Evaluations of the Climatologies of Three Latest Cloud Satellite Products Based on Passive Sensors (ISCCP-H, Two CERES) against the CALIPSO-GOCCP

In this study, the climatologies of three different satellite cloud products, all based on passive sensors (CERES Edition 4.1 [EBAF4.1 and SYN4.1] and ISCCP–H), were evaluated against the CALIPSO-GOCCP (GOCCP) data, which are based on active sensors and, hence, were treated as the reference. Based on monthly averaged data (ocean + land), the passive sensors underestimated the total cloud cover (TCC) at lower (TCC < 50%), but, overall, they correlated well with the GOCCP data (r = 0.97). Over land, the passive sensors underestimated the TCC, with a mean difference (MD) of −2.6%, followed by the EBAF4.1 and ISCCP-H data with a MD of −2.0%. Over the ocean, the CERES-based products overestimated the TCC, but the SYN4.1 agreed better with the GOCCP data. The ISCCP-H data on average underestimated the TCC both over oceanic and continental regions. The annual mean TCC distribution over the globe revealed that the passive sensors generally underestimated the TCC over continental dry regions in northern Africa and southeastern South America as compared to the GOCCP, particularly over the summer hemisphere. The CERES datasets overestimated the TCC over the Pacific Islands between the Indian and eastern Pacific Oceans, particularly during the winter hemisphere. The ISCCP-H data also underestimated the TCC, particularly over the southern hemisphere near 60° S where the other datasets showed a significantly enhanced TCC. The ISCCP data also showed less TCC when compared against the GOCCP data over the tropical regions, particularly over the southern Pacific and Atlantic Oceans near the equator and also over the polar regions where the satellite retrieval using the passive sensors was generally much more challenging. The calculated global mean root meant square deviation value for the ISCCP-H data was 6%, a factor of 2 higher than the CERES datasets. Based on these results, overall, the EBAF4.1 agreed better with the GOCCP data.

Geolocation and diagnostics of trunk pipelines using an active acoustic sensor

The article discusses the issue of determining the location of the main pipeline to identify the deviation of its position from the design due to climatic factors. The possibility of using active sensors capable of operating both as diagnostic devices installed on the main pipeline and as a beacon is investigated, according to the signal of which it is possible to determine the location of this sensor with acceptable accuracy. The optimal operating frequencies are determined by mathematical modeling to ensure the maximum data transfer rate and the required signal-to-noise ratio. The simulation results show the possibility of assessing the attenuation of signals in the ground, as well as determining the limiting range of the method.

On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds

Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved.

Pattern recognition on FPGA for aerospace applications

This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.

EXPERIMENTS USING SMARTPHONE-BASED VIDEOGRAMMETRY FOR LOW-COST CULTURAL HERITAGE DOCUMENTATION

The rapid development of 3D scanning technology is a welcome progress in the field of tangible cultural heritage documentation. While active sensors such as handheld Time-of-Flight (ToF) cameras and lidar have recently generated much hype, developments in low-cost imaging sensors have also seen long strides in recent decades. This paper aims to see the potential of videogrammetry for the purposes of heritage documentation. This technique has existed for decades, but we argue that when combined with modern smartphone sensors and proper photogrammetric processing workflow it may present an interesting low-cost solution for 3D scanning. Furthermore, the paper wishes to address the requirement for a certain geometric quality in heritage documentation and how the proposed method may fulfil them. For this reason, comparisons between the videogrammetric result and traditional DSLR close range photogrammetry will be described to determine its suitability for heritage documentation. Results show that using modern low-cost smartphone imaging sensors, a good compromise between geometric quality and overall cost in the context of cultural heritage recording is possible to achieve.

A simulation-experiment-based assessment of retrievals of above-cloud temperature and water vapor using a hyperspectral infrared sounder

Measuring atmospheric conditions above convective storms using spaceborne instruments is challenging. The operational retrieval framework of current hyperspectral infrared sounders adopts a cloud-clearing scheme that is unreliable in overcast conditions. To overcome this issue, previous studies have developed an optimal estimation method that retrieves the temperature and humidity above high thick clouds by assuming a slab of cloud. In this study, we find that variations in the effective radius and density of cloud ice near the tops of convective clouds lead to non-negligible spectral uncertainties in simulated infrared radiance spectra. These uncertainties cannot be fully eliminated by the slab-cloud assumption. To address this problem, a synergistic retrieval method is developed here. This method retrieves temperature, water vapor, and cloud properties simultaneously by incorporating observations from active sensors in synergy with infrared radiance spectra. A simulation experiment is conducted to evaluate the performance of different retrieval strategies using synthetic radiance data from the Atmospheric Infrared Sounder (AIRS) and cloud data from CloudSat/CALIPSO. In this experiment, we simulate infrared radiance spectra from convective storms through a combination of a numerical weather prediction model and a radiative transfer model. The simulation experiment shows that the synergistic method is advantageous, as it shows high retrieval sensitivity to the temperature and ice water content near the cloud top. The synergistic method more than halves the root-mean-square errors in temperature and column integrated water vapor compared to prior knowledge based on the climatology. It can also improve the quantification of the ice water content and effective radius compared to prior knowledge based on retrievals from active sensors. Our results suggest that existing infrared hyperspectral sounders can detect the spatial distributions of temperature and humidity anomalies above convective storms.

A V-SLAM Guided and Portable System for Photogrammetric Applications

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

Physical Layer Intercept Probability in Wireless Sensor Networks over Fisher–Snedecor ? Fading Channels

In this paper, we analyze the physical layer security (PLS) of an arbitrarily dimensioned wireless sensor network (WSN) in the presence of an unauthorized attacker. Various scheduling schemes have been exploited in order to enhance the secure transmission of reliable links impaired by Fisher–Snedecor F fading. The path loss among active nodes is also considered. The exact intercept probability expressions are derived recalling an optimal scheduling scheme (OS), a scheduling policy based on a specific cumulative distribution function (CS), and round-robin scheduling as a baseline. The asymptotic behavior of the intercept metric is also presented in a simpler form with acceptable accuracy. The secrecy diversity orders are defined and the security–reliability tradeoff of WSN is specified. Numerical results are provided to demonstrate the interplay of various main/wiretap channel conditions, the distances among nodes, the number of active sensors, and the average main-to-eavesdropper’s signal ratio in order to upgrade the quality of the WSN secrecy performance. Additionally, the impact of the outage probability on the intercept probability is defined for a variety of scenarios under which either the CS or OS scheme could be selected as suitable for PLS enhancement. The obtained results are verified by independent Monte Carlo simulations.

The Simultaneous Localization and Mapping (SLAM)-An Overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.