Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Virtual Reality Interactive Method and Device Based on Wireless Communication Tracking

Virtual reality is a computer system that creates a virtual world and then experiences through multiple senses. It is generated by a computer and stimulated by perception systems such as hearing, vision, touch, taste, and smell, providing users with a personal experience. Human-computer interaction is one of the core technologies of virtual reality. Wireless communication is the transmission of communications over long distances between multiple nodes without propagation through conductors or cables and can be carried out using radios, radios, etc. Wireless communication includes a variety of fixed, mobile, and portable applications such as two-way radios, mobile phones, personal digital assistants, and wireless networks. Other examples of radio wireless communication are GPS, garage door remotes, wireless mice, etc. Most wireless communication technologies use radio, including Wi-Fi with distances of just a few meters, but also deep space networks that communicate with Voyager 1 and distances of over millions of kilometers. With the continuous development of sensors and other supporting hardware facilities, the current development of human-computer interaction in virtual reality has made rapid progress. In the research to be conducted in this article, the virtual reality system used in this article cleverly integrates the three characteristics of immersion, interactivity, and conception, so that the experimenter can obtain more realistic data in comparison. To this end, this article first gives a general introduction to virtual reality technology and wireless communication tracking technology and then explains how to use wireless communication tracking technology to make the virtual reality interactive system smoother and smoother, as well as the introduction of its devices. This article explores and analyzes the possible or existing problems of wireless communication tracking technology in virtual reality interaction, hoping to contribute to the wider application of wireless communication tracking technology in virtual reality interaction. The positioning experiment on the wireless mobile signal identification points can be obtained. Among the 40 sensor nodes that are randomly deployed, when the interval of adjusting the mobile signal identification point to broadcast the current position information is 5 s, the average positioning error of the node is about 1.5 m; when the interval is 3 s, the average positioning error of the node is about 1.76 m. It can be seen that the positioning error of the node increases as the interval between the mobile signal identification points increases, which is consistent with the simulation detection result. When the node position of the target signal identification point is chosen to calculate does not just stay on the node communication circle, it introduces a certain localization distance difference, and the further the target signal identification point is from the position of the signal circle, the greater the error. Irregularity of RSS due to environmental changes analyzes the maximum error and provides the factors influencing the error and analyzing the maximum error and provide the factors that influence it.

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.

A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

Influence of Noise on Fault Diagnosis of Transformer based on Vibration Signal

Monitoring transformer vibration signals is a universal application method to realize the diagnosis of internal mechanical faults of transformers. However, the actual transformer operating is interfered by the noise of the surrounding electrical equipment, which reduces the accuracy of the vibration signal identification. This paper simulate the typical noise sources in the actual transformer operating environment, including fan noise and surrounding equipment fault noise, and explore the impact of different noise sources on the transformer vibration signal.

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.