Devices In Different Locations Research Articles

Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations

Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility and transferability of implemented models are recommended since a reference dataset for local pollen of interest needs to be collected for each device to classify pollen, which is complex and time-consuming. Therefore, it would be beneficial to incorporate the reference dataset collected from other devices in different locations. However, it must be considered that laser-induced data are prone to device-specific noise due to laser and detector sensibility. This study collected data from two Rapid-E bioaerosol identifiers in Serbia and Italy and implemented a multi-modal convolutional neural network for pollen classification. We showed that models lost their performance when trained on data from one and tested on another device, not only in terms of the recognition ability but also in comparison with the manual measurements from Hirst-type traps. To enable pollen classification with just one model in both study locations, we first included the missing pollen classes in the dataset from the other study location, but it showed poor results, implying that data of one pollen class from different devices are more different than data of different pollen classes from one device. Combining all available reference data in a single model enabled the classification of a higher number of pollen classes in both study locations. Finally, we implemented a domain adaptation method, which improved the recognition ability and the correlations of transferred models only for several pollen classes.

Performance Analysis and Optimization for the MAC Protocol in UAV-Based IoT Network

Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2-D plane based on the CSMA/CA protocol. Due to UAV's continuous mobility, the communication durations of devices in different locations with UAV are not only time-limited, but also vary from each other. To analyze the throughput performance of uplink multiple access control (MAC) protocol, we propose a new analysis model to deal with the communications heterogeneity in the network. Firstly, we divide the devices in the coverage into different clusters according to their communication durations. Then, a quitting probability indicating the probability that a device quits the UAV's coverage at each time slot is clarified. A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis. Besides, we also propose a modified CSMA/CA protocol which fully considers the heterogeneity of the access time and adaptively allocates the time resource among the devices in different clusters. Finally, the effects of retry limit, initial contention window size, the density of the devices, UAVs speed and coverage area are discussed in the simulation section.

Novel design of smart sleep-lighting system for improving the sleep environment of children.

BACKGROUND: Children struggle to fall asleep by themselves because of their physiological characteristics. Therefore, research has been carried on various devices (such as a smartphone) to assist in improving the sleep quality of children. However, all such devices need to be controlled by parents and do not have functions for monitoring the sleep environment.OBJECTIVE: In this paper, a smart sleep-lighting system that includes a sleep-lighting device and a smartphone dongle is developed to improve the sleep environment of children.METHODS:The temperature, humidity, and luminance of the sleep environment are monitored and analyzed by the sleep-lighting device to control multi-color light and audio components. The colored light emitted by the multi-color light can be adjusted to improve the sleep atmosphere. Also, the audio component can play white noise to induce sleep. In addition, parents can use a smartphone dongle with a multi-channel wireless communication method to monitor and control one or more lighting devices in different locations in real time.RESULTS: For environmental monitoring, average difference between proposed device and commercial sensor from chamber setting temperature 15C to 35C was 0.588C 0.10C, and average error value of the humidity measurement was 0.74% at 40% 60% RH. Also, the manufactured sleep-lighting device shows good performance in multi-color light emission, and playing of white noise. As result, the smartphone connected to the proposed smartphone dongle enables monitoring and control of the proposed lighting device in a wireless well.CONCLUSIONS:The manufactured sleep-lighting device has a high-precision temperature and humidity sensor and a luminance sensor that can accurately monitor the sleeping environment. The lighting device can play white noise to induce sleep in children. Also, a multi-color LED light is operated via a smartphone application to improve the sleep atmosphere. The measured data will be sent to the lighting device and processed together with sleep environment data in order to improve the sleep quality. Additionally, the final system will be tested for real end-users with clinical experiments by sleep research center of a university hospital.

Active authentication with reinforcement learning based on ambient radio signals

Active authentication of mobile devices such as smartphones and ipads is promising to enhance security to access confidential data or systems. In this paper, we propose an active authentication scheme, which exploits the physical-layer properties of ambient radio signals to identify mobile devices in indoor environments. More specifically, we discriminate mobile devices in different locations by analyzing the ambient radio sources, because the received signal strength indicator set of the ambient signals measured by a smartphone is usually different from that observed by its spoofer located in another area. We formulate the interactions between the legitimate mobile device and its spoofer as an active authentication game, in which the receiver chooses its test threshold in the hypothesis test in the spoofing detection, while the spoofer chooses its attack strength. In a dynamic radio environment with unknown attack parameters, we propose a learning-based authentication algorithm based on the physical-layer properties of the ambient radio environments. Simulation results show that the proposed scheme accurately detects spoofers in typical indoor environments.

Possible risks due to exposure of workers and patients with implants by TETRA transmitters

Several studies have demonstrated that mobile telephones that use different technologies, such as Global System for Mobile Communication (GSM) or Universal Mobile Telecommunication System (UMTS), have the potential to influence the functionality of active electronic implants, including cardiac pacemakers. According to these studies, a few safety measures, such as maintaining minimum distances of 25 cm between implants and transmitters, are sufficient to avoid such effects. Terrestrial Trunked Radio (TETRA) has become a well-established communication standard in many countries, including Germany and Austria. TETRA transmitters are typically used by police forces and emergency services. Employees and volunteers working for such institutions are often in close contact with patients, causing TETRA transmitters to potentially have an impact on the functionality of the implants of patients. Therefore, the main focus of our study was to investigate the functionality of several types of implants when exposed to TETRA transmitters. Moreover, we investigated the difference in the degree of exposure of users of TETRA transmitters when they carry the devices in different locations near the body, and when they use them in different positions near the head. Our results show that a compliance distance of 30 cm between implant and transmitter is sufficient to exclude any influence on the examined implants. All examined exposure conditions demonstrated that the levels were well below recommended limits. If a user wants to minimize their exposure, use of transmitters in front of the mouth leads to somewhat lower exposure when compared to typical mobile phone like use.

N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.

This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.

The Altcriss project on board the International Space Station

The Altcriss project aims to perform a long term survey of the radiation environment on board the International Space Station. Measurements are being performed with active and passive devices in different locations and orientations of the Russian segment of the station. The goal is to perform a detailed evaluation of the differences in particle fluence and nuclear composition due to different shielding material and attitude of the station. The Sileye-3/Alteino detector is used to identify nuclei up to Iron in the energy range above ≃60 MeV/ n. Several passive dosimeters (TLDs, CR39) are also placed in the same location of Sileye-3 detector. Polyethylene shielding is periodically interposed in front of the detectors to evaluate the effectiveness of shielding on the nuclear component of the cosmic radiation. The project was submitted to ESA in reply to the AO in the Life and Physical Science of 2004 and data taking began in December 2005. Dosimeters and data cards are rotated every 6 months: up to now three launches of dosimeters and data cards have been performed and have been returned with the end of expedition 12 and 13.