Types Of Sensor Data Research Articles

Development and simulation of sensor-based irrigation control strategies for cotton using the VARIwise simulation framework

Feedback control systems offer opportunities to accommodate spatial and temporal differences in crop water requirement and to improve the automated irrigation of field crops via real-time data from in-field plant, soil–water and evaporation sensing. This paper describes two sensor-based strategies applied to irrigation control, ‘Iterative Learning Control’ (ILC) and custom-designed ‘Iterative Hill Climbing Control’ (IHCC), implemented in the control simulation and evaluation framework ‘VARIwise’. Simulation of an irrigated cotton crop using soils and merged 1999–2004 weather data of SE Queensland, Australia, and represented by the performance of the well-validated cotton growth and production model OZCOT, permitted the relative performance of differing sensor data types and availability to be evaluated (both as alternatives and in combination) in meeting the requirement to optimise either crop yield or water use efficiency. These simulations indicated that ILC would perform better at maintaining soil–water deficit, whilst IHCC would be better at maximising crop yield when plant and soil sensors were utilised in combination. This work demonstrates that the optimal choice of field sensor(s) and control strategy will be a function of the irrigation objective and the spatial and temporal availability and type of field measurements.

Recognising indoor/outdoor activities of low entropy people using Bluetooth proximity and object usage data

– Recognizing daily life activities and human behaviour from contextual information is a challenging task. The purpose of the research work in this paper is to develop a system that can detect indoor and outdoor daily life activities of low entropy mobile people such as elderly people and patients with regular routines using non-intrusive sensor and contextual information. , – A framework is proposed that utilises a hierarchical approach in which high-level activities are divided into sub-activities and tasks and recognises the high-level outdoor and indoor activities of daily life. Tasks are recognised at lower level from sensor data and then used by the “activity recogniser” at higher level to recognise the high-level activities. For outdoor activities recognition, wireless proximity data are used, whereas for indoor activities, object usage data obtained through radio frequency identification sensors are used. , – For outdoor tasks, results have shown 100 per cent recognition for experiment 1 and a decrease in recognition from 100 to 82.7 per cent, respectively, for experiment 2-9 due to increase in the entropy of individual tasks. Outdoor activity recognition ranges from 84.1 to 100 per cent. For indoor tasks, generating alternative tasks sequences approach effectively recognised the single tasks that were conducted with objects without any order. Average indoor activity recognition rate remains above 90 per cent. The reason why this approach is able to detect the activities without their distinct features is the planning capability of the Asbru that is used in the modelling of high-level activities. , – The novelty of this research work is a framework that utilises different types of sensor data and recognises both indoor and outdoor daily life activities of individuals.

Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data

Tree species classification accuracy at the individual tree crown (ITC) level depends on many factors, among which in this paper we analyzed: i) the remote sensing data used for the ITC delineation process carried out prior to the classification, and ii) the pixels considered inside each ITC during the classification process. These two factors were analyzed on the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS). ITCs were delineated automatically on ALS and on hyperspectral data. A manual ITC delineation was used as reference in the analysis. The pixel level classification was performed on the hyperspectral bands using a non-linear support vector machine. The classification at ITC level was obtained by applying a majority voting rule to the classified pixels confined by each ITC. The results showed that ITCs automatically delineated from hyperspectral data were usually smaller than those from ALS, and the tree detection rate for hyperspectral data was much lower compared to ALS data (28.4 versus 48.5%). Regarding the classification results, using only manually delineated ITCs a kappa accuracy of 0.89 was obtained, while using only automatically delineated ITCs from hyperspectral or ALS data reduced the kappa values to 0.79 and 0.76, respectively. Slightly different results were achieved using semi-automatic approaches based on both manual and automatically delineated ITC (0.81 and 0.74, respectively). A selection of only certain pixels inside each ITC improved the classification accuracy from 1 to 7 percentage points. A selection based on the spectral values of the pixels was found more influential than the one based on the ALS-derived canopy height model. The best results were obtained after a selection based on the spectral values in the bands in the blue region of the spectrum using either the Otsu method or an ad-hoc percentile-based thresholding method.

