Design of Detection-Jamming Shared Waveform Based on Virtual Force Field Algorithm

Due to the technical barriers between radars and jammers and the poor performance of the traditional detection-jamming shared signal in integrated radar-electronic warfare systems, a new detection-jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this paper. First, a multi-objective and multi-dimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection-jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in non-collaborative radar. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation number, which are highly sensitive to the multi-jagged PRF signals emitted by the non-collaborative radar. According to the VFFA described in this paper, the optimum detection-jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems.

Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors

The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

Recurrence Quantification Analysis of Glottal Signal as non Linear Tool for Pathological Voice Assessment and Classification

Automatic detection and assessment of Vocal Folds pathologies using signal processing techniques knows an extensively challenge use in the voice or speech research community. This paper contributes the application of the Recurrence Quantification Analysis (RQA) to a glottal signal waveform in order to evaluate the dynamic process of Vocal Folds (VFs) for diagnosis and classify the voice disorders. The proposed solution starts by extracting the glottal signal waveform from the voice signal through an inverse filtering algorithm. In the next step, the parameters of RQA are determined via the Recurrent Plot (RP) structure of the glottal signal where the normal voice is considered as a reference. Finally, these parameters are used as input features set of a hybrid Particle Swarm Optimization-Support Vector Machines (PSO-SVM) algorithms to segregate between normal and pathological voices. For the test validation, we have adopted the collection of Saarbrucken Voice Database (SVD) where we have selected the long vowel /a:/ of 133 normal samples and 260 pathological samples uttered by four groups of subjects : persons having suffered from vocal folds paralysis, persons having vocal folds polyps, persons having spasmodic dysphonia and normal voices. The obtained results show the effectiveness of RQA applied to the glottal signal as a features extraction technique. Indeed, the PSO-SVM as a classification method presented an effective tool for assessment and diagnosis of pathological voices with an accuracy of 97.41%

Information–Theoretic Radar Waveform Design under the SINR Constraint

This study investigates the information–theoretic waveform design problem to improve radar performance in the presence of signal-dependent clutter environments. The goal was to study the waveform energy allocation strategies and provide guidance for radar waveform design through the trade-off relationship between the information theory criterion and the signal-to-interference-plus-noise ratio (SINR) criterion. To this end, a model of the constraint relationship among the mutual information (MI), the Kullback–Leibler divergence (KLD), and the SINR is established in the frequency domain. The effects of the SINR value range on maximizing the MI and KLD under the energy constraint are derived. Under the constraints of energy and the SINR, the optimal radar waveform method based on maximizing the MI is proposed for radar estimation, with another method based on maximizing the KLD proposed for radar detection. The maximum MI value range is bounded by SINR and the maximum KLD value range is between 0 and the Jenson–Shannon divergence (J-divergence) value. Simulation results show that under the SINR constraint, the MI-based optimal signal waveform can make full use of the transmitted energy to target information extraction and put the signal energy in the frequency bin where the target spectrum is larger than the clutter spectrum. The KLD-based optimal signal waveform can therefore make full use of the transmitted energy to detect the target and put the signal energy in the frequency bin with the maximum target spectrum.

A Classification Method for Judging the Depth of Chest Compression Based on CNN

The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.