Relevant bibliographies by topics / Sensor data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sensor data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Sensor data processing"

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., A. Hassini, N. Benabadji ., and A. H. Belbachir . "AVHRR Data Sensor Processing." Journal of Applied Sciences 6, no. 11 (2006): 2501–5. http://dx.doi.org/10.3923/jas.2006.2501.2505.

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Guo, Yixuan, and Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research." Journal of Sensors 2021 (September 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/3840103.

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.
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Yang, Yanning, Andrew May, and Shuang Hua Yang. "Sensor data processing for emergency response." International Journal of Emergency Management 7, no. 3/4 (2010): 233. http://dx.doi.org/10.1504/ijem.2010.037008.

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Ko, Dong-beom, Tae-young Kim, Jeong-Joon Kim, and Jeong-min Park. "Sensor Data Collecting and Processing System." Asia-pacific Journal of Multimedia services convergent with Art, Humanities, and Sociology 7, no. 9 (2017): 259–69. http://dx.doi.org/10.14257/ajmahs.2017.09.28.

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Odeberg, Hans. "A tactile sensor data-processing system." Sensors and Actuators A: Physical 49, no. 3 (1995): 173–80. http://dx.doi.org/10.1016/0924-4247(95)01030-0.

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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.

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<p>The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.</p>
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Kammerer, Klaus, Rüdiger Pryss, Burkhard Hoppenstedt, Kevin Sommer, and Manfred Reichert. "Process-Driven and Flow-Based Processing of Industrial Sensor Data." Sensors 20, no. 18 (2020): 5245. http://dx.doi.org/10.3390/s20185245.

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For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
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Tejero, S., U. Siart, and J. Detlefsen. "Coherent and non-coherent processing of multiband radar sensor data." Advances in Radio Science 4 (September 4, 2006): 73–78. http://dx.doi.org/10.5194/ars-4-73-2006.

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Abstract. Increasing resolution is an attractive goal for all types of radar sensor applications. Obtaining high radar resolution is strongly related to the signal bandwidth which can be used. The currently available frequency bands however, restrict the available bandwidth and consequently the achievable range resolution. As nowadays more sensors become available e.g. on automotive platforms, methods of combining sensor information stemming from sensors operating in different and not necessarily overlapping frequency bands are of concern. It will be shown that it is possible to derive benefit from perceiving the same radar scenery with two or more sensors in distinct frequency bands. Beyond ordinary sensor fusion methods, radar information can be combined more effectively if one compensates for the lack of mutual coherence, thus taking advantage of phase information. At high frequencies, complex scatterers can be approximately modeled as a group of single scattering centers with constant delay and slowly varying amplitude, i.e. a set of complex exponentials buried in noise. The eigenanalysis algorithms are well known for their capability to better resolve complex exponentials as compared to the classical spectral analysis methods. These methods exploit the statistical properties of those signals to estimate their frequencies. Here, two main approaches to extend the statistical analysis for the case of data collected at two different subbands are presented. One method relies on the use of the band gap information (and therefore, coherent data collection is needed) and achieves an increased resolution capability compared with the single-band case. On the other hand, the second approach does not use the band gap information and represents a robust way to process radar data collected with incoherent sensors. Combining the information obtained with these two approaches a robust estimator of the target locations with increased resolution can be built.
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Krishnamurthi, Rajalakshmi, Adarsh Kumar, Dhanalekshmi Gopinathan, Anand Nayyar, and Basit Qureshi. "An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques." Sensors 20, no. 21 (2020): 6076. http://dx.doi.org/10.3390/s20216076.

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.
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Manohar, Nathan, Abhishek Jain, and Amit Sahai. "Self-Processing Private Sensor Data via Garbled Encryption." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (2020): 434–60. http://dx.doi.org/10.2478/popets-2020-0081.

