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As quite a potential technology, the Internet of Things is bound to experience a widespread application in a few years. This paper will conduct an in-depth study on the terminal of Internet of Things. Through studying the general architecture of the Internet of Things, according to its application requirements and technical conditions, the paper analyze the factors affecting the development of the Internet of Things, then adopts the module design to design the universal terminal structure of the Internet of Things. Since the energy consumption is a core issue in the wireless sensor network design, the level of energy management strategy will continue to improve with the development of wireless sensor networks. The paper proposes the energy management system based on energy collection. The paper also initially achieves a system which can provide energy permanently to the Internet of Things using solar energy and lithium batteries.

Nickel films were deposited by radio frequency magnetron sputtering on top of polycarbonate substrates. Surface energy of the substrate was measured by means of the contact angle technique. Effects of sputtering parameters on the critical load between the film and the substrate were determined by the universal mechanical testing system. Optimized fabrication parameters and their influence on the critical load between sputtered nickel films and polymer substrate were studied by means of the orthogonal experimental design. Increasing radio frequency power and time improved film critical load. The radio frequency power had a more pronounced effect on critical load than the sputter power. The plasma pretreatment with Ar gas modified the surface, leading to an increased surface energy, improving the chemical bonds between nickel and carbon atoms, and thereby enhanced the critical load. The adhesion mechanism is also discussed in this paper.

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.