Electrical impedance tomography (EIT) is a noninvasive measurement technique that estimates the internal resistivity distribution based on the boundary voltage–current data measured from the surface of the conductor. If a thin stretchable soft material with a certain piezoresistive property is used as the electrical conductor, EIT has the ability to reconstruct the position where the resistivity changes due to the inside pressure contact, so that a large‐scale artificial sensitive skin is provided for robotics. First, the different conduction principles and material types of artificial sensitive skins are discussed, which is next followed by the different driving modes and image reconstruction techniques. Then, details on how EIT is used for robotic skin applications are described. Finally, the development trends and future potentials of EIT‐based robotic skins are expounded.
The power-spectrum sub-band energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties and application in signal detection have been limited. Therefore, the statistical characteristics of the PSER were investigated and a signal detection method based on the PSER was created in this paper. By analyzing the probability and independence of power spectrum bins, as well as the relationship between F and beta distributions, we developed a probability distribution for the PSER. Our results showed that in a case of pure noise, the PSER follows beta distribution. In addition, the probability density function exhibited no relationship with the noise variance—only with the number of bins in the power spectrum. When Gaussian white noise was mixed with the signal, the resulting PSER followed a doubly non-central beta distribution. In this case, the probability density and cumulative distribution functions were represented by infinite double series. Under the constant false alarm strategy, we established a signal detector based on the PSER and derived the false alarm probability and detection probability of the PSER. The main advantage of this detector is that it did not need to estimate noise variance. Compared with time-domain energy detection and local spectral energy detection, we found that the PSER had better robustness under noise uncertainty. Finally, the results in the simulation and real signal showed that this detection method was valid.
The objective is to achieve the goal of carbon neutrality, controlling CO2 emissions is a primary problem to be solved in the current environmental protection field. Eliminating the environmental pollution of dyes in industrial wastewater is also an urgent problem to be solved in the field of environmental protection. The adsorption technique is an effective way to deal with these two issues. Developing high-efficiency adsorbents is an essential work in the adsorption field. In this work, a simple carbonization method has been developed to prepare porous carbon. The morphology and structure of the prepared porous carbon were investigated by scanning electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and surface area measurement. By studying the influence of preparation conditions on the structure and properties of the prepared carbon materials, it was found that the carbon materials prepared at 900 ℃ (PCMCA-900) exhibited a high specific surface area of 1476 m2 g−1 and a high total pore volume of 0.951 cm3 g−1. The PCMCA-900 showed the highest CO2 uptake of 7.67 mmol g−1 at 0 ºC and the adsorption capacity of PCMCA-900 decreased by less than 5% after 10 times recycling. Moreover, when PCMCA-900 was used as an adsorbent to remove Congo red in water, an adsorption capacity of 652.3 mg g−1 was achieved. Therefore, potassium citrate-derived porous carbon is a desirable candidate for excellent CO2 adsorption and Congo red in industrial wastewater.
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