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AbstractWe introduce garbled encryption, a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions.We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an “alarm” state.We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop.
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Dissertations / Theses on the topic "Sensor data processing"

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Yelasani, kailash kumar yadav. "ECONOMIZED SENSOR DATA PROCESSING WITH VEHICLE PLATOONING." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2305.

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We present platooning as a special case of crowd-sensing framework. After offering a policy that governs platooning, we review common scenarios and components surrounding platooning. We present a prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that beyond the commonly reported benefits of platooning, there are substantial savings in acquisition and processing of sensory data sharing the road. Our results show that data transmission can be reduced to low of 3% compared to normal data transmission using a platoon formation with sensor sharing.
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Ma, Ding. "Miniature data acquisition system for multi-channel sensor arrays." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Spring2010/d_ma_042610.pdf.

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Thesis (M.S. in electrical engineering)--Washington State University, May 2010.<br>Title from PDF title page (viewed on July 23, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 55-57).
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Petersson, Henrik. "Multivariate Exploration and Processing of Sensor Data-applications with multidimensional sensor systems." Doctoral thesis, Linköpings universitet, Tillämpad Fysik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14879.

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A sensor is a device that transforms a physical, chemical, or biological stimulus into a readable signal. The integral part that sensors make in modern technology is considerable and many are those trying to take the development of sensor technology further. Sensor systems are becoming more and more complex and may contain a wide range of different sensors, where each may deliver a multitude of signals.Although the data generated by modern sensor systems contain lots of information, the information may not be clearly visible. Appropriate handling of data becomes crucial to reveal what is sought, but unfortunately, that process is not always straightforward and there are many aspects to consider. Therefore, analysis of multidimensional sensor data has become a science.The topic of this thesis is signal processing of multidimensional sensordata. Surveys are given on methods to explore data and to use the data to quantify or classify samples. It is also discussed how to avoid the rise of artifacts and how to compensate for sensor deficiencies. Special interest is put on methods being practically applicable to chemical gas sensors. The merits and limitations of chemical sensors are discussed and it is argued that multivariate data analysis plays an important role using such sensors. The contribution made to the public by this thesis is primarily on techniques dealing with difficulties related to the operation of sensors in applications. In the second paper, a method is suggested that aims at suppressing the negative effects caused by unwanted sensor-to-sensor differences. If such differences are not suppressed sufficiently, systems where sensors occasionally must be replaced may degrade and lose performance. The strong-point of the suggested method is its relative ease of use considering large-scale production of sensor components and when integrating sensors into mass-market products. The third paper presents a method that facilitates and speeds up the process of assembling an array of sensors that is optimal for a particular application. The method combines multivariate data analysis with the `Scanning Light Pulse Technique'. In the first and fourth papers, the problem of source separation is studied. In two separate applications, one using gas sensors for combustion control and one using acoustic sensors for ground surveillance, it has been identified that the current sensors outputs mixtures of both interesting- and interfering signals. By different means, the two papers applies and evaluates methods to extract the relevant information under such circumstances.<br>En sensor är en komponent som överför en fysikalisk, kemisk, eller biologisk storhet eller kvalitet till en utläsbar signal. Sensorer utgör idag en viktig del i flertalet högteknologiska produkter och sensorforskning är ett aktivt område. Komplexiteten på sensorbaserade system ökar och det blir möjligt att registrera allt er olika typer av mätsignaler. Mätsignalerna är inte alltid direkt tydbara, varvid signalbehandling blir ett väsentligt verktyg för att vaska fram den viktiga information som sökes. Signalbehandling av sensorsignaler är dessvärre inte en okomplicerad procedur och det finns många aspekter att beakta. Av denna anledning har signalbehandling och analys av sensorsignaler utvecklats till ett eget forskningsområde. Denna avhandling avhandlar metoder för att analysera komplexa multidimensionella sensorsignaler. En introduktion ges till metoder för att, utifrån mätningar, klassificera och kvantifiera egenskaper hos mätobjekt. En överblick ges av de effekter som kan uppstå på grund av imperfektioner hos sensorerna och en diskussion föres kring metoder för att undvika eller lindra de problem som dessa imperfektioner kan ge uppkomst till. Speciell vikt lägges vid sådana metoder som medför en direkt applicerbarhet och nytta för system av kemiska sensorer. I avhandlingen ingår fyra artiklar, som vart och en belyser hur de metoder som beskrivits kan användas i praktiska situationer.<br>Sensor,
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Yang, Yanning. "Wireless sensor data processing for on-site emergency response." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/8501.

