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Fine particulate matter (PM) is associated with an increased risk of respiratory and cardiovascular diseases. Fine PM absorbs water molecules at high relative humidity, and then their size grows. Such hygroscopic growth causes a large error when monitoring PM concentrations. To lower the relative humidity, monitors use an indirect heating device, which is large and consumes large amounts of power. The problem with conventional particle separators is that their efficiency depends on temperature and humidity, and their traditional structure, which lets air flow downward. As such, this paper addresses these problems and presents a PM monitor with a new type of dryer that is free from these problems. The proposed monitor requires less energy and has an efficient dehumidifier and a new structure in which air flows upward. The presented experiments were conducted to compare the proposed device with a reference monitor managed by a governmental institute, and to evaluate the effect of the dehumidifier, the relative precision of the proposed devices, and the correlation with the reference monitor. The experimental results showed that the proposed monitor satisfies the U.S. EPA indicators for class III monitors.As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.Nowadays, a large number of sensors are employed in the oceans to collect data for further analysis, which leads to a large number of demands for battery elimination in electronics due to the size reduction, environmental issues, and its laborious, pricy, and time-consuming recharge or replacement. Numerous methods for direct energy harvesting have been developed to power these low-power consumption sensors. Among all the developed harvesters, piezoelectric energy harvesters offer the most promise for eliminating batteries from future devices. These devices do not require maintenance, and they have compact and simple structures that can be attached to low-power devices to directly generate high-density power. In the present study, an atlas of 85 designs of piezoelectric energy harvesters in oceanic applications that have recently been reported in the state-of-the-art is provided. The atlas categorizes these designs based on their configurations, including cantilever beam, diaphragm, stacked, and cymbal configurations, and provides insightful information on their material, coupling modes, location, and power range. A set of unified schematics are drawn to show their working principles in this atlas. Moreover, all the concepts in the atlas are critically discussed in the body of this review. Different aspects of oceanic piezoelectric energy harvesters are also discussed in detail to address the challenges in the field and identify the research gaps.In order to solve multiple unmanned aerial vehicle (UAV) dynamic collision avoidance, a cooperative obstacle avoidance algorithm considering UAV's kinematic constraints has been developed. In the proposed algorithm, the useful information of UAVs is screened out by a Heartbeat information filtering mechanism and fused by the user datagram protocol (UDP) communication method, which improves communication performance among UAVs. In addition, the velocity obstacle (VO) method combined with cubic uniform B-spline curve is used to avoid obstacles and generate smooth paths, which can be applied to practical scenes. Finally, dynamic and static obstacle avoidance simulations are carried out to verify the effectiveness of the proposed algorithm.Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.Content Delivery Network (CDN) technology is one of the core technologies for performance optimization of mobile WEB applications, but it often encounters bottlenecks in video storage. In this paper, based on studying the basic technical model of distributed real-time transcoding CDN, we propose an overall technical architecture of a distributed real-time transcoding CDN system, present the business process of distributed real-time transcoding CDN resource negotiation in the model, design a resource storage policy model and algorithm, including the hot storage ratio resource storage value evaluation method and storage resource allocation algorithm. Through simulation experiments, we verify the technical efficiency of the distributed real-time transcoding CDN system by simulation experiments.At the beginning of the current century, Ethernet-based communication networks began to be implemented in industrial applications. Some previously used protocols were migrated to Ethernet networks, while many others were strictly developed for this communication medium. Numerous industrial Ethernet protocols do not deliver all the capabilities provided by the Ethernet. For example, limitations may arise associated with wireless communication, use of dedicated switching devices, or operation solely for certain topologies. On the other hand, new technologies are now available, such as software defined networks (SDN), that add new features to Ethernet-based communication systems. In this paper, an EtherCAT network in combination with SDN is analyzed. EtherCAT network may only consist of devices with an implemented EtherCAT protocol stack. Therefore, regular Ethernet switches cannot typically be used in this network and, hence, special network infrastructure may be required to create topologies other than standard line topology. It is shown, however, that this limitation can be overcome by the application of SDN. In addition, a definition of datagram forwarding rules (called SDN flows here) is given, and we demonstrate that EtherCAT datagrams can be sent through routes that are required for proper EtherCAT network operation.In rail transport, various automatic protection systems are available to ensure the safe operation of trains and to facilitate automation and optimization tasks. For this purpose, a set of physical balises is used, which are placed at fixed points along the railway track. Based on the information provided by these balises, different information is displayed to the driver, and control actions are generated. The use of physical balises located at fixed points does not allow for automatic protection actions on sections of track where they are not installed. This is a major drawback as in many cases, temporary automatic protection actions are necessary on sections of the railway line without balises due to various circumstances (work on the track, accidents, etc.). To solve this problem, this paper presents a solution called announcement signals and automatic braking using virtual balises (ASAB-VB). This proposal allows the incorporation of virtual balises at points on the track where it is necessary to temporarily perform automatic protection actions. For this purpose, the ASAB-VB system allows obtaining the train position in real-time and storing a digital map of the track that will be made by each train. This digital map includes geographic information about the balises (both physical and virtual ones) located on the track. At the same time, the train position is obtained by merging the information provided by a GNSS, an odometer, and an inertial system (gyro and accelerometers).