Where and what: Using smartphones to predict next locations and applications in daily life

This paper investigates the prediction of two aspects of human behavior using smartphones as sensing devices. We present a framework for predicting where users will go and which app they will use in the next ten minutes by exploiting the rich contextual information from smartphone sensors. Our first goal is to understand which smartphone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting generic (i.e., user-independent) behavioral patterns and study how generic behavior models can improve the predictive performance of personalized models. Experimental validation was conducted on the Lausanne Data Collection Campaign (LDCC) dataset, with longitudinal smartphone data collected over a period of 17 months from 71 users.

The Smartphone As a Platform for Wearable Cameras in Health Research

The Microsoft SenseCam, a small camera that is worn on the chest via a lanyard, increasingly is being deployed in health research. However, the SenseCam and other wearable cameras are not yet in widespread use because of a variety of factors. It is proposed that the ubiquitous smartphones can provide a more accessible alternative to SenseCam and similar devices. To perform an initial evaluation of the potential of smartphones to become an alternative to a wearable camera such as the SenseCam. In 2012, adults were supplied with a smartphone, which they wore on a lanyard, that ran life-logging software. Participants wore the smartphone for up to 1 day and the resulting life-log data were both manually annotated and automatically analyzed for the presence of visual concepts. The results were compared to prior work using the SenseCam. In total, 166,000 smartphone photos were gathered from 47 individuals, along with associated sensor readings. The average time spent wearing the device across all users was 5 hours 39 minutes (SD=4 hours 11 minutes). A subset of 36,698 photos was selected for manual annotation by five researchers. Software analysis of these photos supports the automatic identification of activities to a similar level of accuracy as for SenseCam images in a previous study. Many aspects of the functionality of a SenseCam largely can be replicated, and in some cases enhanced, by the ubiquitous smartphone platform. This makes smartphones good candidates for a new generation of wearable sensing devices in health research, because of their widespread use across many populations. It is envisioned that smartphones will provide a compelling alternative to the dedicated SenseCam hardware for a number of users and application areas. This will be achieved by integrating new types of sensor data, leveraging the smartphone's real-time connectivity and rich user interface, and providing support for a range of relatively sophisticated applications.

이기종 센서 네트워크를 위한 데이터 중심적 센서 미들웨어

Wireless sensor networks need middleware system for efficiently managing the constrained resource and sensing data because they need different sensing data type and protocol to communicate with heterogeneous sensor networks. Thus this paper proposes data-centric sensor middleware for heterogeneous sensor networks. The proposed middleware have to support various query processing of user applications, high-level request of users, manage heterogeneous sensor systems and universal sensing data type for node and user application.

Sensing Technologies for Societal Well-Being

Sensing technologies by design are calibrated for accuracy against an expected measurement scale. Sensor calibration and signal processing criteria are one type of sensor data, while the sensor readings are another. Ensuring data accuracy and precision from sensors is an essential, ongoing challenge, but these issues haven’t stopped the potential for pervasive application use. Technological advances afford an opportunity for sensor data integration as a vehicle for societal well-being and the focus of ongoing research. A lean and flexible architecture is needed to acquire sensor data for societal well-being. As such, this research places emphasis on the acquisition of environmental sensor data through lean application programming protocols (APIs) through services such as SMS, where scant literature is presented. The contribution of this research is to advance the research that integrates sensor data with pervasive applications.

Multi-sensor track-before-detect for complementary sensors

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. It is a non-linear non-Gaussian estimation problem, typically requiring numerical methods. While, in general, TkBD schemes are a more computationally expensive method of tracking, removal of the detection threshold allows for the detection and tracking of targets at much lower signal-to-noise ratios. In this paper we develop TkBD algorithms for multiple asynchronous dissimilar sensors. The problem of radar and IRST fusion is presented as an example and the performance of TkBD is compared with a Multi-Hypothesis Tracker. In the example, the TkBD algorithm is able to utilise IRST data to produce high accuracy in azimuth and elevation well beyond the conventional operating range of the sensor.