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This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders' requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9).
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Wilking, Benjamin [Verfasser]. "Generic sensor data fusion in information space and a new approach to processing dense sensor data / Benjamin Wilking." Ulm : Universität Ulm, 2018. http://d-nb.info/1151938157/34.

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Kallumadi, Surya Teja. "Data aggregation in sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/2387.

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Murshed, Md Golam. "Energy efficient data gathering in wireless sensor networks." Thesis, University of Aberdeen, 2013. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=210783.

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Along with the rapid growth of Wireless Sensor Networks, a wide range of challenges have come to existence to make the network more robust and versatile. Gaining energy efficiency and maximizing network lifetime are the most important of all that can affect the performance of the network directly. In this thesis, a number of research aspects related to energy efficient data gathering have been investigated and some promising proposals are presented. In large, hierarchical multi-hop Wireless Sensor Networks, power consumption characteristics of the static sensor nodes and data traffic distribution across the network are largely determined by the node position and the adopted routing protocol. In this thesis, these phenomena of the network are addressed analytically and we proposed some methods to divide the monitoring field into partitions that act as the basis for even load distribution in the network. We proposed an algorithm to calculate the area of the partitions that exploits the energy efficient features of optimal transmission range. The partition works as the bedrock of the other proposals in this thesis. Considering the influential factors of the proximity and the recent state of the network, we also developed a routing protocol that minimises over all energy consumption of the network and is able to dynamically select a route to the sink. Further, we proposed a rotational order for data gathering scheme that works along with the routing protocol to ensure load balancing and to alleviate data congestion around the sink. Clustered organization of the nodes in sensor networks can further save energy consumption and facilitates scope for better network management. In this thesis, we address the fact that equal sized clusters can cause unbalanced data traffic around the sink. So, we propose a method to calculate suitable cluster radii in different regions of the monitoring field in order to form clusters of different sizes. To ensure unequal clusters in the field, a cluster construction procedure is also proposed targeting minimal data generation, minimal energy consumption and providing capacity for reliability preservation. Furthermore, the notion of redundant nodes and the outlines of a possible solution to identify and deactivate redundant nodes are explained in this thesis. Since the clusterheads play an important role as coordinators in the clusters, it is vital that there is a clusterhead in every cluster all the time. In this thesis, a message optimal and distributed leader election algorithm is proposed to select a new clusterhead in case of unexpected and unnoticed failure of a clusterhead node. Detailed analysis and simulation of the proposed methods clarify the effectiveness of the research. In comparison with other methods of similar kind, our methods confirm better balanced energy dissipation, energy efficient route selection, message optimal clusterhead selection and prolonged lifetime of the network.
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Zhu, Wenyao. "Time-Series Feature Extraction in Embedded Sensor Processing System." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281820.

