The development of telecommunication generation is faster compared to the development of infrastructure in rural areas. This paper proposes an antenna having capability of multi-generation (MG) communications of 2G, 3G, 4G and 5G for rural area wireless communications. The antenna is also functioning for mobile cognitive radio base station (MCRBS) for post-disaster communications in either rural or urban areas. In this paper, the antenna is designed to work at the operating frequencies of 2G–5G in Indonesia, which are expected in between 0.8 GHz to 6 GHz. To cover any areas with radius of 5 km, we propose a Vivaldi antenna, called as MG-Vivaldi antenna, tested by a series of computer simulations, which is realized using aluminum with dimension of 50 cm × 100 cm. We obtain an MG-Vivaldi antenna having return loss RL ≤ −10 dB with gain G > 8 dB. We expect that the proposed MG-Vivaldi antenna contributes to the development of rural area communications as well as contributions for disaster mitigations.
Digital transformation (DT) has been introduced in all industries for improving the way of running business. Industries have embraced technology to different extents, as not all technologies can be equally implemented, even in semantically similar processes. This can be explained by the fact that the hype cycle of emerging technologies differently impacts industries from the time a concrete technology has been discovered and introduced, to the time when technology reaches its full potential of use in real processes, or so-called maturity. This paper presents a literature review on Digital transformation papers, which use the case study method in their researches and/or compare digital transformation case studies across different industries. The purpose of the analysis of case studies is to bring some light to real digital transformation processes and their success in practice. Analysed papers are selected from the Web of Science database based on content, industry and number of papers within it.
This paper presents new experimental results from a prototype Spectral LADAR, which combines active multispectral and 3D time-of-flight point cloud imaging. The physical domain unification of these imaging modalities based on a pulse modulated supercontinuum source enables substantially higher fidelity images of obscured targets compared to the data domain fusion of passive hyperspectral cameras and conventional LADAR imagers. Spectral LADAR produces 3D spectral point clouds with unambiguously associated 3D image points and spectral vectors, promoting improved object classification performance in cluttered scenes. The 3D shape and material spectral signature of objects may be acquired in daylight or darkness, behind common glass, and behind obscurants such as foliage and camouflage. These capabilities are demonstrated by data obtained from test scenes. These scenes include plastic mine-like objects obscured by foliage, distinction of hazardous explosives inside plastic containers versus innocuous decoy materials, and 3D spectral imaging behind ordinary glass windows. These scenes, at effective ranges of approximately 40 meters, are imaged with nanosecond-regime optical pulses spanning 1.08 μm to 1.62 μm divided into 25 independently ranged spectral bands. The resultant point cloud is spectrally classified according to material type. In contrast to other active spectral imaging techniques, Spectral LADAR is well suited to operate at high pixel and frame rates and at considerable stand-off distances. A combination of favorable attributes, including eye safe wavelengths, relatively small apertures, and very short (single pulse) receiver integration time, bear the potential for this technique to be used on robotic platforms for on-the-move imaging and high area coverage rates.
Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user's tasks and perform them on his/her behalf or facilitate the action completion. In this paper, we propose a framework that predicts user's actions by learning his/her behavior when interacting with the smart environment. We prepare the datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Our solution achieves a significant improvement for all target transducers studied and most combinations of parameters yields better results than the base case.
Row guidance is an important aspect of precision agriculture. Navigation system based on Global Positioning System has been well developed, however Global Positioning System cannot provide enough information on crop row and sometimes suffers from disturbance from the environment. So it is necessary to develop local sensor that can detect crop row during harvesting tasks. This article aims to integrate a self-developed mechanical detection sensor into a row guidance control system. The mechanical row detection device based on angular sensor was used to detect crop row and return detected values as deviation for row guidance controller. The control method was introduced after linearizing the kinematic model of farm vehicle, then the road test platform was introduced. The test was conducted under three different speeds. We used Global Positioning System and inertial measuring unit to record the position and heading of the vehicle. For the road test, the average cross track error are below 0.1m, which meet the demand of navigation of combine corn harvester.
The monitoring of urban waste water for agriculture use provides a smart solution for testing the quality of water by using array of sensors and the measured value is displayed in LCD. The major objective of this paper includes the estimation of water quality parameters, for instance, pH, Turbidity, Temperature, BOD, TDS that helps to identified the deviations in the parameters and provides an alert messages when there is an abnormal level i.e., the value exceeds the predefined threshold or the standard value set in the Arduino Mega 2560 Controller. These extreme values indicated chemical spills, treatment plant issues or the problems in supply pipes which may causes severe problem in terms of the cultivation of crops and quality of the soil anomaly detection of water quality setup using a GSM module, the data is stored in a cloud and server is connected with an IoT to sent message to the government and provides a remedial measure to over come these problems and helps the farmers to improve the sales and business processes.
Remote sensing technologies are widely used in maritime surveillance applications. Nowadays, spaceborne Synthetic Aperture Radar (SAR) systems provide outstanding capabilities for target detection at sea for large areas independently from the weather conditions. The generated value added target detection product is composed by complementary information from the Automatic Identification System (AIS). Resulting information layers provides a more reliable picture on the maritime situation awareness. This paper describes the approach of SAR-AIS data fusion and its visualization means developed for Near Real Time (NRT) Applications for Maritime Situational Awareness by the Maritime Security Lab at the Ground Station in Neustrelitz, part DLR’s German Remote Sensing Data Center (DFD). Presented implementation is based on combination of many open source geospatial libraries and frameworks (e.g., GDAL/OGR, Geoserver, PostgresSQL) and shows their effectiveness in the context of complex automated data processing in the frame of NRT requirements.
The last few decades have seen an exponential increase in the capability of computing devices with an equally substantial decrease in their form factors. The first wave was the ubiquitous mobile connectivity which gave way to the widespread adoption of the Internet of Things (IoT) paradigm and its later incarnation: The Internet of Everything (IoE) wherein almost every device is endowed with network communication capabilities. However, from a singular device's resource standpoint, many of these devices may not be able to fulfill the requirements of applications that run on them. An effective way to achieve substantial computing capability is to offload the computation to devices over the network. In this work, we explore mechanisms to optimize offloading decisions while minimizing transmission power. Our simulations show that substantial gains can be achieved from this offloading mechanism.
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