A Monte Carlo Analysis of Sensor Fusion Algorithm for the Estimation of Aerodynamic Angles in a Mini Aerial Vehicle

An Extended Kalman Filter (EKF) fusion algorithm has been developed for estimating the aerodynamic angles in a Mini Aerial Vehicle (MAV). The knowledge on the aerodynamic angles would be helpful in designing control laws during the high angle of attack flights. This involves an algorithm that computes pseudo measurements of Angle of Attack (AOA) and Side Slip Angle (SSA) which are fused with typical sensor data for the estimation of aerodynamic angles. The current work focuses on testing the robustness of the estimation algorithm to uncertain aircraft parameters inherited from the manufacturing errors, assembly errors and also from prolonged usage of the aircraft. For this purpose, a Monte Carlo simulation has been carried out using number of randomly generated aircraft parameters in the flight simulation program, and the true states of the aircraft are generated. With few of these true states, a zero mean white Gaussian noise is added and the sensor data has been simulated. Using the simulated sensor data, the aerodynamic angles are estimated. It should be noted that the estimation algorithm has been provided with correct aircraft parameters, whereas the flight simulation has been carried out with randomly generated aircraft parameters. This paper brings out the robustness of the estimation algorithm to changes in the aircraft parameters propagated from manufacturing / assembly errors.

Type-2 Fuzzy Kalman Hybrid Application for Dynamic Security Monitoring Systems based on Multiple Sensor Fusion

Abstract Multiple sensors fusion plays a major role in situation awareness, especially in modern dynamic security monitoring and tracking applications. In this paper, we focus on a specific effective set of complementary sensors [Laser (for speed measurements), Sonar (for space scanning) and RF (for access rights)]. A novel multi-agent system is obtained by fusing the above types of sensory data taking advantages of similarity and complementarity concepts. Furthermore, Kalman Filter is utilized to track next state estimates of agent(s) in uncertain environment. Finally, our proposed system transforms system state to be able to make a security awareness decision, using type-2 fuzzy logic system to handle exhibited uncertainty, for asset scenery under surveillance. It is shown that the system performance can exhibit promising improvements for this dynamic security monitoring situation as a result of using the above complementary heterogeneous set of sensors.

Data Indicating Malfunction Sign of Hydroelectric Power Plants

Kyushu Electric Power Co., Inc. collects different types of sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are in operation. We have to identify malfunction signs from among the collected sensor data. In this paper, we describe a method for identifying the conditions that could cause a malfunction; our method consists of two identification stages. In the first stage, we identify malfunction signs, which are different from normal-condition data, and in the second stage, we monitor aging degradation. Our proposed method is based on the use of a one-class support vector machine and a normal support vector machine. The experimental results obtained in this study show that our proposed method can be employed to identify malfunction signs, which are different from normal-condition data, and to monitor aging degradation.

DESIGN AND IMPLEMENTATION OF LOCATION AND SITUATION BASED SERVICES FOR A PERVASIVE MOBILE ADVENTURE GAME

This paper describes the design and implementation of a location and situation based pervasive mobile adventure game named Sportix. The prototype uses different types of sensor data – including 3D acceleration data and XPS – to determine the current position and activity of the player. Depending on the firm classification, data and quests are retrieved accordingly from a central information system and visualised for the player on the mobile device. By solving quests and performing sports the player is able to participate in the game.

대칭모형 기반 SLAM : M-SLAM

미지의 영역에서 작업을 수행하고자 하는 이동로봇은 주변의 지도가 없을 뿐만 아니라 자신의 위치도 알 수 없다. 이러한 환경의 극복을 위해 가장 많이 쓰이는 방법이 SLAM(Simultaneous Localization And Mapping)이다. SLAM 분야에서 가장 많이 쓰이는 방법은 EKF (Extended Kalman Filter) 기반의 SLAM이다. 최적의 센서 융합 기법이지만 odometeric error 등을 보상하기 위해서는 복잡한 과정이 점차 증가하게 된다. 사람은 SLAM 방식을 이용하여 낯선 장소에서 마음속의 지도를 쉽게 작성하지만 로봇의 경우 SLAM을 수행하는 것은 매우 어렵고 시간이 오래 걸린다는 단점이 생기는 것이 다. 이러한 단점의 보완을 위하여 본 논문에서는 대칭모형 SLAM(M-SLAM)을 제안한다. M-SLAM은 대칭에 사용할 모형을 미리 정하고 센서로 받아들인 데이터를 모형과 비교하여 대칭된 모형을 맵에 적용시켜서 작업의 양을 줄이는 방법이다. M-SLAM은 적은 특징점을 이용하여 선택된 대칭 도형과의 유사성 판별을 이용하는 방법이므로 특징점이 적은 거리센서에 사용하기 적합한 특성을 가지고 있다고 할 수 있다. 특징점이 적어도 된다는 장점은 SLAM의 시간을 크게 줄여 줄수 있다. The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometeric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm(called M-SLAM).