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Embedded sensor-based systems mounted with tens or hundreds of sensors can collect enormous time-series data, while the data analysis on those time-series is commonly conducted on the remote server-side. With the development of microprocessors, there have been increasing demands to move the analysis process to the local embedded systems. In this thesis, the objective is to inves- tigate the possibility of the time-series feature extraction methods suitable for the embedded sensor processing systems.As the research problem raised from the objective, we have explored the traditional statistic methods and machine learning approaches on time-series data mining. To narrow down the research scope, the thesis focuses on the similarity search methods together with the clustering algorithms from the time-series feature extraction perspective. In the project, we have chosen and implemented two clustering algorithms, the K-means and the Self-Organizing Map (SOM), combined with two similarity search methods, the Euclidean dis- tance and the Dynamic Time Warping (DTW). The evaluation setup uses four public datasets with labels, and the Rand index (RI) to score the accuracy. We have tested the performance on accuracy and time consumption of the four combinations of the chosen algorithms on the embedded platform.The results show that the SOM with DTW can generally achieve better accuracy with a relatively longer inferring time than the other evaluated meth- ods. Quantitatively, the SOM with DTW can do clustering on one time-series sample of 300 data points for twelve classes in 40 ms using the ESP32 embed- ded microprocessor, with a 4 percentage of accuracy advantage than the fastest K-means with Euclidean distance in RI score. We can conclude that the SOM with DTW algorithm can be used to handle the time-series clustering tasks on the embedded sensor processing systems if the timing requirement is not so stringent.<br>Inbyggda sensorbaserade system monterade med tiotals eller hundratals senso- rer kan samla in enorma tidsseriedata, medan dataanalysen på dessa tidsserier vanligtvis utförs på en fjärrserver. Med utvecklingen av mikroprocessorer har behovet att flytta analysprocessen till de lokala inbäddade systemen ökat. I detta examensarbete är målet att undersöka vilka tidsserie-extraktionsmetoder som är lämpliga för de inbäddade sensorbehandlingssystemen.Som forskningsproblem för målet har vi undersökt traditionella statistik- metoder och maskininlärningsmetoder för tidsserie-data mining. För att be- gränsa forskningsområdet fokuserar examensarbet på likhetssökningsmetoder tillsammans med klusteralgoritmer från tidsserieens feature extraktionsper- spektiv. I projektet har vi valt och implementerat två klusteralgoritmer, K- means och Self-Organizing Map (SOM), i kombination med två likhetssök- ningsmetoder, det euklidiska avståndet och Dynamic Time Warping (DTW). Resultaten utvärderas med fyra offentliga datasätt med märkt data. Randin- dex (RI) används för att utvärdera noggrannheten. Vi har testat prestandan för noggrannhet och tidsförbrukning för de fyra kombinationerna av de valda al- goritmerna på den inbäddade plattformen.Resultaten visar att SOM med DTW i allmänhet kan uppnå bättre nog- grannhet med en relativt längre inferenstid än de andra utvärderade metoder- na. Kvantitativt kan SOM med DTW uföra klustring på ett tidsserieprov med 300 datapunkter för tolv klasser på 40 ms med en ESP32-inbäddad mikropro- cessor, vilket är en 4-procentig förbättring i noggrannhet i RI-poäng jämfört med det snabbaste K-medel klustringen med Euklidiskt avstånd. Vi drar slut- satsen att SOM med DTW algoritmen kan användas för att hantera tidsserie- klusteruppgifter på de inbäddade sensorbehandlingssystemen om tidsbehovet inte är så strängt.
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Danna, Nigatu Mitiku, and Esayas Getachew Mekonnen. "Data Processing Algorithms in Wireless Sensor Networks får Structural Health Monitoring." Thesis, KTH, Bro- och stålbyggnad, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-72241.

