13.45 [IQR: 12.64-13.75], p < 0.001) at admission compared with those without depression. In multivariate analyses, RDW was used for independently predicting the depression after ICH at 3 months (OR: 2.832 [95% CI: 1.748-4.587], p < 0.001). After adjusting the underlying confounding factors, the odds ratio (OR) of depression after ICH was 4.225 (95% CI: 1.686-10.586, p = 0.002) for the highest tertile of RDW relative to the lowest tertile. With an AUC of 0.703 (95% CI: 0.649-0.757), RDW demonstrated a significantly better discriminatory ability relative to CRP and WBC. RDW as an indicator for predicting depression after ICH had an optimal cutoff value of 13.68, and the sensitivity and specificity were 63.4% and 64.6%, respectively. Conclusions: Elevated RDW level predicted post-ICH depression at 3 months, confirming RDW as an effective inflammatory marker for predicting depression after ICH.

Utility of serum amyloid A as a potential prognostic biomarker of aneurysmal subarachnoid hemorrhage.

Objectives: Inflammation plays a vital role in the aneurysmal subarachnoid hemorrhage (aSAH), while serum amyloid A (SAA) has been identified as an inflammatory biomarker. The present study aimed to elucidate the relationship between SAA concentrations and prognosis in aSAH. Methods: From prospective analyses of patients admitted to our department between March 2016 and August 2022, aSAH patients with complete medical records were evaluated. Meanwhile, the healthy control group consisted of the age and sex matched individuals who came to our hospital for healthy examination between March 2018 and August 2022. SAA level was measured by enzyme-linked immunosorbent assay kit (Invitrogen Corp). The Glasgow Outcome Scale (GOS) was used to classify patients into good (GOS score of 4 or 5) and poor (GOS score of 1, 2, or 3) outcome. Results: 456 patients were enrolled in the study, thereinto, 200 (43.86%) patients had a poor prognosis at the 3-months follow-up. Indeed, the SAA of poor outcome group were significantly increased compared to good outcome group and healthy control group [36.44 (32.23-41.00) vs. 28.99 (14.67-34.12) and 5.64 (3.43-7.45), P < 0.001]. In multivariate analyses, SAA served for independently predicting the poor outcome after aICH at 3 months [OR:1.129 (95% CI, 1.081-1.177), P < 0.001]. After adjusting the underlying confounding factors, the odds ratio (OR) of depression after aSAH was 2.247 (95% CI: 1.095-4.604, P = 0.021) for the highest tertile of SAA relative to the lowest tertile. With an AUC of 0.807 (95% CI, 0.623-0.747), SAA demonstrated an obviously better discriminatory ability relative to CRP, WBC, and IL-6. SAA as an indicator for predicting poor outcome after aSAH had an optimal cut-off value of 30.28, and the sensitivity and specificity were 61.9 and 78.7%, respectively. Conclusions: Elevated level of SAA was associated with poor outcome at 3 months, suggesting that SAA might be a useful inflammatory markers to predict prognosis after aSAH.

RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme.

For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

A New Projected Active Set Conjugate Gradient Approach for Taylor-Type Model Predictive Control: Application to Lower Limb Rehabilitation Robots With Passive and Active Rehabilitation.

In this paper, a three-order Taylor-type numerical differentiation formula is firstly utilized to linearize and discretize constrained conditions of model predictive control (MPC), which can be generalized from lower limb rehabilitation robots. Meanwhile, a new numerical approach that projected an active set conjugate gradient approach is proposed, analyzed, and investigated to solve MPC. This numerical approach not only incorporates both the active set and conjugate gradient approach but also utilizes a projective operator, which can guarantee that the equality constraints are always satisfied. Furthermore, rigorous proof of feasibility and global convergence also shows that the proposed approach can effectively solve MPC with equality and bound constraints. Finally, an echo state network (ESN) is established in simulations to realize intention recognition for human-machine interactive control and active rehabilitation training of lower-limb rehabilitation robots; simulation results are also reported and analyzed to substantiate that ESN can accurately identify motion intention, and the projected active set conjugate gradient approach is feasible and effective for lower-limb rehabilitation robot of MPC with passive and active rehabilitation training. This approach also ensures computational when disturbed by uncertainties in system.

[Changes in myocardial enzyme spectrum and electrocardiogram after Salmonella food poisoning].

OBJECTIVE: To study the changes in myocardial enzyme spectrum in relation to electrocardiogram (ECG) in Salmonella food poisoning. METHODS: The myocardial enzyme spectrum and ECG of 56 patients with Salmonella food poisoning were examined, with 34 normal subjects serving as the control group. RESULTS: In the food poisoning group, the myocardial enzyme activities was increased in 36 cases (64.29%) within 2 days after the poisoning and the ECG of 33 cases (58.93%) showed abnormal changes within 1-4 days. The levels of creatine phosphoskinase (CPK) and alpha-hydroxybutyrate acid dehydrogenase (alpha-HBDH) in poisoning group were obviously higher than those in the control group (P<0.01). CONCLUSION: Routine examination of myocardial enzyme spectrum and ECG helps define early changes in patients with Salmonella food poisoning for clinical treatment decision.

Blood pressure variability and morning blood pressure surge in elderly Chinese hypertensive patients.

This retrospective study aimed to investigate blood pressure variability (BPV), morning systolic blood pressure surge (MBPS), and the associated factors in 513 elderly (65 years and older) and 188 younger (younger than 65 years) hypertensive Chinese patients who had ambulatory blood pressure monitoring (ABPM) at our hospital from January 1, 2002, to December 31, 2010. The MBPS was lower in the younger patients compared with the elderly patients, and it was highest in the 75 years and older and younger than 80 group (29.0±13.4 mm Hg). Compared with other groups, the 80 years and older group had a significant increase in BPV (P<.05). Multiple linear regression analysis showed that body mass index (BMI; P<.01), up to 50 years of smoking (P≤.03), and circadian blood pressure variation (P<.01) were factors associated with MPBS. In summary, systolic BPV and MBPS were increased in elderly Chinese hypertensive patients, and the MBPS was associated with BMI, years of smoking, and circadian blood pressure variation.