Daylight-driven rechargeable antibacterial and antiviral nanofibrous membranes for bioprotective applications.

Emerging infectious diseases (EIDs) are a significant burden on global economies and public health. Most present personal protective equipment used to prevent EID transmission and infections is typically devoid of antimicrobial activity. We report on green bioprotective nanofibrous membranes (RNMs) with rechargeable antibacterial and antiviral activities that can effectively produce biocidal reactive oxygen species (ROS) solely driven by the daylight. The premise of the design is that the photoactive RNMs can store the biocidal activity under light irradiation and readily release ROS under dim light or dark conditions, making the biocidal function "always online." The resulting RNMs exhibit integrated properties of fast ROS production, ease of activity storing, long-term durability, robust breathability, interception of fine particles (>99%), and high bactericidal (>99.9999%) and virucidal (>99.999%) efficacy, which enabled to serve as a scalable biocidal layer for protective equipment by providing contact killing against pathogens either in aerosol or in liquid forms. The successful synthesis of these fascinating materials may provide new insights into the development of protection materials in a sustainable, self-recharging, and structurally adaptive form

Detection and diagnosis of paralysis agitans

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

Posture-specific breathing detection

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

Noninvasive suspicious liquid detection using wireless signals

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

Buried Object Sensing Considering Curved Pipeline

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 (LWC) detects any moving entity. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying an LWC as the receiver has benefits in terms of a wider coverage area, covering blind and semiblind 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

Design of High-Voltage Switch-Mode Power Amplifier Based on Digital-Controlled Hybrid Multilevel Converter

Compared with conventional Class-A, Class-B, and Class-AB amplifiers, Class-D amplifier, also known as switching amplifier, employs pulse width modulation (PWM) technology and solid-state switching devices, capable of achieving much higher efficiency. However, PWM-based switching amplifier is usually designed for low-voltage application, offering a maximum output voltage of several hundred Volts. Therefore, a step-up transformer is indispensably adopted in PWM-based Class-D amplifier to produce high-voltage output. In this paper, a switching amplifier without step-up transformer is developed based on digital pulse step modulation (PSM) and hybrid multilevel converter. Under the control of input signal, cascaded power converters with separate DC sources operate in PSM switch mode to directly generate high-voltage and high-power output. The relevant topological structure, operating principle, and design scheme are introduced. Finally, a prototype system is built, which can provide power up to 1400 Watts and peak voltage up to ±1700 Volts. And the performance, including efficiency, linearity, and distortion, is evaluated by experimental tests

Theoretical and Experimental Study of the Effects of Impact Drilling Parameters on the Properties of Surrounding Rock Damage

Using a self-designed hydraulic impact drilling test-bed and rock core drill, six groups of cylindrical granite specimens (93 mm dia. × 200 mm) containing central axial holes formed either by impact or nonimpact drilling methods were tested in uniaxial compression to failure on an Instron 1346 universal testing machine to investigate their mechanics and damage properties. The longitudinal acoustic wave velocities were measured before testing. The rock specimens were grouped according to the method of drilling the central hole (impact load exerted by different impact power and different frequencies for an approximately identical impact power, or nonimpact drilling). In this study, a statistical constitutive damage model based on Weibull distribution was used to calculate the degree of rock damage after drilling center holes. The experimental curves were measured to analyze the damage evolution process and the radius of rock damage. These indicate that rock damage increased with the increase of impact power and decreased with increasing impact frequency at constant impact power. This was also verified by the measured longitudinal wave velocity in all rock specimens. These results have significance for guiding the design of composite rock drilling tools that are dedicated to improving rock-breaking efficiency

Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB

Software Design of a Monitoring and Management System in the Internet of Things

Internet of Things concept can be applied to all aspects of life, such as electricity, agriculture, transportation, energy and habitation. Most of the common monitoring systems in the Internet of Things (IoT) [1] suffer from lack of versatility as they apply only to a particular application area. Software design of the monitoring and management system for the IoT, as proposed in the paper, has high levels of flexibility and can be used in different application scenarios. The system, based on B/S mode, builds a web platform in the application layer. After setting the software in the application layer, we can configure the sensor network in the sense layer or modify the parameters of hardware and sensor. The system can show the data in the form of list, curve and video [2]

Scalable Parallel Distributed Coprocessor System for Graph Searching Problems with Massive Data

The Internet applications, such as network searching, electronic commerce, and modern medical applications, produce and process massive data. Considerable data parallelism exists in computation processes of data-intensive applications. A traversal algorithm, breadth-first search (BFS), is fundamental in many graph processing applications and metrics when a graph grows in scale. A variety of scientific programming methods have been proposed for accelerating and parallelizing BFS because of the poor temporal and spatial locality caused by inherent irregular memory access patterns. However, new parallel hardware could provide better improvement for scientific methods. To address small-world graph problems, we propose a scalable and novel field-programmable gate array-based heterogeneous multicore system for scientific programming. The core is multithread for streaming processing. And the communication network InfiniBand is adopted for scalability. We design a binary search algorithm to address mapping to unify all processor addresses. Within the limits permitted by the Graph500 test bench after 1D parallel hybrid BFS algorithm testing, our 8-core and 8-thread-per-core system achieved superior performance and efficiency compared with the prior work under the same degree of parallelism. Our system is efficient not as a special acceleration unit but as a processor platform that deals with graph searching applications