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The gradual deterioration and failure of old buildings, bridges and other civil engineering structures invoked the need for Structural Health Monitoring (SHM) systems to develop a means to monitor the health of structures. Dozens of sensing, processing and monitoring mechanisms have been implemented and widely deployed with wired sensors. Wireless sensor networks (WSNs), on the other hand, are networks of large numbers of low cost wireless sensor nodes that communicate through a wireless media. The complexity nature and high cost demand of the highly used wired traditional SHM systems have posed the need for replacement with WSNs. However, the major fact that wireless sensor nodes have memory and power supply limitations has been an issue and many efficient options have been proposed to solve this problem and preserve the long life of the network. This is the reason why data processing algorithms in WSNs focus mainly on the accomplishment of efficient utilization of these scarce resources. In this thesis, we design a low-power and memory efficient data processing algorithm using in-place radix-2 integer Fast Fourier Transform (FFT). This algorithm requires inputs with integer values; hence, increases the memory efficiency by more than 40% and highly saves processor power consumption over the traditional floating-point implementation. A standard-deviation-based peak picking algorithm is next applied to measure the natural frequency of the structure. The algorithms together with Contiki, a lightweight open source operating system for networked embedded systems, are loaded on Z1 Zolertia sensor node. Analogue Device’s ADXL345 digital accelerometer on board is used to collect vibration data. The bridge model used to test the target algorithm is a simply supported beam in the lab.
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Jardak, Christine [Verfasser]. "The storage and data processing in wireless sensor networks / Christine Jardak." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1024800121/34.

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Books on the topic "Sensor data processing"

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Sensor array signal processing. CRC Press, 2001.

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Naidu, Prabhakar S. Sensor array signal processing. 2nd ed. CRC Press, 2009.

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Multi-sensor data fusion with MATLAB. Taylor & Francis, 2010.

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Data acquisition for sensor systems. Chapman & Hall, 1997.

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Raol, J. R. Multi-sensor data fusion with MATLAB. Taylor & Francis, 2010.

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Raol, J. R. Multi-sensor data fusion with MATLAB. Taylor & Francis, 2010.

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Raol, J. R. Multi-sensor data fusion with MATLAB. CRC Press, 2010.

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Sensor modelling, design and data processing for autonomous navigation. World Scientific, 1999.

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L, Hall David. Mathematical techniques in multi-sensor data fusion. 2nd ed. Artech House, 2004.

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Clark, James J. Data Fusion for Sensory Information Processing Systems. Springer US, 1990.

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Book chapters on the topic "Sensor data processing"

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Berns, Karsten, Alexander Köpper, and Bernd Schürmann. "Sensor Data Processing." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65157-2_8.

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Colubri, Andrés. "Reading Sensor Data." In Processing for Android. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2719-0_7.

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McGrath, Michael J., and Cliodhna Ní Scanaill. "Processing and Adding Vibrancy to Sensor Data." In Sensor Technologies. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6014-1_5.

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Koch, Wolfgang. "On Recursive Batch Processing." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_5.

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Wang, Fusheng, Chunjie Zhou, and Yanming Nie. "Event Processing in Sensor Streams." In Managing and Mining Sensor Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-6309-2_4.

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Aggarwal, Charu C., and Jiawei Han. "A Survey of RFID Data Processing." In Managing and Mining Sensor Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-6309-2_11.

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Wang, Lixin, Lei Chen, and Dimitris Papadias. "Query Processing in Wireless Sensor Networks." In Managing and Mining Sensor Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-6309-2_3.

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Colubri, Andrés. "Driving Graphics and Sound with Sensor Data." In Processing for Android. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2719-0_8.

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Chang, Marcus, and Andreas Terzis. "Data Gathering, Storage, and Post-Processing." In The Art of Wireless Sensor Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40009-4_15.

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Elhoseny, Mohamed, and Aboul Ella Hassanien. "An Encryption Model for Data Processing in WSN." In Dynamic Wireless Sensor Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92807-4_7.

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Conference papers on the topic "Sensor data processing"

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Cecchi, Daniele, Bartolome Garau, Elena Camossi, Alessandro Berni, and Emanuel Coelho. "Sensor-driven glider data processing." In OCEANS 2015 - Genova. IEEE, 2015. http://dx.doi.org/10.1109/oceans-genova.2015.7271466.

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Lauer, Johannes, Nicolas Billen, and Alexander Zipf. "Processing crowd sourced sensor data." In the Sixth ACM SIGSPATIAL International Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2533828.2533839.

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Martinez, David R. "ISR sensor processing and data exploitation." In 2010 IEEE Radar Conference. IEEE, 2010. http://dx.doi.org/10.1109/radar.2010.5494390.

