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An electronic fuze is a one-shot system that has a long storage life and high mission criticality. Fuzes are designed, developed, and tested for high reliability (over 99%) with a confidence level of more than 95%. The electronic circuit of a fuze is embedded in the fuze assembly, and thus is not visible. Go/NoGo fuze assembly mission critical testing does not provide prognostic information about electrical and electronic circuits and subtle causes of failure. Longer storage times and harsh conditions cause degradation at the component level. In order to calculate accrued damage due to storage and operational stresses, it is necessary to perform sample-based accelerated life testing after a certain time and estimate the remaining useful life of mission critical parts. Reliability studies of mechanical parts of such systems using nondestructive testing (NDT) have been performed, but a thorough investigation is missing with regards to the electronic parts. The objective of this study is to identify weak links and estimate the reliability and remaining useful life of electronic and detonating parts. Three critical components are identified in an electronic fuze circuit (1) a diode, (2) a capacitor, and (3) a squib or detonator. The accelerated test results reveal that after ten years of storage life, there is no significant degradation in active components while passive components need to be replaced. The squib has a remaining useful life (RUL) of more than ten years with reliability over 99%.

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems, such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people’s living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7%. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications.

In our daily life, inadvertent scratching may increase the severity of skin diseases (such as atopic dermatitis, etc.). However, people\ud rarely pay attention to this matter, so the known measurement behavior of the movement is also very little. Nevertheless, the behavior and\ud frequency of scratching represent the degree of itching, and the analysis of scratching frequency is helpful to the doctor's clinical dosage. In this\ud paper, a novel system is proposed to monitor the scratching motion of a sleeping human body at night. The core device of the system are just a\ud Leaky coaxial cable (LCX) and a router. Commonly, LCX is used in the blind field or semi blind field in wireless communication. The new idea is\ud that the leaky cable is placed on the bed, then the state information of physical layer of wireless communication channels is acquired to identify the\ud scratching motion and other small body movements in the human sleep process. The results show that it can be used to detect the movement and its\ud duration. Channel state information (CSI) packet is collected by card installed in the computer based on the 802.11n protocol. The characterization\ud of the scratch motion in the collected channel state information is unique, so it can be distinguished from the wireless channel amplitude variation\ud trend.

Objective: Non-invasive respiration detection methods are of great value to healthcare applications and disease diagnosis with their advantages of minimizing the patient’s physical burden and lessen the requirement of active cooperation of the subject. This method avoids extra preparations, reduces environmental constraints, and strengthens the possibility of real-time respiratory detection. Furthermore, identifying abnormal breathing patterns in real-time is necessary for the diagnosis and monitoring of possible respiratory disorders. Method: A non-invasive method for detecting multiple breathing patterns using C-band sensing technique is presented, which is used for identifying different breathing patterns in addition to extract respiratory rate. We first evaluate the feasibility of this non-contact method in measuring different breathing patterns. Then, we detect several abnormal breathing patterns associated with certain respiratory disorders at real time using C-band sensing technique in indoor environment. Results: Mean square error (MSE) and correlation coefficient (CC) are used to evaluate the correlation between C-band sensing technique and contact respiratory sensor. The results show that all the MSE are less than 0.6 and all CC are more than 0.8, yielding a significant correlation between the two used for detecting each breathing pattern. Clinical Impact: C-band sensing technique is not only used to determine respiratory rates but also to identify breathing patterns, regarding as a preferred noncontact alternative approach to the traditional contact sensing methods. C-band sensing technique also provides a basis for the non-invasive detection of certain respiratory disorders., Normal breath for one minute.

© 2019 Elsevier Ltd This paper presents the application of wireless sensing at C-band operating at 4.8 GHz technology (a potential Chinese 5G band). A wireless transceiver is used in the indoor environment to monitor different body motions of a woman experiencing an eclamptic seizure. The body movement shows unique wireless data which carries the wireless channel information. The results indicate that using higher features increases the accuracy from 3% to 4% for classifying data from different body motions. All of the four classifiers are compared by using six performance metrics such as accuracy, recall, precession, specificity, F-measure and Kappa. The values from these metrics indicate the better performance of SVM as compared to other three classifiers, the results indicate that the eclamptic seizures are easily differentiated from other body movements by applying the aforementioned classifiers.

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are na&iuml, ve 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.

Internet of Thing (IoT) in the wireless sensing utilizing 5G C-Band is the modern technology and taking 5G C-Band wireless sensing to the higher paradigms. The healthcare sector utilizing 5G C-Band technology will increase the efficiency of monitoring the fall and other body motions with maximum capacity and extensive spectrum range. We have proposed the unique system operating at 4.8 GHz frequency for measuring the wireless channel information (WCI) of post-surgical falls and other related human activities. The overall system exploit the low cost simple wireless devices including NIC card, RF signal generator, Omni directional antenna and a desktop PC. The fall and other human activities are classified by using machine-learning algorithms like Support Vector Machine (SVM), Naive Bayes and Decision Tree. The three main kernel functions RBF, Linear and polynomial were used for the classification along with the 10 time domain features. The performance of the classifiers increases by using the maximum number of features. We have concluded that the SVM proves to be the best classifier for our wireless data by measuring the performance metrics like accuracy (above 90 %), F-measure, Precession, Specificity, Recall, and Kappa coefficient. The error rate for SVM proved to be the less as compared with the other two classifiers. The accuracy increases with the rate of 4 to 5 %. Our approach shows more feasibility for detecting the post-surgical fall in hospitals and more reliable with maximum accuracy.

This research presents the usage of modern 5G C-Band sensing for health care monitoring. The focus of this research is to monitor the respiratory symptoms for COPD （Chronic Obstructive Pulmonary Disease）. The C-Band sensing is used to detect the respiratory conditions, including normal, abnormal breathing and coughing of a COPD patient by utilizing the simple wireless devices, including a desktop system, network interface card, and the specified tool for the extraction of wireless channel information with Omni directional antenna operating at 4.8 GHz frequency. The 5G sensing technique enhances the sensing performance for the health care sector by monitoring the amplitude information for different respiratory activities of a patient using the above-mentioned devices. This method examines the rhythmic breathing patterns obtained from C-Band sensing and digital respiratory sensor and compared the result.

Utilizing fifth‐generation (5G) sensing in the health care sector with increased capacity and massive spectrum range increases the quality of health care monitoring systems. In this paper, 5G C‐band sensing operating at 4.8 GHz is used to monitor a particular body motion of multiple sclerosis patients, especially the tremors and breathing patterns. The breathing pattern obtained using 5G C‐band technology is compared with the invasive breathing sensor to monitor the subtle chest movements caused due to respiration. The 5G C‐band has a huge spectrum from 1 to 100 GHz, which enhances the capacity and performance of wireless communication by increasing the data rate from 20 Gb/s to 1 Tb/s. The system captures and monitors the wireless channel information of different body motions and efficiently identifies the tremors experienced since each body motion induces a unique imprint that is used for a particular purpose. Different machine learning algorithms such as support vector machine, k‐nearest neighbors, and random forest are used to classify the wireless channel information data obtained for various human activities. The values obtained using different machine learning algorithms for various performance metrics such as accuracy, precision, recall, specificity, Kappa, and F‐measure indicate that the proposed method can efficiently identify the particular conditions experienced by multiple sclerosis patients.