This letter presents design and implementation of a system solution, where light-weight wireless devices are used to identify a moving object within underground pipeline for maintenance and inspection. The devices such as transceiver operating at S band are deployed for underground settings. Finer-grained Channel information in conjunction with leaky wave cable detects any moving entity. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying a leaky wave cable as the receiver has benefits in terms of a wider coverage area, covering blind and semi blind zones. The system fully exploits the variances of both amplitude and phase information of Channel information as the performance indicators for motion detection. The experimental results demonstrate greater level of accuracy.Index Terms-Channel information, Leaky Wave Cable (LWC), Access Point (AP).
Cerebellar Ataxia (CA) is a neurological disease with the symptom of poor coordination of movement and balance disorders. In clinical medicine, the heel-knee-shin test and the rapid alternating movements test are important basis for assessing CA. Based on the above tests, this paper presents a non-contact method and investigates the feasibility of this method for detecting CA. This body sensor networks uses wireless devices operating in the C-band frequency range to capture data of both types of tests without intrusiveness. The obtained data of tests contains massive useful information about human health which is really significant to subjects. But the information can be so subtle that people ignore the value hidden in it. So utility pattern mining (UPM) is used for the purpose of mining subjects' activity pattern. We find that the subjects' activity pattern differs greatly in amplitude information. We extracted the amplitude information that is helpful for analyzing the test' results to determine whether the test is positive or negative. Then we use different kind of algorithms to classify the data samples. Among them, support vector machine (SVM) has the best classification effect on both tests. In the heel-knee-shin test, the coincidence rate (π) is 98.7%, the sensitivity (Se) is 98.9% and the specificity (Sp) is 98.5%. In the rapid alternating movements test, the π is 99.4%, Se is 99.8% and Sp is 99%. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate CA monitoring in a patient-friendly and flexible environment. INDEX TERMS Cerebellar Ataxia, body sensor networks, C-band, pattern mining.
Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity.
Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol.
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