Sensory methodologies and the taste of water

Describing the taste of water is a challenge since drinking water is supposed to have almost no taste. In this study, different classical sensory methodologies have been applied in order to assess sensory characteristics of water and have been compared: sensory profiling, Temporal Dominance of Sensations and free sorting task. These methodologies present drawbacks: sensory profile and TDS do not provide an effective discrimination of the taste of water and the free sorting task is efficient but does not enable data aggregation. A new methodology based on comparison with a set of references and named “Polarized Sensory Positioning” (PSP) has been developed enabling to easily define the sensory characteristics of water without presenting too many samples. Finally, this method provides a new type of sensory data requesting dedicated data analysis.

Data Correlation-Based Clustering Algorithm in Wireless Sensor Networks

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.

Remote sensing data types and techniques for lahar path detection: A case study at Mt Ruapehu, New Zealand

Mt Ruapehu is New Zealand's most active onshore volcano. In 2007, the volcano produced a large lahar following a break-out from the summit Crater Lake. Here, satellite and airborne remote sensing and image processing is used to extract the path of the lahar using ASTER and SPOT5 visible and near infra-red imagery, ALOS-PALSAR L-band synthetic aperture RADAR data, and airborne LiDAR. The results obtained from each of these datasets were compared to the lahar deposit manually digitized from aerial photography. SPOT5 imagery produced the most accurate map of the lahar deposit (77% correct), even though these data were acquired a year after the event. This is attributed to the spatial resolution of the data. The ALOS-PALSAR coherence mapping calculated from images acquired 2 months before and nine months after the lahar was not as accurate as that obtained using the optical imagery (43% correct), but this was still considered an important tool for acquiring data during cloudy periods. LiDAR topographic data, collected to constrain geomorphic changes caused by the lahar, was the least accurate in terms of mapping the lahar path (28% correct). No single technique was deemed to be the most accurate under all circumstances, and a combination of data types would produce the best results. By combining the satellite and LiDAR data, it was possible to accurately classify 92% of the lahar path.

A LONG-TERM FIELD MONITORING SYSTEM WITH FIELD SERVERS AT A GRAPE FARM

Monitoring image data at a field site provides important and useful information concerning plant and environmental conditions for agricultural users. We propose an image monitoring system that makes it possible to work in harsh areas, collect large amounts of data in real time, and analyze the data according to users’ requests. The system is constructed for use via the Internet with Web-based sensor nodes, an agent program, and Web analysis applications. Web-based sensor nodes such as Field Servers allow the monitoring of various types of sensor data, including image data, via the Internet. The agent program performs complicated operations on the sensor nodes from a remote site via the Internet; it can also analyze monitoring data easily using Web applications. To evaluate the effectiveness of the image monitoring system, we executed a long-term field experiment at a grape farm in Obuse-town, Nagano, Japan. Four types of sensor data concerning environmental condition and 2 types of image data concerning plant detail and field overview were collected and stored at a remote site via the Internet. The image data were analyzed using a filtering approach operated by the agent program and Web applications which were used for calculations in a background subtraction model. The proposed system has worked stably since May 2006 and provided results in which approximately 84% of unnecessary image data were filtered. Due to its construction with a distributed architecture based on the Internet, this system has the advantage that non-expert users can easily obtain the monitoring data on the Web and image processing can be provided for users without changing the system architecture.

A Comparison of Detection Performance for Several Track-before-Detect Algorithms

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.

Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks

The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.

Key issues in making and using satellite-based maps in ecology: A primer

The widespread availability of satellite imagery and image processing software has made it relatively easy for ecologists to use satellite imagery to address questions at the landscape and regional scales. However, as often happens with complex tools that are rendered easy to use by computer software, technology may be misused or used without an understanding of some of the limitations or caveats associated with a particular application. The results can be disappointment when maps are less accurate than expected or incorrect decisions when they are treated as truth. In this paper, we discuss several key issues which are critical to ensuring the effectiveness and value of remote sensing products, but which are also sometimes sources of confusion: (1) direct versus indirect models of land surface properties and processes, (2) differences between class-based and continuous mapping models, (3) scale, and (4) accuracy assessment. We illustrate our points with examples from the application of satellite imagery to forest management issues in the Pacific Northwest, USA. While our examples focus largely on Landsat image data, the issues we discuss have broad relevance across sensor data types, land cover properties, and geographic locations.