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A novel adaptive heart rate monitoring and filtering system based on algebraic distance minimization has been designed. This system uses frequency-modulated continuous-wave radar for non-contact monitoring of parameters such as respiration, heart rate, and heart rate variability (HRV). By employing an improved algebraic distance minimization technique, the system suppresses static noise from the environment, enhances the signal-to-noise ratio of the intermediate-frequency signal, and reduces the complexity of subsequent signal processing. To address issues such as low-frequency interference from radar signals reflecting off fixed targets around the subject and the impact of respiratory harmonics on heart rate measurement, this study introduces a respiratory harmonic filter and an adaptive notch filter to eliminate higher-order respiratory harmonic interference. This approach allows for the adaptive decomposition and reconstruction of vital sign signals and final estimation of respiratory heart rate based on the relationship between harmonics. Comparative experiments with intensive care unit medical equipment have validated the superiority and robustness of the proposed algorithm. Experimental results show that the RMSE and MRE of the method are 2.66% and 2.7%, respectively. After multiple measurements across various subjects, the method achieved an average accuracy of 96.5%, demonstrating higher measurement accuracy than other methods in all cases. Additionally, the algorithm also enables the detection of heart rate variability.

In this paper, a novel integrated SHE-NLC control is proposed for the mitigation of unwanted lower-order harmonics in the cascaded H-bridge multilevel inverter. The proposed algorithm has been developed by hybridizing the NLC and SHE methods, in an attempt to keep the merits of both the individual control. The switching angles and nearest levels are calculated by applying the Genetic Algorithm (GA). The proposed technique reduces the calculation time and implementation complexity, thus can be a viable alternative to the real time implementation for SHE. Integrated NLC-SHE control is tested by varying the modulation index and load dynamics. This technique resulted in the reduction in Total Harmonic Distortion (THDs) in load voltage and current. Comparative analysis of NLC, SHE and Integrated NLC-SHE technique is also performed on the CHB nine-level inverter. There is significant reduction in voltage and current THD values and power losses. Efficiency of the inverter is increased. The efficacy of the proposed control CHB nine-level is tested on MATLAB Simulink environment and further validated by experimental results.

In order to eliminate the harmonics of the grid connected inverters accurate harmonic detection is essential. Though there are many kinds of different harmonic detection methods are available, it can hardly be achieved without the help of grid frequency detection of the fundamental component. In this paper a novel Lock-In Amplifier based Frequency Locked Loop (LIA-FLL) is proposed for the improved harmonic elimination of the grid-connected three phase inverters. Due to the outstanding performance of the LIA-FLL, the harmonics can be detected and compensated accurately under the grid frequency variation. To demonstrate the superiority of the proposed method, it is applied to a 10 kW three phase grid connected inverters. The results obtained by the proposed method are compared with those obtained by the harmonic elimination method with no FLL. The principle of LIA-FLL is discussed and its performance for the harmonic elimination is proved by the simulation and experiments with a 10KW three phase grid connected inverter under the highly polluted grid condition.

The dynamics of a machine tool have an important influence on the quality and efficiency of the machining process. In this paper, the modal parameters identification method of machine tools under normal cutting excitation is proposed based on the fact that the random components in the cutting force can provide an effective excitation. However, the generated cutting force during machining contains strong periodic components and does not satisfy the white noise assumption. Directly applying the operational modal analysis (OMA) method will face serious harmonic interference. Therefore, it is difficult to ensure the accuracy of the identified parameters. The cepstrum editing method is proposed to eliminate the periodic component. And the modal parameters are extracted from the remaining signal by using the OMA method. The experimental results show that the proposed method works well under normal cutting conditions.

This work introduces a 49-level Asymmetrical Inverter (AMLI) with boosted output based on the cascaded operation of two 7-Level Modified Packed U-Cell inverters (MPUC-7). The converter is capable of operation with a boosted voltage of up to 1.714 times the maximum DC voltage employed. It requires only 12 active switches and 4 voltage sources. With the sources set in the ratio of 14:7:2:1, the 7-level output of the two converters is so utilized that the 72 = 49-level output voltage is generated across the load. A detailed explanation of level formation is discussed. This converter is operated using an Artificial Neural Network (ANN) which is trained for the harmonic elimination in the output voltage waveform. For the calculation of optimum angles, a meta-heuristic based Genetic Algorithm (GA) technique is employed. The generation of 49-level output requires 24 transitions in one quarter of a cycle. All these angles are generated for various desired output voltages, and the ANN is trained offline for the same. The converter and its control are simulated in MATLAB/Simulink® environment, and the results are verified on the experimental setup. The multilevel output thus obtained is nearly sinusoidal and the Total Harmonic Distortion (THD) thus produced is under the specified limit of IEEE.