دانلود کتاب پیشرفت‌ها در پردازش تصویر، هوش مصنوعی و رباتیک هوشمند

Bioimpedance (BI) measurement is a well-known and commonly used non-destructive material investigation technique [1,2]. Besides being non-destructive, the method’s attractiveness arises from its relatively simple and cost-effective instrumentation requirements, easy mass production, and efficient implementation procedures [2]. BI methods hold the promise of creating a portable, wearable spectrometer capable of performing numerous measurements virtually and invisibly at any time of day, even during physical activity [3]. Consequently, there is great interest in in vivo BI measurements, especially in human studies [4,5]. The method’s popularity in this field depends on its ability to determine the in vivo body composition of test subjects using prototypes and commercially available devices that are simple, user-friendly, and highly standardized at the same time [1,2,6–8]. The body composition parameters that can be determined by BI measurements are extracellular fluid, intracellular fluid, total body water, fat mass, fat-free mass, etc. [4]. Monitoring these features provides new perspectives for valuable population studies in the field of public health [4]. For example, monitoring the whole-body content of multi-ethnic groups [9], integration into e-Health programs [10], application in the training of athletes [11], and other clinical applications [12] can be considered. The implementation of modern BI measurements beyond a single frequency (50 kHz) supports multiple-frequency and BI spectrum-based data collection [4]. In such cases, an AC (alternating current) excitation signal at a very low amplitude is usually applied to dedicated surface electrodes through the biological system under investigation, while the other surface electrodes are used to record the parameters (potential or voltage) of the resulting electric field [4]. In the case of multi-frequency implementations, measurement systems have been developed to simultaneously apply several excitation signals of different frequencies, while others measure the BI spectrum using a swept sine signal [2,4]. BI spectroscopy (BIS) has several advantages over other technologies since it maximizes the amount of useful information extracted from the data to characterize biological structures [4]. The excitation frequency range for BIS equipment is usually systematically shifted between 5 kHz and a few hundred kHz [4]. Typically, the frequency domain below 5 kHz is referred to as the low-frequency range [4]. In this paper, measurements of high impedances are conducted to investigate whether the underlying BIS measurement procedure and the self-developed prototype are capable of detecting model parameters as accurately as possible over the full frequency range. In order to accomplish these tasks, a new neural network-based data processing software is developed. The neural networks have already been used to estimate the parameters of the impedance spectrum in solving the inverse problem [13]. In addition, the Hilbert transformation has been used for parameter estimation involving neural networks [14]; however, it has not been used for spectroscopic inverse problem solutions. When the Hilbert transformation is applied, it ensures that the corresponding real and imaginary components of the impedance spectra are appropriately selected during the optimization process. Selecting the real and imaginary spectra in this manner and then applying the inverse procedure results in a more accurate determination of the R and C values compared to not using the Hilbert transformation. This software extracts the model parameters from the imported BIS data. Based on [15], specific physical models have been developed that include the impedance of the excitation electrodes, as well as the impedance of the material under investigation based on the Cole–Cole model [16] for the realization of this study. Two groups of physical models, called phantoms, have been constructed. The first group is used for neural network training, while the second group is used to verify the accuracy and robustness of the technology through the integration of ultra-precise components. Besides developing BIS technology, application development is also actively in progress, which will lead to the primary use of self-developed prototypes in biotechnological and clinical applications.