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Li, Bingcheng. "Network dynamics based sensor data processing." In Automatic Target Recognition XXX, edited by Timothy L. Overman, Riad I. Hammoud, and Abhijit Mahalanobis. SPIE, 2020. http://dx.doi.org/10.1117/12.2558194.

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Shafagh, Hossein, Lukas Burkhalter, and Anwar Hithnawi. "Talos a Platform for Processing Encrypted IoT Data." In SenSys '16: The 14th ACM Conference on Embedded Network Sensor Systems. ACM, 2016. http://dx.doi.org/10.1145/2994551.2996536.

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Jit, Biswas, Zhu Yongwei, Zhang Haihong, Jayachandran Maniyeri, Chen Zhihao, and Guan Cuntai. "Information processing of optical sensor data in ambient applications." In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2014. http://dx.doi.org/10.1109/issnip.2014.6827631.

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Seo, Seungmin, Sejin Chun, Byungkook Oh, and Kyong-Ho Lee. "SDPA: Sensor Data Processing Architecture for Modeling Semantic Data from Sensor Steams." In 2015 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2015. http://dx.doi.org/10.1109/iri.2015.13.

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Pani, Abhilash, Jinendra Gugaliya, and Mekapati Srinivas. "Data Driven Soft Sensor for Condition Monitoring of Sample Handling System (SHS)." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101423.

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Gas sample is conditioned using sample handling system (SHS) to remove particulate matter and moisture content before sending it through Continuous Emission Monitoring (CEM) devices. The performance of SHS plays a crucial role in reliable operation of CEMs and therefore, sensor-based condition monitoring systems (CMSs) have been developed for SHSs. As sensor failures impact performance of CMSs, a data driven soft-sensor approach is proposed to improve robustness of CMSs in presence of single sensor failure. The proposed approach uses data of available sensors to estimate true value of a faulty sensor which can be further utilized by CMSs. The proposed approach compares multiple methods and uses support vector regression for development of soft sensors. The paper also considers practical challenges in building those models. Further, the proposed approach is tested on industrial data and the results show that the soft sensor values are in close match with the actual ones.
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Kwon, Soonmok, Jongmin Shin, Dongmin Yang, and Cheeha Kim. "Practical approach to sensor data gathering." In 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2008. http://dx.doi.org/10.1109/issnip.2008.4762046.

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Yingwen Chen, Hong Va Leong, Ming Xu, Jiannong Cao, K. C. C. Chan, and A. T. S. Chan. "In-Network Data Processing forWireless Sensor Networks." In 7th International Conference on Mobile Data Management (MDM'06). IEEE, 2006. http://dx.doi.org/10.1109/mdm.2006.96.

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Reports on the topic "Sensor data processing"

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Rhodes, William T. Optical Digital Algebraic Processing for Multi-Sensor-Array Data. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada167196.

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Spina, John F. Integrated RF Sensor Signal/Data Processing Information Analysis Center (IAC). Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada401075.

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Thomas, Maikel A., Heidi Anne Smartt, and Robert F. Matthews. Processing large sensor data sets for safeguards : the knowledge generation system. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1039393.

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Dodge, D. Correlation Processing Of Local Seismic Data: Applications for Autonomous Sensor Deployments. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1016983.

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Wicker, Steven B. Self-Configuring Wireless Transmission and Decentralized Data Processing for Generic Sensor Networks. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425425.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada422190.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Howard, Kevin. A Proof of Concept for 10x+ Efficiency Gains for Multi-Sensor Data Fusion Utilizing a Howard Cascade Parallel Processing System. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417911.

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Fuentes, Anthony, Michelle Michaels, and Sally Shoop. Methodology for the analysis of geospatial and vehicle datasets in the R language. Cold Regions Research and Engineering Laboratory (U.S.), 2021. http://dx.doi.org/10.21079/11681/42422.

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The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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