Construction and Signal Feature Processing of Gold Nanobiosensors Based on the Internet of Things

With the continuous development of signal amplification technology and nanotechnology, more and more electrochemical sensors combining nanotechnology and signal amplification technology are applied in the field of analysis. In this paper, combined with the Internet of Things technology, the construction of gold nanobiosensors and signal characteristic processing are carried out. In this paper, a T-rich DNA probe is used as the recognition element, modified on the electrode surface, combined with DNA-modified nanogold particle amplification technology, and the electroactive substance peg amine is used as the signal molecule to develop a highly sensitive electrochemical biosensor for the detection of melamine. The sensor has good specificity and sensitivity, and the detection limit is as low as 0.5 NM. In addition, by combining sensors with the Internet of Things technology, melamine monitoring and signal characteristic processing can be carried out in real time. This model can easily achieve the purpose of accurate and quantitative analysis of melamine toxins and can be effective for food safety.

Application of digital signal processing and machine learning for Electromyography: A review

This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

A Study of the Effect of Fusion of Multi-Sensor and Cutting Chip Color on the Lifetime of Cutting Tool Coated with TiAlN

In the mold machining process, the cutting tool is worn with machining time, thereby affecting the surface accuracy, leading to poor workpiece dimensions, even fracture. At present, many studies have used multiple sensors to detect the machining conditions of cutting tool and workpiece, including indirect measurement method and direct measurement method. The indirect measurement method, which has been studied widely, mainly uses sensors to capture signals for subsequent data analysis; the direct measurement method mainly analyzes the state of cutting shear zone. Due to the cut-in of cutting tool in the machining process, the workpiece is dislocated rapidly, generating considerable amount of heat, which is transferred to the chips, inducing color change on the surface of chips. Many engineers with machining experience often judge the machining state and tool life according to the chips. The engineers' experience is digitized in this study, and indirect measuring sensors are used to predict the tool life, so as to attain the objective for smart manufacturing, the average percentage error of MAPE using single vibration and voltage eigenvalues as input features is 10%, the voltage signal characteristic values and vibration signal characteristic values are combined. Finally, the chip surface chromaticity eigenvalue is combined with signal characteristic value. The average prediction error of BP-LM method is 7.85%, the average prediction error of GRNN method is 6.59%. Therefore, when the eigenvalue of chip surface chromaticity is added to the prediction result, it can enhance the accuracy of cutting tool wear value prediction more effectively than single sensor signal characteristic value.

Evaluation of Ferromagnetic Steel Hardness Based on an Analysis of the Barkhausen Noise Number of Events

Measurements are carried out of the Barkhausen noise (BN) and hardness on specimens where changes in hardness were obtained due to strain hardening (S235 and DC01 steels) and due to thermochemical treatment (AMS 6414 steel). A method is presented of processing the recorded BN signal to extract diagnostic information. The BN number of events is selected as the signal characteristic property to develop relevant correlations. A new methodology is presented for the development of correlations between the Barkhausen noise number of events and hardness. A possibility is indicated of developing correlations with a high R2 determination coefficient. The method limitations are specified.