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Remote Sensing Data Fusion
Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving operations and functions without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors. An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task. Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030.
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving functions, without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors. An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task. Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030.
Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.
keywords : HRV, accelerometers, stress;
Crowd sensing (also known as participatory sensing, or mobile crowdsensing) is a means of collecting people’s surrounding information via mobile sensing devices. Its highly expressive and powerful sensing capabilities can carry out a big sensing project by fragmenting tasks into small pieces. The key to success is to get more participants to collect higher quality data.
Gyrotrons are among the most powerful sources of coherent radiation that operate in CW and long pulse regimes in the sub-THz and the THz frequency ranges of the electromagnetic spectrum, i.e. between 0.3 THz and 3.0 THz (corresponding to wavelengths from 1.0 to 0.1 mm). This region, which spans between the frequency bands occupied by various electronic and photonic devices, respectively, is habitually called a THz power gap. The underlying mechanism of the operation of the gyrotron involves a formation of bunches of electrons gyrating in a helical electron beam and their synchronous interaction with a fast (i.e. having a superluminal phase velocity) electromagnetic wave, producing a bremsstrahlung radiation. In contrast to the slow-wave tubes, which utilize tiny structures with dimensions comparable to the wavelength of the radiation, the gyrotrons have a simpler resonant system (cavity resonator) with dimensions that are much greater than the wavelength. This allows much more powerful electron beams to be used and thus higher output powers to be achieved. Although in comparison with the classical microwave tubes the gyrotrons are characterized by greater volume and weight due to the presence of bulky parts (such as superconducting magnets and massive collectors where the energy of the spent electron beam is dissipated) they are much more compact and can easily be embedded in a sophisticated laboratory equipment (e.g. spectrometers, technological systems, etc.) than other devices such as free-electron lasers (FEL) and radiation sources based on electron accelerators. Nowadays, the gyrotrons are used as powerful sources of coherent radiation in the wide fields of high-power sub-THz and THz science and technologies .
Han-Chuan Hsieh was received a B.S. degree in Electrical Engineering from National Taipei University of Technology (NTUT), in 1998, and an M.S. degree in Communication Engineering from Tatung Institute of Technology, Taipei, Taiwan, in 2008. He has been a Ph.D. degree in Department of Electrical Engineering of National Taiwan University of Science and Technology (NTUST).
Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living.
Laser absorption spectroscopy (LAS) is an absorption spectroscopic method that employs a laser as the light source and measures the chemical concentration based on detection of a variation of laser beam intensity after transmission along the optical path.
LPWAN stands for Low Power Wide Area Network; LPWAN provides long-distance communication for rural and urban areas to support IIoT devices considered by a ten-year provision time to acclimate IIoT applications with higher extensibility, availability of intelligent monitoring infrastructure for a small portion of data exchanges. LoRa is favorable to use with smart sensing applications working IIoT non-authored spectrum. NBIoT is suitable for supporting agriculture and environmental data collection and observations, industrial data tracking and monitoring, inventory tracking, smart billing, and smart buildings, smart metering, and smart cities. Machine-to-machine (M2M) communication uses the Bluetooth Low Energy (BLE) technique for the data communication, the other IIoT applications used in healthcare, smart agriculture, intelligent home, smart vehicles, smart city, smart gadgets, and industries use the cognitive LPWAN, LoRA, Sigfox. There is a need to mix most LPWAN technologies in heterogeneous IIoT applications to provide more efficient and convenient intelligent services. In heterogeneous IIoT applications, there a need to mix most LPWAN technologies to provide more efficient and convenient intelligent services. This will be deployed by cognitive LPWAN
With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.
With the growth of satellite and airborne-based platforms, remote sensing is increasing attention in the last decades. Everyday, sensors acquire data with different modalities and several resolutions. Leveraging on their complementary properties is a key scientific challenge, usually called remote sensing data fusion.